arXiv (All)
open access
Agentic Multi-Turn Reasoning: A Fairness Approach
Recent advances in Large Language Models (LLMs) have enabled agentic systems capable of solving complex tasks through multi-turn planning, tool use, verification, and memory updates. However, learning agentic systems remains difficult due to two fundamental challenges, i.e., (1) long-horizon credit
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
- Beyond Symmetric Agents: Cognitive Diversity and Multi-Agent Debate in Small Language Models#1130717
- A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agents#1049743
- AutoCRAT: Within-trajectory Joint Control of Stochasticity and Compute for LLM Reasoning#799234
- AutoCRAT: Within-trajectory Joint Control of Stochasticity and Compute for LLM Reasoning#797811
- Optimal Skill Selection for LLM Agents with Provable Bicriteria Guarantees#1129365
Record · ID 1118035
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