arXiv (All)
open access
Verification as an Architectural Layer for LLM Agents: A V-Model Design, and a Pilot Study of Its Deterministic Core
Large language model (LLM) agents built on the ReAct pattern concentrate four responsibilities in one model: selecting a strategy, choosing each action, formatting it, and judging whether the result is adequate. Nothing outside the generative loop can reject its output, so an agent that cannot make
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
- Toollery: Scaling LLM Agents to Thousands of Skills and Tools#1036901
- SkillGym: Training Skill-Use Agents with Automatic Verifiable Environment Generation#1141860
- Break It Down, Pass It On: Cross-Task Skill Transfer in LLM Agents#471273
- Verication-driven closed-loop multi-agent large language modelframework for code-compliant structural design#441888
- EvolveTrade: Experience-Driven Policy Refinement for Self-Evolving LLM Trading Agents#1011363
Record · ID 1115893
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