arXiv (All)
open access
SAGE: Mitigating Long-Horizon Reasoning Biases via Topological Guidance
Long-horizon reasoning remains a central challenge for large language models (LLMs) under sparse-reward regimes. We argue that this brittleness arises from two biases induced by complex reasoning spaces: an exploration bias, where models are drawn toward locally plausible but structurally unstable b
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
- 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
- Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models#984842
- MIRAGE: Multi-Perspective Creative Language Model Reasoning with Reinforcement Learning Guidance#1035951
- Rewarding Novel Deductions: Solver-guided Process Rewards for Logical Reasoning#1128919
Record · ID 1078263
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