TRIM: Reducing AI-Generated CodeSlop via Agent Trajectory Minimization
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 code...
| Authors | Alex Mathai, Shobini Iyer, Aleksandr Nogikh et al. |
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| Primary category | cs.SE |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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