# Experiment Execution Log

**Experiment:** Speculative Decoding Cross-Domain Analysis
**Date:** 2025-11-28
**Status:** Data collection complete, analysis in progress

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## Session Timeline

### 09:25 - Initial Setup
- **Original Goal:** Analyze TiDAR (arXiv:2511.08923) draft rejection patterns
- **Planned:** Options 1 (rejection analysis) + 5 (cross-domain) + 3 (ablation)
- **Created:** Experiment planning system with templates
- **Created:** Full 603-line experiment plan

### 09:26 - Phase 1+2 Execution (Options 1 & 5)
- **Started:** Autonomous researcher with Gemini 3 Pro
- **Approach:** Agent chose speculative decoding simulation (Qwen models)
  - Rationale: TiDAR implementation not available
  - Draft: Qwen2.5-0.5B
  - Verifier: Qwen2.5-7B
- **Domains Tested:**
  - Code: HumanEval (30 samples)
  - Math: GSM8K (subset)
  - Translation: Flores-200 En-Fr
  - Data-to-Text: WebNLG

**Duration:** ~15 minutes
**Status:** ✅ Complete

**Key Results:**
- Code: 14.0% rejection (LOWEST - contradicts hypothesis)
- Translation: 34.9% rejection (HIGHEST)
- Math: 26.1% rejection
- Early tokens: 27.4% rejection vs Late: 22.3%

### 10:30 - Phase 3 Execution (Option 3)
- **Started:** Attention mask ablation study
- **Models:** DistilGPT-2 (draft) + GPT-2 (verify)
- **Masks Tested:**
  1. TiDAR Original (hybrid bidirectional+causal)
  2. Fully Causal
  3. Fully Bidirectional
  4. Windowed (k=32)
  5. Strided (stride=4)
- **Domains:** Code (50), Math (100), Translation (100)

**Duration:** ~15 minutes
**Status:** ✅ Complete

**Key Results:**
- Code best: Windowed (20.0% acceptance)
- Math/Translation best: Causal (31.2%/31.8%)
- TiDAR mask NEVER optimal
- Throughput best: Bidirectional (1.5x-2.5x)

### 10:45 - Scientific Rigor Review
- **Question Raised:** Does simulation approach have scientific validity?
- **Investigation:** Searched for official TiDAR implementation
- **Finding:** Code not yet released ("coming soon" on https://tidarlm.github.io/)
- **Decision:** Cannot reproduce TiDAR exactly

**Critical Analysis:**
- ❌ Speculative decoding ≠ TiDAR (diffusion-based drafting)
- ❌ Different architecture means results don't validate paper
- ✅ Results are valid for speculative decoding itself
- ✅ Insights are novel and publishable

**Decision:** Pivot to Option C - reframe as speculative decoding study

### 11:00 - Experiment Consolidation
- **Action:** Created new unified experiment directory
- **Name:** `20251128-speculative-decoding-cross-domain-analysis`
- **Scope:** Comprehensive analysis of draft-verify dynamics
- **Deliverable:** Research paper on speculative decoding
- **Future Work:** TiDAR comparison when code releases

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## Data Locations

### Phase 1-2: Cross-Domain Rejection Analysis
**Directory:** `20251128-092557-analyze-the-tidar-hybrid-diffusion-autoregressive/`
**Log:** `/logs/agent.log`
**Results:** Agent-generated report in log
**Models:** Qwen2.5-7B + Qwen2.5-0.5B
**Data Size:** ~440KB log file

### Phase 3: Attention Mask Ablation
**Directory:** `20251128-103004-investigate-the-sensitivity-of-tidars-hybrid-diffu/`
**Log:** `/logs/agent.log`
**Results:** Agent-generated report in log
**Models:** DistilGPT-2 + GPT-2
**Data Size:** TBD

### Consolidated Experiment
**Directory:** `20251128-speculative-decoding-cross-domain-analysis/`
**Status:** Active - analysis phase
**Data:** Copying from phase directories

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## Experimental Decisions & Rationale

### Decision 1: Use Autonomous Researcher
**Why:** Efficient exploration of research space
**Result:** Completed 3 phases in 45 min vs. estimated 6-7 hours
**Trade-off:** Agent chose simulation over implementation
**Lesson:** Need to verify approach aligns with scientific goals

### Decision 2: Accept Simulation Approach Initially
**Why:** Trusted autonomous agent's judgment
**Result:** Fast results but wrong architecture
**Lesson:** Always validate approach matches research objectives

### Decision 3: Investigate Scientific Rigor
**Why:** User questioned validity of simulation
**Action:** Searched for official TiDAR code
**Finding:** Not available, simulation doesn't match paper
**Outcome:** Critical reframing required

### Decision 4: Pivot to Speculative Decoding Study
**Why:** Cannot do TiDAR without code, but have valid spec dec data
**Benefit:** Can publish rigorous results now
**Trade-off:** Different from original goal
**Future:** Run TiDAR comparison when code releases

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## Hypotheses Tested

### H1: Code has higher rejection than prose (syntax constraints)
**Result:** ❌ FALSIFIED
**Data:** Code 14.0% vs Translation 34.9%
**Implication:** Syntax helps prediction, not hurts

### H2: Early position has higher rejection than late
**Result:** ✅ SUPPORTED
**Data:** Early 27.4% vs Late 22.3% (p < 0.05)
**Implication:** Context establishment is bottleneck

### H3: Rare tokens rejected more than common
**Result:** ⚠️ WEAK SUPPORT
**Data:** Rare 24.6% vs Common 23.1% (1.5% gap)
**Implication:** Frequency less important than domain

### H4: Throughput varies by domain
**Result:** ✅ SUPPORTED
**Data:** Code 26.7 t/s vs Translation 18.3 t/s (45% gap)
**Implication:** Domain-specific optimization needed

### H5 (NEW - Ablation): TiDAR mask is optimal
**Result:** ❌ FALSIFIED
**Data:** TiDAR never won in any domain
**Implication:** Domain-adaptive masking needed

### H6 (NEW - Ablation): Causal has highest rejection
**Result:** ❌ FALSIFIED
**Data:** Causal had HIGHEST acceptance (31.2%/31.8%)
**Implication:** Full context critical for verification

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## Compute Resources

### GPU Usage
**Hardware:** NVIDIA GB10 (128GB VRAM)
**Utilization:** Clean throughout (0% at start/end)
**Conflicts:** None (vLLM stopped, Ollama disabled)
**Memory:** Models ran in Docker containers

### Time Breakdown
- Phase 1-2: 15 minutes
- Phase 3: 15 minutes
- Setup/planning: 15 minutes
- Analysis/consolidation: 30 minutes
- **Total:** ~75 minutes active work

### Cost
**GPU hours:** ~1.25 hours
**Cloud cost equivalent:** $0 (local execution)
**Modal equivalent cost:** ~$2-3 for 1.25 hours A100

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## Lessons Learned

### 1. Always Verify Approach Matches Goals
**Issue:** Agent chose simulation without verifying it matched TiDAR
**Lesson:** Explicitly check implementation matches paper's architecture
**Fix:** Add validation step in autonomous researcher workflow

### 2. Scientific Rigor > Speed
**Issue:** Fast results don't matter if they don't answer the question
**Lesson:** 45-minute simulation < 1-week proper implementation if needed
**Fix:** Pause and validate before accepting "efficient" alternatives

### 3. Code Availability Research
**Issue:** Assumed recent paper would have code
**Lesson:** Always check code availability before planning experiments
**Fix:** Add "find official implementation" as first step

### 4. Pivot is OK if Rigorous
**Issue:** Original goal (TiDAR) impossible without code
**Lesson:** Reframing to speculative decoding is valid if done properly
**Fix:** Clear documentation of pivot rationale and scope change

### 5. Agent Autonomy Needs Constraints
**Issue:** Agent has freedom to choose approach
**Lesson:** Need explicit constraints (e.g., "use official implementation only")
**Fix:** Add architectural constraints to research objectives

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## Next Steps

### Immediate (Today)
1. ✅ Consolidate experiment data
2. ✅ Create unified experiment directory
3. ✅ Document pivot decision
4. 🔄 Extract quantitative results from logs
5. ⏳ Create result tables

### Short-term (This Week)
1. Statistical significance tests
2. Visualization generation (heatmaps, charts)
3. Analysis code cleanup
4. Paper draft v1

### Medium-term (Next Week)
1. Paper revision
2. Code release preparation
3. Blog post draft
4. Submission preparation

### Future Work
1. Monitor TiDAR code release
2. Reproduce analysis with actual TiDAR
3. Comparative study: spec dec vs TiDAR diffusion drafting
4. Extend to more domains (code+math+translation+data-to-text → +summarization, +Q&A)

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## Open Questions

1. **Why does syntax help drafting?**
   - Hypothesis: Predictable structure reduces uncertainty
   - Test: Compare random code vs. well-formatted code

2. **Can we predict optimal mask from domain properties?**
   - Hypothesis: Entropy/structure metrics predict best mask
   - Test: Analyze domain characteristics vs. mask performance

3. **Do findings generalize to other model pairs?**
   - Test: Different draft/verify model combinations
   - Test: Different model scales (0.5B/7B vs 1B/13B vs 7B/70B)

4. **How do findings apply to TiDAR's diffusion drafting?**
   - Answer: Must wait for code release
   - Prediction: Similar domain effects, different magnitude

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## References & Links

**Original Paper:**
- TiDAR: https://arxiv.org/abs/2511.08923
- Project: https://tidarlm.github.io/

**Related Work:**
- Speculative Decoding: Leviathan et al. (2023)
- Medusa: Cai et al. (2024)
- Draft-Verify survey: TBD

**Our Experiment:**
- Session log: `~/docs/sessions/development/20251128-experiment-system-tidar-setup.md`
- Planning: `~/workspace/experiments/planned/ideas/20251128-tidar-draft-rejection-cross-domain.md`
- Active: `~/workspace/experiments/active/20251128-speculative-decoding-cross-domain-analysis/`

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**Last Updated:** 2025-11-28 11:00
**Next Update:** 2025-11-29 (after data extraction)
**Maintained by:** bioinfo