eScholarship
metadata only
Are More Tokens Rational? Inference-Time Scaling in Language Models as Adaptive Resource Rationality
Human reasoning is characterized by the rational use of resources to optimize performance under constraints. Recently, inference-time scaling has improved the reasoning performance of Large Language Models by increasing test-time computation. Instruction-tuned (IT) models explicitly generate long re
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
- Adaptive Test-Time Compute Allocation for Block Diffusion Language Models in Complex Reasoning#665649
- Adaptive Test-Time Compute Allocation for Block Diffusion Language Models in Complex Reasoning#664149
- GPTBIAS: A Comprehensive Framework for Evaluating Bias in Large Language Models#924392
- GPTBIAS: A Comprehensive Framework for Evaluating Bias in Large Language Models#920161
- DIP: Dynamic In-Context Planner For Diffusion Language Models#792685
Record · ID 1089908
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