Autoconference Analyze

Read all available conference artifacts from CONF_DIR. Do not skip files — the more context, the better the analysis.

Required files (fail if missing):

• conference.md — Extract: goal, metric, baseline value, researcher count, max_rounds, strategy assignments
• conference_results.tsv — The aggregated results table

Optional files (read if present, skip gracefully if absent):

• researcher_{ID}_results.tsv for each researcher ID found in conference.md — Individual iteration histories
• poster_session_round_*.md — Knowledge sharing transcripts from each round's symposium phase
• peer_review_round_*.md — Adversarial review transcripts from each round's review phase
• synthesis.md — Intermediate synthesis notes if generated mid-conference
• final_report.md — The conference's own synthesis output

Extraction targets while reading:

• From conference.md: goal statement, target metric name and direction (maximize/minimize), baseline metric value, number of researchers, their assigned strategies, whether a Devil's Advocate was assigned
• From conference_results.tsv: columns (researcher_id, round, iteration, metric_value, strategy_description, kept/reverted, notes)
• From poster session files: what knowledge was shared, which researcher shared it, which round
• From peer review files: what claims were challenged, what was overturned, reviewer reasoning

Step 2: Trajectory Comparison

For each researcher, compute the following metrics from their researcher_{ID}_results.tsv:

Convergence speed: Number of iterations from start to the iteration where the researcher first achieved their personal best metric. Lower is faster.

Exploration breadth: Count of distinct strategy categories tried. Strategies are "distinct" if they describe fundamentally different approaches (e.g., "regularization tuning" vs. "architecture change" are distinct; "L1 regularization" vs. "L2 regularization" are the same category). Use your judgment to cluster.

Success rate: (number of iterations marked 'kept') / (total iterations). Express as a percentage.

Final metric vs baseline improvement: (final_best_metric - baseline_metric) / baseline_metric * 100%. Label as improvement or regression depending on metric direction.

Notable finding: One sentence describing the most significant discovery or pattern for this researcher.

Produce the following comparison table in the report:

After the table, write 2-3 sentences summarizing the key trajectory differences. Who converged fastest? Who explored most broadly? Was there a trade-off between speed and final metric?

Step 3: Failure Mode Analysis

Identify patterns of failure across all researchers. A "failure" is any iteration marked reverted or any claim marked challenged/overturned in peer review.

Substep 3a — Reverted strategy clustering: Go through all researcher TSV files and collect every row where kept = reverted. Group these by strategy type (same clustering logic as Step 2). For each cluster, record:

• Strategy type name
• How many times it was tried across all researchers
• How many researchers tried it
• Whether it ever worked for anyone (any kept row in the same cluster)

Present as a table:

Verdict should be one of: dead end (never worked), researcher-specific (worked for some but not others), timing-dependent (worked in later rounds but not early), conditional (worked only with specific prerequisites).

Substep 3b — Peer review overturns: From peer_review_round_*.md files, list every claim that was challenged or overturned. For each:

• The claim made
• The round it was challenged in
• The reason given for the challenge
• Whether the researcher recovered (did they produce a better result after the challenge?)

Substep 3c — Systematic failure patterns: Look for failures that afflicted multiple researchers in the same round. If 3+ researchers tried the same type of strategy and it failed in round N, that is a systematic failure. Flag these explicitly: "Systematic failure in Round N: [description]. Possible cause: [hypothesis]."

Step 4: Cross-Researcher Insight Extraction

Analyze knowledge transfer effectiveness using the poster_session_round_*.md files.

Substep 4a — Knowledge transfer audit: For each piece of knowledge shared in a poster session:

• Who shared it
• What round it was shared in
• Which researchers were present (all, unless the file indicates otherwise)
• Whether any researcher subsequently adopted that technique (check their TSV rows after that round)
• The metric impact of adoption (compare metric in the round before adoption vs. 2 rounds after)

Substep 4b — Transfer effectiveness table:

Substep 4c — Breakthrough propagation: Identify whether any single researcher's breakthrough had outsized influence. A "breakthrough" is any iteration that produced a metric improvement >2x the average per-iteration improvement for that researcher. If a breakthrough was shared and caused measurable improvements in others, record:

• "Researcher R{ID}'s discovery of [technique] in Round N propagated to R{ID2} and R{ID3}, producing combined improvements of X%."

If no poster session files exist, write: "No poster session transcripts available — transfer analysis skipped."

Step 5: Insight Taxonomy

Produce a structured taxonomy of all actionable insights extracted from the conference. An insight is any finding that could be applied to a future similar problem.

Assign each insight a type from this controlled vocabulary:

• technique — A specific method or approach that improved the metric
• anti-pattern — A specific method or approach that consistently failed
• meta-strategy — An insight about how to run the search process itself (e.g., "explore broadly in early rounds, exploit in later rounds")
• domain-insight — A fact about the problem domain that influenced strategy selection
• tooling — An observation about tools, libraries, or infrastructure

Confidence scoring:

• high — Observed by 2+ researchers, consistent result, no contradictory evidence
• medium — Observed by 1 researcher with clear causal mechanism, or by 2+ researchers with one contradictory instance
• low — Observed once, causation unclear, or contradicted by another researcher

Transferability:

• yes — Applies broadly to similar problems in this domain
• conditional — Applies only under specific conditions (describe them)
• no — Specific to this dataset/problem instance

Aim for completeness — a thin taxonomy is a failed analysis. Extract at least one insight per researcher per round if the data supports it.

Step 6: Novelty Assessment

For each insight in the taxonomy (Step 5), score it on two additional dimensions:

Predictability score (was this obvious from the baseline?):

• obvious — Any domain expert would have predicted this from the problem statement
• plausible — An expert might have guessed this, but it wasn't certain
• surprising — This would not have been predicted without running the conference

Multi-agent advantage (did the conference format enable this discovery?):

• yes — This insight required multiple researchers, knowledge transfer, or adversarial review to emerge
• possibly — Could have been found by a single researcher, but conference accelerated it
• no — Any single researcher running enough iterations would have found this

Add these columns to the insight taxonomy table, or present as a supplementary table referencing insight numbers.

After the table, write a "Conference ROI" paragraph: Given the total number of researcher-iterations run, how much insight was produced? Was the conference format justified, or could a single deep autoresearch loop have achieved similar results? Be honest — if the answer is "a single researcher would have been sufficient," say so.

Step 7: Write Report

Write {CONF_DIR}/analysis-report.md with the following structure. Do not truncate any section — each section must be fully populated.

# Conference Analysis Report

**Conference Goal:** [from conference.md]
**Baseline Metric:** [value and metric name]
**Best Achieved Metric:** [value, by which researcher, in which round]
**Total Researcher-Iterations:** [sum across all researchers]
**Analysis Generated:** [current date]

---

## 1. Trajectory Comparison
[Full table and summary from Step 2]

## 2. Failure Mode Analysis

### 2a. Reverted Strategy Clusters
[Table from Step 3a]

### 2b. Peer Review Overturns
[List from Step 3b]

### 2c. Systematic Failure Patterns
[Findings from Step 3c, or "None detected."]

## 3. Cross-Researcher Insight Extraction

### 3a. Knowledge Transfer Audit
[Table from Step 4b]

### 3b. Breakthrough Propagation
[Findings from Step 4c]

## 4. Insight Taxonomy
[Full table from Step 5]

## 5. Novelty Assessment
[Extended table from Step 6 + Conference ROI paragraph]

## 6. Recommended Next Steps

[Write 3-5 concrete, actionable recommendations based on the analysis. Each recommendation should cite at least one specific insight from the taxonomy. Format as a numbered list with one sentence of justification per item.]

After writing, confirm: "Analysis complete. Report written to {CONF_DIR}/analysis-report.md. [N] insights extracted, [M] failure modes identified. Top recommendation: [first item from §6]."

Error Handling

Missing researcher TSV files: Proceed with conference_results.tsv only. Note in the report which researchers had no individual TSV available and flag that trajectory analysis for those researchers is approximate.

Truncated conference (stopped before max_rounds): Note the stopping round prominently at the top of the report. Distinguish between: user-interrupted, convergence-triggered, or error-stopped. Adjust trajectory analysis accordingly.

No poster session files: Skip Step 4 and note the absence. Knowledge transfer analysis requires these files.

Contradictory data (e.g., a strategy marked both kept and reverted in different rows): Flag it explicitly in the relevant section and use the later-round entry as the authoritative record.