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arXiv:2505. 12992v4 Announce Type: replace-cross Abstract: Inference-time scaling techniques have significantly bolstered the reasoning capabilities of large language models (LLMs) by harnessing additional computational effort at inference without retraining.
arXiv:2505.16782v3 Announce Type: replace Abstract: Large Language Models (LLMs) have shown impressive performance on complex tasks through Chain-of-Thought (CoT) reasoning. However, conventional CoT...
arXiv:2610.29792v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) improves the performance of Large Language Models (LLMs) by retrieving external knowledge. Recently, some RAG meth...
Thought‑Like‑Pro is a learning framework that enhances large language models’ reasoning by combining imitation learning with a Prolog logic engine. The approach imitates chain‑of‑thought processes verified by symbolic reasoning, allowing models to formulate rules from instructions and convert Prolog‑derived reasoning trajectories into natural language. Experiments show significant reasoning improvements and robust generalization with only a marginal performance drop on distributional extrapolation tasks.
arXiv:2510. 08647v2 Announce Type: replace-cross Abstract: Recent developments have enabled advanced reasoning in Large Language Models (LLMs) via long Chain-of-Thought (CoT), trading efficiency during inference for performance.
arXiv:2607. 01585v1 Announce Type: cross Abstract: Predicate invention (PI), the creation of new predicates to extend the hypothesis space, remains a critical bottleneck in Inductive Logic Programming (ILP).