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A Survey of Reasoning with Foundation Models
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Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation. It serves as a fundamental methodology in the field of Artificial General Intelligence (AGI). With the ongoing development of foundation models, e.g., Large Language Models (LLMs), there is a growing interest in exploring their abilities in reasoning tasks. In this paper, we introduce seminal foundation models proposed or adaptable for reasoning, highlighting the latest advancements in various reasoning tasks, methods, and benchmarks. We then delve into the potential future directions behind the emergence of reasoning abilities within foundation models. We also discuss the relevance of multimodal learning, autonomous agents, and super alignment in the context of reasoning. By discussing these future research directions, we hope to inspire researchers in their exploration of this field, stimulate further advancements in reasoning with foundation models, and contribute to the development of AGI.
Forward citations
Cited by 13 Pith papers
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VLM-R$^3$: Region Recognition, Reasoning, and Refinement for Enhanced Multimodal Chain-of-Thought
VLM-R3 uses reinforcement learning to teach a multimodal model when and where to crop an image during its chain-of-thought, improving scores on several visual reasoning benchmarks.
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Rewarding each parallel reasoning path by Monte-Carlo-Shapley marginal contribution, scored by a generative reward model, lifts Pass@16 on AIME24/AIME25/AMC23 by 4-90% relative over Parallel-R1 with a fifth of the tra...
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The Shape of Reasoning: Topological Analysis of Reasoning Traces in Large Language Models
Topological features of reasoning-trace embeddings correlate with Smith-Waterman alignment to expert AIME solutions more than graph metrics do, but the paper does not validate this out of sample.
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Towards Unified Neurosymbolic Reasoning on Knowledge Graphs
TUNSR unifies propositional and first-order logic reasoning in one model for knowledge graph link prediction and reports state-of-the-art results on 19 datasets across four reasoning scenarios.
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Unleashing Embodied Task Planning Ability in LLMs via Reinforcement Learning
A 7B LLM trained with sparse completion rewards and a GRPO-style algorithm reaches state-of-the-art on ALFWorld and ScienceWorld.
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Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
LLMs perform much worse on causal questions built from post-cutoff news articles, suggesting their apparent causal skill is mostly memorization, and a general-knowledge prompt method only partly closes the gap.
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Demystifying Reasoning Dynamics with Mutual Information: Thinking Tokens are Information Peaks in LLM Reasoning
Reasoning tokens like 'Hmm' and 'Wait' mark steps where a model's internal state carries unusually high dependence with the correct answer, and suppressing them hurts accuracy.
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Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning
Tool-Star combines cold-start supervised fine-tuning with a multi-tool self-critic reinforcement learning algorithm and hierarchical rewards to improve LLM tool-use reasoning.
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CoVeR: Conformal Calibration for Versatile and Reliable Autoregressive Next-Token Prediction
CoVeR is a cluster-aware conformal decoding method that claims full-sequence coverage for LLM outputs without the (1-alpha)^L decay of prior conformal beam search.
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Implicit Reasoning in Large Language Models: A Comprehensive Survey
A survey organizing implicit (silent) reasoning in LLMs into three execution paradigms, plus evidence, benchmarks, and challenges.
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Learning from Diverse Reasoning Paths with Routing and Collaboration
QR-Distill filters, routes, and collaboratively distills multiple teacher reasoning paths into two 7B student models, but its superiority claims are weakened by unfair baselines and contradictory ablations.
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CoRE: Enhancing Metacognition with Label-free Self-evaluation in LRMs
A training-free and label-free detector of cyclic hidden-state patterns triggers early exit during chain-of-thought reasoning, reducing token length while mostly preserving or improving accuracy.
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Synergizing Logical Reasoning, Knowledge Management and Collaboration in Multi-Agent LLM System
SynergyMAS combines a graph database with a Clingo logic solver, corrective RAG, and Theory of Mind prompts in a hierarchical multi-agent team, demonstrated on a Smart Home Energy Management case study.
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