Agent-Based LLMs for Data Analysis and Fixing Software Bugs
summary
Demonstrates autonomous, multimodal agents that debug and patch real GitHub repositories via a tailored AGI ACI agent computer interface with commands like navigate repo edit lines and run tests; a benchmark runs 619 task instances across 17 JavaScript libraries and finds over 80% require visual input for fixes and ~40% of issues take one to four hours for a human; techniques include MCTS, KV‑cache optimization and test‑time compute that yields roughly a 10% improvement and practical production workflows.
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chapters (15)
- 0:00Introduction
- 1:22Agent landscape basics
- 3:07Multi-agent architectures
- 5:13Agent limitations
- 7:04Multimodal bug fixing
- 9:18Agent Computer Interface debugging
- 13:17Test time compute scaling
- 17:43Reinforcement learning in-context
- 19:30Tree search reflection
- 21:17Tree search optimization
- 23:24KV cache efficiency
- 24:28Hybrid test time scaling
- 27:23Evaluating agents SWE-bench
- 28:50Scaling unit tests
- 31:18Key takeaways and future of agent-based debugging
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