Gary Marcus claimed that Anthropic's Claude kernel uses classical symbolic AI with an IF-THEN structure, detailing 486 branch points and 12 nesting levels. However, the post's author expresses skepticism, suggesting the description points more to complex, accumulated code than classical AI algorithms.
ReaComp compiles LLM reasoning into symbolic program synthesizers to overcome the inefficiency and unreliability of LLMs on hard program synthesis tasks. These standalone solvers achieve higher accuracy and efficiency, outperforming LLMs and significantly reducing token usage in neuro-symbolic hybrid settings.
This research explores a novel approach to solving math word problems by integrating the power of language models with the precision of symbolic solvers. The method aims to leverage both natural language understanding and formal mathematical reasoning to achieve robust solutions.
This research introduces Visual-to-Symbolic Analytical Solution Inference (ViSA), an AI capability to recover analytical solutions of physical fields from visual observations. It presents ViSA-R2, a self-verifying pipeline mimicking a physicist's reasoning, and ViSA-Bench, a new benchmark for evaluating models in this domain.
François Chollet discusses the future of AGI, predicting its arrival around 2030, and introduces NDI lab's mission to develop a new, "optimal" machine learning paradigm based on symbolic program synthesis. He critiques deep learning's limitations and outlines NDI's high-risk, high-reward strategy for foundational AI advancement.
This research investigates using an agentic language model feedback framework to generate high-quality planning domains from augmented natural language descriptions. It evaluates the impact of various symbolic feedback mechanisms, like landmarks and plan validation output, in conjunction with heuristic search over model space to optimize domain quality.
This research introduces a symbolic reasoning scaffold to address systematic limitations in LLMs' structured logical reasoning, such as conflating hypothesis generation and propagating weak inferences. It operationalizes Peirce's tripartite inference, enforcing logical consistency through algebraic invariants, notably the 'Weakest Link bound' to prevent conclusion reliability from exceeding its least-supported premise.
This research paper introduces a coin-flip model to predict the performance of symbolic and prompt-based LLM programs using a few in-domain examples and a performance prior. It finds that symbolic programs exhibit an "all or nothing" performance prior, while prompt programs have a diffuse prior.