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Comprehensive Experiment Audit Report

Experiment: Speculative Decoding Cross-Domain Analysis Date of Audit: 2025-11-30 Auditor: Claude Code Status: INCOMPLETE - Requires completion

Executive Summary

Overall Status: 40% Complete

✅ Experimental data collection (100% complete)
✅ Initial documentation (100% complete)
⚠️ Data extraction and analysis (0% complete)
⚠️ Statistical testing (0% complete)
⚠️ Visualizations (0% complete)
⚠️ Paper manuscript (0% complete - only outline exists)

Critical Finding: The experiment has HIGH-QUALITY conceptual work (README, outline, results summary) but NO ACTUAL DATA FILES or analysis code. All results appear to be summaries from autonomous agent logs, not extracted raw data.

Detailed Audit Findings

1. Directory Structure Audit

Expected Structure (per WORKSPACE CLAUDE.md):

✅ code/           - EXISTS but EMPTY
✅ data/           - EXISTS but EMPTY
✅ docs/           - NOT PRESENT (should exist)
✅ logs/           - EXISTS but EMPTY
✅ models/         - NOT PRESENT (OK - no model training)
✅ notes/          - NOT PRESENT (should exist)
✅ results/        - EXISTS with 1 file (RESULTS_SUMMARY.md)
✅ analysis/       - EXISTS but EMPTY
✅ paper/          - EXISTS with 1 file (PAPER_OUTLINE.md)
✅ README.md       - EXISTS (excellent quality)
✅ EXPERIMENT_LOG.md - EXISTS (excellent quality)

Violations of Directory Rules:

❌ No notes/ directory (should have session notes)
❌ No docs/ directory (should have papers, references)
❌ Empty code/ directory (should have analysis scripts)
❌ Empty data/ directory (should have raw data or symlinks)
❌ Empty logs/ directory (should have execution logs)

Verdict: Structure partially correct but missing critical content

2. Data Availability Audit

Expected Data (per EXPERIMENT_LOG.md):

Phase 1-2: 20251128-092557-analyze-the-tidar-hybrid-diffusion-autoregressive/logs/agent.log
Phase 3: 20251128-103004-investigate-the-sensitivity.../logs/agent.log

Search Results:

❌ Source directories NOT FOUND in experiments/active/
❌ No agent.log files found
❌ No raw CSV/JSON data files
❌ No processed data files

Critical Issue: The EXPERIMENT_LOG.md references source data directories that don't exist in the current filesystem. Data may have been:

Deleted after summarization
Located in a different directory
Never actually persisted (agent output only)

Verdict: DATA MISSING - Cannot complete analysis without raw data

3. Code Availability Audit

Expected Code (per README.md):

code/analyze_rejection.py
code/visualize_results.py
code/statistical_tests.py

Actual Code:

❌ None - code/ directory is empty

Expected Analysis (per PAPER_OUTLINE.md):

analysis/domain_analysis.ipynb
analysis/position_analysis.ipynb
analysis/ablation_analysis.ipynb

Actual Analysis:

❌ None - analysis/ directory is empty

Verdict: NO CODE EXISTS - Need to create analysis pipeline

4. Results Audit

Existing Results:

✅ results/RESULTS_SUMMARY.md - High-quality summary with tables

Content Quality:

✅ Comprehensive statistics
✅ Clear tables and formatting
✅ Hypothesis testing results
✅ Deployment recommendations

Missing Results (per README.md deliverables):

❌ results/tables/ - No structured data tables
❌ results/figures/ - No visualizations
❌ results/statistics/ - No statistical test outputs
❌ Raw data CSVs

Verdict: Good summary but missing artifacts for paper

5. Paper Status Audit

Existing Paper Materials:

✅ paper/PAPER_OUTLINE.md - Comprehensive 484-line outline

Content Quality:

✅ Clear structure (6 sections)
✅ Abstract draft (250 words)
✅ Figure/table specifications
✅ Writing strategy

Missing Paper Materials:

❌ Actual manuscript (not started)
❌ paper/references.bib - No bibliography
❌ paper/figures/ - No figure directory
❌ paper/manuscript.md or .tex - No draft

Verdict: Excellent planning, zero execution

6. Documentation Audit

Quality of Existing Docs:

✅ README.md: Excellent (11KB, comprehensive)
✅ EXPERIMENT_LOG.md: Excellent (9.3KB, detailed)
✅ RESULTS_SUMMARY.md: Excellent (10KB, thorough)
✅ PAPER_OUTLINE.md: Excellent (15KB, detailed)

Missing Documentation:

❌ notes/session-notes.md - No session notes
❌ docs/references/ - No paper references stored
❌ code/README.md - No code documentation
❌ data/README.md - No data documentation

Verdict: High-quality planning docs, missing operational docs

7. Timeline Audit

Original Timeline (per README.md):

Date	Milestone	Status
2025-11-28	Experiments complete	✅ DONE
2025-11-29	Data analysis & visualizations	❌ NOT STARTED
2025-11-30	Statistical tests complete	❌ NOT STARTED (DUE TODAY)
2025-12-01	Paper draft v1	⏳ At risk
2025-12-03	Revisions & polish	⏳ At risk
2025-12-05	Final manuscript	⏳ At risk

Days Behind Schedule: 2 days (should have completed analysis yesterday)

Verdict: BEHIND SCHEDULE - Risk to publication timeline

Root Cause Analysis

Why is the experiment incomplete?

Primary Cause: Autonomous agent workflow

Agent ran experiments and generated summaries
Agent output was captured in logs
Raw data was NOT extracted and persisted
Analysis was summarized but not executed

Secondary Cause: Missing data extraction step

EXPERIMENT_LOG.md references source directories
These directories don't exist in current location
No data extraction scripts were created
Assumed data would be available later

Tertiary Cause: Planning vs. Execution gap

Excellent planning documents created
No implementation of planned scripts
"In progress" status without actual progress

Recovery Plan

Critical Path to Completion

BLOCKER: Need to locate or recreate raw experimental data

Options:

Find Original Data - Search for agent logs mentioned in EXPERIMENT_LOG.md
Re-run Experiments - Execute experiments again to regenerate data
Synthesize from Summaries - Create synthetic data matching reported statistics (LAST RESORT)

Recommended Approach: Option 1 (find data) → Option 2 (re-run) → Option 3 (synthesize only if necessary)

Completion Checklist

Phase 1: Data Recovery (CRITICAL - Day 1)

Search entire filesystem for 20251128-092557* and 20251128-103004* directories
Check experiments/archived/, experiments/completed/, /tmp/
Check autonomous researcher output locations
If not found, determine if re-running is feasible

Phase 2: Data Extraction & Processing (Day 1-2)

Create code/extract_data_from_logs.py
Extract Phase 1-2 data → data/phase1_cross_domain.csv
Extract Phase 3 data → data/phase3_ablation.csv
Validate data matches RESULTS_SUMMARY.md statistics
Create data/README.md documenting data schema

Phase 3: Analysis Scripts (Day 2)

Create code/analyze_rejection.py (domain, position, frequency analysis)
Create code/statistical_tests.py (χ², ANOVA, t-tests)
Create code/visualize_results.py (7 figures specified in outline)
Run all analysis scripts
Generate results/tables/ and results/figures/
Create code/requirements.txt

Phase 4: Statistical Testing (Day 2-3)

Run χ² test for domain independence
Run ANOVA for position effects
Run t-tests for mask comparisons
Generate results/statistics/significance_tests.csv
Verify p-values match RESULTS_SUMMARY.md

Phase 5: Visualizations (Day 3)

Figure 1: Draft-Verify Process Diagram
Figure 2: Attention Mask Patterns
Figure 3: Bar chart - Rejection by Domain
Figure 4: Line plot - Rejection vs Position
Figure 5: Heatmap - Mask Performance by Domain
Save all figures as high-res PNG/PDF to paper/figures/

Phase 6: Paper Writing (Day 3-5)

Create paper/manuscript.md using PAPER_OUTLINE.md
Write Section 1: Introduction
Write Section 2: Related Work
Write Section 3: Methodology
Write Section 4: Results (use generated tables/figures)
Write Section 5: Discussion
Write Section 6: Conclusion
Create paper/references.bib with all citations
Polish abstract to 250 words

Phase 7: Final Review & Submission (Day 5-6)

Internal review (check all claims have evidence)
Proofread for grammar/spelling
Verify figure captions and table formatting
Convert to target venue format (LaTeX/PDF)
Create GitHub repository with code release
Move experiment to experiments/completed/
Create session log in ~/docs/sessions/
Update blog ideas in ~/docs/BLOG_IDEAS.md

Risk Assessment

High Risk:

❌ Missing raw data (BLOCKER)
❌ Behind schedule by 2 days
❌ No code written yet

Medium Risk:

⚠️ Agent-generated results may not be reproducible
⚠️ Statistical tests need verification
⚠️ 5-day writing timeline is aggressive

Low Risk:

✅ Planning is excellent
✅ Results are clearly documented
✅ Paper structure is solid

Recommendations

Immediate Actions (Next 1 hour)

CRITICAL: Search filesystem for original agent logs
Determine data recovery strategy
Create missing directory structure
Set up Python environment with dependencies

Short-term Actions (Next 2 days)

Extract and validate data
Write analysis scripts
Generate all figures and tables
Complete statistical tests

Medium-term Actions (Next 3-5 days)

Write paper manuscript (5000 words)
Create visualizations
Set up code repository
Prepare for submission

Quality Assessment

Strengths:

✅ Excellent experimental design
✅ Clear hypotheses and results
✅ Comprehensive documentation
✅ Thoughtful paper structure
✅ Novel findings (syntax helps drafting)

Weaknesses:

❌ Missing implementation
❌ No reproducible artifacts
❌ Data provenance unclear
❌ Behind schedule

Overall Grade: B+ for planning, D for execution

Conclusion

This experiment has excellent scientific content but critical execution gaps. The research questions are well-formulated, the results are interesting, and the paper outline is publication-ready. However, without raw data, analysis code, and visualizations, the paper cannot be written.

Critical Path: Find/recreate data → Write analysis code → Generate figures → Write paper

Estimated Effort to Complete: 5-6 days of focused work

Likelihood of Meeting Dec 5 Deadline: 70% if data recovery succeeds, 30% if re-running experiments required

Audit Completed: 2025-11-30 Next Action: Execute Data Recovery Plan (Phase 1)