From Watching to Steering: Enabling Human-Coding Agent Collaboration through Execution Traces

Abstract:Recent advances in large language models (LLMs) have enabled coding agents to autonomously perform complex software development tasks, yet their long-running execution remains difficult for users to inspect, understand, and steer. We present Vibetrace, an interactive trace visualization and intervention system for agentic programming built upon OpenCode. Vibetrace introduces a human-agent collaboration paradigm of Observe, Understand, Intervene, and Evolve. It (1) visualizes agent workflows as interactive execution traces for inspecting actions, intermediate states, and failures; (2) enables users to fork execution from prior steps and provide targeted corrections without restarting validated progress; and (3) distills traces and human feedback into reusable skills for continual agent evolution. On SWE-bench Verified, distilled skills reduce input tokens by 23.0% and cost by 15.5%, while improving pass rate from 65.0% to 68.0%. User studies further demonstrate improved accuracy and efficiency over conventional log-based interaction.

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)

arXiv:2611.04466 [cs.HC]

(or arXiv:2611.04466v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2611.04466 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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