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🧠 AI🟢 BullishImportance 6/10
TRAJEVAL: Decomposing Code Agent Trajectories for Fine-Grained Diagnosis
arXiv – CS AI|Myeongsoo Kim, Dingmin Wang, Siwei Cui, Farima Farmahinifarahani, Shweta Garg, Baishakhi Ray, Terry Yue Zhuo, Rajdeep Mukherjee, Varun Kumar|
🤖AI Summary
Researchers introduce TRAJEVAL, a diagnostic framework that breaks down AI code agent performance into three stages (search, read, edit) to identify specific failure points rather than just binary pass/fail outcomes. The framework analyzed 16,758 trajectories and found that real-time feedback based on trajectory signals improved state-of-the-art models by 2.2-4.6 percentage points while reducing costs by 20-31%.
Key Takeaways
- →TRAJEVAL decomposes AI agent trajectories into search, read, and edit stages for fine-grained performance diagnosis.
- →All analyzed agents examine approximately 22x more functions than necessary, indicating universal inefficiencies.
- →Different AI models show distinct failure patterns: GPT-5 locates code well but targets edits poorly, while Qwen-32B fails at file discovery.
- →Real-time trajectory feedback improved model performance by 2.2-4.6 percentage points while cutting costs by 20-31%.
- →The framework enables predictive analysis, achieving model-level Pass@1 prediction within 0.87-2.1% mean absolute error.
Mentioned in AI
Models
GPT-5OpenAI
#ai-agents#code-analysis#performance-optimization#machine-learning#diagnostic-framework#github#trajeval#model-evaluation
Read Original →via arXiv – CS AI
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