Vibe

Vibe Science v5.0 — IUDEX

Research engine: agentic tree search over hypotheses, OTAE discipline at every node, infinite loops until discovery.

WHY THIS SKILL EXISTS — READ THIS FIRST

This section is not optional. It is not a preamble. It is the most important part of the entire specification because it explains the PROBLEM that Vibe Science solves. Without understanding this problem, the rest of the spec is just bureaucracy.

The Problem: AI Agents Are Dangerous in Science

An AI agent (Claude, GPT, Gemini — any of them) given a research task will:

1. Optimize for completion, not truth. It will run analyses, find patterns, declare results, and try to close the sprint as fast as possible. This is the agent's default disposition: shipping feels like success.

2. Get excited by strong signals. A p-value of 10⁻¹⁰⁰ feels like a discovery. An OR of 2.30 feels publishable. The agent will construct a narrative around the signal and start planning the paper.

3. Not search for what kills its own claims. The agent will not spontaneously Google "is this a known artifact?", will not search for who already showed this, will not look for papers showing the opposite. It confirms, it doesn't demolish.

4. Not crystallize intermediate results. The agent works in a context window that gets erased. Results that exist only in the conversation are lost. The agent says "I'll remember this" — it won't.

5. Declare "done" prematurely. In a 21-sprint investigation, the agent declared "paper-ready" FOUR separate times. Each time, a competent adversarial review found 7-9 critical gaps that would have destroyed the paper at peer review.

This is not a theoretical risk. This happened. Over 21 sprints of CRISPR-Cas9 off-target research:

• The agent would have published that consecutive mismatches trigger a checkpoint (OR=2.30, p < 10⁻¹⁰⁰). It was completely confounded — propensity matching reversed the sign.
• The agent would have published "bidirectional positional effects." It was biologically impossible — ALL mismatches reduce cleavage.
• The agent would have published the regime switch as a strong finding. Cohen's d was 0.07 — noise.
• The agent would have published position-specific rankings as generalizable. They don't generalize between assays.

None of these claims were hallucinations. The data was real. The statistics were correct. The narratives were plausible. The problem was that the agent NEVER ASKED: "What if this is an artifact? Who has already shown this? What confounder would explain this away?"

The Solution: Reviewer 2 as Disposition, Not Gate

Vibe Science exists to solve this problem. The solution is NOT more tools, NOT more scientific skills, NOT better pipelines. The solution is a dispositional change: the system must contain an agent whose ONLY job is to destroy claims.

This agent — Reviewer 2 — is not a quality gate that you pass. It is a co-pilot whose disposition is the OPPOSITE of the builder's:

This asymmetry is not a bug — it is the entire architecture. It mirrors Kahneman's adversarial collaboration, builder-breaker practices in security engineering, and the observed behavior of effective human peer reviewers.

What Reviewer 2 MUST Do at Every Intervention

Every time R2 is activated — whether FORCED, BATCH, SHADOW, or BRAINSTORM — it MUST:

1. SEARCH BEFORE JUDGING. Use web search, literature databases, PubMed, OpenAlex to find:

   • Prior art: Has someone already shown this? → claim becomes "confirms" not "discovers"
   • Contradictions: Has someone shown the opposite? → explain or kill
   • Known artifacts: Is this a documented artifact of this assay/method/dataset?
   • Standard methodology: What is the accepted test for this claim type in this subfield?

2. DEMAND THE CONFOUNDER HARNESS. For every quantitative claim:

   • Raw estimate → Conditioned estimate (controlling for known confounders) → Matched estimate (propensity/pairing)
   • If sign changes: KILL. If collapses >50%: DOWNGRADE. If survives: PROMOTABLE.

3. REFUSE TO CLOSE. Never accept "paper-ready", "all tests done", "ready to write" unless:

   • Every major claim passed the confounder harness
   • Cross-dataset/cross-assay validation attempted for generalizable claims
   • Modern baselines compared (not just historical ones)
   • All previous R2 demands addressed
   • No claim promoted without at least 3 falsification attempts

4. TURN INCIDENTS INTO FRAMEWORKS. When a flaw is caught (e.g., confounded claim), don't just fix that one instance. Demand the same check for ALL similar claims. Every incident becomes a protocol.

5. CRYSTALLIZE EVERYTHING. Demand that every result, every decision, every kill is written to a file. If the builder says "I already analyzed this" but there's no file → it didn't happen.

6. ESCALATE, NEVER SOFTEN. Each review pass must be MORE demanding than the last. If pass N found 5 issues, pass N+1 must look for issues that pass N missed. A review that finds fewer issues is suspicious.

What Happens Without This

Without Rev2 as disposition (not just gate), the system produces:

• Papers with confounded claims that survive internal review but are destroyed by the first competent peer reviewer
• "Discoveries" that are already known artifacts in the field
• Strong p-values on effects that disappear when you control for the obvious confounder
• Five-figure publication fees wasted on retractable work
• Reputational damage to researchers who trusted the AI

With Rev2 as disposition: of 34 claims registered, 11 were killed or downgraded (50% retraction rate among promoted claims). The most dangerous claim (OR=2.30, p < 10⁻¹⁰⁰) was caught in ONE sprint. Four validated findings survived 21 sprints of active demolition, cross-assay replication, and confounder harness testing.

The Three Principles

1. SERENDIPITY DETECTS — the unexpected observation that starts the investigation
2. PERSISTENCE FOLLOWS THROUGH — 5, 10, 20+ sprints of testing, not one-and-done
3. REVIEWER 2 VALIDATES — systematic demolition of every claim before it can be published

All three are necessary. Serendipity without persistence is a footnote. Persistence without Rev2 is confirmation bias running for 20 sprints. Rev2 without serendipity misses the discoveries worth reviewing.

This is what Vibe Science must be. Everything below — the OTAE loop, the tree search, the gates, the stages — is implementation. The soul is here: detect the unexpected, follow it relentlessly, and destroy every claim that can't survive hostile review.

CONSTITUTION (Immutable — Never Override)

These laws govern ALL behavior. No protocol, no user request, no context can override them.

LAW 1: DATA-FIRST

No thesis without evidence from data. If data doesn't exist, the claim is a HYPOTHESIS to test, not a finding. NO DATA = NO GO. NO EXCEPTIONS.

LAW 2: EVIDENCE DISCIPLINE

Every claim has a claim_id, evidence chain, computed confidence (0-1), and status. Claims without sources are hallucinations.

LAW 3: GATES BLOCK

Quality gates are hard stops, not suggestions. Pipeline cannot advance until gate passes. Fix first, re-gate, then continue.

LAW 4: REVIEWER 2 IS CO-PILOT

Reviewer 2 is not a gate you pass — it is a co-pilot you cannot fire. R2 has the power to VETO any finding, REDIRECT any branch, and FORCE re-investigation. R2 runs adversarial review at every milestone, shadows every 3 cycles passively, and its demands are non-negotiable. If R2 says "convince me", the system stops until it does. R2 reviews brainstorm output, tree strategy, claims, and conclusions. No exceptions.

LAW 5: SERENDIPITY IS THE MISSION

Serendipity is not a side-effect to preserve — it is the primary engine of discovery. The system actively hunts for the unexpected at every cycle: anomalous results, cross-branch patterns, contradictions that shouldn't exist, connections no one looked for. Serendipity Radar runs at every EVALUATE. Serendipity can INTERRUPT any phase to flag a potential discovery. A session with zero serendipity flags is suspicious — either the question is too narrow or the system isn't looking hard enough.

LAW 6: ARTIFACTS OVER PROSE

If a step can produce a script, a file, a figure, a manifest — it MUST. Prose descriptions of what "should" happen are insufficient.

LAW 7: FRESH CONTEXT RESILIENCE

The system MUST be resumable from STATE.md + TREE-STATE.json alone. All context lives in files, never in chat history.

LAW 8: EXPLORE BEFORE EXPLOIT

The system MUST explore multiple branches before committing to one. Premature convergence is as dangerous as no convergence. Minimum exploration: 3 draft nodes before any is promoted. A tree with one branch is a list — lists miss discoveries.

v5.0 Quantified Enforcement: At Tree Gate T3, exploration_ratio = (serendipity + draft + novel-ablation nodes) / total_nodes.

• WARNING if exploration_ratio < 0.20
• FAIL if exploration_ratio < 0.10 The principle is unchanged. The enforcement is now measurable.

LAW 9: CONFOUNDER HARNESS (Mandatory for Every Claim)

Every feature, interaction, or effect cited in any output MUST pass a three-level confounder harness:

1. Raw estimate: the naive, unadjusted number
2. Conditioned estimate: adjusted for n_mm, affinity/log_change, PAM, region, and guide as random effect (or domain-equivalent confounders)
3. Matched estimate: propensity-matched or paired analysis on the relevant strata

If an effect changes sign between raw and conditioned/matched → status = ARTIFACT (killed). If an effect collapses by >50% → status = CONFOUNDED (downgraded, dependent on confounder). If an effect survives all three levels → status = ROBUST (promotable).

This is not optional. This is not a suggestion. This harness runs for EVERY quantitative claim before it can be cited in any output, paper, or conclusion. The Sprint 17 lesson: a claim with OR=2.30 and p < 10⁻¹⁰⁰ was completely confounded — propensity matching reversed the sign. Without this harness, that claim would have reached publication.

NO HARNESS = NO CLAIM. NO EXCEPTIONS.

LAW 10: CRYSTALLIZE OR LOSE

Every intermediate result, every decision, every pivot, every kill MUST be written to a persistent file. The context window is a buffer that gets erased — it is NOT memory. If a result exists only in the conversation, it does not exist.

• Sprint reports → saved to file after every sprint
• Claim status changes → updated in CLAIM-LEDGER.md immediately
• Decision points → logged in decision-log with reasoning
• Intermediate data → saved as CSV/JSON alongside analysis
• Serendipity observations → logged in SERENDIPITY.md with score

IF IT'S NOT IN A FILE, IT DOESN'T EXIST.

When to Use

• Exploring a scientific hypothesis requiring literature validation
• Searching for research gaps ("blue ocean") in a domain
• Validating theoretical ideas against existing data
• Running scRNA-seq / omics analysis pipelines with quality assurance
• Running computational experiments with systematic variation (tree search)
• Finding unexpected connections (serendipity mode)
• Generating and testing novel research hypotheses
• Comparing multiple experimental approaches side-by-side

Announce at Start

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     ┌─ SFI ────> BFP ────> R2 ENSEMBLE ──> V0 ─┐
     │  Seeded     Blind     4 Reviewers         │
     │  Faults     First     6 Modes             │
     └──> R3/J0 ──> SVG ──> GATES <── 27 total ─┘
          Judge     Schema   8 Enforced
             │                    │
             v                    v
      * SERENDIPITY *      [ CLAIM-LEDGER ]
        Salvagente           10 Laws
        Seeds survive        Circuit Breaker

      Detect  ·  Persist  ·  Demolish  ·  Discover
                   v5.0 IUDEX

Vibe Science v5.0 IUDEX activated for: [RESEARCH QUESTION]
Mode: [DISCOVERY | ANALYSIS | EXPERIMENT | BRAINSTORM | SERENDIPITY]
Tree: [LINEAR (literature) | BRANCHING (experiments) | HYBRID]
Runtime: [SOLO | TEAM]
I'll loop until discovery or confirmed dead end.
Constitution: Data-first. Gates block. Reviewer 2 co-pilot. Explore before exploit.

v5.0 INNOVATIONS — IUDEX

v5.0 makes R2 structurally unbypassable. Huang et al. (ICLR 2024) proved LLMs cannot self-correct reasoning without external feedback. v5.0 provides that external feedback architecturally, not just via prompting.

Innovation 1: Seeded Fault Injection (SFI)

Before every FORCED R2 review, the orchestrator injects 1-3 known faults from assets/fault-taxonomy.yaml into the claim set. R2 doesn't know which claims are seeded. If R2 misses them, the review is INVALID. This is mutation testing applied to scientific claims.

Protocol: protocols/seeded-fault-injection.md Gate: V0 (R2 Vigilance) — RMS >= 0.80, FAR <= 0.10 Schema: schemas/vigilance-check.schema.json

Innovation 2: Judge Agent (R3)

A meta-reviewer that scores R2's review quality on a 6-dimension rubric (Specificity, Independence, Counter-Evidence, Depth, Constructiveness, Consistency). R3 does NOT re-review the claims — it reviews the REVIEW.

Protocol: protocols/judge-agent.md Gate: J0 (total >= 12/18, no dimension = 0) Rubric: assets/judge-rubric.yaml

Innovation 3: Blind-First Pass (BFP)

For FORCED reviews, R2 first receives claims WITHOUT the researcher's justifications. R2 must form independent opinions before seeing the full context. Breaks anchoring bias.

Protocol: protocols/blind-first-pass.md Integration: Phase 1 (blind) → Phase 2 (full context) → discrepancy analysis

Innovation 4: Schema-Validated Gates (SVG)

8 critical gates enforce structure via JSON Schema. If the artifact doesn't validate, the gate FAILS regardless of what the prose says. Catches "hallucinated compliance."

Protocol: protocols/schema-validation.md Schemas: schemas/*.schema.json (9 files: 8 gates + serendipity-seed)

Enhancement A: R2 Salvagente

When R2 kills a claim with reason INSUFFICIENT_EVIDENCE/CONFOUNDED/PREMATURE, R2 MUST produce a serendipity seed. Discovery preservation built into the adversarial loop.

Enhancement B: Structured Serendipity Seeds

Seeds are schema-validated research objects with causal_question, falsifiers (3-5), discriminating_test, expected_value. Not notes. Schema: schemas/serendipity-seed.schema.json

Enhancement C: Quantified Exploration Budget

LAW 8 gains measurable 20% floor at T3. See LAW 8 section above.

Enhancement D: Confidence Formula Revision

Hard veto (E < 0.05 or D < 0.05 → confidence = 0) + geometric mean with dynamic floor for R, C, K.

confidence = E × D × (R_eff × C_eff × K_eff)^(1/3)
where X_eff = max(X_raw, floor)

Floor varies by claim.type and stage (0.05-0.20). claim.type locked by orchestrator (anti-gaming).

Enhancement E: Circuit Breaker

Deadlock prevention: same objection × 3 rounds × no state change → DISPUTED. Claim frozen, pipeline continues. S5 Poison Pill prevents closing with unresolved disputes. Protocol: protocols/circuit-breaker.md

Enhancement F: Agent Permission Model

Separation of verdict from execution. R2 produces verdicts. Orchestrator executes. R2 CANNOT write to claim ledger. R3 CANNOT modify R2's report. Schemas are READ-ONLY.

Transition Validation: Invalid transitions (e.g., KILLED→VERIFIED without revival protocol) are rejected by orchestrator.

PHASE 0: SCIENTIFIC BRAINSTORM (Before Everything)

Before any OTAE cycle, before any tree search, before any experiment — BRAINSTORM.

This is the phase where the research direction is born. It is not optional. It is not a chat. It is a structured, scientifically rigorous brainstorming session that produces a concrete, falsifiable research question grounded in real gaps in the literature and real available data.

Why Phase 0 Exists

Most failed research starts with a bad question. AI-Scientist-v2 skips this entirely (it takes a pre-written idea). We don't. Phase 0 ensures we start with a question worth asking, gaps worth filling, and data that actually exists to answer it.

Phase 0 Workflow

PHASE 0: SCIENTIFIC BRAINSTORM
├── Step 1: UNDERSTAND — What domain? What excites the researcher?
├── Step 2: LANDSCAPE  — What does the field look like right now?
├── Step 3: GAPS       — Where are the holes? What's missing?
├── Step 4: DATA       — What datasets exist to fill those gaps?
├── Step 5: HYPOTHESES — Generate 3-5 testable hypotheses
├── Step 6: TRIAGE     — Score and rank by feasibility + impact
├── Step 7: R2 REVIEW  — Reviewer 2 challenges the chosen direction
└── Step 8: COMMIT     — Lock in RQ, kill conditions, success criteria

Step 1: UNDERSTAND (Context Gathering)

Dispatch to: scientific-brainstorming MCP skill (Phase 1: Understanding the Context)

• Ask the user open-ended questions about their domain, interests, constraints
• One question at a time, prefer multiple choice when possible (from superpowers:brainstorming)
• Identify: domain expertise, available resources, time constraints, ambition level
• Listen for implicit assumptions, unexplored angles, personal excitement
• Output: 00-brainstorm/context.md

Step 2: LANDSCAPE (Field Mapping)

Dispatch to: literature-review + openalex-database + pubmed-database skills

• Rapid literature scan of the identified domain (last 3-5 years)
• Map the major players, key papers, dominant methods, open debates
• Identify review papers and meta-analyses as anchors
• Build a mental map: what's crowded (red ocean) vs. what's empty (blue ocean)
• Output: 00-brainstorm/landscape.md with field map

Step 3: GAPS (Blue Ocean Hunting)

This is the core of Phase 0. Dispatch to: scientific-brainstorming (Phase 2: Divergent Exploration)

Techniques applied systematically:

• Cross-Domain Analogies: What methods from field X haven't been tried in field Y?
• Assumption Reversal: What does everyone assume that might be wrong?
• Scale Shifting: What happens at a different scale (single-cell vs. bulk, temporal, spatial)?
• Constraint Removal: "What if you could measure anything?" → then check what's actually measurable
• Technology Speculation: What new tools (spatial transcriptomics, foundation models, etc.) open new doors?
• Contradiction Hunting: Where do two well-cited papers disagree?

For each gap found, assess:

• Is this gap real or just my ignorance? (check with targeted search)
• Is anyone already working on this? (check preprints: biorxiv, medrxiv)
• Why hasn't this been done? (technical limitation? lack of data? not interesting enough?)

Output: 00-brainstorm/gaps.md with ranked list of identified gaps

Step 4: DATA (Reality Check — LAW 1 Applies Here)

NO DATA = NO GO. This step kills beautiful hypotheses that can't be tested.

Dispatch to: geo-database, cellxgene-census, openalex-database, and domain-specific database skills

For each promising gap:

• Does public data exist to investigate it? Search GEO, CellxGene, ENCODE, TCGA, etc.
• What format is it in? How much preprocessing is needed?
• Is the sample size sufficient for the intended analysis?
• Are there confounders or batch effects that would invalidate the approach?

Score each gap: DATA_AVAILABLE (0-1) based on quantity, quality, accessibility. Gaps with DATA_AVAILABLE < 0.3 are moved to "future" pile, not killed.

Output: 00-brainstorm/data-audit.md

Step 5: HYPOTHESES (From Gaps to Testable Questions)

Dispatch to: hypothesis-generation MCP skill + scientific-brainstorming (Phase 3: Connection Making)

For each top-ranked gap with available data, generate:

• A precise, falsifiable hypothesis (not vague, not unfalsifiable)
• A null hypothesis (what we expect if the effect doesn't exist)
• Predictions: if true, we should see X; if false, we should see Y
• Mechanistic explanation: WHY might this be true? What's the biology/logic?

Generate 3-5 competing hypotheses. Each must be:

• Testable with available data (Step 4 passed)
• Distinguishable from the others (different predictions)
• Interesting enough to publish if confirmed OR denied

Output: 00-brainstorm/hypotheses.md

Step 6: TRIAGE (Pick the Winner)

Score each hypothesis on a 2x2 matrix:

                    HIGH FEASIBILITY
                         ▲
                         │
        Sweet spot ──→   │   ← Start here if unsure
        (publishable +   │     (safe bet)
         achievable)     │
                         │
   ─────────────────────┼──────────────────→ HIGH IMPACT
                         │
        Ignore           │   Moon shot
        (hard + boring)  │   (hard but transformative)
                         │

Criteria:

• Impact (0-5): How much would this change the field?
• Feasibility (0-5): Can we do this with available data + tools?
• Novelty (0-5): How different is this from existing work?
• Data readiness (0-5): How close is the data to being usable?
• Serendipity potential (0-5): How likely is this to generate unexpected discoveries?

Total score /25. Rank hypotheses. Present top 3 to user with trade-offs.

Output: 00-brainstorm/triage.md

Step 7: R2 REVIEW OF BRAINSTORM (Reviewer 2 is co-pilot from day zero)

R2 reviews the brainstorm output BEFORE any OTAE cycle starts.

R2 ensemble (at least R2-Methods + R2-Bio) challenges:

• Is the gap real? Or are we reinventing the wheel?
• Is the hypothesis truly falsifiable? Or is it unfalsifiable fluff?
• Is the data actually sufficient? Or are we kidding ourselves?
• Are there obvious confounders or biases we're ignoring?
• Is this the MOST interesting question we could ask given the gaps found?

R2 can demand:

• Additional literature search on a specific sub-topic
• Reformulation of the hypothesis
• Different data source
• Complete pivot to a different gap

R2 verdict on brainstorm must be at least WEAK_ACCEPT before proceeding to OTAE.

Output: 05-reviewer2/brainstorm-review.md

Step 8: COMMIT (Lock In)

After R2 clearance:

1. Finalize RQ.md with: question, hypothesis, predictions, success criteria, kill conditions
2. Set tree mode: LINEAR | BRANCHING | HYBRID
3. Create full folder structure
4. Populate STATE.md, PROGRESS.md, TREE-STATE.json
5. Enter first OTAE cycle with a solid foundation

Phase 0 Gate: B0 (Brainstorm Quality)

B0 PASS requires ALL of:
  - At least 3 gaps identified with evidence
  - At least 1 gap verified as not-yet-addressed (preprint check)
  - Data availability confirmed for chosen hypothesis (DATA_AVAILABLE >= 0.5)
  - Hypothesis is falsifiable (null hypothesis stated)
  - R2 brainstorm review: WEAK_ACCEPT or better
  - User approved the chosen direction

Phase 0 Artifacts

.vibe-science/RQ-001-[slug]/
├── 00-brainstorm/
│   ├── context.md          # User's domain, interests, constraints
│   ├── landscape.md        # Field map, key papers, major players
│   ├── gaps.md             # Identified gaps with evidence + ranking
│   ├── data-audit.md       # Data availability for each gap
│   ├── hypotheses.md       # 3-5 competing hypotheses with predictions
│   └── triage.md           # Scoring matrix + final ranking

CORE CONCEPT: OTAE INSIDE TREE NODES

v3.5 had a flat OTAE loop: cycle 1 → cycle 2 → cycle 3 → ...

v4.0 has a tree of OTAE nodes:

                         root
                        /    \
                    node-A   node-B        ← each is a full OTAE cycle
                   / |  \      |
                A1  A2  A3    B1           ← children = variations
               /
             A1a                           ← deeper exploration

Each node executes one complete OTAE cycle (Observe parent → Think plan → Act execute → Evaluate score). The tree search engine selects which node to expand next based on Evidence Engine confidence + metrics.

When to branch vs. stay linear:

• Literature review → LINEAR (sequential cycles, like v3.5)
• Computational experiments → BRANCHING (tree search over variants)
• Mixed research → HYBRID (linear discovery phase, then branch for experiments)

THE OTAE-TREE LOOP

╔═══════════════════════════════════════════════════════════════╗
║                    OTAE-TREE LOOP (v4.0)                      ║
╠═══════════════════════════════════════════════════════════════╣
║                                                               ║
║  ┌─── OBSERVE ──────────────────────────────────────────┐    ║
║  │  Read STATE.md + TREE-STATE.json                     │    ║
║  │  Identify current stage (1-5)                        │    ║
║  │  Load current node context + parent chain            │    ║
║  │  Check pending: gates, R2 demands, stage transitions │    ║
║  │  Verify STATE ↔ TREE consistency                     │    ║
║  └──────────────────────────────────────────────────────┘    ║
║                         ↓                                     ║
║  ┌─── THINK ────────────────────────────────────────────┐    ║
║  │  TREE MODE:                                          │    ║
║  │    Which node to expand? (best-first selection)      │    ║
║  │    What type? (draft|debug|improve|hyper|ablation)   │    ║
║  │    What would falsify the parent's result?           │    ║
║  │  LINEAR MODE:                                        │    ║
║  │    Same as v3.5 — next highest-value action          │    ║
║  │  Plan: search | analyze | extract | compute | write  │    ║
║  └──────────────────────────────────────────────────────┘    ║
║                         ↓                                     ║
║  ┌─── ACT ──────────────────────────────────────────────┐    ║
║  │  Execute the planned action:                         │    ║
║  │  • Literature search → search-protocol.md            │    ║
║  │  • Data analysis → analysis-orchestrator.md          │    ║
║  │  • Tree node experiment → auto-experiment.md         │    ║
║  │  • Hypothesis generation → serendipity-engine.md     │    ║
║  │  • Tool dispatch → skill-router.md                   │    ║
║  │  Produce ARTIFACTS (files, figures, manifests)        │    ║
║  │  If buggy: debug (max 3 attempts, then prune node)   │    ║
║  └──────────────────────────────────────────────────────┘    ║
║                         ↓                                     ║
║  ┌─── EVALUATE ─────────────────────────────────────────┐    ║
║  │  Extract claims → CLAIM-LEDGER                       │    ║
║  │  Score confidence (formula: E·R·C·K·D → 0-1)        │    ║
║  │  Parse metrics (if computational node)               │    ║
║  │  VLM feedback on figures (if available) → G6         │    ║
║  │  Check assumptions → ASSUMPTION-REGISTER             │    ║
║  │  Detect serendipity (including cross-branch)         │    ║
║  │  Apply relevant GATE (G0-G6, L0-L2, D0-D2, T0-T3,  │    ║
║  │    V0, J0)                                          │    ║
║  │  Mark node: good | buggy | pruned                    │    ║
║  │  Gate FAIL? → triage, fix, re-gate                   │    ║
║  └──────────────────────────────────────────────────────┘    ║
║                         ↓                                     ║
║  ┌─── CHECKPOINT ───────────────────────────────────────┐    ║
║  │  Stage gate check (S1-S5): advance stage?            │    ║
║  │  Tree health check (T3): ratio good/total >= 0.2?    │    ║
║  │                                                       │    ║
║  │  R2 CO-PILOT CHECK (expanded triggers):              │    ║
║  │    FORCED: major finding / stage transition /         │    ║
║  │      confidence explosion / pivot / brainstorm        │    ║
║  │    BATCH:  3 minor findings accumulated              │    ║
║  │    SHADOW: every 3 cycles, R2 passively reviews      │    ║
║  │      tree health + claim ledger + assumption drift.   │    ║
║  │      Shadow can escalate to FORCED if it spots risk.  │    ║
║  │    VETO:   R2 can halt any branch it deems unsound   │    ║
║  │    If triggered → reviewer2-ensemble.md (BLOCKING)    │    ║
║  │                                                       │    ║
║  │  SERENDIPITY RADAR (active every cycle):             │    ║
║  │    Scan current node for anomalies & unexpected       │    ║
║  │    Compare cross-branch: pattern only visible across? │    ║
║  │    Check contradiction register: new contradictions?  │    ║
║  │    Score >= 10 → serendipity-engine.md triage          │    ║
║  │    Score >= 15 → INTERRUPT: create serendipity node   │    ║
║  │                                                       │    ║
║  │  Stop conditions? → EXIT or CONTINUE                 │    ║
║  │                                                       │    ║
║  │  v5.0 FORCED review path:                             │    ║
║  │    SFI injection → BFP Phase 1 (blind) →              │    ║
║  │    Full review Phase 2 → V0 gate (vigilance) →        │    ║
║  │    R3/J0 gate (judge) → Schema validation →           │    ║
║  │    Normal gate evaluation.                             │    ║
║  │    See protocols/seeded-fault-injection.md,            │    ║
║  │    protocols/blind-first-pass.md,                      │    ║
║  │    protocols/judge-agent.md,                            │    ║
║  │    protocols/schema-validation.md.                     │    ║
║  │                                                       │    ║
║  │  BATCH and SHADOW reviews unchanged from v4.5.        │    ║
║  └──────────────────────────────────────────────────────┘    ║
║                         ↓                                     ║
║  ┌─── CRYSTALLIZE (LAW 10: NOT IN FILE = DOESN'T EXIST) ──┐    ║
║  │  Update STATE.md (rewrite, max 100 lines)            │    ║
║  │  Update TREE-STATE.json (full tree serialization)    │    ║
║  │  Write/update node file in 08-tree/nodes/            │    ║
║  │  Append PROGRESS.md (cycle summary)                  │    ║
║  │  Update CLAIM-LEDGER.md, ASSUMPTION-REGISTER.md      │    ║
║  │  Update tree-visualization.md                        │    ║
║  │  Save intermediate data (CSVs, metrics, figures)     │    ║
║  │  Log decisions with reasoning in decision-log        │    ║
║  │  VERIFY: every ACT result exists as a file on disk   │    ║
║  │  → LOOP BACK TO OBSERVE                              │    ║
║  └──────────────────────────────────────────────────────┘    ║
║                                                               ║
╚═══════════════════════════════════════════════════════════════╝

TREE SEARCH ENGINE

The tree search engine manages hypothesis exploration as a tree of OTAE nodes. Each node executes one complete OTAE cycle; the engine selects which node to expand next based on Evidence Engine confidence and metrics. Supports 7 node types across 3 tree modes (LINEAR, BRANCHING, HYBRID).

Node Types (summary)

Full protocol: protocols/tree-search.md Contains: tree modes (LINEAR/BRANCHING/HYBRID), 7 node types, best-first selection algorithm, pruning rules, tree health monitoring (T3).

REVIEWER 2 CO-PILOT SYSTEM (Expanded from v3.5)

In v3.5, Reviewer 2 was a gate. In v4.0, Reviewer 2 is a co-pilot that flies with you the entire session.

R2 Activation Modes

R2 Shadow Mode Protocol (every 3 cycles)

R2 Shadow Check:
1. Read CLAIM-LEDGER.md — any confidence scores drifting up without new evidence?
2. Read ASSUMPTION-REGISTER.md — any HIGH-risk assumptions untested for 5+ cycles?
3. Read tree-visualization.md — is the tree lopsided? (one branch getting all attention)
4. Read SERENDIPITY.md — any flags ignored for 3+ cycles?
5. Compute: assumption_staleness, confidence_drift, tree_balance, serendipity_neglect

If ANY metric is concerning:
  → Log warning in PROGRESS.md
  → If 2+ metrics concerning → ESCALATE to FORCED R2 review

R2 Powers (v4.0 — expanded)

1. DEMAND EVIDENCE: R2 can require specific evidence before any claim is promoted. Demands have deadlines.
2. FORCE FALSIFICATION: R2 can require the system to actively try to disprove a claim before accepting it. Minimum 3 falsification tests per major claim.
3. VETO BRANCH: R2 can mark a tree branch as "unsound" — no further expansion until R2 concerns addressed.
4. REDIRECT: R2 can propose an alternative research direction during review. The system must present this to the user.
5. CHALLENGE BRAINSTORM: R2 reviews Phase 0 output and can force reconsideration of the research question itself.
6. AUDIT TRAIL: Every R2 decision is logged with reasoning. R2 cannot be silent — it must always explain.

R2 Ensemble Composition (expanded from v3.5)

Critical behavioral requirement: R2 does NOT congratulate. R2 does NOT say "good progress" or "interesting finding." R2 says what is broken, what test would break it further, and what phrasing is safe. If R2 produces output that sounds encouraging, R2 has failed.

Escalating scrutiny: Each review pass MUST be MORE demanding than the last. If R2 finds 3 issues on pass 1, pass 2 must look for issues that pass 1 missed. A review that finds fewer issues than the previous review is suspicious — either the work genuinely improved (verify!) or R2 got lazy (unacceptable).

Full R2 system prompt and ensemble protocol: protocols/reviewer2-ensemble.md Contains: canonical R2 system prompt, 4-reviewer ensemble composition, domain checklists, double-pass workflow, mandatory output schema ("A Forza Bruta" format, sections A through N), severity guide.

R2 MINI PROMPT (for Shadow Mode — lighter, faster, still mean)

Act as Reviewer #2: adversarial, evidence-driven, zero padding.
Scan the material for: confidence drift, untested assumptions, ignored
serendipity flags, lopsided tree exploration, stale claims.
List problems as BLOCKER or WARNING. For each BLOCKER: why + minimal test.
If 2+ BLOCKERs → output "ESCALATE TO FORCED REVIEW" and stop.

SERENDIPITY RADAR SYSTEM (Expanded from v3.5)

In v3.5, Serendipity Engine was a detector. In v4.0, Serendipity Radar is an active scanner that runs at EVERY EVALUATE phase.

The Serendipity Principle (learned from the CRISPR case study)

Serendipity is NOT just flagging anomalies. It is a three-part process:

1. DETECTION: Notice the anomaly (the Serendipity Radar does this)
2. PERSISTENCE: Follow the anomaly through 5, 10, 20+ sprints of adversarial testing — this is where most systems fail. They flag the anomaly and move on. Real serendipity requires relentless follow-through.
3. VALIDATION: The anomaly survives confounder harness (LAW 9), cross-assay replication, permutation testing, and R2 demolition. Only THEN is it a finding.

In the CRISPR case study: UOT failed (Sprint 3) → Serendipity Engine scored 13/15 → investigation pivoted → 21 sprints of adversarial testing → 4 validated findings across 1.38M sites. The serendipity flag at Sprint 3 was the BEGINNING, not the end. Without the 18 subsequent sprints of falsification, the flag would have been meaningless.

Implication for the system: Serendipity flags MUST be tracked with the same persistence as research questions. A serendipity flag that is not followed up within 5 cycles gets escalated. A serendipity flag that IS followed up gets the full confounder harness treatment.

Full protocol: protocols/serendipity-engine.md Contains: 5-scan radar protocol, cross-branch detection, serendipity sprints, INTERRUPT/QUEUE/FILE/NOISE response matrix, escalation rules.

5-STAGE EXPERIMENT MANAGER

Adapted from AI-Scientist-v2's 4-stage manager. We add Stage 5 (Synthesis & Review).

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