5arXiv cs.AI (Artificial Intelligence)·
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.
Related guides (2)
Related events (8)
TraceLab: Real-world coding agent workload traces reveal serving optimization opportunities
Researchers at UW release TraceLab, a dataset of ~4,300 coding-agent sessions comprising ~350,000 LLM steps and ~430,000 tool calls drawn from real day-to-day use of Claude Code and Codex. Analysis reveals characteristic patterns: long autonomous loops, long contexts with short outputs, heavily-tailed tool call distributions, and high but imperfect prefix cache hit rates. The findings motivate concrete serving-system improvements including lower-overhead tool calling, append-length-aware prefill, and improved KV-cache management. The dataset, pipeline, and analysis code are publicly released.
SWE-Pruner Pro uses agent's internal representations for efficient context pruning in coding agents
SWE-Pruner Pro is a new context pruning method for coding agents that leverages the agent's own internal representations to decide which lines of tool output to keep or discard, rather than relying on a separate classifier. A lightweight head converts these internal representations into keep-or-prune labels per line, augmented with a length-aware embedding. Evaluated across two open-weight backbones and four multi-turn benchmarks, the method saves up to 39% of prompt and completion tokens while maintaining task quality, and on MiMo-V2-Flash additionally improves SWE-Bench Verified resolve rate by +3.8%.
STRACE framework improves LLM agent optimization via structural trajectory analysis and causal extraction
Researchers introduce STRACE (Structural TRajectory Analysis and Causal Extraction), a framework for constructing high signal-to-noise optimization contexts for long-horizon LLM agents. At the batch level, STRACE mines failure patterns to filter redundant traces; within each trace, it performs causal localization over a textual dependency graph to isolate root-cause steps. On the formal verification benchmark VeruSAGE-Bench, STRACE achieves a 1.4× success-rate improvement (42.5% to 58.5%) over human-expert-designed agents, outperforming standard context-filtering baselines.
E3 framework reduces LLM agent token costs 91% by estimating task complexity before execution
Researchers introduce E3 (Estimate, Execute, Expand), a framework that addresses over-reading behavior in LLM agents by having them estimate task complexity and execute a minimum viable path before expanding scope. On MSE-Bench, a 121-edit deterministic benchmark, E3 matches 100% task success while cutting cost by 85%, tokens by 91%, and file inspections by 92% versus strong baselines. The authors also validate the approach on a live GPT-4o agent editing a real open-source library, graded against an actual pytest suite. The work formalizes the Agent Cognitive Redundancy Ratio (ACRR) and positions task-aware execution as a step toward engineering-grounded AI.
RedAct framework protects procedural skills in agent execution traces via selective redaction and watermarking
Researchers introduce RedAct, a framework for releasing agent execution traces without exposing proprietary procedural skills (tool invocations, decision logic, error-recovery strategies). The system localizes sensitive information, rewrites traces while preserving audit-critical evidence, and embeds behavioral watermarks for provenance tracking. To evaluate the approach, the authors construct CapTraceBench, a benchmark of 75 long-horizon tasks and 154 skills across seven domains. RedAct reduces normalized skill transfer from 44.7–67.1% on raw traces to below the no-skill baseline, while watermark detection achieves 93.6–100% true positive rate with under 2% false alarms.
SelfCompact: Model-driven adaptive context compaction for long agent traces
Researchers propose SelfCompact, a scaffold that lets language models decide when and how to compact their own accumulated context during long agentic runs, rather than relying on fixed token-threshold triggers. The system pairs a compaction tool with a lightweight rubric specifying when to invoke or suppress compaction based on trajectory structure (e.g., sub-task completion vs. mid-derivation). Evaluated across six benchmarks and seven models, SelfCompact matches or exceeds fixed-interval summarization while reducing per-question token cost by 30-70%, with gains of up to 18.1 points on math tasks and 5-9 points on agentic search. The work identifies a 'meta-cognitive gap' in unprompted models and shows it can be closed via scaffolding without fine-tuning.
Probe-and-Refine Tuning improves coding agent performance via iterative repository guidance refinement
A new arXiv paper introduces probe-and-refine tuning, a procedure that uses synthetic bug-fix probes to iteratively improve AGENTS.md repository guidance files for LLM-based coding agents without requiring an agent loop during tuning. Evaluated on SWE-bench Verified with Qwen3.5-35B-A3B, the method achieves 33.0% mean resolve rate versus 28.3% for a static knowledge base baseline and 25.5% for an unguided baseline. The improvement is attributed to coverage gains—refined guidance helps agents locate the correct files rather than improving patch quality—and a step-budget experiment shows guidance is necessary for agents to productively use larger compute budgets.
Retrying vs Resampling in AI Control: Safety Tradeoffs in Coding Scaffolds
This paper analyzes two strategies for handling flagged actions in AI coding scaffolds—retrying (blocking risky actions and continuing) and resampling (drawing multiple samples from the same context)—from an AI control perspective that treats the model as potentially adversarial. The authors find that retrying backfires because the untrusted model can exploit monitor rationale to craft stealthier attacks, while resampling avoids this information leakage. Using Claude Opus 4.6 as the untrusted model and MiMo-V2-Flash as the monitor on the BashArena benchmark, they show that drawing five samples per step and auditing on maximum suspicion score raises safety from 61% to 71% at a 0.3% audit budget. Two findings contradict prior work: auditing on maximum (not minimum) suspicion scores is better, and executing the least suspicious sample yields only marginal safety gains.