arXiv (All)
open access
CT-SAFR: Safe and Interpretable Chain-of-Thought Reasoning for Autonomous Robots: A Multi-Layered Verification Framework for Trustworthy AI-Driven Robotic Decision Making
Chain-of-Thought (CoT) prompting enables LLMs to perform explicit, step-by-step reasoning, creating opportunities for sophisticated autonomous robots. However, recent research reveals that reasoning models verbalize their actual decision processes only 25-39% of the time, with faithfulness degrading
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 Textual Chain-of-Thought: A Survey on Action-Grounded Reasoning in Autonomous Driving#822096
- Neuro-Formal Verification: Agentic Language-Agnostic Formal Program Reasoning#1084328
- Verify to Amplify: Improving Reasoning via Learned Chain-of-Thought Verification#996884
- Reasoning Beyond Language: A Comprehensive Survey on Latent Chain-of-Thought Reasoning#798609
- Reasoning Beyond Language: A Comprehensive Survey on Latent Chain-of-Thought Reasoning#797196
Record · ID 996521
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