Disagree to Explore, Agree to Commit: Routing-Guided Test-Time Scaling for Software Agents
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
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arXiv:2610.09633v1 Announce Type: new Abstract: The evaluation of coding agents generally strives to be as realistic as possible. In our study, we collect 4,782 agent sessions of real software engine...
The evaluation of coding agents generally strives to be as realistic as possible. In our study, we collect 4,782 agent sessions of real software engineers in JetBrains IDEs, which we call Production S...
arXiv:2602.18998v2 Announce Type: replace Abstract: LLM agents are increasingly expected to operate as general-purpose systems that resolve real-world user requests, yet their dynamic scaling behavio...
TEX is a new test‑time scaling framework that uses execution‑driven cross‑validation to generate and refine tests for language‑model agents. By running agents in parallel and cross‑executing each test against every patch, TEX builds a $K imes K$ feedback matrix that guides objective refinement without sharing verbose traces. On the SWT‑Bench Verified benchmark, TEX reaches 86.4% accuracy—setting a new state‑of‑the‑art and improving over single‑agent baselines by 9.0%—and even random selections from its outputs outperform the previous best score.
arXiv:2608. 00106v1 Announce Type: new Abstract: Agentic systems must decide not only what answer to produce, but which reasoning and execution operations should precede it.
arXiv:2607. 11399v1 Announce Type: cross Abstract: Large language model agents are increasingly executed not by a single model call, but by an execution harness that manages observation, context, control, action, state, and verification.