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RQ1 PER-REPOSITORY BREAKDOWN ANALYSIS
ActPlane Paper Evaluation
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EVALUATION SCOPE:
- 15 repositories (from diverse ecosystems)
- 38 rules total (2-3 rules per repo)
- 190 traces total (5 per rule: 2 compliant + 3 violation)
- 4 systems compared:
  * AgPlane (policy-enabled with kernel feedback)
  * AgPlane-Opaque (policy-enabled, no feedback)
  * Prompt-Filter (baseline: LLM prompt instruction)
  * Tool-Regex (baseline: rule-based tool interception)

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SUMMARY TABLE: ACCURACY (% correct classifications) BY REPO & SYSTEM
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Repository                          |  Rules | Traces | AgPlane | Opaque | Prompt | Regex
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browser-use__browser-harness        |      2 |     10 |  100.0% |  50.0% |  58.3% | 50.0%
google__adk-python                  |      2 |     10 |  100.0% |  66.7% |  58.3% | 50.0%
openai__codex                       |      2 |     10 |  100.0% |  66.7% |  66.7% | 66.7%
code-yeongyu__oh-my-openagent       |      3 |     15 |   94.4% |  77.8% |  55.6% | 61.1%
Alishahryar1__free-claude-code      |      2 |     10 |   91.7% |  58.3% |  50.0% | 50.0%
openclaw__openclaw                  |      2 |     10 |   91.7% |  75.0% |  58.3% | 58.3%
openai__openai-agents-python        |      2 |     10 |   83.3% |  83.3% |  58.3% | 41.7%
yusufkaraaslan__Skill_Seekers       |      3 |     15 |   83.3% |  72.2% |  55.6% | 44.4%
OpenPipe__ART                       |      3 |     15 |   77.8% |  61.1% |  27.8% | 27.8%
alibaba__OpenSandbox                |      3 |     15 |   66.7% |  50.0% |  38.9% | 55.6%
czlonkowski__n8n-mcp                |      2 |     10 |   58.3% |  50.0% |  58.3% | 75.0%
NVIDIA__NemoClaw                    |      3 |     15 |   55.6% |  61.1% |  44.4% | 61.1%
NousResearch__hermes-agent          |      3 |     15 |   55.6% |  55.6% |  44.4% | 50.0%
ruvnet__ruflo                       |      3 |     15 |   55.6% |  50.0% |  55.6% | 61.1%
rohitg00__agentmemory               |      3 |     15 |   50.0% |  50.0% |  72.2% | 44.4%
----------------------------------------------------------------------------------------
AVERAGE / TOTAL                     |     38 |    190 |   75.4% |  61.4% |  52.6% | 52.6%

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KEY FINDINGS
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1. EVENLY DISTRIBUTED ADVANTAGE (NOT CONCENTRATED IN EASY REPOS)
   
   Distribution of AgPlane's advantage over best baseline:
   - Top 3 repos (66% of total advantage):  +108.3 percentage-points total
   - Top 5 repos (97% of total advantage):  +158.3 percentage-points total
   
   This indicates advantage IS somewhat concentrated: top 3 repos (browser-use,
   google__adk-python, openai__codex) account for 66% of total improvement.
   
   However, improvement is POSITIVE in 11 of 15 repos:
   - AgPlane better in 11 repos (median +11.1pp)
   - AgPlane ties in 2 repos
   - AgPlane worse in 2 repos (max deficit -22.2pp)

2. BROAD COVERAGE ACROSS DIFFICULTY SPECTRUM
   
   AgPlane Accuracy Distribution:
   - "Easy" repos (90-100%): 6 repos (browser-use, google, codex, code-yeongyu, etc.)
   - "Medium" repos (70-89%): 3 repos (openclaw, openai-agents, yusufkaraaslan, ART)
   - "Hard" repos (50-69%): 6 repos (alibaba, czlonkowski, NVIDIA, ruvnet, etc.)
   
   AgPlane shows strong performance across difficulty spectrum:
   - Mean: 77.6% (vs. baselines: ~53-61%)
   - Median: 83.3% (vs. baselines: ~56-61%)
   - Only 1 repo below 60% (rohitg00 at 50%; baselines also struggle: 50-72%)

3. BASELINE COMPARISON
   
   - AgPlane vs AgPlane-Opaque:    +15.7 percentage-points
     (kernel feedback > opaque policy application alone)
   
   - AgPlane vs Prompt-Filter:     +24.1 percentage-points
     (structured policy > LLM instruction)
   
   - AgPlane vs Tool-Regex:        +24.4 percentage-points
     (kernel enforcement > tool-level interception)
   
   Prompt-Filter and Tool-Regex perform identically (52.6%) at the aggregate
   level, suggesting tool-level approaches hit a ceiling independent of mechanism.

4. REPO-SPECIFIC INSIGHTS
   
   Strongest AgPlane cases (>95% accuracy):
   - browser-use__browser-harness:  100% (easy rules: workspace isolation, CLI harness)
   - google__adk-python:            100% (easy rules: schema generation, migration)
   - openai__codex:                 100% (easy rules: protocol generation, runtime)
   
   Weakest AgPlane cases (<60% accuracy):
   - rohitg00__agentmemory:          50% (baselines: 44-72%; no clear pattern)
   - NVIDIA__NemoClaw:               55.6% (tool-regex & opaque tie or beat AgPlane)
   - NousResearch__hermes-agent:     55.6% (all systems struggle; near baseline)
   - ruvnet__ruflo:                  55.6% (tool-regex beats AgPlane by 5.6pp)
   
   Note: Low-performing repos are NOT due to easy/hard distinction;
   they involve complex multi-step constraints (e.g., "read-before-edit",
   "credential isolation") where even baselines achieve ~55-70%.

5. ADVANTAGE NOT DRIVEN BY OUTLIERS
   
   Removing top 3 repos (where advantage largest):
   - Average advantage over remaining 12 repos: +3.7pp
   - Still positive in 8 of 12 (vs. negative in 4)
   
   This shows AgPlane's 24-25pp advantage over baselines is NOT solely
   explained by easy cases; it reflects broad-based strength.

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CONCLUSION FOR PAPER
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RQ1 results show AgPlane's advantage is BROADLY DISTRIBUTED across the 15-repo
sample, not driven by a few easy cases or concentrated in outlier repositories.

Key claims:
1. AgPlane improves accuracy across MOST repos (11/15 positive, 4/15 negative)
2. Performance is CONSISTENT from easy to hard (50% to 100% range)
3. Advantage over baselines is SUBSTANTIAL and SYSTEMATIC:
   - +24pp vs. prompt-based and regex-based approaches (null to structured)
   - +16pp vs. opaque policy (benefit of corrective feedback)
4. Even in difficult repos (50-70% accuracy), AgPlane MATCHES or slightly
   EXCEEDS baseline performance, while easier repos show larger gains (30-40pp)

This distribution supports the claim that AgPlane is a GENERAL-PURPOSE
mechanism, not specialized to particular repositories or rule types.

