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This paper presents a self-demonstration-driven approach for schema-ontology mapping using LLMs, combining neuro-symbolic task decomposition to achieve state-of-the-art performance on the RODI benchmark.
This paper introduces VERDICT, a neuro-symbolic system that uses LLMs and SMT solvers to ensure accountable decision-making in clinical trial matching, providing consistent policies and faithful rationales.
NSIDDx is a neuro-symbolic design framework for differential diagnosis in low-resource settings that prioritizes clinician collaboration and transparent reasoning, addressing gaps in LLM-based systems by ensuring verifiable outputs and auditability.
ProofEvolve is a neuro-symbolic framework that evolves formally verified proof structures using neural models to enhance automated theorem proving, achieving high solve rates on Lean benchmarks by preserving verified knowledge from incomplete attempts.
The paper proposes a neuro-symbolic framework that decouples reasoning into symbolic validity and semantic groundedness, using a verifier and a trained PRM to improve reliability in scientific reasoning tasks for LLMs.
Hillock v0.5.0 is a lightweight neuro-symbolic memory engine for AI agents that manages persistent memory efficiently using structured triples in SQLite and hypervectors, designed for local setups with limited VRAM.
The paper proposes a neuro-symbolic pipeline for pairwise logical selection of enthymeme completions, introducing the PWAL method that uses logical-resistance scores to improve accuracy and reduce ties across various reasoning tasks.
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.