arXiv (All)
open access
The Interplay of Harness Design and Post-Training in LLM Agents
Tool-integrated LLM agents are often wrapped within a harness: the scaffolding that determines which tools are exposed, how they are described, and what auxiliary information accompanies each per-step observation. While agents are routinely post-trained, this scaffolding is typically treated as a fi
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Understanding Agent-Reactive Bugs at the Model-Harness Boundary: An Empirical Study of LLM Agent Issue Reports#381813
- ActiveSaddler: Automated Curriculum Learning for Agent Harness Optimization#1169625
- ActiveSaddler: Automated Curriculum Learning for Agent Harness Optimization#1155841
- Mid-Harness: Scaling Actions Between Model and Harness for Terminal Agents#1145149
- Mid-Harness: Scaling Actions Between Model and Harness for Terminal Agents#1136882
Record · ID 1128616
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive