arXiv (All)
open access
EvoX: Rethinking the Agent Harness as a Runtime Policy
Large language model (LLM) agents increasingly interact with tools and environments, yet plausible model decisions do not always translate into successful execution due to tool-call mismatches, interrupted workflows, and unsupported completion claims. We present EvoX, a Rust-based, multi-model agent
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
- Harness Engineering in LLM Tool Use via Agent-Native Reusable Tool Primitives#822166
- OpenRE-Bench: A Benchmark Harness for Multi-Agent Requirements Engineering with Mined GitHub Issues#1647004
- OpenRE-Bench: A Benchmark Harness for Multi-Agent Requirements Engineering with Mined GitHub Issues#1616955
- PDEU-Bench: Benchmarking the Personalized Planning Lifecycle of Tool-Calling LLM Agents#1129540
- Safe, Persistent, and Evolving Agent Harness for Understanding Partially Observable Worlds#1248832
Record · ID 1649036
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