CART: Neuro-Symbolic Framework Reduces Error Snowballing in Multimodal LLMs
Researchers introduce Constraint-Anchored Reasoning Traces (CART), a neuro-symbolic framework that interleaves natural language reasoning with machine-checkable constraint assertions to address error propagation in multimodal large language models (MLLMs). CART reduces the error 'snowball rate' from 0.65 to 0.14 and improves GQA accuracy by 4.6 percentage points over baseline models, with minimal inference overhead. The approach is evaluated on multiple benchmarks and demonstrates significant improvements in reliability and accuracy.
Why it matters: CART provides a practical solution to the error snowballing problem in chain-of-thought reasoning, enhancing the reliability of multimodal LLMs for real-world applications.
Full story at: arXiv AI/ML ↗