SPIN Unprocessed July 3, 2026 ai_technology research
Revisiting Chain-of-Thought Reasoning under Limited Supervision: Semi-supervised Chain-of-Thought Learning
View original on arxiv.orgSummary
arXiv:2607.01511v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning has emerged as an effective approach for activating latent reasoning capabilities in large language models. However, most existing CoT methods use reasoning chains mainly as inference-time prompts, while the generated reasoning traces are rarely reused as semi-supervised learning signals. In this report, we define \textbf{Semi-supervised Chain-of-Thought Learning} and propose \textbf{Semi-CoT}, a simple framework th
SpinGraph analysis pending — check back after processing.
Ask AI about this story
See how AI engines summarize this narrative — one click, prompt included.
More from arXiv Artificial Intelligence
View all →- Profit-Based Counterfactual Explanations for Product Improvement: A Case Study of Manga Sales in Japan
- SemHash-LLM: A Multi-Granularity Semantic Hashing Framework for Document Deduplication
- Safe and Adaptive Cloud Healing: Verifying LLM-Generated Recovery Plans with a Neural-Symbolic World Model
- Hawk: Harnessing Hardware-Aware Knowledge for High-Performance NPU Kernel Generation
- EO-Agents: A Three-Agent LLM Pipeline for Earth Observation Hypothesis Generation
- Scaling Trends for Lie Detector Oversight in Preference Learning
Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO