5 papers
Rationale-Guided Policy Optimization: Learning to Reason with Adaptive Rationale Scaffolding
Hoang Phan, Minh Pham, Chau Pham +3
On-policy reinforcement learning has become a central paradigm for improving the reasoning abilities of large language models. However, its effectiveness is often limited by reward…
SpecMind: Cognitively Inspired, Interactive Multi-Turn Framework for Postcondition Inference
Cuong Chi Le, Minh V. T Pham, Tung Vu Duy +4
Specifications are vital for ensuring program correctness, yet writing them manually remains challenging and time-intensive. Recent large language model (LLM)-based methods have sh…
Where Knowledge Collides: A Mechanistic Study of Intra-Memory Knowledge Conflict in Language Models
Minh Vu Pham, Hsuvas Borkakoty, Yufang Hou
In language models (LMs), intra-memory knowledge conflict arises when inconsistent information about the same subject is encoded within the model's parametric knowledge. Prior work…
When Names Disappear: Revealing What LLMs Actually Understand About Code
Cuong Chi Le, Minh V. T. Pham, Cuong Duc Van +3
Large Language Models (LLMs) achieve strong results on code tasks, but how they derive program meaning remains unclear. We argue that code communicates through two channels: struct…
SWE-Synth: Synthesizing Verifiable Bug-Fix Data to Enable Large Language Models in Resolving Real-World Bugs
Minh V. T. Pham, Huy N. Phan, Hoang N. Phan +3
Large language models (LLMs) are transforming automated program repair (APR) through agent-based approaches that localize bugs, generate patches, and verify fixes. However, the lac…