arXiv (All)
open access
ReFract: Benchmarking Perspective Awareness in Language Model Agents with Text World Models
Large Language Model (LLM) agents are increasingly deployed in high-stakes settings such as industrial maintenance and equipment fault troubleshooting, where workers occupy a variety of roles. A capable agent must therefore act in a way that is calibrated to user's role: taking actions and providing
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
- ReFract: Benchmarking Perspective Awareness in Language Model Agents with Text World Models#1181701
- Grounded Checklist Partial Credit for Agent Skill Trajectories#780804
- Grounded Checklist Partial Credit for Agent Skill Trajectories#779429
- Beyond the Verdict: Agentic Code Judging with Verifiable Feedback#1344257
- Beyond the Verdict: Agentic Code Judging with Verifiable Feedback#1319062
Record · ID 1188034
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