arXiv (All)
open access
VeriHarness: Scaling Agentic Verification for Long-Horizon Tasks
As LLM agents undertake increasingly complex, long-horizon tasks, verifying their outputs becomes increasingly challenging. We study how verification capability can be strengthened with a fixed base model, without access to reference answers or grading rubrics at test time. Repeated sampling yields
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
- Schwarz: Solver-Aware Agentic Program Verification#818849
- Schwarz: Solver-Aware Agentic Program Verification#808857
- NexForge: Scaling Agent Capabilities through Requirement-Driven Task Synthesis for LLMs#381815
- UniACE: A Unified Framework for Evaluating LLM Agentic Capabilities#820473
- Maintaining Benchmarks Against Increasingly Capable Agents: Detection and Remediation of Unearned Passes#1119335
Record · ID 1155907
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