arXiv (All)
open access
Input-Conditioned Search for Efficient Test-Time Scaling of Recursive Reasoners
People often tackle difficult problems by thinking more deeply or exploring more possibilities. Similarly, recursive reasoners, which reason by repeatedly applying a learned update rule to latent states, can also scale inference through more updates (depth) or additional trajectories generated by pe
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
- Thinking-Free Policy Initialization Makes Distilled Reasoning Models More Effective and Efficient Reasoners#1357045
- Towards Better Exploration in Sequential Test-Time Scaling#1144599
- When LLM Meets Tree Search: A Systematic View of Inference as Search in Large Language Models#799599
- Teaching Vampire New Tricks: An Experimental Study of Neural Clause Selection#1110602
- Parason: Revealing Subtask and Trial Parallelism in LLM Reasoning#1342591
Record · ID 1344048
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