arXiv (All)
open access
Dynamic K-Head: Difficulty-Supervised Budget Selection for Continuous Latent Reasoning
Chain-of-thought (CoT) improves reasoning but incurs substantial autoregressive rationale cost. Continuous CoT moves intermediate computation into recurrent hidden states, yet typically uses a fixed number of updates even though the best budget varies across questions and accuracy need not improve m
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 Verbalized Confidence: Calibrating Reasoners with Differentiable Readouts#1129470
- The Price of Thought: Does Test-Time Reasoning Pay in LLM Trading?#1105264
- RewardWeaver: Long-Horizon Interactive Learning for Language Agents via Self-Evolving Reward Adaptation#1234487
- Learning Counterfactual World Models for Embodied Reasoning under Partial Observability#973459
- When Learned Context Planning Fails to Beat Strong Retrieval: A Controlled Study of Planning, Routing, and Reranking for Long-Context QA#1063187
Record · ID 1342890
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