208 citations · 407 across the 28 of their papers we have counts for
33 papers
ANTE: Anticipate, Then Execute -- Learning Internal World Models for Coding Agents from Verified Beliefs
Yi Ge, Kangrui Ruan, Jifan Chen +6
Rapid advances in large language models have made software engineering agents capable of tackling complex coding tasks. Within a containerized environment, a coding agent receives…
Teaching Agents to Code Reliably
Muhammad Ahmed Mohsin, Myeongsoo Kim, Kangrui Ruan +3
Autonomous coding agents solve repository issues by reading code, running commands, editing files, and submitting patches. Extra inference-time compute yields gains only when it pr…
AHEAD: Adaptive Hindsight with Environment-Augmented Distillation for Agentic RL
Xiaolong Jin, Dingmin Wang, Vijay Lingam +1
Training multi-turn LLM agents with reinforcement learning typically relies on trajectory-level rewards, which assign a uniform advantage to every step and cannot identify which de…
CODESTRUCT: Code Agents over Structured Action Spaces
Myeongsoo Kim, Joe Hsu, Dingmin Wang +3
LLM-based code agents treat repositories as unstructured text, applying edits through brittle string matching that frequently fails due to formatting drift or ambiguous patterns. W…
CodeScout: Contextual Problem Statement Enhancement for Software Agents
Manan Suri, Xiangci Li, Mehdi Shojaie +5
Current AI-powered code assistance tools often struggle with poorly-defined problem statements that lack sufficient task context and requirements specification. Recent analysis of…
Coherence Collapse: Diagnosing Why Code Agents Fail After Reaching the Right Code
Myeongsoo Kim, Dingmin Wang, Siwei Cui +6
Code agents resolve 65-70% of SWE-bench Verified issues, but Pass@1 cannot tell us why the rest fail, and, as we show, capable-model failures are systematically misdiagnosed withou…