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arXiv:2606. 28480v1 Announce Type: cross Abstract: As large language models and harness frameworks continue to advance, agents operating in terminals are increasingly capable of performing a broader range of general computer-use tasks beyond coding.
The study investigates how individual components of a coding harness—planning, action space, and context management—affect autonomous coding agents’ performance. By fixing the execution loop and varying these components across 176 settings on SWE‑Bench Verified and Terminal‑Bench 2.1, the authors find that context management is most valuable when context windows are tight, staging rule‑based elision before LLM summarization yields the best efficiency, planning serves as an accuracy scaffold for weaker models and a cost saver for stronger ones, and predefined tools help models with limited bash skills while bash‑capable models benefit from a bash‑only interface. Trajectory‑level analysis shows that context management lengthens execution paths, planning alters where trajectories terminate, and the action space determines code granularity, offering a modular framework for future harness design.
arXiv:2610.23707v1 Announce Type: new Abstract: A multi-step agent task fails when a single tool it needs is missing, yet the tools an agent may need are too many to list: the roughly 8,200 tools on...
arXiv:2604. 00073v3 Announce Type: replace-cross Abstract: There has been growing interest in building agents that can interact with digital platforms to execute meaningful enterprise tasks autonomously.
AT-AT (Automatic Tool Optimization from Agent Traces) is an agentic tool optimizer that improves shared toolsets offline using historical agent executions while keeping agents unchanged. It employs a lightweight coding agent to read agent trajectories and evaluation results, selectively editing tool descriptions and implementations when allowed. In benchmarks, AT-AT achieves the highest task completion rates in Airline and Telecom domains at lower cost than strong baselines, and it also improves performance on TerminalBench-2 and TBLite, while reducing cost or error rates on production MCP servers.
OpenCollab is a multi‑agent coding framework that unifies organization design, enforces experimental control on a shared runtime, and tracks execution via fine‑grained event streams. It introduces the metric Adherence to measure whether the declared organization is actually realized, showing that small configuration changes can shift adherence from 47.2% to 97.2%. Experiments demonstrate that a two‑coder workflow built on OpenCollab achieves new state‑of‑the‑art performance against mainstream harnesses while using the fewest tokens, and that a well‑designed organization can outperform strong existing harnesses.