TRIM: Reducing AI-Generated CodeSlop via Agent Trajectory Minimization
2026-07-20 • Software Engineering
Software EngineeringArtificial IntelligenceOperating Systems
AI summaryⓘ
The authors found that code generated by AI coding agents tends to be bigger and messier than human-written code because of leftover changes made during the agent's trial-and-error process. This buildup of unnecessary edits, which they call CodeSlop, makes the code harder to maintain over time. To fix this, the authors created an algorithm called TRIM that reduces these redundant changes by focusing on the agent's editing steps, not just the final code. Their tests show TRIM effectively trims down CodeSlop with little impact on code quality and is more efficient than some other methods.
Coding agentsCode generationCodeSlopRedundancyAgent trajectoryPatchDelta DebuggingCode maintenanceSpeculative editsAlgorithm efficiency
Authors
Alex Mathai, Shobini Iyer, Aleksandr Nogikh, Petros Maniatis, Franjo Ivancic, Junfeng Yang, Baishakhi Ray
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.