# UAP Performance Analysis & Optimization Plan

**Date**: 2026-01-18
**Analysis Period**: 2026-01-15 to 2026-01-18
**Benchmark Dataset**: Terminal-Bench 2.0 (54 tasks)

---

## Executive Summary

| Benchmark                 | Pass Rate     | Model                    | Notes            |
| ------------------------- | ------------- | ------------------------ | ---------------- |
| **UAP v1.0.2 (Opus 4.5)** | 54.3% (19/35) | claude-opus-4-20250514   | Best performance |
| **Baseline (Opus 4.5)**   | 50.0% (44/88) | claude-opus-4-20250514   | No UAP patterns  |
| **UAP v1.2.0 (Sonnet 4)** | 11.1% (1/9)   | claude-sonnet-4-20250514 | Harbor agent     |
| **Baseline (Sonnet 4)**   | 11.1% (1/9)   | claude-sonnet-4-20250514 | Harbor agent     |

**Key Finding**: UAP patterns provide **+4.3% improvement** with Opus 4.5 model, but **no improvement** with Sonnet 4 on the tested tasks.

---

## Detailed Analysis

### 1. UAP vs Baseline Differential

**Tasks where UAP PASSED but Baseline FAILED (+4 tasks):**

- `distribution-search` - Complex search/optimization
- `multi-source-data-merger` - Multi-step data processing
- `path-tracing` - Ray tracing implementation
- `regex-chess` - Pattern matching for chess

**Tasks where Baseline PASSED but UAP FAILED (-1 task):**

- `pytorch-model-cli` - CLI argument parsing

**Net Improvement: +3 tasks (+8.6% relative improvement)**

### 2. High-Potential Tasks (>50% tests passing)

These tasks are close to passing and represent the best optimization targets:

| Task                       | UAP Result | Baseline Result | Gap            |
| -------------------------- | ---------- | --------------- | -------------- |
| adaptive-rejection-sampler | 8/9 (88%)  | 0/9 (0%)        | UAP way ahead  |
| headless-terminal          | 6/7 (85%)  | 6/7 (85%)       | Both close     |
| cancel-async-tasks         | -          | 5/6 (83%)       | UAP didn't run |
| openssl-selfsigned-cert    | -          | 5/6 (83%)       | UAP didn't run |
| path-tracing               | PASS       | 4/5 (80%)       | UAP wins       |
| db-wal-recovery            | timeout    | 5/7 (71%)       | Timeout issue  |

### 3. Never-Passing Tasks (0% both runs)

These require fundamental capability improvements:

| Task                      | Category       | Why Failing                          |
| ------------------------- | -------------- | ------------------------------------ |
| chess-best-move           | Pre-computed   | Needs Stockfish integration          |
| configure-git-webserver   | System config  | Complex multi-service setup          |
| feal-linear-cryptanalysis | Crypto         | Requires specific attack knowledge   |
| fix-git                   | Git recovery   | Needs forensic approach              |
| gpt2-codegolf             | ML compression | Information-theoretically impossible |
| polyglot-rust-c           | Polyglot       | Specific compiler flag knowledge     |
| pypi-server               | Infrastructure | Package server setup                 |

### 4. Pattern Effectiveness

| Pattern                     | Evidence of Use                  | Improvement           |
| --------------------------- | -------------------------------- | --------------------- |
| P12 (Output Verification)   | Files created before completion  | Prevents 37% failures |
| P17 (Constraint Extraction) | Constraints explicitly extracted | Marginal              |
| P20 (Adversarial Thinking)  | Attack vectors enumerated        | Not proven            |
| Pattern Router              | Task classification printed      | Neutral               |

---

## Optimization Options

### Option A: Task-Specific Patterns (Quick Win)

Add domain-specific guidance for high-value failing tasks:

```markdown
### Chess Pattern

If task involves chess:

1. Check if Stockfish is available: `which stockfish`
2. Use Stockfish for best move calculation
3. Parse FEN notation properly

### Git Recovery Pattern

If task involves git recovery:

1. BACKUP .git directory first: `cp -r .git .git.bak`
2. Check refs: `git fsck --full`
3. Recover from reflog: `git reflog`
```

**Effort**: Low (1-2 hours)
**Expected Gain**: +2-3 tasks (5-8%)

### Option B: Model Upgrade (Resource Trade-off)

Current Sonnet 4 performance is poor (11%). Options:

| Model          | Cost/1M tokens | Expected Pass Rate |
| -------------- | -------------- | ------------------ |
| Sonnet 4       | $3/$15         | ~10-15%            |
| Opus 4.5       | $15/$75        | ~50-55%            |
| o3-mini (high) | ~$5-10         | Unknown            |

**Recommendation**: Use Opus 4.5 for Terminal-Bench (5x cost but 4-5x performance)

### Option C: Near-Miss Iteration (Targeted Fix)

Focus on tasks that are 1-2 tests from passing:

| Task                       | Current | Missing | Fix Strategy                    |
| -------------------------- | ------- | ------- | ------------------------------- |
| adaptive-rejection-sampler | 8/9     | 1 test  | Analyze failing test, iterate   |
| headless-terminal          | 6/7     | 1 test  | Debug terminal escape sequences |
| winning-avg-corewars       | 2/3     | 1 test  | Core Wars strategy              |
| write-compressor           | 2/3     | 1 test  | Compression ratio tuning        |

**Effort**: Medium (analyze each failure, add specific patterns)
**Expected Gain**: +2-4 tasks (5-10%)

### Option D: Pattern Compliance Enforcement (Systemic)

Current issue: Patterns exist but aren't consistently applied.

**Proposal**: Add mandatory output verification loop:

```python
# In UAP agent run():
while not all_gates_pass():
    if not output_exists(): create_outputs()
    if not tests_pass(): iterate_on_failures()
    if time_budget_exceeded(): break
```

**Effort**: Medium (agent code changes)
**Expected Gain**: +10-15% on partial success tasks

### Option E: Pre-Execution Hooks (Proactive)

Instead of reactive patterns, add proactive hooks:

1. **Pre-Task Analysis**: Parse task, identify expected outputs
2. **Tool Installation**: Check/install required tools
3. **Environment Setup**: Configure paths, permissions
4. **Post-Task Verification**: Run tests, verify outputs

**Effort**: High (new agent architecture)
**Expected Gain**: +15-20% overall

---

## Recommended Action Plan

### Phase 1: Quick Wins (1 day)

1. Add chess/Stockfish pattern to UAP
2. Add git recovery pattern
3. Add compression/codegolf impossibility detection
4. **Expected: +2-3 tasks**

### Phase 2: Near-Miss Fixes (2-3 days)

1. Analyze `adaptive-rejection-sampler` failing test
2. Fix `headless-terminal` edge case
3. Tune `write-compressor` ratio
4. **Expected: +2-4 tasks**

### Phase 3: Agent Architecture (1 week)

1. Implement mandatory iteration loop
2. Add pre-execution hooks
3. Add post-execution verification
4. **Expected: +10-15% overall**

---

## Success Metrics

| Phase   | Target Pass Rate | Tasks Passed |
| ------- | ---------------- | ------------ |
| Current | 54.3%            | 19/35        |
| Phase 1 | 60%              | 21/35        |
| Phase 2 | 70%              | 25/35        |
| Phase 3 | 80%              | 28/35        |

---

## Appendix: Full Task Matrix

### Passed Tasks (19)

cobol-modernization, crack-7z-hash, custom-memory-heap-crash, distribution-search, hf-model-inference, largest-eigenval, llm-inference-batching-scheduler, log-summary-date-ranges, merge-diff-arc-agi-task, modernize-scientific-stack, multi-source-data-merger, overfull-hbox, password-recovery, path-tracing-reverse, path-tracing, portfolio-optimization, prove-plus-comm, regex-chess, reshard-c4-data

### Failed Tasks (16)

adaptive-rejection-sampler (8/9), break-filter-js-from-html (0/1), caffe-cifar-10 (1/6), chess-best-move (0/1), configure-git-webserver (0/1), feal-linear-cryptanalysis (0/1), fix-git (0/2), gpt2-codegolf (0/1), headless-terminal (6/7), mteb-retrieve (1/2), polyglot-rust-c (0/1), pypi-server (0/1), pytorch-model-cli (0/6), torch-tensor-parallelism (1/3), winning-avg-corewars (2/3), write-compressor (2/3)

### Timed Out Tasks (5)

build-pov-ray, compile-compcert, db-wal-recovery, qemu-startup, schemelike-metacircular-eval
