arXiv (All)
open access
On the Token Value Inequality in Efficient Reasoning
Chain-of-Thought reasoning has enabled large language models to achieve substantial performance gains on complex tasks. However, these gains come at the cost of dramatically increased token consumption. This raises a fundamental question: is every token in the reasoning trace equally valuable? We pr
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 with Reasoning Skills: Fewer Tokens, More Accuracy#126561
- A*-Thought-V2: Efficient Latent Reasoning via Geometric Dynamics of LLM#984715
- Is Large Language Model Performance on Reasoning Tasks Impacted by Different Ways Questions Are Asked?#177097
- Least-to-Most Prompting Enables Complex Reasoning in Large Language Models#16580
- Latent Chain-of-Thought as Planning: Decoupling Reasoning from Verbalization#1118470
Record · ID 1119043
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