hypothesis-batch

name	hypothesis-batch
description	Standalone BATCH generator of research hypotheses for ONE topic — enumerates many (behavior + mechanism) candidates into a TREE library (hypothesis_library.json), each behavior scored with an IMPACT check and each mechanism with a NOVELTY check, with LLM semantic dedup. It does NOT refine, review, pilot, converge to a single idea, or build an experiment plan — but it DOES score every behavior for IMPACT and every mechanism hypothesis for NOVELTY (separate scores only — no combined ranking). Can run MANY consecutive rounds in ONE invocation via `— rounds: R` (each round adds ~N_BEHAVIORS and persists to the library before the next; later rounds rebuild their banlist from the accumulated library, so earlier behaviors are never regenerated) — e.g. `— rounds: 5, n-behaviors: 10` aims for ~50 distinct behaviors. Its only selector is BEHAVIOR_SOURCE = discover (mine new behaviors + mechanisms) | given (behavior fixed, generate mechanisms only); there is no `mode` flag. Behavior axis is enumerated from /mechanism-behavior-discovery's discovery strategies; mechanism axis from /mechanism-explore's combination strategies. Fully INDEPENDENT of /auto: it never reads or writes research_memory.json and never touches /auto, /auto-claim, or /idea-creator. Use when the user wants to mass-produce diverse, deduplicated, impact- & novelty-scored behavior+mechanism hypotheses on a topic (e.g. 'batch generate hypotheses', '批量生成假设', '多样化假设库', 'build a hypothesis library for X'). Candidates are promoted into /auto MANUALLY by the user later — this skill performs no handoff.
argument-hint	<topic> [— behavior-source: discover | given] [— behavior: <text | Bk>] [— n-behaviors: N] [— rounds: R]

Hypothesis Batch — Tree Library Generator (novelty-scored)

Mass-produce diverse, deduplicated, impact- & novelty-scored (behavior → mechanism) hypotheses for a single topic and persist them as a tree in hypothesis_library.json. Each behavior carries an IMPACT score (is the phenomenon important) and each mechanism carries a NOVELTY score (is the method new). This is the divergent, non-converging counterpart of the discovery claim stage: same two generation axes, but it enumerates many candidates instead of committing to one, and it stops after the impact + novelty checks — no refinement, review, pilot, ranking, or experiment plan.

What this is / is NOT

• IS: generate (behavior + mechanism) candidates → semantic dedup → impact score per behavior + novelty score per mechanism → persist to the tree library.
• Is NOT: /auto-claim or part of /auto. It does not call /auto-claim or /idea-creator, does not read or write research_memory.json, and does not produce FINAL_PROPOSAL.md / EXPERIMENT_PLAN.md / task.md. Promotion into an /auto round is a manual step the user does later.
• Is NOT convergent — it never ranks down to one idea, never refines, never pilots, never reviews/plans. (Impact + novelty scoring are the only evaluative steps it performs, and they are reported separately — never combined into a rank.)
• Domain-general — topic is a parameter; nothing is hard-coded to any single domain.

Modes — BEHAVIOR_SOURCE (the only selector)

The single selector is BEHAVIOR_SOURCE = discover | given, set with — behavior-source:. There is no mode flag — this skill does not take one; if — mode: is passed it is ignored.

• BEHAVIOR_SOURCE = discover (default when no — behavior: is given) — behavior is NOT specified, so generate behavior conjectures too. Enumerate the behavior axis from /mechanism-behavior-discovery's four discovery strategies (target N_BEHAVIORS new behaviors), then for each new behavior generate the mechanism axis (per /mechanism-explore's combination strategies). Grows the tree on both axes.

• BEHAVIOR_SOURCE = given (auto-selected when — behavior: is supplied) — behavior is fixed, so generate mechanism conjectures only. The behavior axis does not grow. The — behavior: value is resolved two ways:

  • existing node id (e.g. — behavior: B3) → reuse that behavior node;
  • free text (e.g. — behavior: "模型在多轮对话中倾向维持首轮立场") → create one new behavior node from it (no five-bars mining; it is taken as given), then append mechanisms under it. Append only new mechanism children; propose no new behaviors.

Resolution & conflicts:

• behavior-source: given requires a — behavior:; if absent, halt and ask for the behavior (do not silently fall back).
• behavior-source: discover with a — behavior: present → the explicit behavior-source wins (discover); report the mismatch in one line and ignore — behavior: for generation.
• If — behavior-source: is omitted, derive it: — behavior: present → given; absent → discover.

Constants

• BEHAVIOR_SOURCE = (auto) — discover (default; no — behavior:) or given (a — behavior: is supplied). Override with — behavior-source:. See "Modes".
• N_BEHAVIORS = 10 — target count of new behaviors to add per round (discover only). Override with — n-behaviors: N.
• ROUNDS = 1 — number of consecutive generation rounds to run in this single invocation. Each round runs the full generate → dedup → impact/novelty → persist cycle (Phases 3–6), adding ~N_BEHAVIORS new behaviors (discover) or new mechanisms (given) and writing them to hypothesis_library.json before the next round starts — so the library accumulates round-by-round. Because every round rebuilds its banlist from the just-updated library, behaviors/mechanisms produced in earlier rounds are never regenerated in later rounds (they become part of the banlist). Set with — rounds: R. With — rounds: 5, n-behaviors: 10, one invocation aims for ~50 distinct behaviors across 5 accumulating passes. The round loop self-terminates early if a round adds nothing after dedup (topic saturated — report it).
• REVIEWER_BACKEND = llm-chat — external LLM via llm-chat MCP for generation, semantic dedup, and novelty scoring (model defers to LLM_MODEL).
• NOVELTY_WEB = true — when true, run one quick web/arXiv search per surviving behavior to ground both the impact and novelty scores (cheap: per-behavior, not per-mechanism). Set false for a pure-LLM estimate. This skill produces lightweight triage scores only; for a publication-grade verdict on a chosen candidate, run the standalone /impact-check (behavior) or /novelty-check (mechanism) on it separately.
• LIBRARY_FILE = hypothesis_library.json — the single canonical tree file (project root). One topic per file; created on first run. No other view file is generated.

The Two Axes

Behavior axis — read /mechanism-behavior-discovery SKILL.md, use its four discovery strategies as lenses (discover only):

1. Cross-domain transfer 2. Borrow from the human sciences 3. Cross-modal transfer 4. Reuse past CS results. Each behavior passes the five bars (Real / Non-obvious / Specific / Robust / Tractable) and is a one-sentence falsifiable phenomenon with its triggering condition.

Mechanism axis — follow /mechanism-explore: read its SKILL.md and generate one hypothesis per combination strategy it defines (per behavior). Do not hardcode a count here — use whatever strategy set /mechanism-explore currently specifies. At time of writing those are:

• Mechanistic evidence — Location → Causal Intervention
• Capability / editing — Location → Tuning & Editing
• Complete account — Location → Causal Intervention → Formation Tracing
• Explaining a model — Location → Unit Interpretation
• Decision reliability — Location → Unit Interpretation → Decision Auditing

Each mechanism hypothesis names (a) the internal object (layer / head / neuron / SAE feature / direction / circuit), (b) the predicted causal relation (ablate→effect, steer→dose-response, patch→localization), and (c) ≥1 boring null (memorization / surface feature / shortcut / tokenizer / position). A strategy that does not fit a behavior may be skipped with a one-line reason.

Workflow

Structure: Phases 0–2 are one-time setup. Phases 3–6 are the per-round body, repeated ROUNDS times (Phase 6 persists the library at the end of every round, so round k+1's Phase 3 banlist is built from everything rounds 1..k produced → no re-generation of earlier ideas). Phase 7 reports the aggregate across all rounds. When ROUNDS = 1 this is exactly the original single-pass behavior.

Phase 0 — Resolve topic, behavior-source, rounds & load library

1. Parse topic (required; ask in one line if missing). Compute topic_slug (lowercase kebab-case) for record-keeping only. Read N_BEHAVIORS and ROUNDS overrides if supplied.
2. Resolve BEHAVIOR_SOURCE per the "Modes" resolution rules (validate the given/discover ↔ — behavior: consistency; halt/ask or report mismatch as specified).
3. If hypothesis_library.json exists: read it as the current tree. Cross-topic guard: if its topic differs semantically from this run's topic, halt and ask (rename/move the old file, or confirm overwrite) — never silently overwrite another topic's library. Else start an empty tree {topic, topic_slug, created, updated, behaviors: []}.
4. If BEHAVIOR_SOURCE = given, resolve the target behavior node now: existing Bk → reuse; free text → create one new behavior node (statement = the text; mark source: "given").

Phase 1 — (optional) ground from landscape

If idea-stage/LANDSCAPE.md exists, read its gaps/narrative and pass them as grounding into the brainstorm + novelty prompts. If absent, proceed purely generatively (do not run /research-lit).

Phase 2 — Load the two axes' framing + the two scorers

Read /mechanism-behavior-discovery (only needed for discover) and /mechanism-explore SKILL.md in full for generation, plus /impact-check and /novelty-check for the Phase 5 scoring criteria (reference only — copy nothing into outputs). Hold in context for Phases 3–5.

Round loop (Phases 3–6 ×ROUNDS). Run the following cycle once per round, round = 1..ROUNDS. At the start of each round, re-read hypothesis_library.json (it grew in the previous round's Phase 6) and rebuild the banlist from its current contents, so earlier rounds' behaviors/mechanisms are banned and never regenerated. Persist (Phase 6) at the end of every round before starting the next. If a round adds 0 survivors after dedup, the topic is saturated for this run — stop the loop early and note it in the Phase 7 report rather than spinning further rounds.

Phase 3 — Brainstorm (single llm-chat call, per round)

One llm-chat call per round. The model is stateless, so the prompt MUST paste the banlist = all existing behavior statements currently in the library, including those added by earlier rounds of this same invocation (and, in given, existing mechanism (directions + hypothesis) for the target behavior) and require outputs that are NOT in it or close variants. Branch on BEHAVIOR_SOURCE:

• discover: ask for N_BEHAVIORS new behaviors that span all of /mechanism-behavior-discovery's discovery strategies (cover the different lenses within this one call) and are mutually distinct; for each, name the discovery_strategy used and a one-to-two-sentence discovery_strategy_detail stating how the idea was reached (the concrete provenance — e.g. which cross-discipline finding/method was borrowed and mapped onto what, or which past CS result was reused in which new setting); plus one mechanism hypothesis per applicable /mechanism-explore combination strategy, with object + causal relation + boring null, and five-bars one-liners.
• given: do no behavior generation; keep the fixed behavior and ask only for new mechanism hypotheses across /mechanism-explore's combination strategies not already present for it.
• Both branches — planned data + model per mechanism. For every mechanism, also propose its planned data (existing dataset first per /mechanism-behavior-discovery: provenance = existing/adapted/constructed, source, planned used_n / n_pairs, and a one-line note on how it elicits the behavior or builds the intervention pairs) and target model (id + size, as a list). This mirrors /auto-claim's Resources framing — triage-level, not a binding plan. Save the raw response to the run trace.

Phase 4 — Semantic dedup (LLM, two layers)

Use llm-chat for semantic judgment (not string match):

1. Behavior layer (discover only; skipped in given): flag each new behavior new or duplicate-of:<Bk>; drop duplicates (merge extra nuance into the existing node's notes if valuable).
2. Mechanism layer: within each surviving/target behavior, flag each new mechanism new or duplicate-of:<Bk-Mj>; drop duplicates.

Phase 5 — Impact (per behavior) + Novelty (per mechanism) check & score

Score per behavior (one call covering that behavior and all its mechanism variants), not per leaf separately — impact attaches to the behavior, novelty to each mechanism:

1. If NOVELTY_WEB, run one quick web/arXiv search for the behavior + its mechanism angle to ground both scorers (per behavior; cheap).
2. Call llm-chat once per behavior with two referee roles:
   • Impact referee on the behavior (following /impact-check's dimensions): return score (1–10, 10 = clearly important), a one-line rationale (why it matters + who would build on it), and a recommendation (PROCEED / PROCEED WITH CAUTION / DEPRIORITIZE).
   • Novelty referee on each mechanism variant (following /novelty-check): return score (1–10, 10 = clearly new), the single closest prior work, a one-line differentiation, and (optional) a one-line caveat. Both are lightweight triage scores, not publication-grade verdicts.
3. Write impact into the behavior node and novelty into each mechanism leaf (schema below). Stamp method (llm-chat / llm-chat+web) and date on both. Save the impact + novelty traces.

Phase 6 — Merge into the tree & persist (end of each round)

1. Assign ids by max-suffix+1 (never reuse): behaviors B<n>; mechanisms B<n>-M<m>.
2. Append survivors under the right behavior / discovery_strategy. Set status: "candidate", stamp batch (date + run index + round index, e.g. 2026-06-23.run01.r03) and updated.
3. Write hypothesis_library.json (the single canonical file) now, before the next round — this persisted state is what the next round reads as its banlist. Then return to Phase 3 for the next round (until ROUNDS is reached or a round adds 0 survivors).

Phase 7 — Report (aggregate across all rounds)

Print: topic, BEHAVIOR_SOURCE, ROUNDS requested vs. actually run (note early stop if a round saturated), a one-line per-round ledger (round → behaviors/mechanisms added → running total), total #behaviors added / total, #mechanisms added / total, #semantic duplicates dropped (summed across rounds), impact score distribution across all behaviors (min/median/max) and novelty score distribution across all mechanisms (min/median/max, count ≥8), and file paths. Remind that promotion into /auto is manual (write the chosen behavior into a task.md, then run /auto).

Library Schema (canonical JSON — nested tree, one topic per file)

{
  "topic": "<topic>",
  "topic_slug": "<slug>",
  "created": "<YYYY-MM-DD>",
  "updated": "<YYYY-MM-DD>",
  "behaviors": [
    {
      "id": "B1",
      "source": "discover",
      "discovery_strategy": "Cross-domain transfer",
      "discovery_strategy_detail": "<how this idea was reached: the concrete provenance of the conjecture — e.g. which finding/method from which discipline was borrowed and mapped onto what here, or which past CS result was reused in which new setting>",
      "statement": "<one-sentence phenomenon + trigger>",
      "five_bars": {"real":"","nonobvious":"","specific":"","robust":"","tractable":""},
      "impact": {
        "score": 8,
        "rationale": "<one line: why it matters + who would build on it>",
        "recommendation": "<PROCEED | PROCEED WITH CAUTION | DEPRIORITIZE>",
        "method": "llm-chat+web",
        "date": "<YYYY-MM-DD>"
      },
      "gaps": ["<optional, e.g. G1 if grounded on LANDSCAPE.md>"],
      "notes": "<optional merged nuance>",
      "mechanisms": [
        {
          "id": "B1-M1",
          "strategy": "Mechanistic evidence",
          "directions": ["Location", "Causal Intervention"],
          "hypothesis": "<internal object + predicted causal relation>",
          "boring_null": "<>=1 boring null>",
          "data": {
            "provenance": "existing | adapted | constructed",
            "source": "<dataset name / where it comes from>",
            "used_n": "<planned size or split; for interventions e.g. n_pairs>",
            "note": "<one line: how this data elicits the behavior / how the intervention pairs are built>"
          },
          "model": ["<model id + size, e.g. Llama-3-8B>"],
          "novelty": {
            "score": 7,
            "closest": "<closest prior work>",
            "differentiation": "<one line: why this is different>",
            "caveat": "<optional>",
            "method": "llm-chat+web",
            "date": "<YYYY-MM-DD>"
          },
          "status": "candidate"
        }
      ]
    }
  ]
}

source on a behavior is discover (mined) or given (supplied via — behavior:). status lifecycle is owned by the user: candidate → queued → explored (flip by hand when promoted into an /auto round). This skill only ever writes candidate.

Output Protocols

• Output Versioning Protocol — hypothesis_library.json is a living document updated in place (not timestamped); save raw llm-chat passes (generation, dedup, novelty) to the run trace instead.
• Output Manifest Protocol — log hypothesis_library.json to MANIFEST.md on first creation.
• Output Language Protocol — machine fields (ids, strategy/direction names, source, status, data.provenance, data.source, model ids, impact.score/recommendation/method/date, novelty.score/method/date) stay English; free-text behavior/mechanism/discovery_strategy_detail/data.note/rationale/differentiation follows the project language.

Review Tracing

After each mcp__llm-chat__chat call (generation, dedup, impact + novelty scoring), save the trace per shared-references/review-tracing.md to .mechanist/traces/hypothesis-batch/<date>_run<NN>/. With ROUNDS > 1, put each round's traces in a r<NN>/ subfolder (e.g. .mechanist/traces/hypothesis-batch/<date>_run<NN>/r03/) so every round's passes are kept separately.

Key Rules

• The only selector is behavior-source: discover | given. There is no mode flag; — mode: is ignored if passed.
• Stay decoupled from /auto. Never read/write research_memory.json; never edit /auto, /auto-claim, or /idea-creator; never emit task.md / FINAL_PROPOSAL.md / EXPERIMENT_PLAN.md. If decoupling and convenience conflict, choose decoupling.
• Generation + impact/novelty scoring only. No refinement, review, pilot, or ranking-to-one. Diversity, dedup, an impact score per behavior, and a novelty score per mechanism are the whole job — impact and novelty are reported separately, never combined into a rank.
• Score everything. No behavior is persisted without an impact field; no mechanism leaf without a novelty field, nor without planned data + model.
• Semantic dedup, not string match — reworded duplicates must be caught by the LLM judgment pass.
• Never reuse ids; append-only growth so the tree is a stable, citable backlog.
• Rounds accumulate, never repeat. With — rounds: R, persist the library at the end of every round and rebuild the next round's banlist from it, so behaviors/mechanisms from earlier rounds are banned (not regenerated). A round that adds 0 survivors after dedup means the topic is saturated — stop early and say so; never pad with near-duplicates to fill the round.
• One topic per file; cross-topic invocation halts and asks rather than overwriting.
• Large file handling: if Write fails on size, retry via Bash heredoc silently.