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cs.AI2026
Improving Large Language Models for Code through Runtime Program-State Reasoning
Hongwei Li, Spandan Garg, Yufan Huang
Large language models receive limited explicit training in reasoning about runtime program states. We study whether training models to reason about runtime program states improves…
cs.AI2026
Terminus-4B: Can a Smaller Model Replace Frontier LLMs at Agentic Execution Tasks?
Spandan Garg, Vikram Nitin, Yufan Huang
Modern coding agents increasingly delegate specialized subtasks to subagents, which are smaller, focused agentic loops that handle narrow responsibilities like search, debugging or…
cs.SE2026
Debug2Fix: Can Interactive Debugging Help Coding Agents Fix More Bugs?
Spandan Garg, Yufan Huang
While significant progress has been made in automating various aspects of software development through coding agents, there is still significant room for improvement in their bug f…