arXiv:2608. 13228v1 Announce Type: new Abstract: Agent harnesses combine retrieval, routing, state, provenance, and verification, but locally successful components may disagree on shared state.
arXiv:2608. 16630v1 Announce Type: cross Abstract: Repository-scale coding requires an agent to keep tests, imports, configuration, and migration rules consistent within a bounded context window.
Agent evaluations tell us that a model picked the wrong tool, but rarely why. We introduce canary tools: diagnostic probe tools planted in an agent's Model Context Protocol (MCP) tool set, each engineered to probe one specific tool-selection weakness.
The paper introduces Enrich‑Retrieve‑Rank, a scalable method for discovering capabilities in large agent ecosystems. It replaces in‑context routing with an offline enrichment step that converts sparse metadata into searchable profiles, followed by an online retrieve‑then‑rank pipeline that returns a ranked shortlist without invoking candidates. Experiments show that as the number of capabilities grows from 10 to 7,278, the new approach maintains higher top‑1 accuracy and reduces cost by 70× compared to full‑context baselines.
arXiv:2608. 04719v1 Announce Type: new Abstract: Agent evaluations tell us that a model picked the wrong tool, but rarely why.
SemVerBench is a benchmark that evaluates how well large language models (LLMs) understand and apply version-constraint resolution semantics, such as determining whether a version satisfies constraints like ^1.2.3 or >=2.0. The study finds that many models struggle with certain corner cases, with GPT‑5.1 performing poorly while Claude and Opus perform much better. The authors suggest that the failures stem from an activation/application gap rather than a lack of knowledge, and recommend that coding agents delegate version resolution to a dedicated resolver tool.
arXiv:2606. 07316v1 Announce Type: cross Abstract: Byzantine collaboration among large-language-model agents requires a finality-control primitive: given delivered stochastic, structured natural-language proposals, the protocol must decide whether the round supports a commit, what kind of commit, or a typed safe abort.
The paper introduces State‑Conditioned Minimal Sufficient Evidence Recovery (SER), a method that, given a coding agent’s current state, reconstructs a compact set of evidence passages that collectively provide all facts needed for the agent’s next decision. Using the SERBench dataset of 500 held‑out states from 45 repositories, the authors show that their MSS‑Complement approach recovers a complete evidence set for 73.0 % of states with five items and 80.6 % with eight, outperforming baseline ranking methods. The study also demonstrates that this set‑level policy improves downstream performance on AMA‑Bench and highlights the importance of retrieving missing facts rather than merely re‑ranking similar passages.
arXiv:2608. 02680v1 Announce Type: cross Abstract: Tool-using language-model agents repeatedly rediscover procedures they have already executed, producing traces that mix reusable structure with retries, exploration, accidental ordering, and repeated lookups.
arXiv:2606. 07316v2 Announce Type: replace-cross Abstract: Can a committee of LLM agents reach agreement that is certifiable at the level of meaning, not only at the level of a label?
arXiv:2604. 00392v2 Announce Type: replace-cross Abstract: Agents that synthesize their own tools ship a second artifact alongside each answer: a software library that future tasks reuse, compose, and depend on.
arXiv:2607. 17136v1 Announce Type: cross Abstract: Agentic computer-use RL is reported in single runs, and those numbers mislead.
arXiv:2608. 08466v1 Announce Type: new Abstract: Modern LLM agents are often improved by modifying prompts, tools, or workflows manually, while the executable scaffold surrounding the model---the \emph{harness}---is typically treated as a fixed artifact after deployment.