arXiv:2606. 08529v1 Announce Type: new Abstract: Published agent capability scores conflate what a model can do with what its scaffold lets it do, and the magnitude of this elicitation gap is not well characterized under controlled conditions.
arXiv:2603. 10044v2 Announce Type: replace-cross Abstract: A safety score earned on a benchmark need not predict how the same model behaves once it is wrapped in an agentic scaffold the benchmark never tested.
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.
arXiv:2607. 17044v1 Announce Type: cross Abstract: Multi-step enterprise agent tasks fail in a characteristic way: single-pass inference has no checkpoint between deciding an answer and committing to it.
arXiv:2607. 22585v1 Announce Type: new Abstract: Public leaderboards for coding agents typically rank systems by model name and pass rate, while the surrounding harness (the scaffold that issues tools, manages context, and decides when to stop) is often under-specified.
The paper introduces Belief-Calibrated Optimization (BCO), a method that records and updates a persistent in‑context document representing an agent’s belief about how the environment responds to edits. By continually revising this world model as new candidates are evaluated, BCO improves the performance of frozen LLM agents across five benchmarks, outperforming a control lacking the world model. An offline ablation shows that the document’s content provides reusable, accurate predictions of environmental responses, beyond mere form.
arXiv:2608. 05519v1 Announce Type: new Abstract: Agent benchmarks usually measure task completion and treat resource use as an auxiliary statistic.
arXiv:2608. 04719v1 Announce Type: new Abstract: Agent evaluations tell us that a model picked the wrong tool, but rarely why.
arXiv:2607. 10569v1 Announce Type: cross Abstract: Modern coding agents expose multiple tool surfaces -- IDE primitives, bash, and Model Context Protocol (MCP) code-execution -- and the field has shipped three contradictory claims about which one matters.
arXiv:2608. 09629v1 Announce Type: new Abstract: Self-evolving agents are usually built around prescribed optimization pipelines: the framework decides how to gather evidence, revise a persistent artifact, select candidates, and stop.
SheetMind is a Manager‑Action‑Reflection framework that evaluates how much spreadsheet agent performance derives from the agents themselves versus the shared action interface. In a controlled study on all 221 tasks of the SheetCopilot Benchmark, replacing the high‑level action API with primitive cell operations drops accuracy by 47.1 points, while adding a Reflection Agent improves performance by 4.5 points and a Manager by 1.4 points. The framework also shows that decomposition changes failure modes, reducing silent wrong outputs from 33% to 25%, and that GPT‑5 and GPT‑5‑mini achieve similar performance, whereas GPT‑3.5 underperforms significantly.
arXiv:2608. 08654v1 Announce Type: new Abstract: How much an AI coding agent costs to run can depend more on the agent scaffolding that drives it than on the interface through which it reaches its tools.
The paper investigates how altering the harness—specifically the way a coding agent manages context and tool outputs—affects performance when the underlying model and task remain unchanged. Two harness configurations were compared on three coding benchmarks: a control that preserves the full conversation in order, and a treatment that mechanically shortens older tool results to keep the context tight. Across all benchmarks, the treatment increased the mean per‑task fail‑to‑pass fraction and, in some cases, the number of complete solutions, demonstrating that the harness itself can significantly influence a frozen model’s effectiveness.