TRIM: Reducing AI-Generated CodeSlop via Agent Trajectory Minimization

TRIM algorithm reduces AI-generated code bloat by minimizing agent trajectories

Researchers introduce 'CodeSlop' as a formal concept describing residual, functionally unnecessary edits that accumulate in AI-generated code as coding agents iterate toward solutions. The paper presents TRIM (Trajectory-guided Redundancy Identification and Minimization), which reduces CodeSlop by 17.9–32.9% across agentic scaffolds by minimizing agent trajectories rather than targeting redundant code directly. TRIM achieves this with roughly half the validation cost of baseline approaches like Delta Debugging and negligible performance regression, addressing a compounding maintainability problem as agents take on larger shares of codebases.