Neuro-Symbolic
Large Foundation Model.

Large language models (LLMs) and multimodal large language models (MLLMs) have unlocked impressive reasoning ability. However, when tasks demand precise logical guarantees, explicit structure, or reliable multi-step deduction, purely neural approaches often fall short. Their reasoning can be fluent yet brittle: explanations may sound convincing while quietly violating basic logical constraints.

Neuro-symbolic approaches aim to close this gap by coupling LLMs with symbolic representations and reasoning procedures. Symbolic reasoning enables compositionality, support program-like manipulation of knowledge, and good traceability and interpretability. Neural models, in turn, offer strong capabilities in learning, perception, and pattern discovery. When combined, they allow us to:

• Improve faithfulness: Reasoning steps can be tractable and verifiable, thus enabling faithful reasoning.
• Enhance interpretability: Symbolic traces and proofs provide human-inspectable explanations.
• Enable compositional generalization: Logic and constraints naturally capture reusable structures and rules.
• Support robustness & safety: Formal checks help enforce invariants in high-stakes scenarios.
• Extend beyond text: In multimodal settings, symbols provide a common reasoning layer over images, text, and other signals.

This page collects a set of Neuro-Symbolic Large Foundation Model projects—covering both symbolic-guided LLM reasoning and multimodal symbolic reasoning—that explore these ideas in different settings and benchmarks.