#neural-symbolic News & Analysis

7 articles tagged with #neural-symbolic. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

7 articles

AIBullisharXiv – CS AI · Jun 27/10

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eMoT: evolving Memory-of-Thought via Symbolic Anchoring and Memory Corrosion

Researchers introduce eMoT (evolving Memory-of-Thought), a framework that enhances LLM reasoning by treating reasoning processes as dynamic, evolving memories rather than static sequences. The system combines memory corrosion mechanisms, symbolic anchoring for deterministic computation, and consistency refinement to reduce hallucinations and improve multi-step reasoning accuracy, achieving 100% on Game of 24 and significant gains on mathematical benchmarks.

AIBullisharXiv – CS AI · May 77/10

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A Foundation Model for Zero-Shot Logical Rule Induction

Researchers introduce Neural Rule Inducer (NRI), a pretrained foundation model enabling zero-shot logical rule induction without task-specific retraining. By encoding domain-agnostic statistical properties instead of literal identities, NRI generalizes across different predicates and demonstrates robustness to label noise and spurious correlations, advancing toward foundation models for symbolic reasoning.

AINeutralarXiv – CS AI · 4d ago6/10

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CQD-SHAP: Explainable Complex Query Answering via Shapley Values

Researchers introduce CQD-SHAP, a framework that explains how neural models answer complex queries over incomplete knowledge graphs by computing the contribution of each query component using Shapley values from game theory. This approach addresses the black-box nature of existing complex query answering methods and demonstrates consistent effectiveness across multiple datasets.

AINeutralarXiv – CS AI · Jun 96/10

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Neural Scalable Symbolic Search Framework for Complex Logical Queries with Multiple Free Variables

Researchers introduce NS3, a neural-symbolic framework that improves complex query answering over knowledge graphs by approximating joint rankings of multi-variable answers without exhaustive enumeration. The method demonstrates substantial performance gains across benchmarks and includes a new joint-ranking dataset extending evaluation to three free variables.

AINeutralarXiv – CS AI · May 276/10

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Reliable Extraction of Clinical Follow-Up Instructions: A Hybrid Neural-Symbolic Pipeline

Researchers developed a hybrid neural-symbolic pipeline for extracting clinical follow-up instructions from outpatient notes, pairing medical actions with future dates. The system significantly outperformed generative AI models (GPT-4o-mini and LLaMA-3) at linking actions to dates, achieving 99.7% F1 score on seen data versus 51-57% for baselines, demonstrating that symbolic reasoning outperforms pure language generation for structured clinical extraction tasks.

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AINeutralarXiv – CS AI · May 116/10

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Neurosymbolic Framework for Concept-Driven Logical Reasoning in Skeleton-Based Human Action Recognition

Researchers introduce a neurosymbolic framework that combines neural networks with symbolic logic for skeleton-based human action recognition, enabling interpretable AI models that explain their decisions through human-readable logical rules rather than operating as black boxes.

AINeutralarXiv – CS AI · Mar 266/10

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GeoSketch: A Neural-Symbolic Approach to Geometric Multimodal Reasoning with Auxiliary Line Construction and Affine Transformation

Researchers introduce GeoSketch, a neural-symbolic AI framework that solves geometric problems through dynamic visual manipulation, including drawing auxiliary lines and applying transformations. The system combines perception, symbolic reasoning, and interactive sketch actions, achieving superior performance on geometric problem-solving benchmarks compared to static image processing methods.