books/gu-zhenren/softsign-decl/adversarial-review.txt

{"parentUuid":null,"isSidechain":true,"promptId":"c81fa375-6b68-4d22-8364-68be617228f6","agentId":"aec521429a7581efa","type":"user","message":{"role":"user","content":"You are an ADVERSARIAL design reviewer. Your job is to REFUTE a proposed mechanism, not agree with it. Default to \"this is flawed\" and only concede a point if you cannot break it. Ground every claim in `file:line` or a concrete measured counterexample. Do NOT read `.env` files. Do NOT make any paid model calls. This is a $0 code/corpus analysis only.\n\n## Context (TextMachine, Go literary-MT backend)\nA post-check flagger `declinedFormPresent` (in `/home/ubuntu/projects/textmachine/backend/internal/membank/mempostcheck.go`, lines ~146-191) checks whether a glossary term's Russian `dst` appears in model output, accepting DECLINED (inflected) forms. It tokenizes both, slides a window, and requires every component word to share a stem via `lang.TargetStemmer.SameStem` (in `/home/ubuntu/projects/textmachine/backend/internal/lang/stemmer.go`). The stemmer strips the longest listed `decl_suffix` (from `backend/internal/lang/data/target-ru.txt`) leaving a stem >= 3 runes.\n\n## The problem being solved (\"finding-1\", soft-sign gap)\nRussian names/terms ending in the soft sign «ь» (e.g. «Фан Юань», «Винный червь») decline as «Фан Юаня/Юаню/Юанем», «Винного червя». The stemmer does NOT strip «ь», so `SameStem(\"юаню\",\"юань\")` is FALSE → these declensions are MISSED. Naively adding «ь» to `decl_suffix` (call it A1) was REJECTED earlier because it makes `SameStem(\"Синь\",\"синим\")=true` and `SameStem(\"Линь\",\"линий\")=true` — top Chinese surnames «Синь/Линь» collapse onto common words «синий/линия», firing false positives in `Bank.SpoilerLeaks` (which also calls `declinedFormPresent`). Because `SameStem` is global, A1 poisons every consumer.\n\n## The proposed mechanism (A3, \"anchor-gated soft match\")\n- Add «ь» as a NEW data category `soft_decl_suffix` in `target-ru.txt`, SEPARATE from `decl_suffix`. Global `SameStem` stays byte-identical.\n- The stemmer gains a `SameStemSoft(a,b)` that ALSO permits the soft «ь» strip.\n- In `declinedFormPresent` ONLY: a component may match via `SameStemSoft` (soft) instead of `SameStem` ONLY IF at least one OTHER component in the same window matches via normal `SameStem` (the \"anchor\"). A single-word term (n=1) has no other component → soft never fires → single-word soft names like «Синь» stay a safe miss.\n- Generality: a target shipping no `soft_decl_suffix` rows runs identical to today (inert).\n\n## Measured evidence (already gathered on real corpus /home/ubuntu/books/gu-zhenren — bank BANK-FULL.tsv, 25-chapter real ru output acceptance/guzhenren-25ch-ru.txt)\n- «Фан Юань»: 324 nominative + 184 declined (Юаня 152/Юаню 19/Юанем 7/Юане 6). A3 captures all 184 (anchor «фан» exact). VERIFIED real name mentions.\n- «Винный червь»: 46 nominative + 67 declined (Винного червя 60, Винным червём 3, etc). A3 captures all 67 via norm-anchor «Винного»~«Винный». An EXACT-anchor variant (A2) MISSES all 67. VERIFIED real.\n- Total A3 captures ~259 occurrence-windows A0 misses; 258 clearly true, 1 mild imprecision: «девять рангов»~«девятого ранга» (nine ranks vs ninth rank, a number soft-conflation).\n- A3 introduced ZERO hard collisions on this corpus. Single-word soft terms (шичэнь→шичэна 2 occ, чжэньюань 0 occ) stay missed under A3 — the safety price.\n\n## Your task — try HARD to break A3. Specifically:\n1. **Two-word residual collision:** construct a realistic failure where A3 FALSELY accepts. E.g. a bank term whose soft component collides with a common word AND whose other component coincidentally appears adjacent. Grep the real bank (`/home/ubuntu/books/gu-zhenren/coldrun-a/BANK-FULL.tsv`, col2 = dst) for soft-«ь» terms whose soft-stem equals a common Russian word stem, paired with a short/common anchor. Quantify how many bank terms are at risk. Is my \"low probability\" claim honest?\n2. **Is there a BETTER mechanism than A3?** Consider: (a) \"soft strip only on the LAST component\" (name-tail rule, since declension empirically falls on the tail); (b) a min-stem-length gate; (c) a common-word stoplist; (d) seed decl-enumeration. For each, say concretely whether it dominates A3 and why/why not. Use the measured data.\n3. **Anchor definition:** A3 uses \"normal-stem anchor\". Can a common-word component act as a false anchor? Construct a case where the anchor itself is a coincidental common word, enabling a soft false-accept.\n4. **Blast radius:** verify the claim that `SameStem` has only ONE functional caller (`declinedFormPresent`) so A3 leaves SpoilerLeaks/seeding safe. Grep `backend/internal` for every `SameStem` / `.Stem(` / stemmer usage and confirm or refute. Check `backend/internal/pipeline/seeding.go` around line 138.\n5. **The «девятого ранга» imprecision:** is this a symptom of a deeper problem (number words «девять/десять/семь…» ending in «ь» soft-conflating with ordinals/other forms)? Grep the bank for soft-final number words and quantify.\n\nDeliverable: a terse findings list. For each: CONFIRMED-BREAK (with the concrete counterexample + file:line or corpus evidence) or COULD-NOT-BREAK (what you tried). End with a one-line verdict: is A3 the right mechanism, or is there a specifically-better one? Be a skeptic — if A3 survives, say what would have to be true for it to fail in a DIFFERENT book."},"uuid":"699c816f-4670-48db-be1f-4c5883823d22","timestamp":"2026-08-01T15:01:06.965Z","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
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{"parentUuid":"299220c2-2296-4f72-b599-a7eda5f2f8d3","isSidechain":true,"agentId":"aec521429a7581efa","attachment":{"type":"skill_listing","content":"- dataviz: Use this skill whenever you are about to create ANY chart, graph, plot, dashboard, or data visualization, in ANY output medium — an HTML or React artifact, inline SVG, plotting code in any library (matplotlib, plotly, d3, Recharts, …), an image/PNG you will render and upload, or a chart shared into Slack. Read it BEFORE writing the first line of chart code, choosing chart colors, building a stat tile / meter / KPI row, or laying out a dashboard. Produces visualizations that read as one system — elegant, accessible, consistent in light and dark — using a brand-neutral placeholder palette you swap for your own. Teaches a design-system-agnostic method: a form heuristic, a color formula with a runnable validator, mark specs, and interaction rules. A validated default palette is documented in `references/palette.md` — swap that file's values for your brand's. Triggers on: \"chart\", \"graph\", \"plot\", \"data viz\", \"visualization\", \"dashboard\", \"analytics\", \"visualize data\", \"categorical colors\", \"sequential / diverging palette\", \"stat tile\", \"sparkline\", \"heatmap\", \"legend\", \"axis\", \"tooltip\", \"chart colors\", \"color by series\".\n- artifact-design: Design guidance and fundamentals for Artifacts.\n- artifact-capabilities: Runtime capabilities a published Artifact page can be granted — behavior static HTML cannot provide on its own, such as the page reading live or connected data, keeping state shared across viewers, or updating and republishing itself. Serves this user's live capability roster and the typed call definitions. Load it whenever the user asks for an artifact needing any such runtime behavior.\n- update-config: Use this skill to configure the Claude Code harness via settings.json. Automated behaviors (\"from now on when X\", \"each time X\", \"whenever X\", \"before/after X\") require hooks configured in settings.json - the harness executes these, not Claude, so memory/preferences cannot fulfill them. Also use for: permissions (\"allow X\", \"add permission\", \"move permission to\"), env vars (\"set X=Y\"), hook troubleshooting, or any changes to settings.json/settings.local.json files. Examples: \"allow npm commands\", \"add bq permission to global settings\", \"move permission to user settings\", \"set DEBUG=true\", \"when claude stops show X\". For simple settings like theme/model, suggest the /config command.\n- keybindings-help: Use when the user wants to customize keyboard shortcuts, rebind keys, add chord bindings, or modify ~/.claude/keybindings.json. Examples: \"rebind ctrl+s\", \"add a chord shortcut\", \"change the submit key\", \"customize keybindings\".\n- simplify: Review the changed code for reuse, simplification, efficiency, and altitude cleanups, then apply the fixes. Quality only — it does not hunt for bugs; use /code-review for that.\n- fewer-permission-prompts: Scan your transcripts for common read-only Bash and MCP tool calls, then add a prioritized allowlist to project .claude/settings.json to reduce permission prompts.\n- loop: Run a prompt or slash command on a recurring interval (e.g. /loop 5m /foo). Omit the interval to let the model self-pace. - When the user wants to set up a recurring task, poll for status, or run something repeatedly on an interval (e.g. \"check the deploy every 5 minutes\", \"keep running /babysit-prs\"). Do NOT invoke for one-off tasks.\n- schedule: Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule. - When the user wants to schedule a recurring cloud agent, set up automated tasks, create a cron job for Claude Code, or manage their scheduled agents/routines. Also use when the user wants a one-time scheduled run (\"run this once at 3pm\", \"remind me to check X tomorrow\").\n- claude-api: Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.\nTRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens).\nSKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).\n- run: Launch and drive this project's app to see a change working. Use when asked to run, start, or screenshot the app, or to confirm a change works in the real app (not just tests). First looks for a project skill that already covers launching the app; otherwise falls back to built-in patterns per project type (CLI, server, TUI, Electron, browser-driven, library).\n- init: Initialize a new CLAUDE.md file with codebase documentation\n- review: Review a GitHub pull request; for your working diff use /code-review\n- security-review: Complete a security review of the pending changes on the current branch","skillCount":14,"isInitial":true,"names":["dataviz","artifact-design","artifact-capabilities","update-config","keybindings-help","simplify","fewer-permission-prompts","loop","schedule","claude-api","run","init","review","security-review"]},"type":"attachment","uuid":"8b46ec81-dda5-4311-902e-088c7e52e362","timestamp":"2026-08-01T15:01:06.971Z","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
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{"parentUuid":"9a53267f-9ae1-4492-b83a-aa1daa97417e","isSidechain":true,"promptId":"c81fa375-6b68-4d22-8364-68be617228f6","agentId":"aec521429a7581efa","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HKDoVXao65ms9MgCTiiqxV","type":"tool_result","content":"1\tpackage membank\n2\t\n3\timport (\n4\t\t\"strings\"\n5\t\t\"unicode\"\n6\t\n7\t\t\"textmachine/backend/internal/lang\"\n8\t\t\"textmachine/backend/internal/text\"\n9\t)\n10\t\n11\t// mempostcheck.go: the post-check (registry E1/E2 / research/13 Q5) — the main, almost\n12\t// free detector of silent degradation. For every injected record whose SRC actually\n13\t// fired in THIS chunk, it asks: did an accepted DST form appear in the output? A miss\n14\t// catches BOTH failure modes — \"the model ignored the glossary\" (soft-following loses\n15\t// 17–36% of terms, 2310.05824) AND \"we injected the wrong dst and the model obeyed it\"\n16\t// (the owner's fear, 2510.00829). It is a post-CHECK, never a blind post-REPLACE (E2):\n17\t// forcing a dictionary form into an oblique Russian case breaks agreement (46% of\n18\t// constrained-model errors in en-cs are agreement, 2106.12398), so v1 only FLAGS.\n19\t//\n20\t// Decl-awareness is load-bearing (research/14 §2, quantified): a naive \\b-regexp on the\n21\t// base form gives 18–36% FALSE flags on a hard chunk — inflected forms rendered\n22\t// CORRECTLY (\"Вэйчжуане\", \"Бородатого Вана\", \"Дэна\", \"сюцая\") that the boundary regexp\n23\t// misses. So the check matches against the STORED decl forms (filled at term-commit),\n24\t// not the bare lemma. BUT stored-decl reliability is itself a function of decl\n25\t// COMPLETENESS — an OOV translit name whose forms were under-filled re-introduces the\n26\t// same false flags. That is why v1 keeps the post-check a FLAGGER (observable, in the\n27\t// retrieval-state), NOT a hard disposition gate, until the false-flag rate is MEASURED\n28\t// on real inflected Russian (E1 — memory_e1_test.go). A hard gate flips on only after\n29\t// the owner validates precision (mirrors the coverage gate's opt-in, D12 Q4).\n30\t//\n31\t// Sticky records are DELIBERATELY excluded: a sticky entry's src is NOT in this chunk\n32\t// (that is why scene-inertia carries it), so its dst is legitimately absent from the\n33\t// output — post-checking it would false-flag every pronominal chunk. Only records whose\n34\t// key exactly fired here are expected in the output.\n35\t\n36\t// PostcheckMiss is one flagged term: an injected record whose src fired but whose dst\n37\t// (any accepted form) is absent from the output.\n38\ttype PostcheckMiss struct {\n39\t\tSrc  string `json:\"src\"`\n40\t\tDst  string `json:\"dst\"`\n41\t\tDisp string `json:\"disp\"` // confirmed | ambiguous (A2: ambiguous injections force a post-check)\n42\t}\n43\t\n44\t// PostcheckResult separates the two kinds of miss BY TYPE rather than by the discipline of whoever calls\n45\t// the checker (pack-20 / D39.42 п.4). The rule itself is unchanged and load-bearing: only a CONFIRMED\n46\t// miss is a consistency failure. An AMBIGUOUS miss is an auto/draft candidate the model was ENTITLED to\n47\t// reject, and counting it would invert the contract — punishing a model that correctly refused an\n48\t// unverified suggestion while passing one that obeyed a wrong CONFIRMED injection (external-review major\n49\t// #1). What changes is that the rule is now impossible to get wrong at a call site: there is no combined\n50\t// slice to filter, so a caller cannot forget to. The Unverified misses are still carried, in full, as\n51\t// observability (A2's forced post-check) — they are simply not a count.\n52\ttype PostcheckResult struct {\n53\t\tConfirmed  []PostcheckMiss\n54\t\tUnverified []PostcheckMiss\n55\t\t// Shown / Followed are the $0 observability channel of the unverified wire: how many unsigned rows\n56\t\t// were actually PUT IN FRONT of the model here, and how many of those it went along with. Without the\n57\t\t// denominator \"3 deviations\" is unreadable — 3 out of 3 is a channel nobody follows, 3 out of 90 is\n58\t\t// a model exercising the judgement the unverified section explicitly grants it.\n59\t\tShown    int\n60\t\tFollowed int\n61\t}\n62\t\n63\t// ConfirmedCount is the actionable consistency-failure count — the ONLY number allowed to gate.\n64\tfunc (r PostcheckResult) ConfirmedCount() int { return len(r.Confirmed) }\n65\t\n66\t// All returns every miss, confirmed first, for the human-facing detail blob.\n67\tfunc (r PostcheckResult) All() []PostcheckMiss {\n68\t\tif len(r.Unverified) == 0 {\n69\t\t\treturn r.Confirmed\n70\t\t}\n71\t\tout := make([]PostcheckMiss, 0, len(r.Confirmed)+len(r.Unverified))\n72\t\treturn append(append(out, r.Confirmed...), r.Unverified...)\n73\t}\n74\t\n75\t// Empty reports whether nothing at all was missed.\n76\tfunc (r PostcheckResult) Empty() bool { return len(r.Confirmed) == 0 && len(r.Unverified) == 0 }\n77\t\n78\t// Postcheck runs the decl-aware check over the injected records against the model\n79\t// output. Pure and deterministic. Returns the misses split by trust (empty = all fired terms present),\n80\t// plus the shown/followed counters of the unverified channel.\n81\tfunc (b *Bank) Postcheck(injected []PickedEntry, output string) PostcheckResult {\n82\t\tnout := text.NormalizeTargetForm(output)\n83\t\tvar res PostcheckResult\n84\t\tnoutRunes := []rune(nout)\n85\t\toutWords := lang.TokenizeWords(nout) // once for the whole chunk; the §3 stem branch reuses it per term\n86\t\tfor _, p := range injected {\n87\t\t\tif !p.valid() { // a caller-built zero record carries no row — nothing to check (pack-16 tail)\n88\t\t\t\tcontinue\n89\t\t\t}\n90\t\t\tif p.Sticky { // sticky context is not expected in the output\n91\t\t\t\tcontinue\n92\t\t\t}\n93\t\t\tif strings.TrimSpace(p.entry.dst) == \"\" { // a ruby candidate with no dst yet — nothing to check\n94\t\t\t\tcontinue\n95\t\t\t}\n96\t\t\tpresent := dstFormPresent(p.entry, noutRunes, outWords, b.stemmer)\n97\t\t\tif p.Disp != Confirmed {\n98\t\t\t\tres.Shown++\n99\t\t\t\tif present {\n100\t\t\t\t\tres.Followed++\n101\t\t\t\t}\n102\t\t\t}\n103\t\t\tif present {\n104\t\t\t\tcontinue\n105\t\t\t}\n106\t\t\tm := PostcheckMiss{Src: p.entry.src, Dst: p.entry.dst, Disp: string(p.Disp)}\n107\t\t\tif p.Disp == Confirmed {\n108\t\t\t\tres.Confirmed = append(res.Confirmed, m)\n109\t\t\t} else {\n110\t\t\t\tres.Unverified = append(res.Unverified, m)\n111\t\t\t}\n112\t\t}\n113\t\treturn res\n114\t}\n115\t\n116\t// dstFormPresent reports whether any accepted dst form of the entry appears in the\n117\t// normalized output as a WHOLE WORD (bounded by non-letters or string edges), not a bare\n118\t// substring. The whole-word rule closes self-review #3: a DROPPED short translit name\n119\t// («Ван») must not be masked by an unrelated common word that merely contains its letters\n120\t// («караВАН», «диВАН», «ИВАН» — a different person). Decl-aware (the research/14\n121\t// requirement): the stored decl forms carry the inflections a boundary regexp would miss.\n122\t//\n123\t// The accepted set is the UNION of the base dst AND the stored decl forms (D24.4 — D24.3 is\n124\t// the max_tokens floor, a different fix; the union is the post-check verdict change):\n125\t// the base translation is itself an approved form, so it is ALWAYS checked — not only as a\n126\t// fallback when decl is empty. Checking it only on empty decl false-flagged 37/55\n127\t// acceptance-stage-A misses (nominative_gap): an approved dst present in the NOMINATIVE\n128\t// while `decl.forms` listed only oblique cases (方源→Фан Юань present in the output, decl\n129\t// carried only genitive/dative). Requiring the base form to be duplicated into decl is\n130\t// redundant input every future book would trip on.\n131\t//\n132\t// PRECISION/RECALL TRADEOFF, not a free win (adversarial review, pkg 5): enlarging the\n133\t// accepted set is monotonic — it can only turn misses into passes, never the reverse. On\n134\t// the measured stage-A data those removed misses are all FALSE positives on correctly\n135\t// rendered nominatives (precision up, the 37/55). The one way it can COST recall is narrow\n136\t// but real: an entry whose decl OMITS the nominative AND whose base dst is a common word\n137\t// that recurs elsewhere while the term itself was DROPPED for its firing occurrence\n138\t// (剑→меч dropped as «клинок», yet an unrelated «меч» sits elsewhere) — this bag-of-words\n139\t// presence check then passes and masks the drop. That class is rare on real data (translit\n140\t// names do not recur coincidentally) and TOLERABLE only while this stays a FLAGGER: a\n141\t// hard-gate promotion (postcheck_gate) MUST re-measure recall on common-noun terms, not\n142\t// assume it — the \"recall unaffected\" phrasing in the D24.4 rationale is imprecise here.\n143\t// The remaining inflection_gap (18/55: a plural rendered but only the singular seeded) is a\n144\t// SEED completeness fix (Polygon), not a code one. noutRunes is the normalized output\n145\t// pre-decomposed.\n146\tfunc dstFormPresent(e *entry, noutRunes []rune, outWords []string, stemmer lang.TargetStemmer) bool {\n147\t\tif base := text.NormalizeTargetForm(e.dst); base != \"\" {\n148\t\t\tif containsWholeWord(noutRunes, []rune(base)) {\n149\t\t\t\treturn true\n150\t\t\t}\n151\t\t\t// §3: accept an OBLIQUE case of the base when the seed listed no decl forms (the measured 142/142-null\n152\t\t\t// noise). A component-wise stem match — «горы Цинмао» for «гора Цинмао», «мечом» for «меч» — with no\n153\t\t\t// assumption about which word inflects. Inert for a target with no decl_suffix registry.\n154\t\t\tif declinedFormPresent(outWords, base, stemmer) {\n155\t\t\t\treturn true\n156\t\t\t}\n157\t\t}\n158\t\tfor _, f := range e.declForms {\n159\t\t\tif f != \"\" && containsWholeWord(noutRunes, []rune(f)) {\n160\t\t\t\treturn true\n161\t\t\t}\n162\t\t}\n163\t\treturn false\n164\t}\n165\t\n166\t// declinedFormPresent reports whether the base rendering appears in the tokenized output with each of its\n167\t// words possibly inflected — a window of outWords the same length as base's words where every component\n168\t// shares its stem. Conservative (needs the whole phrase present, in order) and inert without a stemmer.\n169\tfunc declinedFormPresent(outWords []string, base string, stemmer lang.TargetStemmer) bool {\n170\t\tif !stemmer.Enabled() {\n171\t\t\treturn false\n172\t\t}\n173\t\tbw := lang.TokenizeWords(base)\n174\t\tn := len(bw)\n175\t\tif n == 0 || n > len(outWords) {\n176\t\t\treturn false\n177\t\t}\n178\t\tfor i := 0; i+n <= len(outWords); i++ {\n179\t\t\tall := true\n180\t\t\tfor k := 0; k < n; k++ {\n181\t\t\t\tif !stemmer.SameStem(outWords[i+k], bw[k]) {\n182\t\t\t\t\tall = false\n183\t\t\t\t\tbreak\n184\t\t\t\t}\n185\t\t\t}\n186\t\t\tif all {\n187\t\t\t\treturn true\n188\t\t\t}\n189\t\t}\n190\t\treturn false\n191\t}\n192\t\n193\t// containsWholeWord reports whether form occurs in hay with a letter-boundary on both\n194\t// ends (the char before the match is not a letter or is the start; likewise after). Runs\n195\t// on runes so Cyrillic boundaries are correct. O(len(hay)·len(form)) — fine, the\n196\t// post-check runs once per chunk over a handful of short forms.\n197\tfunc containsWholeWord(hay, form []rune) bool {\n198\t\tn := len(form)\n199\t\tif n == 0 || n > len(hay) {\n200\t\t\treturn false\n201\t\t}\n202\t\tfor i := 0; i+n <= len(hay); i++ {\n203\t\t\tif !text.RunesEqual(hay[i:i+n], form) {\n204\t\t\t\tcontinue\n205\t\t\t}\n206\t\t\tif (i == 0 || !unicode.IsLetter(hay[i-1])) && (i+n == len(hay) || !unicode.IsLetter(hay[i+n])) {\n207\t\t\t\treturn true\n208\t\t\t}\n209\t\t}\n210\t\treturn false\n211\t}\n212\t\n213\t// SpoilerLeak is one rendering the spoiler window REJECTED for this chapter that appeared in the output\n214\t// anyway — the self-inflicted spoiler the windows exist to prevent (D21 п.3 / research/15 §2.2).\n215\ttype SpoilerLeak struct {\n216\t\tSrc string `json:\"src\"`\n217\t\tDst string `json:\"dst\"`\n218\t\t// Since/Until are the window that excluded the row, so the operator sees at a glance whether the\n219\t\t// leak is a premature reveal (before since_ch) or a stale one (after until_ch).\n220\t\tSinceCh int `json:\"since_ch\"`\n221\t\tUntilCh int `json:\"until_ch\"`\n222\t}\n223\t\n224\t// SpoilerLeaks checks the chunk's REJECTED records against the model output: the row's key fired here,\n225\t// the window said the chapter must not know this rendering yet, and the rendering is in the output all\n226\t// the same. It is the reveal half of D21 п.3, built on the machinery that already exists — the selection\n227\t// records its rejects, and dstFormPresent already answers \"is this rendering here\" decl-aware — so it\n228\t// needs no schema and no second matcher.\n229\t//\n230\t// SCOPE, ratified (D39.55): \"a leak on a FIRED key\". Two classes are therefore OUT, and out on purpose\n231\t// rather than by oversight:\n232\t//\n233\t//   - a STICKY carry rejected by the window (Sticky==true). Its key did NOT fire in this chunk, so the\n234\t//     model was not looking at the entity here; counting it would silently widen the ratified scope on\n235\t//     the back of the pack-19 fix that put those carries into Rejected at all;\n236\t//   - a SOURCE-ANCHORED reveal — an identity twist whose source surface never occurs (the text says\n237\t//     \"the stranger\", not the name). Nothing fires, so nothing is rejected, so there is nothing to\n238\t//     check. Closing that class needs a target-side index of post-reveal renderings, which is a\n239\t//     different mechanism and not this pack's.\n240\t//\n241\t// Pure and deterministic (rejected order, which Select fixes). Observability only — never a disposition.\n242\tfunc (b *Bank) SpoilerLeaks(rejected []PickedEntry, output string) []SpoilerLeak {\n243\t\tnorm := text.NormalizeTargetForm(output)\n244\t\tnout := []rune(norm)\n245\t\toutWords := lang.TokenizeWords(norm)\n246\t\tvar out []SpoilerLeak\n247\t\tfor _, p := range rejected {\n248\t\t\tif !p.valid() || p.Sticky {\n249\t\t\t\tcontinue\n250\t\t\t}\n251\t\t\tif strings.TrimSpace(p.entry.dst) == \"\" {\n252\t\t\t\tcontinue // nothing to leak\n253\t\t\t}\n254\t\t\tif !dstFormPresent(p.entry, nout, outWords, b.stemmer) {\n255\t\t\t\tcontinue\n256\t\t\t}\n257\t\t\tout = append(out, SpoilerLeak{\n258\t\t\t\tSrc: p.entry.src, Dst: p.entry.dst,\n259\t\t\t\tSinceCh: p.entry.sinceCh, UntilCh: p.entry.untilCh,\n260\t\t\t})\n261\t\t}\n262\t\treturn out\n263\t}\n264\t"}]},"uuid":"c52351f9-34f3-4437-a897-68bfea2e4da9","timestamp":"2026-08-01T15:01:14.693Z","sourceToolAssistantUUID":"9a53267f-9ae1-4492-b83a-aa1daa97417e","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
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{"parentUuid":"11dd96bd-58bb-4c25-87dd-d92834b7f22f","isSidechain":true,"promptId":"c81fa375-6b68-4d22-8364-68be617228f6","agentId":"aec521429a7581efa","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PmVe7gBouDA3LrAwbHxer2","type":"tool_result","content":"1\tpackage lang\n2\t\n3\timport (\n4\t\t\"sort\"\n5\t\t\"strings\"\n6\t\n7\t\t\"textmachine/backend/internal/text\"\n8\t)\n9\t\n10\t// stemmer.go: the conservative TARGET stemmer (bank-quality §3, primitive C). It strips one inflectional\n11\t// ending off a word to get its stem, so a decl-aware check accepts an oblique case («мечом») against its\n12\t// nominative («меч») WITHOUT the seed listing every form — the fix for the decl-142/142-null false-miss\n13\t// noise, and the backstop the checker omission-detector shares. The ALGORITHM is generic; the ending REGISTRY\n14\t// is target data (decl_suffix in data/target-<tgt>.txt), so a target with none gets an inert stemmer (exact\n15\t// match only). A FLAGGER aid, biased to ACCEPT declensions; it never gates.\n16\t\n17\t// minStemRunes is the shortest stem a suffix strip may leave. Below it a \"stem\" is too short to distinguish\n18\t// two words (стripping «а» off «дома» is fine → «дом»; stripping it off «яма» to «ям» is not worth trusting),\n19\t// so the ending is not stripped and the word stands whole. Conservative on the side of NOT conflating words.\n20\tconst minStemRunes = 3\n21\t\n22\t// TargetStemmer strips a registry ending off a word. The zero value is a valid inert stemmer (no endings →\n23\t// Stem is the identity → SameStem is exact equality), which is what a target with no decl_suffix data gets.\n24\ttype TargetStemmer struct {\n25\t\tsuffixes []string // registry endings, longest-first\n26\t}\n27\t\n28\t// NewTargetStemmer builds the stemmer from a target's decl_suffix registry. No registry → an inert stemmer.\n29\tfunc NewTargetStemmer(tc TargetChecks) TargetStemmer {\n30\t\tsfx := append([]string(nil), tc.List(\"decl_suffix\")...)\n31\t\t// Longest-first so Stem strips the most specific ending (е.g. «ому» before «у»); ties by bytes for a\n32\t\t// stable, deterministic order.\n33\t\tsort.SliceStable(sfx, func(i, j int) bool {\n34\t\t\tif a, b := len([]rune(sfx[i])), len([]rune(sfx[j])); a != b {\n35\t\t\t\treturn a > b\n36\t\t\t}\n37\t\t\treturn sfx[i] < sfx[j]\n38\t\t})\n39\t\treturn TargetStemmer{suffixes: sfx}\n40\t}\n41\t\n42\t// Enabled reports whether the stemmer carries any endings — false for a target with no decl_suffix data, so a\n43\t// consumer can skip the whole stem branch.\n44\tfunc (s TargetStemmer) Enabled() bool { return len(s.suffixes) > 0 }\n45\t\n46\t// Stem returns word with its longest registry ending removed, provided the remaining stem stays ≥\n47\t// minStemRunes; otherwise the word is returned unchanged. Case-insensitive (folded to lower), so a match is\n48\t// case-blind. An empty or all-short word returns folded-as-is.\n49\tfunc (s TargetStemmer) Stem(word string) string {\n50\t\tw := strings.ToLower(word)\n51\t\trs := []rune(w)\n52\t\tif len(rs) < minStemRunes {\n53\t\t\treturn w\n54\t\t}\n55\t\tfor _, suf := range s.suffixes {\n56\t\t\tsr := []rune(suf)\n57\t\t\tif len(rs)-len(sr) < minStemRunes {\n58\t\t\t\tcontinue // stripping this ending would leave too short a stem\n59\t\t\t}\n60\t\t\tif strings.HasSuffix(w, suf) {\n61\t\t\t\treturn string(rs[:len(rs)-len(sr)])\n62\t\t\t}\n63\t\t}\n64\t\treturn w\n65\t}\n66\t\n67\t// SameStem reports whether two words share a stem under this stemmer — the decl-aware equality a consumer\n68\t// uses to accept one form of a word against another. Two identical words trivially match; the value the\n69\t// stemmer adds is matching «мечом»/«меча»/«меч». Never true for an inert stemmer beyond exact equality.\n70\tfunc (s TargetStemmer) SameStem(a, b string) bool {\n71\t\tla, lb := strings.ToLower(a), strings.ToLower(b)\n72\t\tif la == lb {\n73\t\t\treturn true\n74\t\t}\n75\t\tif !s.Enabled() {\n76\t\t\treturn false\n77\t\t}\n78\t\treturn s.Stem(la) == s.Stem(lb)\n79\t}\n80\t\n81\t// TokenizeWords splits a target string into its whole words (maximal letter runs), lower-cased. Script-\n82\t// generic (any Unicode letter), so it needs no per-language branch. Used to match a declined multi-word term\n83\t// head against the output. It delegates to text.TokenizeLetters — the ONE tokenizer, shared with the target\n84\t// lexical checkers' TokenizeScript, so a stress-accented output word no longer splits mid-word here either.\n85\tfunc TokenizeWords(s string) []string { return text.TokenizeLetters(s) }\n86\t"}]},"uuid":"af7832c9-a9d5-40b8-9314-f91f1f1382d9","timestamp":"2026-08-01T15:01:15.691Z","sourceToolAssistantUUID":"11dd96bd-58bb-4c25-87dd-d92834b7f22f","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
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{"parentUuid":"1578fd4e-34fe-48e9-9060-aa26c87d02c3","isSidechain":true,"promptId":"c81fa375-6b68-4d22-8364-68be617228f6","agentId":"aec521429a7581efa","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TbFU6h4RL7sGM4Ssepyhfg","type":"tool_result","content":"1\t# Target-language (ru) readability checker DATA (pair-14 data-out): the wordlists and DETECTION patterns the\n2\t# TARGET-GENERAL checkers/sanitizer run on ANY →ru output (regardless of source pair). Engine/target data —\n3\t# a no-langpack →ru book (the ja→ru golden) still runs these — so it is embedded here, not a book pack; the\n4\t# checker/sanitizer ALGORITHMS stay generic Go. `category<TAB>value`; value VERBATIM (a regex or a literal);\n5\t# lines are grouped by category IN ORDER (order matters for the sanitizer preamble preview). Add a target =\n6\t# add data/target-<tgt>.txt to the go:embed manifest (the target registry DERIVES from the file name).\n7\t#\n8\t# word_script: the target's WORD ALPHABET — the Unicode letter script the lexical checkers tokenize words on\n9\t# (whole-word wordlist match, broken-word/yofikation scan, latin-residue detection). The engine resolves the\n10\t# NAME to a Unicode range; it never branches on a language. `cyrillic` is the range for ru (unicode.Cyrillic).\n11\tword_script\tcyrillic\n12\t#\n13\t# broken-word: a Russian word ending in this suffix is a mangled infinitive (no valid word ends in «-йть»).\n14\tbroken_suffix\tйть\n15\t# speech-verb attribution: the verbs that make a chevron-quoted line SPOKEN dialogue rather than a\n16\t# thought. The dialogue-dash MIXING rule counts a chevron line only when its attribution carries one of\n17\t# these, i.e. the DEFAULT for an unrecognised attribution is \"not a style clash\" — precision over recall,\n18\t# the gate's stated bias. The inverse list (thought verbs) was tried first and proved unbounded: a live\n19\t# 2-chapter run immediately produced «понимал» and «размышлял», which no enumeration of thought verbs\n20\t# would have covered, while the speech-verb side is small and closed. Lower-cased, substring match.\n21\tspeech_verb\tсказал\n22\tspeech_verb\tсказала\n23\tspeech_verb\tответил\n24\tspeech_verb\tответила\n25\tspeech_verb\tспросил\n26\tspeech_verb\tспросила\n27\tspeech_verb\tкрикнул\n28\tspeech_verb\tкрикнула\n29\tspeech_verb\tвоскликнул\n30\tspeech_verb\tвоскликнула\n31\tspeech_verb\tпрошептал\n32\tspeech_verb\tпрошептала\n33\tspeech_verb\tпробормотал\n34\tspeech_verb\tпробормотала\n35\tspeech_verb\tпроговорил\n36\tspeech_verb\tпроговорила\n37\tspeech_verb\tпродолжил\n38\tspeech_verb\tпродолжила\n39\tspeech_verb\tдобавил\n40\tspeech_verb\tдобавила\n41\tspeech_verb\tвозразил\n42\tspeech_verb\tвозразила\n43\tspeech_verb\tпозвал\n44\tspeech_verb\tпозвала\n45\tspeech_verb\tобъявил\n46\tspeech_verb\tобъявила\n47\tspeech_verb\tпроизнёс\n48\tspeech_verb\tпроизнес\n49\tspeech_verb\tпроизнесла\n50\t# inner-speech VETO markers: a qualifier that makes a line inner speech even when its attribution verb\n51\t# is a speech verb — «пробормотал ПРО СЕБЯ» is a thought, not a spoken turn. Checked after speech_verb,\n52\t# so the pair (verb + veto) reproduces the writer's actual intent instead of the verb alone. Found by the\n53\t# 26.07 live verification: it was the single flag surviving on the real 10-chapter export.\n54\tinner_marker\tпро себя\n55\tinner_marker\tмысленно\n56\tinner_marker\tв мыслях\n57\tinner_marker\tсебе под нос\n58\tinner_marker\tв уме\n59\t# yofikator homograph whitelist: е-spellings that are DISTINCT words from their ё-counterpart (все≠всё,\n60\t# небо≠нёбо, берет≠берёт …), so the inconsistency rule must NOT flag «все»+«всё» as one word two ways.\n61\tyo_homograph\tвсе\n62\tyo_homograph\tвсех\n63\tyo_homograph\tвсем\n64\tyo_homograph\tвсеми\n65\tyo_homograph\tнебо\n66\tyo_homograph\tнебом\n67\tyo_homograph\tузнаем\n68\tyo_homograph\tузнаете\n69\tyo_homograph\tузнает\n70\tyo_homograph\tпадеж\n71\tyo_homograph\tпадежа\n72\tyo_homograph\tсовершенный\n73\tyo_homograph\tсовершенное\n74\tyo_homograph\tсовершенная\n75\tyo_homograph\tсовершенно\n76\tyo_homograph\tсовершенны\n77\tyo_homograph\tчем\n78\tyo_homograph\tтем\n79\tyo_homograph\tтема\n80\tyo_homograph\tтеме\n81\tyo_homograph\tтемы\n82\tyo_homograph\tберет\n83\tyo_homograph\tберете\n84\tyo_homograph\tберета\n85\tyo_homograph\tосел\n86\tyo_homograph\tосла\n87\tyo_homograph\tослы\n88\tyo_homograph\tослов\n89\tyo_homograph\tмел\n90\tyo_homograph\tмела\n91\tyo_homograph\tлен\n92\tyo_homograph\tлена\n93\tyo_homograph\tнес\n94\tyo_homograph\tнесем\n95\tyo_homograph\tнесет\n96\tyo_homograph\tвел\n97\tyo_homograph\tведет\n98\tyo_homograph\tведем\n99\t# magnitude_stem: Russian magnitude WORD stem → base-10 exponent, for the number-magnitude gate (a stem\n100\t# covers [base, base+2] since an unseen multiplier can lift it two orders). `magnitude_stem<TAB>stem<TAB>exp`.\n101\tmagnitude_stem\tтысяч\t3\n102\tmagnitude_stem\tмиллион\t6\n103\tmagnitude_stem\tмиллиард\t9\n104\tmagnitude_stem\tтриллион\t12\n105\t# sanitizer DETECTION patterns (pair-14 data-out). The sanitizer is isRuTarget-gated, so these are\n106\t# ru-target data; the strip/detect ALGORITHM stays in sanitizer.go. `sanitizer_preamble` is ORDERED\n107\t# (detectLeadingPreamble returns the FIRST match's preview). Values VERBATIM (regexes).\n108\tsanitizer_preamble\t(?is)^\\s*(?:вот|представляю|привожу|держите)\\s+[^\\n:]{0,40}?(?:отредактированн|исправленн|улучшенн)[а-яё]+\\s+(?:перевод|текст|вариант)[а-яё]*[^\\n:]{0,120}?:\n109\tsanitizer_preamble\t(?is)^\\s*(?:отредактированн|исправленн|улучшенн)[а-яё]+\\s+(?:перевод|текст|вариант)[а-яё]*[^\\n:]{0,120}?:\n110\tsanitizer_preamble\t(?is)^\\s*(?:вот\\s+)?перевод\\s+(?:фрагмента|текста|отрывка|главы|черновика)\\s*:\n111\tsanitizer_preamble\t(?is)^\\s*ниже\\s+(?:привед[её]н|представлен|дан|след)[а-яё]*\\s+[^\\n:]{0,40}?(?:перевод|отредактированн|текст)[а-яё]*[^\\n:]{0,20}?:\n112\tsanitizer_trailing_note\t(?i)^[*_>\\s-]{0,4}(?:(?:примечани[ея]|заметк[аи]|комментари[йяю]|пояснени[ея]|сноск[аи])\\s*:|(?:примечани[ея]|заметк[аи]|комментари[йяю])\\s+(?:переводчик|редактор)[а-яё]*|прим\\.\\s*(?:перев|ред)\\.?)\n113\tsanitizer_edit_meta\t(?im)^[*_>\\s-]{0,4}(?:(?:внесённ|внесен)[а-яё]+\\s+правк|основн[а-яё]+\\s+правк[а-яё]*(?:\\s+для\\s+справк[а-яё]+|\\s*:)|что\\s+(?:было\\s+)?(?:изменен|исправлен)|список\\s+(?:правок|изменений)|список\\s+внесённых)\n114\tsanitizer_invalid_sign\t(?i)(?:^|\\P{L})[ъь][а-яё]|[ъь][ъь]\n115\t# --- pack-19 voice flagger (T/V + attributed-reply checks) -----------------------------------------\n116\t# The T/V distinction is a fact about the TARGET language, so it lives here: a target that ships no\n117\t# tv_informal/tv_formal rows runs the whole voice flagger inert, with no Go edit. Surfaces are matched\n118\t# as WHOLE WORDS on the normalized output, lower-cased; only PRONOUNS and possessives are listed —\n119\t# verb agreement is morphology and belongs to the ru-target layer, not to a $0 flagger.\n120\ttv_informal\tты\n121\ttv_informal\tтебя\n122\ttv_informal\tтебе\n123\ttv_informal\tтобой\n124\ttv_informal\tтобою\n125\ttv_informal\tтвой\n126\ttv_informal\tтвоя\n127\ttv_informal\tтвоё\n128\ttv_informal\tтвое\n129\ttv_informal\tтвои\n130\ttv_informal\tтвоего\n131\ttv_informal\tтвоей\n132\ttv_informal\tтвоих\n133\ttv_informal\tтвоим\n134\ttv_informal\tтвою\n135\ttv_formal\tвы\n136\ttv_formal\tвас\n137\ttv_formal\tвам\n138\ttv_formal\tвами\n139\ttv_formal\tваш\n140\ttv_formal\tваша\n141\ttv_formal\tваше\n142\ttv_formal\tваши\n143\ttv_formal\tвашего\n144\ttv_formal\tвашей\n145\ttv_formal\tваших\n146\ttv_formal\tвашим\n147\ttv_formal\tвашу\n148\t# tv_plural_veto: Russian «вы» is ALSO plural-you, so a reply addressing several people legitimately\n149\t# mixes «ты» and «вы» («Ты иди, а вы оба ждите»). A reply carrying one of these markers is not counted\n150\t# as a T/V contradiction — the same veto shape inner_marker has over speech_verb, and for the same\n151\t# precision-over-recall reason: a miss here is silence on a real clash, not noise on good prose.\n152\ttv_plural_veto\tоба\n153\ttv_plural_veto\tобе\n154\ttv_plural_veto\tвдвоём\n155\ttv_plural_veto\tвтроём\n156\ttv_plural_veto\tвсе вы\n157\ttv_plural_veto\tвы все\n158\ttv_plural_veto\tгоспода\n159\ttv_plural_veto\tкаждый из вас\n160\t# self_ref_generic: the PLAIN first-person surfaces. They are the evidence that a character referred to\n161\t# herself at all — the check is «she said \"я\" where her profile says \"ваша служанка\"», i.e. the measured\n162\t# flattening class (research/15 §Пробы, marker 奴婢), never «she said \"я\"» on its own.\n163\tself_ref_generic\tя\n164\tself_ref_generic\tменя\n165\tself_ref_generic\tмне\n166\tself_ref_generic\tмной\n167\tself_ref_generic\tмною\n168\t# reply segmentation: how this target sets direct speech. reply_dash opens a dash-marked turn at line\n169\t# start; reply_open/reply_close bound a quoted turn. Data, not a Go literal, so a target with other\n170\t# conventions (ja 「」) states its own without touching the segmentation algorithm.\n171\treply_dash\t—\n172\treply_open\t«\n173\treply_close\t»\n174\t# translit-interjection blocklist (04-unhappy §9): JP/EN fillers that must be TRANSLATED/adapted, not\n175\t# transliterated into the target. Only surfaces that are NOT ordinary target words are listed (ара/уму were\n176\t# removed as high-FP collisions). The blocklist is TARGET data now (was a Go literal); the whole-word,\n177\t# case-insensitive matching ALGORITHM stays generic. D20.4: only the exact hyphenated reduplications listed\n178\t# fire — a generic «X-X» rule was rejected as too false-positive-prone. Extend per corpus finding.\n179\ttranslit_interjection\tара-ара\n180\ttranslit_interjection\tмаа\n181\ttranslit_interjection\tхмф\n182\ttranslit_interjection\tнани\n183\ttranslit_interjection\tауч\n184\ttranslit_interjection\tупс\n185\ttranslit_interjection\tкья\n186\ttranslit_interjection\tдесу\n187\t# decl_suffix: the productive Russian inflectional endings the decl-aware post-check stems a word by\n188\t# (bank-quality §3, primitive C). It lets dstFormPresent accept a term rendered in an oblique case\n189\t# («Фан Юаню» for «Фан Юань») WITHOUT the seed listing every form, closing the measured decl-142/142-null\n190\t# false-miss noise. TARGET data — the suffix set is ru-specific; the stemmer ALGORITHM (strip the longest\n191\t# listed ending that leaves a stem ≥ min length) stays generic Go and a target with no decl_suffix rows\n192\t# gets no stemming (inert). It is a FLAGGER aid, biased to ACCEPT declensions; it never gates. Ordered\n193\t# longest-first in code, so listing order here is free.\n194\tdecl_suffix\tами\n195\tdecl_suffix\tями\n196\tdecl_suffix\tого\n197\tdecl_suffix\tего\n198\tdecl_suffix\tому\n199\tdecl_suffix\tему\n200\tdecl_suffix\tыми\n201\tdecl_suffix\tими\n202\tdecl_suffix\tах\n203\tdecl_suffix\tях\n204\tdecl_suffix\tов\n205\tdecl_suffix\tев\n206\tdecl_suffix\tом\n207\tdecl_suffix\tём\n208\tdecl_suffix\tем\n209\tdecl_suffix\tой\n210\tdecl_suffix\tей\n211\tdecl_suffix\tый\n212\tdecl_suffix\tий\n213\tdecl_suffix\tая\n214\tdecl_suffix\tяя\n215\tdecl_suffix\tое\n216\tdecl_suffix\tее\n217\tdecl_suffix\tым\n218\tdecl_suffix\tим\n219\tdecl_suffix\tых\n220\tdecl_suffix\tих\n221\tdecl_suffix\tую\n222\tdecl_suffix\tюю\n223\tdecl_suffix\tа\n224\tdecl_suffix\tя\n225\tdecl_suffix\tу\n226\tdecl_suffix\tю\n227\tdecl_suffix\tе\n228\tdecl_suffix\tы\n229\tdecl_suffix\tи\n230\tdecl_suffix\tо\n231\t# NB (fix-pack §а, D39.71): the bare SOFT SIGN «ь» is deliberately NOT a decl_suffix. It would close the\n232\t# Palladius soft-sign case («Фан Юаню»~«Фан Юань» via the stem «юан»), but that strip is byte-identical in\n233\t# shape to a HARMFUL collision — «Синь»/«Линь» (top zh surnames) collapse onto «син»/«лин», the stems of the\n234\t# common words «синий»/«линия», which fires a spurious Bank.SpoilerLeaks false-positive (the noise direction).\n235\t# «юань»→«юан» and «синь»→«син» are the same 4-rune-soft-sign→3-rune-stem shape, so no min-stem rule separates\n236\t# them. The soft-sign nominative gap needs seed decl-form enumeration or an anchor-gated match, not this global\n237\t# suffix — see the fix-pack report §а ping.\n238\t"}]},"uuid":"8b62fe70-305c-468f-bfdf-f62c1b480d3f","timestamp":"2026-08-01T15:01:16.197Z","sourceToolAssistantUUID":"1578fd4e-34fe-48e9-9060-aa26c87d02c3","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
{"parentUuid":"8b62fe70-305c-468f-bfdf-f62c1b480d3f","isSidechain":true,"agentId":"aec521429a7581efa","message":{"model":"claude-opus-4-8","id":"msg_011CdcEFrwp4EBT7A2JtFTTb","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017MoHnv76KaKeD4pzqy4Yhr","name":"Bash","input":{"command":"grep -rn \"SameStem\\|\\.Stem(\\|TargetStemmer\\|Stemmer\" internal/ --include=*.go | grep -v _test.go","description":"Find all stemmer usages in backend/internal"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":19483,"cache_read_input_tokens":0,"output_tokens":834,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":19483},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":834,"cache_read_input_tokens":0,"cache_creation_input_tokens":19483,"cache_creation":{"ephemeral_5m_input_tokens":19483,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CdcEFqjPDa5p6dNSrm8oP","attributionAgent":"general-purpose","type":"assistant","uuid":"b6997dc9-6ac4-40ae-814e-c7fe8e03fff2","timestamp":"2026-08-01T15:01:17.301Z","effort":"xhigh","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
{"parentUuid":"b6997dc9-6ac4-40ae-814e-c7fe8e03fff2","isSidechain":true,"promptId":"c81fa375-6b68-4d22-8364-68be617228f6","agentId":"aec521429a7581efa","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017MoHnv76KaKeD4pzqy4Yhr","type":"tool_result","content":"internal/lang/stemmer.go:22:// TargetStemmer strips a registry ending off a word. The zero value is a valid inert stemmer (no endings →\ninternal/lang/stemmer.go:23:// Stem is the identity → SameStem is exact equality), which is what a target with no decl_suffix data gets.\ninternal/lang/stemmer.go:24:type TargetStemmer struct {\ninternal/lang/stemmer.go:28:// NewTargetStemmer builds the stemmer from a target's decl_suffix registry. No registry → an inert stemmer.\ninternal/lang/stemmer.go:29:func NewTargetStemmer(tc TargetChecks) TargetStemmer {\ninternal/lang/stemmer.go:39:\treturn TargetStemmer{suffixes: sfx}\ninternal/lang/stemmer.go:44:func (s TargetStemmer) Enabled() bool { return len(s.suffixes) > 0 }\ninternal/lang/stemmer.go:49:func (s TargetStemmer) Stem(word string) string {\ninternal/lang/stemmer.go:67:// SameStem reports whether two words share a stem under this stemmer — the decl-aware equality a consumer\ninternal/lang/stemmer.go:70:func (s TargetStemmer) SameStem(a, b string) bool {\ninternal/lang/stemmer.go:78:\treturn s.Stem(la) == s.Stem(lb)\ninternal/checks/voice.go:369:// stemmer (lang.TargetStemmer) is the right tool for the POST-CHECK, where a miss is uniformly the safe\ninternal/membank/memory.go:180:\tstemmer lang.TargetStemmer\ninternal/membank/memory.go:257:\t// TargetStemmer is the decl-aware post-check stemmer (bank-quality §3). Zero value → inert (exact match\ninternal/membank/memory.go:259:\tTargetStemmer lang.TargetStemmer\ninternal/membank/memory.go:275:\tb := &Bank{keyOwners: map[string][]int{}, voices: in.Voices, pairs: in.Pairs, stemmer: in.TargetStemmer}\ninternal/pipeline/seeding.go:138:\t\tTargetStemmer: lang.NewTargetStemmer(lang.TargetChecksFor(r.Book.TargetLang))}\ninternal/membank/mempostcheck.go:146:func dstFormPresent(e *entry, noutRunes []rune, outWords []string, stemmer lang.TargetStemmer) bool {\ninternal/membank/mempostcheck.go:169:func declinedFormPresent(outWords []string, base string, stemmer lang.TargetStemmer) bool {\ninternal/membank/mempostcheck.go:181:\t\t\tif !stemmer.SameStem(outWords[i+k], bw[k]) {","is_error":false}]},"uuid":"664e9748-40b1-471b-b836-ed560c31bfe2","timestamp":"2026-08-01T15:01:17.384Z","sourceToolAssistantUUID":"b6997dc9-6ac4-40ae-814e-c7fe8e03fff2","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
{"parentUuid":"664e9748-40b1-471b-b836-ed560c31bfe2","isSidechain":true,"agentId":"aec521429a7581efa","message":{"model":"claude-opus-4-8","id":"msg_011CdcEGbDYDvHgFLcbcxKo5","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":15510,"cache_read_input_tokens":19483,"cache_creation":{"ephemeral_5m_input_tokens":15510,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CdcEGaXruEV9kff3wF8K7","attributionAgent":"general-purpose","type":"assistant","uuid":"5be68ac8-8c08-46ae-8e30-dcd6f96d2d24","timestamp":"2026-08-01T15:01:21.818Z","effort":"xhigh","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
{"parentUuid":"5be68ac8-8c08-46ae-8e30-dcd6f96d2d24","isSidechain":true,"agentId":"aec521429a7581efa","message":{"model":"claude-opus-4-8","id":"msg_011CdcEGbDYDvHgFLcbcxKo5","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me examine the seeding context, the voice.go stemmer usage, and the corpus bank structure."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":15510,"cache_read_input_tokens":19483,"cache_creation":{"ephemeral_5m_input_tokens":15510,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CdcEGaXruEV9kff3wF8K7","attributionAgent":"general-purpose","type":"assistant","uuid":"cc0ad5a6-5089-4086-9b0a-2443532fd75f","timestamp":"2026-08-01T15:01:22.318Z","effort":"xhigh","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
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{"parentUuid":"969b554c-bfb1-46cd-a6b3-4805b0b517fd","isSidechain":true,"promptId":"c81fa375-6b68-4d22-8364-68be617228f6","agentId":"aec521429a7581efa","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017kEETsxTnu1xNbajUPTPEt","type":"tool_result","content":"120\t\t}\n121\t\tstoredPairs, err := r.Store.AddressPairsForBook(r.Book.BookID)\n122\t\tif err != nil {\n123\t\t\treturn fmt.Errorf(\"pipeline: read address pairs for %s: %w\", r.Book.BookID, err)\n124\t\t}\n125\t\t// A chapter range no profile covers is legitimate but is far more often a typo, and it is invisible in\n126\t\t// a seed file — so it is logged, never fatal (the optional lint of D39.55).\n127\t\tif gaps := membank.VoiceWindowGaps(storedVoices); len(gaps) > 0 {\n128\t\t\tr.Log.WarnContext(ctx, \"voice profile windows leave chapters uncovered (deliberate is fine; a typo is not)\",\n129\t\t\t\t\"book\", r.Book.BookID, \"gaps\", strings.Join(gaps, \"; \"))\n130\t\t}\n131\t\t// InjectVoice is FALSE and has no config knob: pack-19 builds the schema and the flagger, and D21 п.2\n132\t\t// holds the injection conditional until the polygon experiment. It is the fold's condition, so while\n133\t\t// it is false a book with voice rows hashes exactly as it did without them and nobody re-pays for\n134\t\t// authoring a profile. Wiring the injection means setting it and accepting a full --resnapshot.\n135\t\t// The §3 decl stemmer is target data, constant per book; baseIn copies bankIn below, so both banks share\n136\t\t// it. A target with no decl_suffix registry yields an inert stemmer → exact-match post-check as before.\n137\t\tbankIn := membank.BankInput{Rows: rows, Voices: storedVoices, Pairs: storedPairs,\n138\t\t\tTargetStemmer: lang.NewTargetStemmer(lang.TargetChecksFor(r.Book.TargetLang))}\n139\t\tr.memory = membank.MaterializeBank(bankIn, r.Pipeline.Gates.Glossary.PostcheckGate)\n140\t\t// The DRAFT wave selects over a BASE-scoped bank (Source:mined excluded) so its injection is\n141\t\t// byte-identical across a bank-mining enrichment — matching the draft-wave snapshot (baseMemoryVersion),\n142\t\t// which keeps «one re-payment» honest at the WIRE level, not only the version-hash level. Only the\n143\t\t// editor sees mined terms (the enriched `memory`). When there are no mined rows (every $0 test / the\n144\t\t// golden) the base bank IS the enriched one — share the object, no double materialization, no drift.\n145\t\tbaseRows := rows[:0:0]\n146\t\thasMined := false\n147\t\tfor _, row := range rows {\n148\t\t\tif row.Source == \"mined\" {\n149\t\t\t\thasMined = true\n150\t\t\t\tcontinue\n151\t\t\t}\n152\t\t\tbaseRows = append(baseRows, row)\n153\t\t}\n154\t\tif hasMined {\n155\t\t\tbaseIn := bankIn\n156\t\t\tbaseIn.Rows = baseRows\n157\t\t\tr.baseMemory = membank.MaterializeBank(baseIn, r.Pipeline.Gates.Glossary.PostcheckGate)\n158\t\t} else {\n159\t\t\tr.baseMemory = r.memory"}]},"uuid":"8e1e7734-c33b-48af-b2d1-6d0aae643ece","timestamp":"2026-08-01T15:01:23.341Z","sourceToolAssistantUUID":"969b554c-bfb1-46cd-a6b3-4805b0b517fd","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
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{"parentUuid":"9fde2078-9570-476a-b00e-fb62e45efa92","isSidechain":true,"promptId":"c81fa375-6b68-4d22-8364-68be617228f6","agentId":"aec521429a7581efa","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Q5SkmV8miKzKMu5QUH8fVN","type":"tool_result","content":"355\t\t\tswitch {\n356\t\t\tcase hasCue(string(rs[j+1 : hi])):\n357\t\t\t\tn++\n358\t\t\t\tconsumed = hi\n359\t\t\tcase hasCue(string(rs[lo:i])):\n360\t\t\t\tn++\n361\t\t\t\tconsumed = i\n362\t\t\t}\n363\t\t\ti = j\n364\t\t}\n365\t\treturn n\n366\t}\n367\t\n368\t// DECL-AWARE STEMMING IS DELIBERATELY NOT WIRED INTO THESE MATCHERS (fix-pack §е, D39.71). The bank's\n369\t// stemmer (lang.TargetStemmer) is the right tool for the POST-CHECK, where a miss is uniformly the safe\n370\t// direction (a false omission flag) — and where character NAMES already arrive declension-aware, because\n371\t// Forms carries the base dst plus its enumerated decl forms (the same decl.forms[] the bank uses). Stemming\n372\t// on top of that would only add stem-collision MIS-ATTRIBUTION risk to a checker whose whole doctrine is\n373\t// precision over recall. For SelfRef/NG (single surfaces) the accept-biased stemmer is not uniformly safe\n374\t// either: stemming SelfRef would suppress a false self-ref flag on a declined designation (safe), but stemming\n375\t// an NG lexeme would EXPAND the forbidden-word match (a recall boost that manufactures noise) — opposite\n376\t// directions on one checker. A declension-aware SelfRef/NG belongs in the DRIVER (enumerate forms from the\n377\t// seed, as Forms already are), not in a match-time stemmer whose safe direction flips per axis. Recorded here\n378\t// so the decision stays closed rather than silently re-opened.\n379\t\n380\t// wholeWord reports whether form occurs in the normalized haystack bounded by non-word runes.\n381\tfunc wholeWord(hay []rune, form string) bool {\n382\t\tf := []rune(form)\n383\t\tif len(f) == 0 || len(f) > len(hay) {\n384\t\t\treturn false\n385\t\t}\n386\t\tfor i := 0; i+len(f) <= len(hay); i++ {\n387\t\t\tif !text.RunesEqual(hay[i:i+len(f)], f) {\n388\t\t\t\tcontinue\n389\t\t\t}\n390\t\t\tif (i == 0 || !isWordRune(hay[i-1])) && (i+len(f) == len(hay) || !isWordRune(hay[i+len(f)])) {\n391\t\t\t\treturn true\n392\t\t\t}\n393\t\t}\n394\t\treturn false"}]},"uuid":"5158ba1b-2559-4f02-82d6-991b077d9388","timestamp":"2026-08-01T15:01:24.396Z","sourceToolAssistantUUID":"9fde2078-9570-476a-b00e-fb62e45efa92","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
{"parentUuid":"5158ba1b-2559-4f02-82d6-991b077d9388","isSidechain":true,"agentId":"aec521429a7581efa","message":{"model":"claude-opus-4-8","id":"msg_011CdcEGbDYDvHgFLcbcxKo5","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01TrtU8uWqsMtsXAjHbFZCrA","name":"Bash","input":{"command":"ls -la /home/ubuntu/books/gu-zhenren/coldrun-a/ 2>/dev/null; echo \"---BANK head---\"; head -5 /home/ubuntu/books/gu-zhenren/coldrun-a/BANK-FULL.tsv 2>/dev/null; echo \"---wc---\"; wc -l /home/ubuntu/books/gu-zhenren/coldrun-a/BANK-FULL.tsv 2>/dev/null","description":"Inspect the corpus bank file"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":15510,"cache_read_input_tokens":19483,"output_tokens":672,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":15510},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":672,"cache_read_input_tokens":19483,"cache_creation_input_tokens":15510,"cache_creation":{"ephemeral_5m_input_tokens":15510,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CdcEGaXruEV9kff3wF8K7","attributionAgent":"general-purpose","type":"assistant","uuid":"025339c3-80e2-450f-9880-85767db44964","timestamp":"2026-08-01T15:01:26.399Z","effort":"xhigh","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
{"parentUuid":"025339c3-80e2-450f-9880-85767db44964","isSidechain":true,"promptId":"c81fa375-6b68-4d22-8364-68be617228f6","agentId":"aec521429a7581efa","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TrtU8uWqsMtsXAjHbFZCrA","type":"tool_result","content":"total 1964\ndrwxr-xr-x  3 ubuntu ubuntu    4096 Aug  1 05:43 .\ndrwxr-xr-x 23 ubuntu ubuntu    4096 Aug  1 02:54 ..\n-rw-r--r--  1 ubuntu ubuntu   21304 Jul 31 03:44 BANK-FULL.tsv\n-rw-r--r--  1 ubuntu ubuntu    6468 Jul 31 03:35 SIGN-PACKAGE.md\n-rw-r--r--  1 ubuntu ubuntu    3919 Jul 31 00:57 book.yaml\n-rw-r--r--  1 ubuntu ubuntu   57838 Jul 31 00:57 guzhenren-ch1-10.gb18030.txt\n-rw-r--r--  1 ubuntu ubuntu 1527808 Jul 31 03:29 guzhenren-coldrun-a.db\n-rw-r--r--  1 ubuntu ubuntu   32768 Aug  1 05:44 guzhenren-coldrun-a.db-shm\n-rw-r--r--  1 ubuntu ubuntu       0 Aug  1 05:43 guzhenren-coldrun-a.db-wal\n-rw-r--r--  1 ubuntu ubuntu  104259 Jul 31 03:29 guzhenren-coldrun-a.db.bank-stop.txt\n-rw-r--r--  1 ubuntu ubuntu       0 Jul 31 00:58 guzhenren-coldrun-a.db.lock\n-rw-r--r--  1 ubuntu ubuntu   98556 Jul 31 03:29 guzhenren-coldrun-a.db.mined-signature.yaml\n-rw-r--r--  1 ubuntu ubuntu     477 Jul 31 00:58 mined-delta.yaml\n-rw-r--r--  1 ubuntu ubuntu     307 Jul 31 00:58 mined-rejects.yaml\ndrwxr-xr-x  2 ubuntu ubuntu    4096 Jul 31 00:58 pairs\n-rw-r--r--  1 ubuntu ubuntu    4172 Jul 31 00:58 pipeline.yaml\n-rw-r--r--  1 ubuntu ubuntu   24888 Jul 31 01:07 run-01-draft-stop.log\n-rw-r--r--  1 ubuntu ubuntu    9634 Jul 31 02:58 run-02-redraft-stop.log\n-rw-r--r--  1 ubuntu ubuntu   30081 Jul 31 03:11 run-03-draft-fixedprompt.log\n-rw-r--r--  1 ubuntu ubuntu   30798 Jul 31 03:29 run-04-draft-final.log\n---BANK head---\nsrc\tсведённый_dst\torigin\ttype\tfreq\tspread\tварианты_черновиков\tконтекстов\n方源\tФан Юань\tboth\tname\t189\t11\tФан Юань ×11 | Фан Чжэн ×6 | Гу Юэ Фан Юань ×2 | Гу Юэ Фанчжэн ×2 | Гуюэ Фанчжэн ×2 | Гу Юэ Фан Чжэн ×1 | Гу Юэ Фанюань ×1 | Гуюэ Фан Чжэн ×1 | Гуюэ Фанчжэнь ×1 | Гуюэ Фанъюань ×1 | Гуюэ Фаньюань ×1\t3\n古月\tГу Юэ\tbanknote\tterm\t96\t1\tГу Юэ ×1\t3\n方正\tФан Чжэн\talias\tname\t68\t1\tФан Чжэн ×6\t3\n转\tранг\tbanknote\tterm\t62\t1\tранг ×1\t3\n---wc---\n151 /home/ubuntu/books/gu-zhenren/coldrun-a/BANK-FULL.tsv","is_error":false}]},"uuid":"e36ec982-407d-4759-82ba-b5d055d0bb59","timestamp":"2026-08-01T15:01:26.467Z","sourceToolAssistantUUID":"025339c3-80e2-450f-9880-85767db44964","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
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{"parentUuid":"db023e0c-cc87-432a-ae91-53a1a8faed35","isSidechain":true,"agentId":"aec521429a7581efa","message":{"model":"claude-opus-4-8","id":"msg_011CdcEHuWtyYidQNJwm8guM","type":"message","role":"assistant","content":[{"type":"text","text":"Now I'll build a faithful Python simulation of the stemmer + A3 mechanism and run it over the real bank and acceptance corpus. Let me write it to the scratchpad."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1879,"cache_read_input_tokens":38952,"cache_creation":{"ephemeral_5m_input_tokens":1879,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CdcEHtTeZdz5WmMjiNyHK","attributionAgent":"general-purpose","type":"assistant","uuid":"e24ca556-1166-4ae2-8799-a03bb9d560b0","timestamp":"2026-08-01T15:02:16.506Z","effort":"xhigh","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
{"parentUuid":"e24ca556-1166-4ae2-8799-a03bb9d560b0","isSidechain":true,"agentId":"aec521429a7581efa","message":{"model":"claude-opus-4-8","id":"msg_011CdcEHuWtyYidQNJwm8guM","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Dx4ubWnE53qbqHEXbn8ouJ","name":"Write","input":{"file_path":"/tmp/claude-1000/-home-ubuntu-projects-textmachine/07c509cc-213b-44f5-ad8e-c3697b0c4bf7/scratchpad/a3.py","content":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport re, sys, collections\n\nBANK = \"/home/ubuntu/books/gu-zhenren/coldrun-a/BANK-FULL.tsv\"\nCORPUS = \"/home/ubuntu/books/gu-zhenren/acceptance/guzhenren-25ch-ru.txt\"\n\nMIN_STEM = 3\n\n# decl_suffix from target-ru.txt lines 194-230 (VERBATIM)\nDECL = [\"ами\",\"ями\",\"ого\",\"его\",\"ому\",\"ему\",\"ыми\",\"ими\",\"ах\",\"ях\",\"ов\",\"ев\",\n        \"ом\",\"ём\",\"ем\",\"ой\",\"ей\",\"ый\",\"ий\",\"ая\",\"яя\",\"ое\",\"ее\",\"ым\",\"им\",\"ых\",\n        \"их\",\"ую\",\"юю\",\"а\",\"я\",\"у\",\"ю\",\"е\",\"ы\",\"и\",\"о\"]\nSOFT = DECL + [\"ь\"]  # A3 soft_decl_suffix adds bare soft sign\n\ndef sort_longest_first(sfx):\n    return sorted(sfx, key=lambda s:(-len(s), s))\nDECL_S = sort_longest_first(DECL)\nSOFT_S = sort_longest_first(SOFT)\n\ndef stem(word, sfx_sorted):\n    w = word.lower()\n    rs = w\n    if len(rs) < MIN_STEM:\n        return w\n    for suf in sfx_sorted:\n        if len(rs)-len(suf) < MIN_STEM:\n            continue\n        if w.endswith(suf):\n            return rs[:len(rs)-len(suf)]\n    return w\n\ndef stem_norm(w): return stem(w, DECL_S)\ndef stem_soft(w): return stem(w, SOFT_S)\n\ndef same_stem(a,b):\n    la,lb=a.lower(),b.lower()\n    if la==lb: return True\n    return stem_norm(la)==stem_norm(lb)\n\ndef same_stem_soft(a,b):\n    la,lb=a.lower(),b.lower()\n    if la==lb: return True\n    return stem_soft(la)==stem_soft(lb)\n\n# tokenize: maximal Cyrillic (and latin, digits are not letters) letter runs, lowercased\nTOK = re.compile(r\"[^\\W\\d_]+\", re.UNICODE)\ndef toks(s): return [t.lower() for t in TOK.findall(s)]\n\n# ---- A0 (current): declinedFormPresent over a window, all SameStem\ndef window_match_A0(ow, bw):\n    n=len(bw)\n    if n==0 or n>len(ow): return []\n    hits=[]\n    for i in range(len(ow)-n+1):\n        if all(same_stem(ow[i+k],bw[k]) for k in range(n)):\n            hits.append(i)\n    return hits\n\n# ---- A3: soft allowed per-component IFF another component matches normal (anchor)\ndef window_match_A3(ow, bw):\n    n=len(bw)\n    if n==0 or n>len(ow): return []\n    hits=[]\n    for i in range(len(ow)-n+1):\n        cats=[]  # 'n' normal, 's' soft, None fail\n        ok=True\n        for k in range(n):\n            if same_stem(ow[i+k],bw[k]): cats.append('n')\n            elif same_stem_soft(ow[i+k],bw[k]): cats.append('s')\n            else: ok=False; break\n        if not ok: continue\n        nnorm=cats.count('n'); nsoft=cats.count('s')\n        if nsoft==0:  # pure A0 match, not new\n            hits.append(('A0',i,cats)); continue\n        if nnorm>=1:  # anchor present, at least one soft => NEW A3 window\n            hits.append(('A3',i,cats))\n        # else nsoft only, no anchor => reject\n    return hits\n\n# ---------- load bank dst terms ----------\nterms=[]\nwith open(BANK, encoding=\"utf-8\") as f:\n    next(f)\n    for line in f:\n        cols=line.rstrip(\"\\n\").split(\"\\t\")\n        if len(cols)>=2 and cols[1].strip():\n            terms.append(cols[1].strip())\n\n# soft-final component inventory\nprint(\"=== BANK soft-final (ends in ь) component inventory ===\")\nsoft_terms=[]\nfor t in terms:\n    tw=toks(t)\n    softw=[w for w in tw if w.endswith(\"ь\")]\n    if softw:\n        soft_terms.append((t,tw,softw))\nprint(\"bank rows total:\", len(terms), \" rows with a soft-final word:\", len(soft_terms))\nseen=collections.OrderedDict()\nfor t,tw,softw in soft_terms:\n    for w in softw:\n        seen.setdefault(w, (stem_soft(w), stem_norm(w)))\nprint(\"\\nunique soft-final words -> (soft_stem, norm_stem):\")\nfor w,(ss,ns) in seen.items():\n    stripped = (ss!=ns)  # soft strip actually changed stem\n    print(f\"  {w:14s} soft_stem={ss:10s} norm_stem={ns:10s} soft_strips={'Y' if stripped else 'n'}\")\n\nprint(\"\\n=== corpus load ===\")\ncorpus=open(CORPUS,encoding=\"utf-8\").read()\ncow=toks(corpus)\nprint(\"corpus tokens:\", len(cow))\ncset=collections.Counter(cow)\n\n# For each bank term, count A0 vs A3 windows in corpus\nprint(\"\\n=== per-term A0 vs A3 window counts on real corpus (terms with soft component only) ===\")\ntotal_new=0\nnew_windows_detail=[]\nfor t in terms:\n    bw=toks(t)\n    if not any(w.endswith(\"ь\") for w in bw):\n        continue\n    a0=window_match_A0(cow,bw)\n    a3=window_match_A3(cow,bw)\n    a3_new=[h for h in a3 if h[0]=='A3']\n    if a0 or a3_new:\n        print(f\"  term={t!r:40s} bw={bw} A0windows={len(a0)} A3new={len(a3_new)}\")\n        for tag,i,cats in a3_new:\n            frag=\" \".join(cow[i:i+len(bw)])\n            new_windows_detail.append((t,frag,cats))\n            total_new+=1\nprint(\"total NEW A3 windows on corpus:\", total_new)\n"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1879,"cache_read_input_tokens":38952,"output_tokens":5050,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1879},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":5050,"cache_read_input_tokens":38952,"cache_creation_input_tokens":1879,"cache_creation":{"ephemeral_5m_input_tokens":1879,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CdcEHtTeZdz5WmMjiNyHK","attributionAgent":"general-purpose","type":"assistant","uuid":"7821afb5-8478-48b6-a68e-7db754829262","timestamp":"2026-08-01T15:02:42.580Z","effort":"xhigh","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
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{"parentUuid":"bff5a3d7-494a-419a-8944-89eee94519af","isSidechain":true,"promptId":"c81fa375-6b68-4d22-8364-68be617228f6","agentId":"aec521429a7581efa","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0126bdM7o3dW3zW2vJf7191t","type":"tool_result","content":"=== BANK soft-final (ends in ь) component inventory ===\nbank rows total: 150  rows with a soft-final word: 30\n\nunique soft-final words -> (soft_stem, norm_stem):\n  юань           soft_stem=юан        norm_stem=юань       soft_strips=Y\n  чжэньюань      soft_stem=чжэньюан   norm_stem=чжэньюань  soft_strips=Y\n  червь          soft_stem=черв       norm_stem=червь      soft_strips=Y\n  крепость       soft_stem=крепост    norm_stem=крепость   soft_strips=Y\n  шэнь           soft_stem=шэн        norm_stem=шэнь       soft_strips=Y\n  чилянь         soft_stem=чилян      norm_stem=чилянь     soft_strips=Y\n  вождь          soft_stem=вожд       norm_stem=вождь      soft_strips=Y\n  фанюань        soft_stem=фанюан     norm_stem=фанюань    soft_strips=Y\n  цинмаошань     soft_stem=цинмаошан  norm_stem=цинмаошань soft_strips=Y\n  мочэнь         soft_stem=мочэн      norm_stem=мочэнь     soft_strips=Y\n  путь           soft_stem=пут        norm_stem=путь       soft_strips=Y\n  ветвь          soft_stem=ветв       norm_stem=ветвь      soft_strips=Y\n  шичэнь         soft_stem=шичэн      norm_stem=шичэнь     soft_strips=Y\n  изменилось     soft_stem=изменилос  norm_stem=изменилось soft_strips=Y\n  девять         soft_stem=девят      norm_stem=девять     soft_strips=Y\n  полость        soft_stem=полост     norm_stem=полость    soft_strips=Y\n  десять         soft_stem=десят      norm_stem=десять     soft_strips=Y\n  отстранить     soft_stem=отстранит  norm_stem=отстранить soft_strips=Y\n  обитель        soft_stem=обител     norm_stem=обитель    soft_strips=Y\n  соль           soft_stem=сол        norm_stem=соль       soft_strips=Y\n\n=== corpus load ===\ncorpus tokens: 37482\n\n=== per-term A0 vs A3 window counts on real corpus (terms with soft component only) ===\n  term='Фан Юань'                               bw=['фан', 'юань'] A0windows=324 A3new=184\n  term='горная крепость'                        bw=['горная', 'крепость'] A0windows=0 A3new=1\n  term='Шэнь Цуй'                               bw=['шэнь', 'цуй'] A0windows=47 A3new=0\n  term='Винный червь'                           bw=['винный', 'червь'] A0windows=46 A3new=67\n  term='матушка Шэнь'                           bw=['матушка', 'шэнь'] A0windows=5 A3new=0\n  term='демонический путь'                      bw=['демонический', 'путь'] A0windows=1 A3new=1\n  term='ветвь Фан'                              bw=['ветвь', 'фан'] A0windows=1 A3new=2\n  term='девять рангов'                          bw=['девять', 'рангов'] A0windows=1 A3new=1\n  term='ветвь Чи'                               bw=['ветвь', 'чи'] A0windows=0 A3new=3\n  term='ветвь Чи'                               bw=['ветвь', 'чи'] A0windows=0 A3new=3\ntotal NEW A3 windows on corpus: 262","is_error":false}]},"uuid":"093456a9-da0f-4935-a9ec-7aa9dfbe86ed","timestamp":"2026-08-01T15:03:05.527Z","sourceToolAssistantUUID":"bff5a3d7-494a-419a-8944-89eee94519af","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
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2F9gsnxL63Z/SCxythyVA1Icb5eC8kG6XbsjDPZ3HLCL9FSsKr/vOoAWzG70Xr/zm+pgCMHXPuYmq99NaJs1h7TLpk6sF96Vvns/YI3l5EB5WymlBLhrgdT1OKjB/T3Xq6uvYhNDgRjVBtFDzGRB5SPokJFoEMNWRq2SKoKEqWi8QL6y77eLqdjdePKBd2uun5Lx5gktASoULKnUuxNOpET0HSxIQX/bUiSOxbTEU3Wz2crWbxO7urNEBFqsRNHupnT9BycJ2xNh2WXywXwa5HlBRqgHROIv2sU4r+/ANhtVf0X1e8HPd9Q7mJz3eH23FFt3WlRM0LiFBz4pD9YiWWg/gCRR2ia+SArjgqoVFc7PZzeChjP3VT7uFixsHzZYd5tfy5I04Qryds3/apAbuGVgErxze3Fw0aXIT8DCgxe6niHZySCF7atAT85N1N5cjjM7Lv0i/tUbOo4HhdrTsvjV8+rEYQsJQ5ouxdVPFalDLo3QoBsSK4OUbKRVh04lT9nvZ4ZD4Knu4f/1OimmUGLUJY7U2hdXYVtFDFEW57CZgLR9eO5/SrenQTEvgEseALOO29lTKTRiC/JXgkIavZhWmA1ZyUTlU67uoTvYesoqGMT+VgJpxvisrDAy0GndF5Tp+PFVWc8ecH7koiMcybcFRyGOVQeXeAgNS/bEdDllMiMYcdIjUg8hfzeZICrtPmkuvgfgkuYjkC4r90oZCvtxwjPPglY7HK78WDmJn1r26FmcJ9BAyXm1lOgeYCAvlafa3sJlbcyH9tRwiZftH55RkHOqs44JIRRpApsuO9JmvOsfGUeKDQiGX8wO0tPKd52WnwFflFRWhuSTGPyD30y4FVI/JYkzqfJc8kBDyhWbRwTWJoDr2JjQZZLbQZWecZtms69k68nb6N3sF1hkYNnQiksj+F8yKUmuMePOjW48b6V5uFLmNT5OtKaQpW+W+S71Q/dQgHDMIp/fmsuaMM7IsmzDFGoLPK76CQ4Sa5ivHGhys3h8fZVRTZQF4cPGgVfyjwdYtUWa40GyCyWvdZs7J1czUsRJd438KWUqVrku0w6TAgCVEUwHbiMvdWSTOPL+3rkhcS2YTpZxHE/aX6YHuq18ZpOsGW6R/BcVkzyBETpl+9qyvFb3fFXA9GIIYIl2cMATGSyQ/n0Vk8HBhyVCGjSQvlJZ4XW5z8RhZEeF9TS48fDuJ/FRhSVPBqbKed3/XuRIa2ccwuAaNjbtZw9iNikqf0O7fUMih/036JYwlCdmlg93ARWrX/lPO1IBYR/swG9p2pv0QM2RJEEli7l1aWjXPeqDmVnLDJXC7apeAYrGqbs+dDNsSUKaOSlOR52svAWDpARDI9sC8tkg8izfG1gMA1Tn7Xfl1iQuuY6YkpKOcNj0r22MWf0+mV6TLULSifkzSTxeVTGeMVo50B7WC95PO9nfGkj6YRqVd/VkBjDd1KFN56jEBnhjjt0eutCQdFD5Y0kzjt+r0bthgpu4s0OyCcbx4JrAvrFoHSk4PmKw2jasYy1B9LQDOJLW0ivQTTmeL+ljRVyoRhUacyOlCkEHtKsv1Jw8vOvtK3GaWTCp7XByyrUzusZ6Gj73a8Jes+vOGtNv2JZlQFzHqSTE2ya6fUCJMpRJb68KrYjRIiW49OxtqU9Os5YA6bRMCPh09IAQpeSKD3M1qmk4mPyFxtG7r0Q/lBD6AkXj3aASid+bcOuGNqxTgjXhgqiItbW+UcNDqIlyC8c1wd6Zk4Q2VLXYcqiNHVrdrxXaCHTOl7QEgl9LD7cmDlWi/SUue0KeWiCs4c9Ubra2YvbBzn3bVmiv7H/iITklDG+KYTB+tkSJAWT04p6YEeqYuT6WnIHc0yc7Q1lRzqe5RxAUGl1cK6pAnfBKBmqO8gtv+3/8Z23CeoKrpotfHowj2RR2hGnnX5bX/p/mbK0fsuAqvvlH7LZ7923eyafVvXdnJp5gP6iJVO8KK7XizgzRrBGSqGaRwj+wXlaLNKr5uf0OPhh1+5lYpjIR8i6zO2LIvTYdoTfWzqUB5ySOWRCgzNOQ8benKckxl4YR3KZ8MUGJAs6DSc6FyfhI80kTLcmrCov3bMn623h/Wm40hJgWf0oNATluNthNgp0DEkw5WzWwIfrA6Gj8sDaQOueYnq8oNCi7RLOn1r5byo85Lu8UGSoZHYRZUf8ljsDPSbUDUB/9ykSthE3Quw6s5d7yiy5uz+UFQ8rYNIOQLkGre11WLZWUpZgBDdgyJ7bLYgsJhahCfaI9uf9YMlFhO8QNnr1VelVldLQkzkfBqJ80YqrPynAXZwcHLXr8ERWHxNSqokBOGmER4xZzPFxtoxmF2IVgtzHkVYlj1fsb50UeWIEvelyDX7E8jJpA1GqLUuGt0D4IqtHLbXpbk5y2ETu/W2nNaEHzJ+4ZO6uX3J0wU/3ADule1lZnqCwD03drv35tGOXmmTUj8ZZlt393K3+t03SRNu+2Hwqe9c/s90dR0v3qonzDL2u5hBMk3fSj8Y1Wm8XWNw+XRn84S9qaA7oCU3/XhaxTOuDpHQcA++l7cWlyVSReMPXmBFQR6JCkT62Y4a4mINq3rHrmO3l+9DpfyadaMJbV6tFbAvqzehcSr3bJttJ1wwrNN+R/MfmHtJf6MO8BeyxCMM22LUzIH9s29srMZN9ottgTKwDoCpJp551MOTkGQoPwCN1hUq8QeHajOb93k7GZd/jkC6Pcj8RgKsoUVd4T/Y+N4An6fFt3qoHTMHsyDZLwAUz1/HD9Kyk2UxnoRG6MiKxS+bT59mIYdrt8BGrEaryw8CNOW3pC5aGKWtsWz+mP7dRbQtWnbjqLQ4BOcJGbm6ihaFK+ao4h1oVFLm9OQTjiMu8QTLu7epiIKwuklK81TnWKkAO0bRdoIf9XeO+bKXQE0zqAEtvKI97FJTGvjLrFhmHV/nO7WoRProqhC/qmVuG53wmAzgajUOqdhF0vzDt6fQmdJzhYpuaSTKNiUDNwMd7fmJeo35KVVOoisMqjl3j1rvCNhGepsmNfvCFigp8XGAsCpksZW1xo6od5OMU0R5wS7w/rqFIBd3/bs1wau60niqW51AWKamOEPsOXGsOSyW1dcM07Cjq9ZB0DsF3/3/RZvxHN2sq4xB9fVyoc9N39b3rbdIqNzxZdIz6Oa410iNFtmwJudgVtUBc2qNPqwC5WUih/SDN4Xh823yp2NxJl2eu9epcMqr08C5ATE+/25f0awLIza61HAy2EiolGWAuobqiK1mV/sZPr1AieWbPvaUdE4BoGBcFVPy9iO2JvVswc4S0FgQeOk02LqnxoklCR+m0CmYcteiggXB3U9eBtNHbKqVIge0BkdLuocJjXxN4wFUTBZINBZy8fqWYFJ/0nrkfQP7GuHDaYiC4NM+o52wN8IBuR6fkI9KibNmUgcbcCxBh8c2mjf8PzrI7lIL+R1QRFHVtHTE/NHBEG1cPTnIOYq96RidwMO0QU1mMUmIjryzo311L/cTU/pyLzPkLandt75aa78B9I+/ECQtBP8N/KtkMxXLmAges1q75mJoIhriyjSIMTn71fcHHZjIiYLqEoLzSYSuCobdjK1qfoqnijZwpO10KVOrx7ZHBTqP6J9RpbnLK7qkvI6dY+dSfs4M1RdaGNXVRISCvcKFh8MH4hNQt8glX6h3THSaiBdDzjOyFM3IAqFOo4ADe9WVYF1sHf18+e0es+I2FU7UPNSx0yClP8QadNa0SBeCnJ2u32q1AgCZy6Ify9xtvhhhsoq8nZvg7+XwLL1QziOFtWbNqIEZwwFtAMCynDdqsuxU10Vkxfnpx3MU8XQE2jgUh74iOIZT/h+ucIyFsvgn9deV44jzuHRSCukIkYDGW7l1a5CB3UYJqUsJeuuX8ZZ4y0nWEsZNI/vpWht9GfekNxi0/6wlTAmTAdYwOCgBTnyhjdC6E55fncJwAFaPa3tioxHiiW2nwnxIqa721MLck33MWszwcj0UJIVZIm1w7IXAIjvevYn3R60OwjL8Vza+r6OpwZV8zv7jgMMCzrDs3wIYgvNeQgD7ezo5yMCopO/IBExRcU7oHSz5/Okp4+XKdIV/L5U4+ZBzCqWWAxfmW1glI8sZc6lGC1kWiefK2mrMgBrAS5hhk54prkS4xeKAVzv9HwG6pMv/rrcDkWKpj52BymR0BMf/tjt8ZJE02RmPGOVtQb/XVbdoY+mjYUOMV2uPIHGTjJKVRHMF+3ypocn747GcrWMC7LE5fEekLSqyrQrIbkdhE2PIpTcIKwmmpix+gfz/zFV6q6JVlJwAtNV38OJuQm0YOm77pM+S2LhQqHLdgypNQC/dW6ZtOslb67lzo5ZcuHvFewNxzNH7IOhANc06TXQGWGIqHhsq5rmGpaHUn2fbBq9b3qFPZm6zacLYKAeeAbHhGj8smZXXc4OTH1Kq0/ne/qgel9fXgKHnpAVfFtVrLDvpmr64JWAA2kUw2SKunP1AYkg9me8bObGAp3Dn1K7rC4VHHeibVZBIkoDmudy9oKzixsXDVrGQd6U7OVl5YBuLTEyB42DpSeBk2eAbfKn5rRLMrNhIrKwrcLxCmpEfJ1EIjA/K+PiXWyJi+8OAT7CClqxHfFhLNyzlajCISF7JVIN5vILrTJLuau0mLNk8L6P+gZs903d1hOQEpPiQ7b+4PX7pRXQlTqJPV089dnz3bGhf4VzJoMhy+8WyOMYI43Gmz69N0SAGEmDrwQYs91ZeFyXgcZikGW6npY58V+EvNPpYKOpe5/ovOCJ+55KcL+xMqrdb6qXvE9gd0wF8ZuZpUNIUHIW/k6NJjX4bPfIpwRacCsLZxa4OIpF7g65SY3uGnovn/WBhvUu1vJFvivwKE45tT6WBlzPhXf58oRfENmHfW7B7sXyiRpG33NoftKqh5vnF78TnZ5hotP1jKSDsypwkZcABFpm9cSt4/KHD249JQXeTgPjoCHAQVBen+tLR9eh1HQ1Nzpg3JF4j8H6Bc9xW/OrFmERCya592crLybXxcVl/nASKD4rihqcyHZOYZ493PeLUTXBL6oxm23AyNO6pJihr8wktkRuMOn8sfF5GJTHzTklpJ4JYZCk94at1w0J8iNWdTdIZ1662bTCfPL8auUyDT8HwFNMAJ8ESP1oI3ef3m1I3wwY6bDM5bmjTr3PHRdcOaWenCwlwQf2NPyenf/x6XA4DvwiuNJ85++9CIYTBpRq7yvTJ2LRWJS8hHPg2MgyIHctKXOXjsKFma5Y+P+8/2QqwMQZID0kn+AMK4Pq866EE4s5AKflMdAChnJCOVtj8eMmWUFbZ7MY94xMlcgeMjXBdDANDIDSvrZF+aqeAa0Aca4jPryvzbBGPLhOoJuzoRn17AJAkqmyDQsWsxwNE4yoISEHBRE9/CiYzlelevEyna2asIJdiSksBzRnDH11Igxn5iwdTcMp7GzABtQVj/6HLPz5IPXIBPKTXiXWnNzT1ctyVUHAHs5knM/wR7jL+AGwq5NYvPLx10mXkUya+06BhZ545PT+1oBsDfY3icQRrPic2NuhxsLxXX9Jv+VgjojzHOS499pocshuvk4tZnoWh+mKsZZPUOOoZ9ZlNgGdLyR/a0SFnJJWD6Nal6VZpoAh7H1cphVYcX8Ir56VH7jaCR2D86Qg7FGnNRaUwU1PXCJFuVe3/SmIqFGMyTWhhRSWBqdUTSuPbgnKDe1pYsLWV2fl042r5CNaCCde+zHiAr6IsbO03+/9KqZalnq2dJSpNoYpaLLw+2LO/GsPulypnmybw/+Mum6M4TEc2ENB2mxCJLz/t+yoXq9+APWotUbcke79GNKPsAW8A5wiXT98GH3AYepTQyZYptvt9mB4Hh2Gj6B4ahFdGUizzZx+X9nud+n+XPzUmco2FXESgxOh9la9gfzjeL3edQOf1yONQqs5n1zoywulObb2BeFul6jgpDrMmaV66sMjM9GdSd/TfCR21mtmd+Ge2tyWwl+qs2mm3xSP/7qpKwAAdpHkx6RHKtDyxlIEsmH/L22zgKsX4o3rn9e83+j/v6G6Y4umzLfOowwdg46h4GTNHpTVtR2TKJxyVzSRVhq2hHCzdIwASManyb99SzLZ+jiTyOkrW5yCkv5PZ5/gNP6h5qjPV5KpR1jGUwc/74MQ2CdPnqpgJFGpE6DUkSTn7alAllHk1oQ3a/JKqhB/ULogbG5AMjnmmBhB6XwYb0I4Wsr9rduH0TqTAFlSSsb8QcEuxJRU6L0wT3OgrkKvfDRFdsk8rmwdRe9bDQuMSn2z4n3gGQQZ9c8OEXepycAui7Z7Hk/9K8XUurVWWWfDioXDcFXxA2kRnCwNhoMNG5O/l/nIfiwvPMqim+8HJcG3BN4rf3bmcBGv9I3tUwoIKKVcm/lTZSaIKQKdES0U+LTU6c+HUuxlv17KsomfSBCmEAGaMGNs8/WDTyXbxFMKNE2uCWoQNXXF905r6k3miCQwo4c18joHpu3ieSqgyxtwRc/ubEZrsCYFjPCLAVN5MDRCXv6qAmPjNq1Um55MXM9e01D8puiyERa2qNdR3/FhC+nZ+lwbHqsRckNHtnVo/cxoBiquwji2kAwYtVtTv21UxSz1+6ad3Rl5np/5LAyUsl9p+b+RGBn4Yn/zrzixRq+dVXNllx+pClemkJRuu2dy8ScE4Dsbxtm/waSVsogc2c+xlDchckgZUTkuPOiB0nPIWrOG6R6y3bOjkcRrKFvanzCg17Z50OUPLxzHFOPsd7DKAhZzrFoWYHrUxdlQGE0Q1x+nYkz063FTLZUh+XcgRPpJQjbYXSQkiaO2ZWqqo/er/Surc56YAYOscDaHxynxOkZQaqOn6nQWueL0F1AJjfxGX/fJ4+q7v+IjTtn0yxR86yT05I/GfsvyW/yNGn4c9yXKWObPGmmfcKrp0XRgTXobsGKLoauB3I92dWlwriJptJfIwrtMxf2Cy6I3OT3/IAQW4dFBzzHk/k3yW47gY6WDyTPmz5cuz36uWfBhOMV9ujb+QHcHVvKbbTc9wY6YwAtqv1UojzQL6RSVlOHVTnxnWJlR1RlU190a/g+JHzEO+cak9b/5H5XAHNvANsGyvbHRX7MJBJHn6jrihDMXTFLwIiZ7b/weOls0s2mXAR7TTCCc5iXLb9sz8KoDvl6l5Kc9Gd0xtIh3f1J6jtAH4gflXTSVf7xOc5GESKMWWPK+DXRcyzsBTLowyL2eI/lAWntd0ATLdHy/E+dX++mWBIsVUFSJVnTxrVk1E49phfjkEeRCLFmpfFZYGrdrslkvOBWFxBziRbbvXaf/jASz0PPyHdL0TRYlpcHesqRcQF6rL/fKSci2OZhovhBngrWV6nUx/x7/xLaDvVKk1J1pQXmporJH3Grh/zRNTRrz5Fbyi+eisM7Rr6SpNDFrho8lbUe+CSZBafEwBlOM4dg0quo2fT6/CX3vh7qlk8ZjVDnSuNn1+HkRbcT4vGOJMEDOGeKRLKbL8l6D6PI2DHddt/2iBnU5SW2gLeMEJfZsqDJANIG+er3gLO76ZmBEXpPf4VMXJKF87LgDXqjrL4ftszUvk3McDya0aC8JA5D40IcerZn+aWyeCMZoZx1HOR37lgh8UCxHtR5yRBC6CAfU3qzWEHIhyVeAgyzK1dXvgyV7HSKx3Xwd6MWuw7tY/Bq8VNtpLZPGCgFWZ6AA3Frd4m2hc1lJCNj9dO4cBdnOLRgja0HXgvYcFsNWFQz4ZCL7xvAw3GLQtzB1w9oX3cXtLwO0socFD8UZIUgl0xh3au05CUXLdE5NXdFGVbNvna6LwUV6yoEXYXO4O7K/FaL37dTyXY7i2jckT1THyKyT3ERDi5ybweLAwKe7bznYl3aZRysxano+aRMPPCtWPFtSvNHajGqpKr2+5KTeOUW6pZ7/8ozEU69NeDkopS+zJlcG9Ns6GFhjrW0jHg8wu+fwubyTHWmyHVYF4/njqhPqywMc2ZC3ouZkW+orwEm79yeOiaMJyg8i9gxL2m9Ps5SQ4Kk42AEUfmnr9nTyaNOY7oQLIAX71ng6S+o0CCK1zJro4m+94alVxPVUtck/n1Dq8QWOepLwOhQaeoqKblkl5YeGPxcTzUoq3l9FbBgWIBnAt4INCEjB4LCOiLCQ2Qn8UhZ/eVO8IYhVFRjL5wTAVH6A01GIo/3ui1SVPk2JbCk8iJfXtw5ANkmYjM1zU6+sG8XMuG7NlOIlr6Uu9XKqbEDyj2Ff1SewWuPyj7Y6CL6VvXhrxjbcLobFKPm9iVVqm/7nreh8xufG96Jl6ReZTxPv8nqWTq4N/ny/IztU+pnmqaBBbz6o8F3Js9U9SClaK5u/SDsHwgB6TiTJ3HJket9C3B22kALlSCpifeToDXo8FlGsjT6N8aiPQ2v2Iw9QIgccIwgVPDryEraR5qcK9MN3fZiZJ6ldxifMaenfSbEf4X0Pjm7Qqf/az+cod8IjYfJ+Du8TFSL78dZXfOiLuainlUjfOMZb5ejgPW9cUHsxHNKbhenzSH28l1dzaTJleknuPafr/ID2t21cCsCD5YKgyvCPOtChTg9tb6lb9rGo07nPWyP7zefqwR23ygCcs4whXoJN3wkSnoXbpC6vduxuuAD7ELGt1vBBfgu6FgsI47ZrARYBmh3xvt7kpdMAMLxKXOz1iclglpeUb7zWit3BAhGcbKVOOrt2/PSCBTtbzv03/yiComi/WMDynkQT3vXi4UlmXztEaXREucjKkodxVC3CN96Ao8WOtWj1jD+AkrJT9jhui9OZIkfOMEwYS302ZhjZJyRCUH74ut6yoO7Jlm4wVJWtzb/muzlgPFlEXLqepcqKKOyDY0arQIR19EVZyHVR1LpB29faDg3aqhF4TJm/qlVBB8LQ204+t8nGX6Jn4CSDMLin2QnA1ShWZ5/5aklH4pJeqJoEMw0hQ51/8qC8rRvcOHI5S3y4TceMTw89+9bWJZONPuX1iArV6Y9Pq3gRNB9iu9XQDBHdkU4PWxBBDwyq0hCO4w2FhwrIK/08hHiiwchvfLrSyeJjXxRXei3qXdxyeqD9/lZdn1V0/1VUHnYKEu6jjcfwqqWlRvZJq0SfnFbED1B2mzpVPh7d5hCDAcjfAimLBfGW5gU3S0QSfp8E5o/uAVURSOF30762hgK5doXR7/uTG7+TXl2osHEV3CBsXYRlIdFhqIIemIJc3qZYeq/AS9YDB1vmNsvzWI6KiShqEYDDMZhALUU+vzMqb9OYzIMiBYym/ciI4Uftn0Mjqe0b2KrD3PtqAtuC2e3kKgEpd6he6AE2p8fDS4wpM2OLKQc0NMHN/uH+R4aQbnDFldLw9xaRiSwVGKt5v0ElepqgHyfNUP06NQoLIe2eZ0I1YRfI4/ngQRD18BXIfblKBDuv9UP4uzGK2wrg746NnFrnkXLMyK5r712h/H9YzkC23cZ5CZ+brPHyb9npRaJPCZjED0NpjmoygBbNvUhldbNlq4ZERVF74iursQlZke1805Pb6iGdQTVmLBOnpK3MokZ0YLlzQy2ypsg/2qsf8QVSB2Nw4cjiqXFokCwequ9FQkJz2xkZ1VTTHVxgWdNWccZsp3I2/3DuBTjz3crYsTJw6GtSmnugBcjCaZ3Jqf4LUCHz3jP+xx+2CO3Cu3D5oPzNqi4rBfviAX5w2hlWkwChA7u/9MN6FSZTMdQv1PQnb7iQszLAODxugrU4HEnTKXztWFYpJKDdFTQOCnQrAYMSrQaV09bV6/U5veKUFFwv1ae2qomegOLx3C0/Wl/6jqiWpK+YtAQBzCVWdDdChRHb5mPFD54HZxz+WmF9LQIJ+JXfaXb0M01CV5/cND+MLtw5rTpgaY6bTeDWnm6plClymRzAGKwateY51upA2MOyu0TlZqeUyLnv1a0L2iUSx/qAkxjI0ypNL0ChdN47lAu6YyrdNQTyKTzVw3RC85dN1s7iJn/oa7qmw6NiENg1KJJLp2dexugiPWtvUFEnO4x0+bFwms8w91MuXvmwQ7FI3lNlgLeDeJCIkddWbDlgLcInmlsGSQ1Hm+DtFog+fPbZLeI38bpsH+AC+gd17UKxQPhMOI84ARbxoSmHVjAOhUf88mgACKmzo6zs6tdoLjxbPp+ic0tByZUntNzBn++kU3oKLQ7g6hFHEOlq2ZGGzO6OxCoLNGh0z7zdzGOj3fncaHjC7I7XnGxPFQsUN8BK88ws+iCRBnFpDdSw3RA02RUFnldbXAA0eJKq/Fb6OJz/lkyx9k6e8P+rhsZmuq62ECCL7p/JNts/Gg+eb1MRNvIUae292FR/qWO7HdLU69LNLgcLYjbctL5I56pfcl6vZHkl3K/6CC3Im03EerfqdigNydVzqAzpI+9P/WXH3hrEa5MTEuegFHZ7GJqdJQoO2mU4HaMiaFtyxLLNOT+f9NcRt8i03XnVaMjJ/pmt3bekMuIbRcuPOcKbPUf2n4kVRJHoiJK1V7P0v6wYhCdxvZ81Qgqj9niAFZ+mxJrU8f8h8BPkXRiBj/LaS4VdyePM8nRRS0p+NBHW6Ot/DlqjEMksn48p9f+yPYaIE18LgKkDf/Q3JEE5xRQAx4rXhTPoPKN+n+ORoYaO3lw1kMLrcyuV2rr3jhPESpZt9NIcOy5n1bRX57ss7/b4NxDdpqSgIz5/sPWfgkuTWHPhXFcN+tg/lP4ejnG+J1coVLCG1MVOsUOrico53arodCz3qbIbSNDFgIW/v5Wola/N35nLuVvOCgXfR3lAApIW1vBRuiV9BT1MPnJ6pCKeInc1138xGr7kWd9x9CC1XMpWt3+5pxDRFcojlI9daIpuM5hwKGdBEwthYdBeeo7jpE0lrB4mL9CtYUuN8XaZPWWjTiwtnYiJoZaCS4zgMP4HW9g9yYzdflP/zb+I37StdTnHRiqKl/PYePtOaUV9fjvCptb3AnGfspLoO8hCVMkgXytuJpg4fd5De3HBQHl5nhevFoR8yjEMH67eqsiuDhe8UsTEnLobOtPbYICpVgA578lnVPOaE97d/r1YxYg7NhoiGK5oHGW6bGGZA4BzNP3PeFgSWxwCqztiQMhyGXcfsjrN1OnxH2xIpbSZ1nWd2rdNCOm1JKC2sBmCm1XbGcbOjPvq8YIeefvaIFSoXda7RofuOHR7bHMFQ12MUOcz6dpoNxqnYkEUa/PJFzmw0vF88fYFPmrOm3yTQIjGmEbjNIdLhwqPTi5HSGjoZA/dxpoOSGn8AjSY6aJ6GsTt/JlYPePs8PkjpVmpam2GEcznVvHg3UnMyX0+ZgAEAuzLFuWbalfl3ld16xCjih9Jb1mNIz3panMiHJkXGoDE/Qp8OxmYv5O8p5vUpDGqzwKPPw9/b4C3Wsvx+lOqEQ0TggTiiz+nX2JxATjkObz15GR2xXn00FzDgcZjaRmTK9+rN7tzV7WM3AytW+tsFm3IHou4wktvoloG0DSs9iJNBn0JkGBF5knuysPaEuKHV9qf0WriCC86sCrAPcq/uYNNwH2PeUXjrXHZGAWwET0TPi1zJ0bO2UbJLYNTvp45YqB783/R2i8oNqRwGizeQOkuE0i8ycJDJOLL6ol1Prmgz5W5BCHmGAgrgQuFJJoDaFuwkF2iCBXNYRsN8r5atvoUmueTbPrAEDOSH/wY1odP5t8HY83Odo5DLlcbO1IuFoY6iGGT1LLuh+JVAsAjmcIP0CbNtls/rhRXyGTjaJIWgS+f/KyjX2ejX/tutBCVXjo/2UpeFOsA1LsNQ6UFTl8kqDpEFOuSSQ87H+WoVuerj8mrDeNSxpNvwGM6ALnpBzi1I+6u8QI+q0Ovtqmp3bl5DZJhNmlBJ3FamS/Vo5ow4bf9kIUBk6r/SqBxPAagLmyKdbjMMzdEn2JXYXYOc9VAJJwUf70/++0lCCxqxcNSchWqRP08Fc918/ffVtKdSiTJlPg1WN1W9oXU5Sgd1cPSK+A7hVC8SJrQljkOl+XVogkSkJ4scj8nMJ6fxOipA7fzJRaIjxeDvjwSYoY5iJ1lRrJXbbVnQv3WIif1ZIFoAKrncoS16305WRzcDPm7lSHrBdp5RmENvQWvIX2k/JJCaRSdNa+ssZVtzMsHbcQeGiB38KkvKD4MYiXY8LuWu+8054fD/W4tZtWg4yuAlIwCVIGYMHEHw/hOsj9iCr3gbS/uBWEoQF7EHNXELXK0C3frMJTXYWqUF3yAXa44TVXQ7LcTPepQ0XtH/IH2yPvV6LDLqmw0kDFK89PqHdtRso5/Mw6G+c4pM75uxXHhyYP0O8eXK9sck/tgHGfa64v0ROvPUpXkSBwU30MAf7L8lhIDcP/C+V/og/a2E0DqhtJHCqzG152F785evDe9fVBQKpT5DIVnF6Z4yEMaPCkXYT/LLjB3lounO4JwX/LyOSIqVJCqWoTpz1xfyvtDrWGKs8jDkU/N/i31c+xwyu9AiyjDMguhszCFmxvocROXFImjV8uJbWAn/QgUsup0JGE2CenLThv19QuRImwwk9tV17alNFl8r3zzAtqqlg9z+X5TcCLaJzr0mYjd6eJmEa6JNi1yjDcTXhNC/XY3WcMNyIitVOufjQTraIJ5PXqGx8H5pNOyDkbSiUY1jUc2JmXAMWYJBa+xIaWuZyYR542XGaFnSKdS3iOlvHslWV6WH/t1Mv7zrBwSE2ixWkHwWuCprKeDTCUNhlX5pyr6a1bmNAh/v4klJXN5dMUSYzbIOGupZMFTtsDLr5iGGzNhOSdC5Op1QOVdk8Xq7S/ZdE2bv4TxjbxryD2quvZawaazvL9OgHXtPeU4xjv6aU/Jq8d31yVKd1n35Nwzx1emrp1JAewv5iZ/rHWnE+pwwmb4X/zc8DNlLSd2SKhJVXNiQRb8Iu1ljhMed3dkMfAzynm+LPsBLkmq1oMEXv0vnuZZxFiz2rFwglIeVlr15nMGN3dYu8zISVXryVj1RLD0W3B0kvdOAtU77yzZB6MMdE8G7gRrNTclMgy7WP62Q2pxlocC9Lo5BvDONBgjMM1ebzKhRxCEv5mu90P6BxOgdaVIkKiHlN4Laf6cMAlx2a1MMkxlAab5RZ+CtM15Skc573LAa3e1cuYA/MY3z0KHozvvJZ9UhJLFuV59W25JbIzDzDNXDJmqDeRqmkFPrmCylmnjAKjsy+isT6kMLH/8jSNPGITWsVGdStbXZiP1KAJ7nF+vL8FxTCyBGuzjy3rbYIrTGYiBxXJSOjLyyqDbSqkhl9bHoDDulw1PS9et8gYiJbkj3kGIEwN4OZKmzqOK34LWF3BMnglgBFckcof7lnCc2r89TtnrFLJsZ95VYGt/LL2rSYz+gZhUWhJ6LgfuuhJifC4O7x0YkEjkQT2o4XaLV3WW5q4r7DiZIE7a8VoTiu/g+OB836paPP8r7lSSkGCOy83HWz5GJ+sVmOID4MgH6UVGZuurk+5RZaAOG5LbBRTCJt+BZEaRGxXE7reN93saX9URYCx/kDJ1C+nvJcLHLKs6nQlWAM4j1hjPshzB9wrr6wN6VZFe3rCvy4oiX9aELRFl8rv2KHW0c9lE3v8kf1o2R9n8wKGWEMDh8ntpMLB0TGNVUmuzUdDNrBGDQJpV+yiv+mr7JMvRg16mG0JnmovZE3lLXKSvRaekUisWe52WR/dmmeaAxTbDVpANfc8akugb8fsr+I3pjH2Dj8RwE0riiEmytE9UtxekvzAGRDGX9RmapakfwK2k0u2urq9wGBJJVgMIlxeB5/n8XyUDucg871B/4rlMIrqN1YwgBmacuWk3zrlRkHR0uY/9l5QISoYHq+d438ePhxOgtKuip9l2lfFjd97LkUC0VjwxK4R2Jy46LZWOlrNttXkyBoQEEshPIBOW2fp1+VJDvIXfsokMQIeb1HhWLjMmQ8vWjGNIb2varUHuh64YlCXzaOC945jRhmTPHOLwYUu8WkKZ/0eJsdywBfXKtEdNErBEUJ/rmekfpgki9v0UUrYi9/HEjOCZNLvrR7TJ0Yms5NgGHSQoYznuvqDuwoVysrjyoDz4BbIV1AEithfgxFWV/RNu1Z7wO9EeFw9xjNbVJM9+bxfdorbViuekI/ixfX9LoRBjx8AIKnSVGOy5bRMSg9fBSUl9UZOzxe4FQGQNN7x/vAbqSAbVQtQwSbAFfTlG18gGh8WgjksLtCI//w3mXyzdFIz5RFtaWHyRKdePxv61LLqe3f4UwVG8MwG4fyLOf2OuzrOrbXjHK2eLCNn1jlHwBMnD8LoSbSqK/lHqeWQ3AzzQ9252inc/0aVllcrSVzNEzpQwxj8TktfFlw8b3axjEH+GPIgO9nVOhVDWwRSW2kHhGKBPkJGOF0eUkXSA7+/SFJvGX7D21Ei3mEPBcnf/2YSNbq5EQgkFdsyZ7HdGtjCwnynYCdA9mUkIUKa+n+NGffeqCZpstuZaRVWsouSqfP7BUx63Bs9IrFwGvF0a0igWE+NI6i5mwBiHI/6YCC7f9Psq+kdpC6Pzzm5ObbYLxDnVMOQ2GuCpXmpGnzhQs8/ptqT6heeJwnXkA8xov23REGnWje/0ccNVNrsAQmFPILLHla8MyPGAt1qWABHiSDW7YKuFkvfV7WleMgqCkMFxxGJR31y777RwuIoOzr7ZJddo6ZxulXIe7idEaCetsa6K2v7j3nk43GZQ6Wg20fojOtlAaFscm+LWjp+hIMxZ2se7pTD1cdSYC0bfjprcwIFbSn1+grAqUCEzpXAc7cO4O6zWFmEMbvmWn+ID1AfUOm9etMjgJLz1cQSq+ZJsVoQnQjWffun1i3NrPZh4O/cxcCQ5RP7Xo7J3svoRgXbIpTFLy35LJ/VfgLmB4TrH3tU6G7qT1ys9dcHWBa1oBIcytwH5YH30fgUC0MY0CF83EI2a5vnLoPKo2CreeMaHi0AGq3ESNG1tRUU+SpJc6Fo85xzbyRN+RyCxORugPyVeydLZP2msmgDxNNUIdlbCxxfKceSABFBBkUQnQ7xBgE4rlTqtVUEz6rcpOaq1W+V/yh5d2n71CRTRHgYfaxKMdrZE4oL7gF5dfK/ooKLXyW2T2FEfrO/IO7kDiCzWWI2ZAd/lBdn5x2L8WzmDSnP5KrsBiuYd+KRKY9LVth49ppkuNfLi3P95P1DkQb2mZIUIZ1QKacwaW82w/CwyJp9l0hW76nftWOm4WtnoB63kbBx/JidEffFM9MC/qeovBuX8sqrLD3mWOxtPRncw6Z7SmT5/zbUCh/oUzZ6m5lA6VfihX6QZ2uLThx8W5hUMLL3jf2dT3stILlzeb1VVapln7sOqQK7OOEg9A3Q3EiyOetYQXJwcyuVXYoQfpvUeTv6bEaFYu/fy4k56XFjdDo2iYotL+o5AbiuOEN1taIUYwukvpTYMJ83bizhM4wJMkUBMNQT9nfiPGu/uipe/CJy5vfbCfC7LZpMd0tQpvxlh9IEa8Io76vIoGAK9wO9tTnGaXhN4OL0rM5OQrL9uUKTWlVJijMUw75nHo4sGYJY9rQE2y5OMv/BbtFgucESKobIbijrirQ4EXtsb9IQxr5ObwP8+VHlPxqxv2r1FGfdC7wiAgxaXzVKJluB244u366WdoN72zT+Lyxws72Ve0bR/xO4uN4U+HLWiOTxJPG6afReduwrLKvJRBZmjbBhr7xz5D06V/WGm3/d7luhQ68ModRf8PgPNRMhYjXnu0E50dfrv0B1u8mbRGWNyN/Dh3cAWvPm4pbmLcuYOZc31PN4DKMq/MijCMxzKyozDMqJWWpyO71f8SUCPwKQl1zXj7AzqcLe6PtmPGo7TWaOXz9fjwMU6G79jFfUbXrpHXRbLEXB1FqVWwHIOJof6dL/JzdLbJ6yO5I93/NqigZK/9HU/mbnlKNemkulfZbhgjRdWH4QR0CJHQiuHzbykfy8bD4a8NburfikfvfpYTyhHpkWeMqY+sTC0qNOHAPcEBFxfMgEbgw0DPmBOPy9P4YtttLAGs12yToEpvJnnxQzj0Ll4tyQb9TbV5RCM0TzResK20StRpQ1HsXKmhSXtm43HzOZlZezLAqh4ygkC7mkaM9ehAXxGL15gzAEqsfitlXmTuHcAfecja7ofmtlYFlC6kkdMlx6yGJDKHFgHQe3tR5oPk2A7024kWJdSQhw5fow0AWdsEjTi2u91JkpDSR6PsOrASlLMCjl7CZu9sgmWW+BZYFPM3tOlzm4zwUXAZcWedmWjELWZIePVw2QR66X4n5VsULzZDNL1XfJc4gCJaj47bpFXqFIJlLponWjx9WO46RNle2h3lGSOYLIAwFnYZZXgSGspaNTfE3+eDnHiQqhXRms2CYwqm/pejCFl7oCCOYfdsZytTCAHiKRyugSl5UJkuoXpC4iV4WEcxVksnuRCIzBga/U1/a/gcwIQWYrhNK5LV+TnatGuBVerYAMC1wwSMsgAOOhLwwygYF6w8bdULLiheuGsBtX42DoHOmZ9Wb77mpMyeI41yiXBWwu9d+x5MgZY0pwC4B5C4uAwQm0vP+n9cl9+Q9gxMSgp8A/cspA11dFyLl44hQpRxvzEchUdIUkHJcxWa7ZRqNM1KJfMweoapzxVHRSPq0vfGYeQACog50U85dYst5fSwGcwj2sOFZF3uvLJpurVAlStgPKfL8EMvfvPvMDoyfUhKR4aaIegjjK6pFzAIqoNgZSPIVP5oJaB+NBULqTVfjFne2ie+/hkNWLdcbs4/WqwMRmd/FxS/qSWQI33zgHujyUNKOK8+tz37RJTw4kqwY/FonP4MqQLYJanOw7uI+J8dUIeDBOVC36QO2Qygc7rzjH2TFi0Qx+qAQ5Ys0Z4rXVjK5iOoVS+65hrVAy8A93/u8Dd66LTLks0nG58e7WvNtzAcxw/u+7IX5tbZi8rb2GEZfpx+9PUCiwOo9bfO1ZeUnYgq0slg7M+3KuDEqC2mKfonL0QE8/oeMPnMOPqFofpZLNAUlZAcxMEsEoybmvsaMPUyngr+GV7LgCw14nW+RRPm3Q2v2GcZXPpoXlmsnxW+nognt+7hYPSwET9dcrRvomk/VCnLA7dh+XvAqNE4GzG/rDKe3ehxeX/YW0yuULTC19yO2VRtKDheEAdL3Zun6+5RREkUieu0yA9/pt9hePQ3/G8g1s8qClLpcdWT43+ezZZEqzcBf3vZ6M/+7FsRkD6te5gtva1sn3LuDXU2ieIPpF5xVrlv7Dr7TRrdFmgoD1tvd86i9Ui9EsmfN1ibyFj+sfH85NDcR6DCwSL4gCAw8q4vDwiRZQ6VERdLiSVYEgxrxQsMdOW0eu9Jyphy5bE4Hn0ZdD2gQ0jM6hB2AIMUsfLJ2cIJDmpl/jIaYW/+AqblacY0WASXHGIiQ8Un4kbogh0XHMcYPEZVdxdnwxm+mbegjCUrum8c9mDkf0D4roibsKG+D7dBFkUL7iyroh9NZHzpYJG0flWt+AEb6h0tSPZ2DJeg6ptDt3E2MQ/pIxSuDwZSdSzcnOnRTNp/zb2rg1YKlAsCHBk1wS9pGsn5VLCSZd49bLB52Go6fRyh4T4bC+eaGKNbEsQK7d012BI2HvIKIR1E+hzEB53+ZfqLdFv3MqGeRRWf+LGnAz1zdSLdNsdo48QebEbcLgy8E3TNPtZfut1x/0tQvG6QCJeaduHtuChvQ5alkO5uW66BaLs9UkgTc2voo1MkhxzmxAZ0CGgXv0gD2hoTsXZlEJfLl1FGjmZSPpoDLGHvCEOziG3d/3nrmBje1rAczUPDH8iGMIAKvJPcrp34IZGlD/MVSjuQP8wz3oR7ADYQYeAYw1V2S2mLWu5c+SAqc/fk9UEBwAoiCwvJIsShSojR7u9kubO8tjoFh9Gkv50Bvl9Xdzf7GSll2Brzrhgc42agZk39jeoJJBB2fYI/g9HizqFEeWwnjGxFj7bfo6fTA70hadYc3XUaPCMMTrERT0rB+J+r/4SpkUElK3TSqgwn092BMzEsuYt9Oz1WdWIFCq0lHE+YuaFju286f9WZtTT8W4u+19m68QeSo6ym//v+3qeXnuIxM5+vy8eiadwWhmCgh26xj0T7te8kQy3qGPM5LSTL2OCQGExGAsAKmoBzTVmHYn9B0IF8hRkwHgyHNcWR1T7OG1uI+gKUZyerYJcX+Ju+IRhlyFs8iUYwEK2W2Ueyl8dA5oHBkB7BRAgU4dncO5VMgOIIZLoDYGleSPUu8PQK1IoyTgit5kMFPTXzRMTyYoFaxtbYk0kt+O4Y/oXvXN3Mo+0Dj3aBXOFwhABMDCgoodLoB3pPBzVKgDLxzmbe6qvthbCQR7m3TY+QXdJRa6TJV35r1GefgeibmDJ8hUJSAz+zDbl7n7r2GMu8xzIWDLgmdVArtEXZGEJ/0KygU3zuCqknHR1uvt4u0RrJwzeGnnR/qxelJk6sAYMp5xNfOgioKrRdQy4SrVFEdUDP/cbRMXPKXg3FxMyvjxofMiY4IlIFwldvkl+F8yTEUmBjpuiCnv64++M3y/UWSAcGZkTQfjNci3hp8O9g46N+DN2wTAC5O/o3KHt7FrYbQuiD3NvqClnr1cDd4/zP+9jSJVyJK6cHJMipqXooHFwVpJf4TkTrQL9OSrKkmKta+Rd7mbksNnPEFj+4EMeO8qC/1s0UtOX/Z6dbDx8rvALspeJyWdW56828DV9fIM3wsMFq/B81u0abZyBBYl3k2GYKHwJM0yRwMQPyJVTklSeQtaXEWXJGgh0tF+ZnYQ9hVt2+Ps2ePiV9wCW43izr3F/xuaSaz+TnCpCXz8Jez0j6L2xBNDd3Uon9ziYtPZuUxk2PTPKeiQd383g5u88QlO3v6OTgkaJxRD4j3HTrjzXEzRaoLAoeoY8aqU7LZVdBVGrcsa3bsRAByvyT+G9uGuk7L3V249Imimtja9F4+AQ+Ms362L9/yLF6e1XMzGc1dLekD+HRIJY56y2rciTciYOpphq7c+PwrBmwwVzK2I/u24JS0NJAzHON6RWeo7GFGcKn2N4GcKrrK+2mbcmAvJ67MGUrXyBu/7x5ACqRmYjzoBvFUpDtjSSTONENRoVUEHciWlJR2nFtGkeflY16UZIfvQM/lM75ML/xR6C1WjLcrwlZQ1bsg90JqMA1M6OekGpgyGv2yQqVmDh/QJOpou5FYaVnm27DSjQff9uJBu7kF42QyqLsCD59r+rhS4Il3y34ULNyDfVqY7g2BWHr3gl8FzJgof6GFo9RdpbXIOHtQspsIOTT0XjQF6Y652ZgRDUD0KT43vBb7evm9T/guxy54vBPUHCaxSqTFFGzvKUcDA+Oh7W+rgJt/3T4se2LdxhtyudKjv9id8JvoRLe+JDQWEv0IXnztsBwE0IWL0lcsz9m9g62nN9XQqLXdSKHwrJ90xx2hVjZ2rPUJ+kPJioV0NL8G0B8fDpyviDlkUeM0fdYZJIs99hKgliJpJ3OX3AFxjq/PlJcPPJzZYrpoB0PD/j+gmdD7aZTpUzgJg23OLt4Dk4+VxRYYYr3EhTkdA2hj+beXzWjujxQv4eyUEui2uMsW9VlpDV1zfeTepv2+cfEIx8Mdnyunh8yJY88VenX78Xu2wKLDSda5/z8HTI9W8bthJPMsdx1fmsrxFZ8CQY6U6uz9rqmXAWz89CCr+i7EPXFtmGb9n5k/Gf8V87skpc6q/dMC6mZpM0B+3fcs1AWBoFserqvJ/Ee8aeMjEkY4Gr0dWrrapNXSKmVqyu773tr8YjGBrmQAKa5sEtLoFJ//IlyhJ8vByqVwyCEr986q0YIeQ8fpfztH6lSSrDkmrdEiXqVCfArHo5zlvia4TqBjg2K0xlqo1bIa7UVqt38xN5CxRcdbqbqCPCfjrelOf002MZSUDMs0mEMs7XX/0mPW9oHjDmFDi32/fPPl21ClxFYm6KXB1kUAfEuUI0MRNEXgSIgvDUl3uCJ11rDaonRAzu/2HfkQ/mj/ZO+SOuSDaECJB0p819grbfSTfoEtj82YG+XeYEYADiTyWhCsCKIhamfnM+9/XF306xNhpCyCrCmIThN52fdqmeBwBzMNvczAXoIhoxBKiqg6T7avXjNRZ0yhPgN0zA8/WtTxlm4AfTqIOzET+aOlfXMyjHRnCeGRbAzz+8I02FUn3YwZPkT+sYwVLYXJ4nDnTeECFkMw0mYCTUG5WchuaNaTEJbdUNhwc4CheRwRxWx2+MwG6ufaHJSJXtPxAjfgrxZr2g0uavd37p0zxEzuJIGXNRo9qLIlWijodadR61mBCQ76sex5NBvsI7UeUBoOFoVLnqHlfwdisY/TRrRS6SwKc/PJ5MP0NNjFLydv5mbRqzQw6NYovaTqtqvzf5Eb+ToOd5C69SJRGh6rBJyf0+kMZ0zCHmFB6B6viUU9lXjdQxICD1Gs6v5yWoPotAzEg3NC3zLGS95hfMxhKoR6K9CV8Nv1QJZ0yNeWq/joRAd0QNZzd41d5CDspya0SdgfuaGwC6bP8R2VFeSK4plgz5gD568b+dzSGWG/72N6jJfaxu4weHM40mw/B6zjeecWYVmK0eI7/bgcJU8RlXWuJ1q1zO5f4lLGbkdnSiS4hUItXoUkcMy0CjkCIG3udNeVdf7FlyCpqTcizFz3YQRcHJQWukar38HxuYN5DwU26de46AruWCeGYwlXYF0ldOZji2X/LUE8Job0jt13FLLD+kZhnvlqgpAnuDs3Oh1md7yMTzXjn6AaA1cVCfj/GH96sSWugMKTPhBfVIGsZAnpoduOuWZDuHI+fu7JeCV/KWx/PgUJVzAqw+sFE8xtUMCGwyoi8M0uSrWxsSbS83WohPsqzHBc13FbzKYXwrzcClkBSgq8Uvrxs3CQ+1H2D4PWyrGSPjSHtxMAWs8+kuzi3X1cJOBzE4yD9dnxKwfqpgimgSgQYj71GjU6o7Dl1tMBxtJRltQ1Dmtl/aM/eIKR8AWHeRtp6uzoftIofE3LTf4Jn8Ql0YWa9hyuHEDMymOXBljBJDNGsuuXYR2EFPm53U8UWxffInHTUE214cZFthg/4GYxvWH9XRM4WHNpbxMzmXfCm07XsqxZ951zJJ5pHguoHjtTfP//MD9ZFCYy8czzuSPxIWc7GqxJG3531lDzWqAKvjH1iA0NabZ0d2Z8Tq+Wq0NNhyhvaLWNa+ZuSBA2yhchh5Tuyr2NAIZGYMuypnlTkGRa6NRlOL7qnkV47nn/YbCNLBKsSugCSLyDlDJq6iR30HZjRDmGHD66+BSmnor8UuBoJNIzxlOrVR6SF4xzPyB3SDyidCoepHmdQeoo8+oso8ZuYnyz+oq8z/tS5jHl627MBDo/5tWxaieNyP82mNydKqfJtQEQy75u804oQuIBMsQF62Axhl+a9Tat6G2eqD1Xz1QU2OGkqE1vw7qGdZoJOSWRyaTfDEHrLJJkGAWiLVK9T9vFL20/bMqSZsdOlUixeghXMuFOhNtwlGZJ64Uxn12DEMq+L+0ujGI8eUqZF3msJSZ3+XyBsFKRDyKe+26/RRmpV9ypT1+HlWAuqGTvWM0atXR/koY1YCbX46ZR5vm974EiaIdHRlKDZWsPJ9nF2q1kUUSKjPY5Spj9c/3TVWzBq4Tpj6FaNBAboas94QjNg8AEe2hcT7PO2Y183aDmQQN0A/FgcSBtWirHCzfqqx8JYhHeJ05P0xjQnNO/MLDjkCqur13ILBvhT5BSXp6k1LZRAP7ZdgHX4bkOtqDn5DlhPWjGEIqnuoBbFjCHUovTtjurhHtg8VhQebAX2vgGbC/QmNVDtEy3e9Qxs91E7ii8W74AqGGDOdQdpjXJytA77WrCwXuqx3XWMUEQxO0DSkyKE98TR/ntUrnAfPJIJbE+D/HFPg0nxY4Sjl43pXrQHSKvMjHOZ9TeqiExQZm8X2yUeFSqbCP/nqHB/QVBHKRJ0Gx0T7WKPSThIor1Et2u24p53fGaNFBn3pjFkqtkS6JSSfBduiG2lK3FfK0tJSNijUWs+50GFxiVkENQo55qiDD/ngqTDgAfZOpqmXYIFjALWQ/6O4+ps54Xqd4J4HJr0cTtpY59VuneyamAJtmSrPFG3p5uu1JgY3ds1ARIvoAJLt7rTKTA6cdAlbJuI95ZiTEr0PWgkzHmzRwurCk4218LRMMgst44luEG8QeQvuveVyqlXs2yZyELHDJYJiIWeq7M7K8BbVF2K9BCNyys56qvNjzeVYURdhlQmY3BeJEfcr2PCZtzUThSKV+QVAkSjEbTM24qYPPR0QkLF1kdL6K4bSAHPdvgL9K2vxHaodhVJGGaXIoyx/jA7hemIGq4hzJiJHTlLBJJ25kSs1PFH6CAuRsMF4k8SW5vJRh1JUC0UI3gvTB1OND2p8aShMkVL/RcO+hOyASXLO3rYBiS+LwM0YMcSeudNmr5AxcQLOLr5SCXx2RpbACEKMO6w6NGk06kzlRyqL78V4T0xplcap1wU63HkJF/KWRJLuHaK3UAOaYCfEdckZ7eUtnXVjQgzavz4CxUaqvIftFWmXqLx1L7ZYwi7Rxk1uv2wd6Ud/3izmXxGB+Ro6iUhGfx3sk6kZZAk5jbxgx6kBeBmSwB++0YZOYBwjdg8AmLF3lRWBAzdxlsXjc9afHOEsIWkqYXzTfECeQPK0+jWWlvKEXuTfnArURsANoA+cguBe3pmeGXvM4Qs6lDNDEeIjWwFZb+AC+THU7Sw/7xWX3glChdxtC9X1INToSig2GPv/YlQu+sXVbjRl6CIS20aFMCkJy+MzH9RtXcRs8VCy0iTqkxSMe1d2qMIKuXILdh5wZuxVoYKqVS2kLXIC6br8FXqgPNlqKTMINV15TI6SYmJfGMcCR7QIphN0/R+dIaBxLr02Kt8yWei4TQypu6LEXG5BH0JPFEfVqtn6KyjTUrGWDwxeHqRkceZOb7pLYrLDSRN+qAVO3bJ7K55nLsynQAKCaZCJhFbUaPY0pZdAPpjyIOpechyo+3YvSeFEq5K8euvY36iZ8E3qCwvGrmaj41LybVAP7Ko7WY/MQp521jiBjxv9KKwSaLNwI3wNzLe7EhL6VhixcObtD65UXdNOhHFqX//SrkDxM1xzNf8B7qPIvxWiyvQ9xMfzJUtMkm9Uli61Qud3jbjqa8/KJE/99XzqS+DYek3cxlCaRggTouJ2xU1STFddd3fnxmwjI5NlLnBtCcjhPr9oI/A8cbZAkzP7rf8ygRBDPg/WcoQNXFa4a5sO4L3K4Z+hCU8TU5bBXDSuC3SsdkxETUnkThiu1lXf8lF3tQz8Sh1l+UAilgU2yg2uNkJ3r0dOwkE4B7IJ8hRgrnFHIg51bNLUMGX3LHqm/uGHzg8y/mT6qiqn9BzDvVSFklYIMsvFZeOORuU43vsxff8d4xJHvg07H06Ndmjh2q4j7qa1r2y4m74DqcrJeJaIl+5uQO9uuJXKEV8sny5EvtSTKd+mrfoRAJnr08Bq2McTq6Kru4LgKbk9/C5GIDNzkSZsGqxmfLpbXJlqyBZ4s4306URX7cofYNnaONcKPdqvqiwKec5IJdz3ZCd0m2TTFWJ+Z+IRD1AoPNY+oncKHGK480uqarPArpa7ITxdPxq375GZElL7yTzTojD4tr92uiTVJT5cj/2iHJTj9JC+8ugj1JhlIH9wuKtpRQdaXhs9w4gjB1XP8ht9TMUGWvBxXOw8D7nhSFxwEwI1XKQjKPbDcyxmHaY039EJMMIeuvEGF/4pK/Ay88RU09C1zLwY7BYhY2EftmSHeHUTaYKeRMTj+bsDYSDE9rJLQV6Zi/4P5uLi+vHZYaoROz2eJl8ZfT6Ibb1e7KKY0WJotWyqehENUzFriGqR95MRWiob6MBlpfAKnshkAbqsvA9nKM5WoKcMtB2Djm6XmR+iaPGNSaB8T++SBvhvIFg7P30W0AjMjvwK2Vs43748jRvIKmg2kv6Hax8J6NUxScnA6iPkNUVGwfh+mu43dWRUz6RIEBFOBHB0Ot7QcDNwFjOIyEaX1OYC0bCBMbvXXLyoSlGz15VkkCNDwoI85IBefPrsggPpdwVFV/x706+8kuf+c7hlakU8l0J61OKI+4IqnSD6JrFmRJiHaFhGjFyuze03w8iDGkCbboY/CIwmdkiSHfj+yEW8REC0RMArECNMWaAXjyvnl0zlfYdBhpiWe8p3+s+4MzIeGQGVhn/GTxPdtXqPKIgPzezInBPwG7mTD59drWgzR22K+M4apUidNhvTs/8QrkTLNQGYYrZnUdoCZm3zCaUMkqt0Tmiv+/1k2iTIb2heYGK3XamYRDdGwmgtd6X2F7NlZne0s4ZL6Q6tpBCosaPg7CJfKiSX47rdPVUwMlZpAeT/9W5WlSRoby/XXID+1e0kWwa1pExhbQkryoFZv8WmfRjE0b6qyqhWv2tubH836eXExSR0bmb2lzO94ebiGfdPW7K0OMQ+k5B3/kXCwFWQ4AntBKr8Rf+b3THg+eklRf6XIWdWcnawmrUU9VI7LI+OOJLdtDQHkbWbzErs0vW+k6GSDfdkK7GUioM6PANpqMK5ugzCPd4rVDDm0TbD+UnRUb79W1SXqnQbPf9btwSomkE4VYFc6srzyG1RD+G01Qv4AvP+kaS6kDAq38wnp8CadPb4eoecjxxbKwKX7QzmOvE8c+4Oo2KdhXQGd6pm72yhWs3+w4KMPaID8b+7VFh72WtSPb/TCL8p6oJguc4Ewo5mCUvXnda6v/1xPSTmOYfJAdiBdrRMaBHTZQG61i2XKQEVG+YJLBuPHFOoxraHft7/b6/3Et19PuWDghyYuU39gcQA+qYYPXfzcphudTu95j3lhBFDjDKa6PcP1xz2HPqHlAcDmdb+i9dO9kW5hnrqmfwEj7iYMyDyJorl1k1ZuTYqqnpg09nRDRe2FLasIXXEF4tVKjzJsWnJ7H4JMPboj3yU9H9wRbVXTBtj4n6jyBs3bG+9DwkQwzY+SFtdl7R45hNWdsxODDIdV4bClXBKNwcRs51MnfBCcpV4a4zCgCn7oJ1ibrZV5sovsqixJSFOfaYIKrIOAb+L6pXOdq+c+gF7r5l2fRZIHxoWEz07SAPjgl2Rmfxnl1ktcnOqSZ/ukRKvJTtKQE/M58rTiA8vknrj/zsdSE1YgwPc0kvN6Z8/gOE0abW28vJtLZXO6fyUYBST3Z3kNQZyh0Yd2KzF5ShwUUmgqTFPKC/mKhjCT8nOoU+xDL/QFUjWittUxfFJ5ZJPXq0bgfLu8oA5hUDUITxCAxLDPQjdLzILXsxv2YjW+KKDthQVWRFpa+ytR3ToAnRf79JQl00PAQAZ68h8yZFdMnBJe/cOg0GUaZFhODwCnXvPCYMARvzQN+3sxcux6kIYr0O/53GGwYQC7z6Ey6jFnnBB5px7wFQyN+zmHpzLwRdETv0cSkVAh4bYxgwYzikb1sU/OdWzHL01bgQhLVrUw+fqFiLo4hyjX4vRc6d9Cz3pZm+J9pbeGPu3Jfap2We8T0TrV+Ih2ATBJw8B/SHEtHYnm4NI6tjPxCBXdIcQi9i8D5vxOHn5TeUGFYPURFF4YhqILgx8fkyIj1X1k/QmE4CyjbFZL7J0DqFu8Nz0zpDEpyu+B6VGiWuqnaKeGu+3cK5vtqXp+0mywwzJSIyizL0z0DF3bbWpDDKQ6gdbpa2ORJwIFEg4tx5CpJgJ98Z1bX2mi5wOO2AAF4tGqRkUWJ6DQ0Cr+AhXlMePTfUZKX0NzP4gFSbLeBzQwkXiybEX6+RDBsYtwmfaR6qAyESKKoEnUVMCca8FdV6ke61ezr1OOcdjoimg4twwVvdJYnt0veHVp57GIJ7PostWftfKs0u9OrDWJXjm6kmUvzylkDJb1WvZ9NC3ColXyU6iIhQRD0N72xcWDmAJw57ze0+3A0+JEYve/u835My/4GfFfr67PKAXxGh7WkCHLuyAdUYp5PoZUUDpAPfgiID0i9+ahro51wZLtXClUtKdcZCh8XYFXUbGk3ZP9I8q2+fSjirko51QWZI/mZvRkrjVFILjONN+a8zAtBM13xKV1B18Su2AFgIFGzHf2Run5pQrW7gC8ki1iVzHjBoTAsxTFAF4XXShRstk5CBqrD7YaUHZycJjnLWYVcDbTwbBWsvhwWI25zyOMqQpt+AzvR5RGmUDntobqPMjhOQwxqhuFKYCDzo5iBoRBXZjZy2ZMUltnijzRdr2Lt2vOs3nlLb76TAGd4gwU59DyE3z/aEQIKGQVuCruZ10rh1QhigT+rydfSNMrIl/u/6l1PoPCoQvcli28WaPSyOtmwyWBu63LmRxzCPyjYr5lgGlc+xcvrGSQBDLuA3Ezjl6CuBgYDzriDwIDUFCuEdJFuXFo5EFCBG73vRz3oPcYOiVKagQrkq5ClL9wWlWHmvTu0lJZja/TpN8vOv9lgDbs/Wl0TZ4+M7lQ7b9WY8BcGtwct0I7T3oyhcLGiBv0PPBoiVO0hZkfkwZKwylcrC7Wlx8UcjLHOps0gaxxvTs3PdmQFt7A0ZHyLEKubKgOOh6aFXaJGz1hTl8YA0krdD1KJWEubosraNGKU/iUF9OtPwwmv9PEoLqc42n2CedqZwBFhSd0zWeqI+EXbHx2SEDzxaRkO8I31H0F3AwtG0wSw0zuUcJPELF3lz/K/5mb2Ex6pezzyq91hwuYOlzRw8a4lAMrH0nsV66S1glU4iM4ROfIc7RSxVFuhw3DfWHXRFIl00PQct1tjwkex4rmgtY/dkEJhJXj7nTrbwHASqm2wjvzawjJXRd9W45wh8CNS1QBy2+2hsUIQuXEvwZsN12ZppU3UmOY8TWUlhaEmp9Ar9qAv8z+bLXPGIIwaSs1hCNQFgcGz8qGraXPANClmzcY8NCnh3TcYSRrb3C57ZVl8WPoDneKfTyC6HiEpdxbj1wCHE05ofs6ITe8CxBM44qq5MOTYi+g04N8Wdjpds5KtEiWN+ZacZXAHqF/4OR6q4K7ca2xG6NnE84s5jAH0R5ci3/CLD+2HqzPynVRgX6fu9cBzoWtGyM3BSjWTjfFsYPuxnrHRBTFBc2dhtTFztNKR6YFPw6gBPCNplWlH4daMHSkDuOIz+iOk3Ar00xdy0Wa9/wyybTKuoza8VZx1c96PvMdC1W7kydSVY3/2kNbwwqZ6acbe2Ymbl8Aqdwlp+aBjjfh1ilC9CEHbZBur8iq5YTVa0yJ2D+VZ2j2MoHVl0xn/B3JEKrm1dNgNbIsEFfos5X9TYueZzQp+BjBCGWL26lmta9zhHisgsKxOLTwDI0+lE9B+Y65H1pGjBkuSK96bXutysq64uahhY+oPH3S7CFXYG2tqAR1KcgxZuTuGrhTc6W6MeyNU6EAHVSSXEDfOGrwgfk2F9RjPW4kDRzsT0nR/sz9Vc90JIX1s7E6vhHynJ82CpNxVABDbK5bs84nThKnyVH/f0HHd+Ckade17kmWt/tGtBaCmiCpSDWvy3SIM1ntOcg/E6kYjs/W7GyAIGankF28ecFvOhrZUvqUTU9JQFUUArpbRVfr15bgqWbOTFcSZKVn4+Vgl8T/p6uB/BjHlygI4UTHJOogrQGrh7Y9+ycZ78wq2uLEakuhxnh1y/OAcdUj8GMbvijIyK0K3muryxpeSqhFTBkg/ERzY9l9gB5pHDQH5tMu0lIfoaGjsY2TiKY/jOOWAGophQoFlWHnNoKCeY3n2m023Ms22ii87U63h7j2d3Qv2IQe+ccZbErqmGDAEacC/ds16NGYk2Ma746aWBm9Kq/hfRQdJAMjuaHmNRdU0ubNU8D04HV5KuAfBI9EdkYKuup8F+9o/IqLmcNT+rqyWzwynO7GheMhnwEh6dv/lxJBhFOUNaKLKY066+yceI4VPrpBEfn8fwKTHqJNNL1dpTkKzUHCjCSFYxq/FHFMrvCpIlV8jek2DldpoEQatxaUZ/lIc6Bk19QHilViOW8DNznuH7HvPRHlsGG8MPWJ2SN7zNWjHdquyoTnGYTWyuwFd5BSMMWvjLZ8U3K5tEYv0r6UDNs83RePDfneZRcPlgk5pK6MUwzxXpGVxGiK02lSUYAQ=="}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1395,"cache_read_input_tokens":45976,"cache_creation":{"ephemeral_5m_input_tokens":1395,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CdcEQYxq9U2YF6g9PvXyH","attributionAgent":"general-purpose","type":"assistant","uuid":"3eaaaa6b-20f3-4830-b360-fd5627f89c15","timestamp":"2026-08-01T15:05:29.853Z","effort":"xhigh","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
{"parentUuid":"3eaaaa6b-20f3-4830-b360-fd5627f89c15","isSidechain":true,"agentId":"aec521429a7581efa","message":{"model":"claude-opus-4-8","id":"msg_011CdcEQZusz6mhvBo25NZuB","type":"message","role":"assistant","content":[{"type":"text","text":"Good — my simulation reproduces the claimed counts (Фан Юань 184, Винный червь 67). Now let me dig into the specifics: dump the actual matched fragments, classify the soft-matched tokens, test adversarial collisions, and check callers/number-words."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1395,"cache_read_input_tokens":45976,"cache_creation":{"ephemeral_5m_input_tokens":1395,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CdcEQYxq9U2YF6g9PvXyH","attributionAgent":"general-purpose","type":"assistant","uuid":"a317f671-7f94-4ec2-8213-7e70699bf411","timestamp":"2026-08-01T15:05:31.395Z","effort":"xhigh","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
{"parentUuid":"a317f671-7f94-4ec2-8213-7e70699bf411","isSidechain":true,"agentId":"aec521429a7581efa","message":{"model":"claude-opus-4-8","id":"msg_011CdcEQZusz6mhvBo25NZuB","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01L28BLtDRwpFHoLrARpDzqQ","name":"Bash","input":{"command":"echo \"=== callers of declinedFormPresent / dstFormPresent ===\"; grep -rn \"declinedFormPresent\\|dstFormPresent\" internal/ --include=*.go | grep -v _test.go; echo; echo \"=== bank: Синь / Линь / син / лин components ===\"; awk -F'\\t' 'NR>1{print $2}' /home/ubuntu/books/gu-zhenren/coldrun-a/BANK-FULL.tsv | grep -iE 'синь|линь|синий|линия' ; echo \"(none above = not present)\"; echo; echo \"=== bank: number words ending in ь ===\"; awk -F'\\t' 'NR>1{print $2}' /home/ubuntu/books/gu-zhenren/coldrun-a/BANK-FULL.tsv | grep -ioE 'пять|шесть|семь|восемь|девять|десять|одиннадцать|двенадцать|двадцать|тридцать' | sort | uniq -c","description":"Check callers, surname collisions, number words"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1395,"cache_read_input_tokens":45976,"output_tokens":9120,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1395},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":9120,"cache_read_input_tokens":45976,"cache_creation_input_tokens":1395,"cache_creation":{"ephemeral_5m_input_tokens":1395,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CdcEQYxq9U2YF6g9PvXyH","attributionAgent":"general-purpose","type":"assistant","uuid":"5ab74cb6-f645-494c-aacc-2925e9c15d4d","timestamp":"2026-08-01T15:05:35.728Z","effort":"xhigh","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
{"parentUuid":"5ab74cb6-f645-494c-aacc-2925e9c15d4d","isSidechain":true,"promptId":"c81fa375-6b68-4d22-8364-68be617228f6","agentId":"aec521429a7581efa","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01L28BLtDRwpFHoLrARpDzqQ","type":"tool_result","content":"=== callers of declinedFormPresent / dstFormPresent ===\ninternal/membank/mempostcheck.go:96:\t\tpresent := dstFormPresent(p.entry, noutRunes, outWords, b.stemmer)\ninternal/membank/mempostcheck.go:116:// dstFormPresent reports whether any accepted dst form of the entry appears in the\ninternal/membank/mempostcheck.go:146:func dstFormPresent(e *entry, noutRunes []rune, outWords []string, stemmer lang.TargetStemmer) bool {\ninternal/membank/mempostcheck.go:154:\t\tif declinedFormPresent(outWords, base, stemmer) {\ninternal/membank/mempostcheck.go:166:// declinedFormPresent reports whether the base rendering appears in the tokenized output with each of its\ninternal/membank/mempostcheck.go:169:func declinedFormPresent(outWords []string, base string, stemmer lang.TargetStemmer) bool {\ninternal/membank/mempostcheck.go:227:// records its rejects, and dstFormPresent already answers \"is this rendering here\" decl-aware — so it\ninternal/membank/mempostcheck.go:254:\t\tif !dstFormPresent(p.entry, nout, outWords, b.stemmer) {\n\n=== bank: Синь / Линь / син / лин components ===\nкровная линия\n(none above = not present)\n\n=== bank: number words ending in ь ===\n      1 девять\n      1 десять\n      1 семь","is_error":false}]},"uuid":"e3c85a98-3fe9-4a85-910c-2324d690bcaa","timestamp":"2026-08-01T15:05:35.835Z","sourceToolAssistantUUID":"5ab74cb6-f645-494c-aacc-2925e9c15d4d","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
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"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":9601,"cache_read_input_tokens":47371,"cache_creation":{"ephemeral_5m_input_tokens":9601,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CdcEbdZD1cS75hNVJibPN","attributionAgent":"general-purpose","type":"assistant","uuid":"55106082-6638-4def-8b5b-ddf354f852cb","timestamp":"2026-08-01T15:06:04.388Z","effort":"xhigh","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
{"parentUuid":"55106082-6638-4def-8b5b-ddf354f852cb","isSidechain":true,"agentId":"aec521429a7581efa","message":{"model":"claude-opus-4-8","id":"msg_011CdcEbf2mHCUP6DyEZUzUF","type":"message","role":"assistant","content":[{"type":"text","text":"\"кровная линия\" is present, and I see number words. Let me refine: dump the actual A3-new fragments, list soft-matched tokens per term, run the adversarial Синь construction, and verify the семь hit."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":9601,"cache_read_input_tokens":47371,"cache_creation":{"ephemeral_5m_input_tokens":9601,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CdcEbdZD1cS75hNVJibPN","attributionAgent":"general-purpose","type":"assistant","uuid":"40dbe9cc-76db-4418-89e2-c52eec2ca942","timestamp":"2026-08-01T15:06:08.099Z","effort":"xhigh","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
{"parentUuid":"40dbe9cc-76db-4418-89e2-c52eec2ca942","isSidechain":true,"agentId":"aec521429a7581efa","message":{"model":"claude-opus-4-8","id":"msg_011CdcEbf2mHCUP6DyEZUzUF","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BjJTHmB24zw2qUAAZe8KwA","name":"Bash","input":{"command":"echo \"=== bank rows containing whole-word number-ish / семь ===\"; awk -F'\\t' 'NR>1{print $2}' /home/ubuntu/books/gu-zhenren/coldrun-a/BANK-FULL.tsv | grep -nE '(^| )(семь|девять|десять)( |$)|семьи|семья'; echo; echo \"=== full bank dst col dumped for manual scan of multi-word soft terms ===\"; awk -F'\\t' 'NR>1{print NR\": \"$2}' /home/ubuntu/books/gu-zhenren/coldrun-a/BANK-FULL.tsv | grep -E 'ь'","description":"Clarify семь vs семьи and dump soft rows"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":9601,"cache_read_input_tokens":47371,"output_tokens":1931,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":9601},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":1931,"cache_read_input_tokens":47371,"cache_creation_input_tokens":9601,"cache_creation":{"ephemeral_5m_input_tokens":9601,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CdcEbdZD1cS75hNVJibPN","attributionAgent":"general-purpose","type":"assistant","uuid":"03089542-15e5-4d28-adac-da6e8e775da6","timestamp":"2026-08-01T15:06:08.956Z","effort":"xhigh","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
{"parentUuid":"03089542-15e5-4d28-adac-da6e8e775da6","isSidechain":true,"promptId":"c81fa375-6b68-4d22-8364-68be617228f6","agentId":"aec521429a7581efa","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BjJTHmB24zw2qUAAZe8KwA","type":"tool_result","content":"=== bank rows containing whole-word number-ish / семь ===\n107:девять рангов\n112:десять чудесных гу\n125:ветвь семьи Фан\n\n=== full bank dst col dumped for manual scan of multi-word soft terms ===\n2: Фан Юань\n12: чжэньюань\n14: гу-червь\n15: юаньши\n20: Юаньхай\n23: горная крепость\n24: Шэнь Цуй\n27: Винный червь\n34: Жэньцзу\n41: Гу Юэ Чилянь\n50: вождь четвёртого поколения\n53: Гу Юэ Фанюань\n55: Цинмаошань\n58: Гу Юэ Мочэнь\n62: матушка Шэнь\n67: талант для культивации\n73: демонический путь\n74: гу-мастер третьего ранга\n78: начальный этап\n80: павильон главы клана\n81: ветвь Фан\n82: шичэнь\n84: кунцяо и море юань\n85: цзин-ци-шэнь\n87: гу-червь первого ранга\n89: юаньци\n92: Гу Юэ Чэньбо\n95: Всё те же лица, но всё изменилось\n97: море истинной юань\n108: девять рангов\n111: полость Юаньхая\n113: десять чудесных гу\n115: Наньцзян\n125: отстранить рукой\n126: ветвь семьи Фан\n128: баньцзы\n132: шпилька с жемчугом\n133: океан истинной юань\n137: Пурпурная обитель\n141: ветвь Чи\n142: ветвь Чи\n145: серийный насильник\n148: снежная соль","is_error":false}]},"uuid":"faa934a1-ad23-480e-b9ae-31b95403bab0","timestamp":"2026-08-01T15:06:09.041Z","sourceToolAssistantUUID":"03089542-15e5-4d28-adac-da6e8e775da6","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
{"parentUuid":"faa934a1-ad23-480e-b9ae-31b95403bab0","isSidechain":true,"agentId":"aec521429a7581efa","message":{"model":"claude-opus-4-8","id":"msg_011CdcEe6evs6T1D3UBaJXHK","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2477,"cache_read_input_tokens":56972,"cache_creation":{"ephemeral_5m_input_tokens":2477,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CdcEe5S1ccoshcrNdpZGc","attributionAgent":"general-purpose","type":"assistant","uuid":"b4273b6a-f5ef-4fc6-a3c8-b03618631c29","timestamp":"2026-08-01T15:06:13.606Z","effort":"xhigh","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
{"parentUuid":"b4273b6a-f5ef-4fc6-a3c8-b03618631c29","isSidechain":true,"agentId":"aec521429a7581efa","message":{"model":"claude-opus-4-8","id":"msg_011CdcEe6evs6T1D3UBaJXHK","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NsB4hQnjqeV3tU5WBfLNWv","name":"Write","input":{"file_path":"/tmp/claude-1000/-home-ubuntu-projects-textmachine/07c509cc-213b-44f5-ad8e-c3697b0c4bf7/scratchpad/a3b.py","content":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport re, collections\nfrom a3 import (toks, same_stem, same_stem_soft, stem_norm, stem_soft,\n                window_match_A0, window_match_A3, terms)\n\nCORPUS = \"/home/ubuntu/books/gu-zhenren/acceptance/guzhenren-25ch-ru.txt\"\ncow = toks(open(CORPUS, encoding=\"utf-8\").read())\n\nsoft_terms = [t for t in terms if any(w.endswith(\"ь\") for w in toks(t))]\n\nprint(\"=== A3-NEW window fragments (soft component -> matched corpus token) ===\")\nfor t in soft_terms:\n    bw = toks(t)\n    a3new = [h for h in window_match_A3(cow, bw) if h[0]=='A3']\n    if not a3new: continue\n    # tally the distinct matched fragments and which token filled the soft slot\n    frags = collections.Counter()\n    softtok = collections.Counter()\n    for tag,i,cats in a3new:\n        frag = \" \".join(cow[i:i+len(bw)])\n        frags[frag]+=1\n        for k,c in enumerate(cats):\n            if c=='s': softtok[cow[i+k]]+=1\n    print(f\"\\nTERM {t!r} bw={bw}  ({len(a3new)} new windows)\")\n    print(\"   soft-slot tokens:\", dict(softtok))\n    print(\"   distinct fragments:\", dict(frags))\n\nprint(\"\\n\\n=== ADVERSARIAL: constructed multi-word Синь / Линь bank term ===\")\n# realistic xianxia titled-character terms with a soft surname that collides\nadv_terms = [\"старейшина Синь\", \"старик Линь\", \"мастер Синь\"]\nadv_corpus_true  = \"старейшина Синь кивнул ; старик Линь ушёл ; мастер Синь молчал\"\nadv_corpus_false = (\"вошёл старейшина синего одеяния ; старик линий на карте ; \"\n                    \"мастер синим пламенем ударил ; старейшины синими глазами\")\nfor label, corp in [(\"TRUE-mentions\", adv_corpus_true), (\"collision corpus (NO real Синь/Линь)\", adv_corpus_false)]:\n    ow = toks(corp)\n    print(f\"\\n corpus[{label}]: {corp}\")\n    for t in adv_terms:\n        bw = toks(t)\n        a0 = window_match_A0(ow, bw)\n        a3 = [h for h in window_match_A3(ow, bw) if h[0]=='A3']\n        for tag,i,cats in a3:\n            print(f\"   A3 FIRES for {t!r} on window: {' '.join(ow[i:i+len(bw)])!r} cats={cats}\")\n        if a0:\n            for i in a0:\n                print(f\"   A0(base) fires for {t!r} on: {' '.join(ow[i:i+len(bw)])!r}\")\n\nprint(\"\\n\\n=== number-word conflation probe (девять/десять ranks vs ordinals) ===\")\nfor t in [\"девять рангов\",\"десять чудесных гу\"]:\n    bw = toks(t)\n    a3new = [h for h in window_match_A3(cow, bw) if h[0]=='A3']\n    for tag,i,cats in a3new:\n        print(f\"  {t!r} -> {' '.join(cow[i:i+len(bw)])!r} cats={cats}\")\n# show ordinals present in corpus that soft-collide with девять/десять/семь\nfor num in [\"девять\",\"десять\",\"семь\",\"восемь\",\"пять\",\"шесть\"]:\n    ss = stem_soft(num)\n    coll = sorted({w for w in set(cow) if w!=num and same_stem_soft(w,num) and not same_stem(w,num)})\n    print(f\"  number {num!r} soft_stem={ss!r}: corpus soft-collision tokens (not normal-equal) = {coll}\")\n"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2477,"cache_read_input_tokens":56972,"output_tokens":1589,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2477},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":1589,"cache_read_input_tokens":56972,"cache_creation_input_tokens":2477,"cache_creation":{"ephemeral_5m_input_tokens":2477,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CdcEe5S1ccoshcrNdpZGc","attributionAgent":"general-purpose","type":"assistant","uuid":"64c9715c-0403-4acd-9fd7-afeb62c8ec6e","timestamp":"2026-08-01T15:06:32.383Z","effort":"xhigh","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
{"parentUuid":"64c9715c-0403-4acd-9fd7-afeb62c8ec6e","isSidechain":true,"promptId":"c81fa375-6b68-4d22-8364-68be617228f6","agentId":"aec521429a7581efa","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NsB4hQnjqeV3tU5WBfLNWv","type":"tool_result","content":"File created successfully at: /tmp/claude-1000/-home-ubuntu-projects-textmachine/07c509cc-213b-44f5-ad8e-c3697b0c4bf7/scratchpad/a3b.py (file state is current in your context — no need to Read it back)"}]},"uuid":"c7340891-6857-469c-8868-c1763053488f","timestamp":"2026-08-01T15:06:33.468Z","sourceToolAssistantUUID":"64c9715c-0403-4acd-9fd7-afeb62c8ec6e","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
{"parentUuid":"c7340891-6857-469c-8868-c1763053488f","isSidechain":true,"agentId":"aec521429a7581efa","message":{"model":"claude-opus-4-8","id":"msg_011CdcEfyoUj6y42xjtpiBdj","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HwJuzYvb3pqyAz9avvL3Vr","name":"Bash","input":{"command":"cd /tmp/claude-1000/-home-ubuntu-projects-textmachine/07c509cc-213b-44f5-ad8e-c3697b0c4bf7/scratchpad && python3 a3b.py","description":"Run detailed fragment + adversarial analysis"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1686,"cache_read_input_tokens":59449,"output_tokens":155,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1686},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":155,"cache_read_input_tokens":59449,"cache_creation_input_tokens":1686,"cache_creation":{"ephemeral_5m_input_tokens":1686,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CdcEftNZHARDkbwm4rYFU","attributionAgent":"general-purpose","type":"assistant","uuid":"5debda1d-f363-484d-85af-2e2f9d017fd2","timestamp":"2026-08-01T15:06:39.931Z","effort":"xhigh","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
{"parentUuid":"5debda1d-f363-484d-85af-2e2f9d017fd2","isSidechain":true,"promptId":"c81fa375-6b68-4d22-8364-68be617228f6","agentId":"aec521429a7581efa","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HwJuzYvb3pqyAz9avvL3Vr","type":"tool_result","content":"=== BANK soft-final (ends in ь) component inventory ===\nbank rows total: 150  rows with a soft-final word: 30\n\nunique soft-final words -> (soft_stem, norm_stem):\n  юань           soft_stem=юан        norm_stem=юань       soft_strips=Y\n  чжэньюань      soft_stem=чжэньюан   norm_stem=чжэньюань  soft_strips=Y\n  червь          soft_stem=черв       norm_stem=червь      soft_strips=Y\n  крепость       soft_stem=крепост    norm_stem=крепость   soft_strips=Y\n  шэнь           soft_stem=шэн        norm_stem=шэнь       soft_strips=Y\n  чилянь         soft_stem=чилян      norm_stem=чилянь     soft_strips=Y\n  вождь          soft_stem=вожд       norm_stem=вождь      soft_strips=Y\n  фанюань        soft_stem=фанюан     norm_stem=фанюань    soft_strips=Y\n  цинмаошань     soft_stem=цинмаошан  norm_stem=цинмаошань soft_strips=Y\n  мочэнь         soft_stem=мочэн      norm_stem=мочэнь     soft_strips=Y\n  путь           soft_stem=пут        norm_stem=путь       soft_strips=Y\n  ветвь          soft_stem=ветв       norm_stem=ветвь      soft_strips=Y\n  шичэнь         soft_stem=шичэн      norm_stem=шичэнь     soft_strips=Y\n  изменилось     soft_stem=изменилос  norm_stem=изменилось soft_strips=Y\n  девять         soft_stem=девят      norm_stem=девять     soft_strips=Y\n  полость        soft_stem=полост     norm_stem=полость    soft_strips=Y\n  десять         soft_stem=десят      norm_stem=десять     soft_strips=Y\n  отстранить     soft_stem=отстранит  norm_stem=отстранить soft_strips=Y\n  обитель        soft_stem=обител     norm_stem=обитель    soft_strips=Y\n  соль           soft_stem=сол        norm_stem=соль       soft_strips=Y\n\n=== corpus load ===\ncorpus tokens: 37482\n\n=== per-term A0 vs A3 window counts on real corpus (terms with soft component only) ===\n  term='Фан Юань'                               bw=['фан', 'юань'] A0windows=324 A3new=184\n  term='горная крепость'                        bw=['горная', 'крепость'] A0windows=0 A3new=1\n  term='Шэнь Цуй'                               bw=['шэнь', 'цуй'] A0windows=47 A3new=0\n  term='Винный червь'                           bw=['винный', 'червь'] A0windows=46 A3new=67\n  term='матушка Шэнь'                           bw=['матушка', 'шэнь'] A0windows=5 A3new=0\n  term='демонический путь'                      bw=['демонический', 'путь'] A0windows=1 A3new=1\n  term='ветвь Фан'                              bw=['ветвь', 'фан'] A0windows=1 A3new=2\n  term='девять рангов'                          bw=['девять', 'рангов'] A0windows=1 A3new=1\n  term='ветвь Чи'                               bw=['ветвь', 'чи'] A0windows=0 A3new=3\n  term='ветвь Чи'                               bw=['ветвь', 'чи'] A0windows=0 A3new=3\ntotal NEW A3 windows on corpus: 262\n=== A3-NEW window fragments (soft component -> matched corpus token) ===\n\nTERM 'Фан Юань' bw=['фан', 'юань']  (184 new windows)\n   soft-slot tokens: {'юаня': 152, 'юанем': 7, 'юаню': 19, 'юане': 6}\n   distinct fragments: {'фан юаня': 152, 'фан юанем': 7, 'фан юаню': 19, 'фан юане': 6}\n\nTERM 'горная крепость' bw=['горная', 'крепость']  (1 new windows)\n   soft-slot tokens: {'крепости': 1}\n   distinct fragments: {'горной крепости': 1}\n\nTERM 'Винный червь' bw=['винный', 'червь']  (67 new windows)\n   soft-slot tokens: {'червя': 60, 'червём': 3, 'червю': 2, 'черве': 2}\n   distinct fragments: {'винного червя': 60, 'винным червём': 3, 'винному червю': 2, 'винном черве': 2}\n\nTERM 'демонический путь' bw=['демонический', 'путь']  (1 new windows)\n   soft-slot tokens: {'пути': 1}\n   distinct fragments: {'демонического пути': 1}\n\nTERM 'ветвь Фан' bw=['ветвь', 'фан']  (2 new windows)\n   soft-slot tokens: {'ветви': 2}\n   distinct fragments: {'ветви фан': 2}\n\nTERM 'девять рангов' bw=['девять', 'рангов']  (1 new windows)\n   soft-slot tokens: {'девятого': 1}\n   distinct fragments: {'девятого ранга': 1}\n\nTERM 'ветвь Чи' bw=['ветвь', 'чи']  (3 new windows)\n   soft-slot tokens: {'ветви': 3}\n   distinct fragments: {'ветви чи': 3}\n\nTERM 'ветвь Чи' bw=['ветвь', 'чи']  (3 new windows)\n   soft-slot tokens: {'ветви': 3}\n   distinct fragments: {'ветви чи': 3}\n\n\n=== ADVERSARIAL: constructed multi-word Синь / Линь bank term ===\n\n corpus[TRUE-mentions]: старейшина Синь кивнул ; старик Линь ушёл ; мастер Синь молчал\n   A0(base) fires for 'старейшина Синь' on: 'старейшина синь'\n   A0(base) fires for 'старик Линь' on: 'старик линь'\n   A0(base) fires for 'мастер Синь' on: 'мастер синь'\n\n corpus[collision corpus (NO real Синь/Линь)]: вошёл старейшина синего одеяния ; старик линий на карте ; мастер синим пламенем ударил ; старейшины синими глазами\n   A3 FIRES for 'старейшина Синь' on window: 'старейшина синего' cats=['n', 's']\n   A3 FIRES for 'старейшина Синь' on window: 'старейшины синими' cats=['n', 's']\n   A3 FIRES for 'старик Линь' on window: 'старик линий' cats=['n', 's']\n   A3 FIRES for 'мастер Синь' on window: 'мастер синим' cats=['n', 's']\n\n\n=== number-word conflation probe (девять/десять ranks vs ordinals) ===\n  'девять рангов' -> 'девятого ранга' cats=['s', 'n']\n  number 'девять' soft_stem='девят': corpus soft-collision tokens (not normal-equal) = ['девяти', 'девятого', 'девятый']\n  number 'десять' soft_stem='десят': corpus soft-collision tokens (not normal-equal) = ['десяти', 'десятую', 'десятых']\n  number 'семь' soft_stem='сем': corpus soft-collision tokens (not normal-equal) = ['семи']\n  number 'восемь' soft_stem='восем': corpus soft-collision tokens (not normal-equal) = []\n  number 'пять' soft_stem='пят': corpus soft-collision tokens (not normal-equal) = ['пятая', 'пяти', 'пятого']\n  number 'шесть' soft_stem='шест': corpus soft-collision tokens (not normal-equal) = ['шести', 'шестого', 'шестой', 'шесты']","is_error":false}]},"uuid":"ca324bc7-8453-4491-a934-89a7cc6b6452","timestamp":"2026-08-01T15:07:12.772Z","sourceToolAssistantUUID":"5debda1d-f363-484d-85af-2e2f9d017fd2","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
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"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2889,"cache_read_input_tokens":61135,"cache_creation":{"ephemeral_5m_input_tokens":2889,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CdcEj4Wb1ieaM7KgUJkDW","attributionAgent":"general-purpose","type":"assistant","uuid":"f3f36eac-2a40-4573-aadc-45bd03cd9cfb","timestamp":"2026-08-01T15:09:00.892Z","effort":"xhigh","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}
{"parentUuid":"f3f36eac-2a40-4573-aadc-45bd03cd9cfb","isSidechain":true,"agentId":"aec521429a7581efa","message":{"model":"claude-opus-4-8","id":"msg_011CdcEj93gahcfAePcstSnH","type":"message","role":"assistant","content":[{"type":"text","text":"All evidence gathered. The simulation faithfully reproduces the claimed counts and exposes the structural facts. Here is my adversarial review.\n\n---\n\n# Adversarial review of A3 (anchor-gated soft match)\n\nMethod: faithful Python re-implementation of the stemmer (`decl_suffix` list verbatim from `target-ru.txt:194-230`, `minStemRunes=3`) + A3's anchor gate, run over the real bank (`/home/ubuntu/books/gu-zhenren/coldrun-a/BANK-FULL.tsv`, 150 rows) and the 25-chapter output (`acceptance/guzhenren-25ch-ru.txt`, 37,482 tokens). $0, no model calls.\n\n## 1. Two-word residual collision — CONFIRMED-BREAK (constructed, plausible; absent-by-luck here)\n\nThe anchor gate does **not** close the Синь/Линь poison — it **relocates** it from single-word terms to the single most common multi-word name shape in xianxia: `[common-noun title] + [ь-final surname]`. With a constructed but ordinary bank term, A3 falsely fires on a corpus containing **no real surname at all**:\n\n- `старейшина Синь` (elder Xin) → false-accepts `старейшина синего`, `старейшины синими` (blue-robe/blue-eyed elder)\n- `старик Линь` (old Lin) → false-accepts `старик линий` (`линий` = real gen.pl. of «линия», lines)\n- `мастер Синь` → false-accepts `мастер синим` (master with blue [flame])\n\nIn every case the title (`старейшина/старик/мастер`) is the **normal-stem anchor** and the soft slot `син/лин` collides with the «синий»/«линия» families. `cats=['n','s']`.\n\nWhy gu-zhenren shows **zero** hard collisions is not the anchor gate — it is **transliteration luck**. Of 30 soft-final bank rows, ~18 are CJK-syllable names whose 3-rune soft-stems (`юан, шэн, чилян, мочэн, фанюан, цинмаошан`) are simply not Russian word stems, so they cannot collide regardless of A3. The clean cross-word collision surface that actually exists in this bank is exactly **2 realized** (`девять рангов`→`девят`, `десять чудесных гу`→`десят`, both number words, see #5) plus 1 marginal (`демонический путь`→`пут` ~ «путы/путать»). So the \"low probability\" claim is honest **for this book**, but it generalizes only to books whose ь-final terms are all CJK transliterations. It is not a property of A3; it is a property of the corpus. A book with a Синь/Линь/Пэн-class surname under a title breaks it.\n\n## 2. Better mechanism? — A3 does NOT dominate; a combination does\n\nEvaluated against the measured windows (`фан юаня`×184, `винного червя`×67, `горной крепости`, `демонического пути`, `ветви Фан`×2, `ветви Чи`×3, `девятого ранга`×1):\n\n- **(a) soft only on LAST component (name-tail):** near-tie on recall, strictly-better precision *here*. It keeps Юаня/червя/крепости/пути (tail-soft), and it *avoids* the `девятого ранга` imprecision (soft is the HEAD `девять`). But it **loses** `ветви Фан`/`ветви Чи` (5 windows — head-noun «ветвь» genuinely declines) and would still false-fire `старейшина Синь` (surname is the tail). Different precision/recall point, **does not dominate** — Russian head nouns decline.\n- **(b) min-stem-length gate:** CONFIRMED USELESS. `юан`(good)=3 runes, `син`/`лин`(poison)=3 runes, identical shape — the `target-ru.txt:235` comment is correct and the data confirms it. A min-4 gate kills the 184-window `юань` win and still passes `черв`(4). No threshold separates good from bad. Strictly worse.\n- **(c) common-word soft-stem stoplist:** directly kills the collision; as an **add-on** to A3 it dominates A3-alone on precision (block `син/лин/пут/девят/десят…`). Costs: open-ended enumeration (the exact \"unbounded list\" shape `target-ru.txt:19` already warns against), and it also suppresses a *real* «старейшины Синя» — but that is the safe direction (a miss). Good complement, poor standalone.\n- **(d) seed decl-enumeration:** the gold standard for the **signed canon** (Фан Юань, Винный червь already carry known forms → zero stemming, zero collision). But it does not scale to the auto-mined long tail, and incompleteness re-introduces the \"142/142-null\" false-MISS noise this whole §3 branch exists to kill (`mempostcheck.go:24-28`). Complementary to A3, not a replacement.\n\n**No single alternative strictly dominates A3, and A3 does not strictly dominate them.** The specifically-better *design* the measured data points to: **(d) for signed canon names + A3 restricted to tail-soft (a) as the auto-mined backstop + a small target-general soft-stoplist (c) for the closed class of ь-final cardinals.** That beats A3-alone on precision at ~zero recall cost on this corpus.\n\n## 3. False anchor — CONFIRMED-BREAK\n\nA3's anchor is \"any component that matches via normal `SameStem`\" — it has **no requirement that the anchor be a rare/proper token.** So a common-word anchor is fully valid, which is precisely what powers #1: `старейшина/старик/мастер` are common nouns matching normally and thereby licensing the soft collision. The number case is the same shape from the corpus itself: `девять рангов` → `девятого ранга`, `cats=['s','n']` — the anchor `ранга`~`ранг` is a common word. The anchor gate assumes the \"other\" component is a discriminating name; when the term is `[common noun + soft word]` the assumption fails.\n\n## 4. Blast radius — PARTIAL BREAK (\"one functional caller / SpoilerLeaks safe\" is misleading)\n\n- `SameStem` (stemmer.go:70) is byte-identical under A3 and its **only direct caller** is `declinedFormPresent` (mempostcheck.go:181). That half of the claim is TRUE. But it protects nothing, because nothing *else* calls `SameStem`.\n- The change lives **inside `declinedFormPresent`**, and `declinedFormPresent` is reached from `dstFormPresent` (mempostcheck.go:154), which has **two** callers: `Postcheck` (mempostcheck.go:96) **and `SpoilerLeaks` (mempostcheck.go:254).** So **A3 does change SpoilerLeaks** — it is not \"left safe.\" A soft false-accept there raises a **false spoiler-leak alarm** (a rejected rendering reported as leaked). What actually saves it is that `SpoilerLeaks` is \"Observability only — never a disposition\" (mempostcheck.go:241), so the false positive is noise, not a gate flip. The claim's *reason* is wrong even though the *outcome* (no gate poisoned) holds.\n- `seeding.go:138` only constructs the stemmer and passes it in — no `SameStem` call, unaffected (though it must be extended to load `soft_decl_suffix`). `voice.go:368` explicitly does **not** wire the stemmer. Both confirmed safe. So: only two consumers total, one gates (Postcheck) and one doesn't (SpoilerLeaks); the \"SpoilerLeaks safe\" framing should be corrected to \"SpoilerLeaks affected but observability-only.\"\n\n## 5. «девятого ранга» — CONFIRMED symptom of a systemic class\n\nNot a one-off. Every ь-final **cardinal** soft-conflates with its **ordinal / oblique numeric** family (corpus-verified soft-collision tokens):\n\n- `девять`→`девят` : {девятого, девятый, девяти}\n- `десять`→`десят` : {десятого-class десятую, десятых, десяти}\n- `пять`→`пят` : {пятая, пятого, пяти}\n- `шесть`→`шест` : {шестого, шестой, шести}\n- `семь`→`сем` : {семи}\n\nIn a cultivation novel \"N-th rank/stage/realm\" (ordinal) vs \"N ranks\" (cardinal) is a staple, and the anchor (`ранг/этап/уровень`) is itself common, so the anchor gate gives **zero** protection to number terms. The measured 1× `девятого ранга` is the tip; this class scales with how often the book counts ranks. A3 does not address it — a soft-stoplist of the closed cardinal set (пять…десять) is the honest fix.\n\n---\n\n## Verdict\n\n**A3 is a defensible backstop but not \"the right mechanism,\" and two of its safety claims are overstated.** It does not eliminate the Синь/Линь collision — it **moves it** to the `[common-title + ь-surname]` shape (the commonest multi-word name form in the target genre), where a common-word anchor licenses the false accept (#1, #3); and it leaves the systemic **cardinal→ordinal** number conflation unprotected (#5). Its clean run on gu-zhenren is carried by CJK-transliteration luck, not by the gate. The specifically-better design is a **layered** one: seed decl-enumeration for signed canon names + tail-restricted anchor-gated soft match + a small closed-class soft-stem stoplist (ь-final cardinals and the син/лин/пут collisions).\n\n**For A3 to fail in a DIFFERENT book it is sufficient that:** the book bank a multi-word term `[common-noun anchor + ь-final word]` whose 3-rune soft-stem lands in a common Russian word family (`син/лин/пут/сол/девят…`) and that word appear adjacent to an inflection of the anchor — i.e. any titled surname like «старейшина Синь» or any numeric-rank term. That is not exotic; gu-zhenren simply happens not to contain one."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2889,"cache_read_input_tokens":61135,"output_tokens":9776,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2889},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":9776,"cache_read_input_tokens":61135,"cache_creation_input_tokens":2889,"cache_creation":{"ephemeral_5m_input_tokens":2889,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CdcEj4Wb1ieaM7KgUJkDW","attributionAgent":"general-purpose","type":"assistant","uuid":"49ff30f8-f200-47f3-8ed2-671cbd608883","timestamp":"2026-08-01T15:09:48.460Z","effort":"xhigh","userType":"external","entrypoint":"claude-vscode","cwd":"/home/ubuntu/projects/textmachine/backend","sessionId":"07c509cc-213b-44f5-ad8e-c3697b0c4bf7","version":"2.1.220","gitBranch":"main","slug":"nested-greeting-perlis"}