Foutse Khomh

A critical-path-aware performance analysis methodology that automatically discovers recurring performance patterns by synthesizing static code features, dynamic execution traces, and kernel-level resource data, finding 13 distinct performance archetypes that appear consistently across different applications, independent of their domain or implementation.

Feedback effectiveness in LLM code repair is not universally applicable across programming languages, challenging previous assumptions about its reliability.

Contextual drift in LLMs can be contained with over 99% effectiveness, drastically reducing the risk of adversarial manipulation in multi-turn interactions.

CoT's code generation prowess isn't a sure thing: its robustness crumbles when input tweaks destabilize key "structural anchors" in the reasoning process.

Forget retraining: this SDN-IoT defense system uses LLMs to safely evolve reinforcement learning policies through interpretable policy updates, slashing catastrophic overloads by 80%.

LLMs can exhibit surprising ethical failures and progressive degradation under sustained adversarial pressure, even when passing standard single-round safety benchmarks.

Forget monolithic LLMs for requirements engineering: QUARE's multi-agent negotiation framework achieves 105% better compliance by explicitly surfacing and resolving inter-quality conflicts.