TRIM: Reducing AI-Generated CodeSlop via Agent Trajectory Minimization

• URL: http://arxiv.org/abs/2607.18161v1
• Date: Mon, 20 Jul 2026 17:06:19 GMT
• Title: TRIM: Reducing AI-Generated CodeSlop via Agent Trajectory Minimization
• Abstract summary: Coding agents are increasingly used to accelerate code generation in many downstream tasks.<n>Despite their value, agent-generated code tends to be larger and more verbose than the corresponding human-written implementation.<n>We show that the cause lies in the agent's own search process.
• Score: 57.14479960309637
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Coding agents are increasingly used to accelerate code generation in many downstream tasks, such as fixing bugs, building applications, and prototyping. However, despite their value as coding assistants, agent-generated code tends to be larger and more verbose than the corresponding human-written implementation. In this work, we show that the cause lies in the agent's own search process: while iterating toward a passing solution, an agent accumulates speculative edits, abandoned hypotheses, and temporary changes that persist into the final patch. This may seem harmless for a single patch, but the problem compounds as agents take responsibility for ever-larger portions of a codebase-a codebase that was once minimal and well-maintained slowly accumulates redundancy faster than it can be cleaned up, drifting to a state that is harder to maintain. Given the magnitude of this problem, we take a step towards alleviating this issue. First, we formally define this phenomenon as CodeSlop-the residual and functionally unnecessary edits commonly seen in AI-generated code. We then introduce our algorithm TRIM (Trajectory-guided Redundancy Identification and Minimization). Rather than minimizing CodeSlop directly, TRIM instead minimizes agent trajectories. As we show empirically, this indirect technique of minimizing CodeSlop is highly effective: TRIM cuts CodeSlop by 17.9%-32.9% across agentic scaffolds, with negligible performance regression. TRIM is also highly efficient, requiring roughly half the validation cost of algorithmic baselines such as Delta Debugging.

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