Tag
The paper introduces SDDL, a neuro-symbolic framework that improves combinatorial optimization accuracy in resource-constrained language models by translating natural-language problems into formal representations, resulting in higher feasibility rates compared to direct-generation and solver-code baselines.
The paper introduces a minimal-core-guided repair method for neuro-symbolic constraint solving, where language models use proofs from unsatisfiable cores to correct translation errors, reducing fabrication in solutions.
Euclid-Omni is a neuro-symbolic framework integrating LLMs, VLMs, and a symbolic solver to address plane geometry problems from calculations to Olympiad-level proofs, using synthetic data generation for training.
This paper introduces an expert-guided neuro-symbolic pipeline combining LLMs for semantic normalization and fuzzy logic to assess compliance with sepsis treatment protocols, providing graded insights from clinical data.
Presents Moose, a neuro-symbolic method that compiles OWL 2 EL ontologies into Sentential Decision Diagrams for differentiable weighted model counting, enabling latent concept learning under partial supervision and providing the first reasoning-shortcut analysis in an OWL EL setting.
Presents NeSy-Spatial, a neuro-symbolic framework that self-evolves spatial reasoning skills by composing tool-use and geometry skills, improving accuracy on spatial reasoning benchmarks.
This paper presents DNSSE, a hybrid framework combining LLM-guided schedule prediction, symbolic constraint solving, and coverage-guided stochastic mutation for verifying distributed parallel AI programs. It detects 2.9x more concurrency bugs than the baseline and raises branch coverage from 68.6% to 91.6% on realistic benchmarks.
Introduces LiFTER, a neuro-symbolic predictor for continuous-time dynamic graph forecasting that grounds predictions in observable temporal facts and executable rules, enabling fully inspectable and verifiable link prediction with competitive accuracy and high explanatory fidelity.
SymDiag is a neuro-symbolic framework that translates chain-of-thought reasoning into symbolic constraints and performs step-level satisfiability checks to localize failures in LLM reasoning, disentangling translation errors from reasoning errors.
This paper proposes a neuro-symbolic closed-loop architecture for laser powder bed fusion, where an in-loop ontology couples symbolic reasoning with statistical learning to control melt pool depth and eliminate overhang dross. Feasibility is demonstrated via a surrogate calibrated to the NIST AM-Bench benchmark.
ConWriter introduces a training-free framework for long-form story generation that maintains narrative consistency through scene-level incremental writing, symbolic state reasoning, and uncertainty-aware risk signals. Evaluated on ConStory-Bench across multiple models and lengths, it aims to prevent consistency errors from propagating in extended contexts.
Introduces CANOE, a multi-agent neuro-symbolic framework for open-ended care plan coordination that uses argumentative computation and human-in-the-loop contestation to improve transparency, safety, and clinical correctness.
ANCHOR-RE is a neuro-symbolic framework that integrates ontology-guided reasoning and external knowledge grounding into LLM inference for biomedical relation extraction, improving F1 scores across multiple benchmarks without fine-tuning.
NeSyFS is a neuro-symbolic framework for LLM agents under partial observability that uses a knowledge graph to represent belief state, combines fast/slow thinking with uncertainty-aware planning, and reflection, showing gains on ALFWorld, Webshop, and ScienceWorld.
Chronofy proposes a three-layer neuro-symbolic framework called Temporal-Logical Decay Architecture (TLDA) that integrates temporal validity directly into RAG systems via temporal subspace embeddings, decay-weighted graph retrieval, and Signal Temporal Logic verification to reduce temporal hallucination.
Proposes SAGE, a neuro-symbolic framework combining language models with cognitive models for pragmatic reasoning, demonstrated on three case studies including referential expression generation and implicatures.
This paper introduces a neuro-symbolic meta-policy for temporal knowledge-graph memory in partially observable reinforcement learning, combining RDF-based graph representations with symbolic memory management heuristics to achieve inspectable and adaptive control.
Introduces Octopus, a neuro-symbolic architecture that combines LLM swarms with algorithmic physics engines for autonomous discovery of cancer vulnerabilities, identifying IGF2 as a biomarker for drug resistance.
Proposes CART, a neuro-symbolic framework that interleaves natural language reasoning steps with symbolic constraint assertions to detect and correct errors early in chain-of-thought traces for multimodal LLMs. Reduces snowball rate from 65% to 14% and improves accuracy on multiple benchmarks.
This paper introduces a neuro-symbolic pipeline for automating LEED v4.1 BD+C compliance verification using small locally deployed language models and deterministic numeric checking. Experiments on four university buildings show that a 4B model outperforms an 8B model, and the deterministic checker corrects arithmetic errors on key credits, though multimodal inputs reduce accuracy.