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Introduces Neuro-Symbolic Drive, a framework that uses rule-grounded reasoning traces from classical planners to fine-tune a driving VLA (Qwen3.5-4B), achieving significant reductions in average displacement error and miss rate compared to standard CoT reasoning.
This paper formalizes the 'Impedance Mismatch' between foundation models and knowledge graphs, and proposes a theoretical roadmap for neuro-symbolic fusion using structured residual streams, vector symbolic architectures, and orthogonal subspace editing.
The author describes building a Minsky brain: a runtime of 40+ LLM agents wired in a connectome and staged phylogenetically to simulate a brain. They ask for advice on where to post this project on Reddit.
IVIE is a neuro-symbolic approach that combines LLMs for creative generation with symbolic validation to produce coherent and playable interactive fiction worlds. Human evaluation shows its worlds are immersive and thematically coherent.
This paper introduces SPIN, a framework for decentralized multi-agent swarm control that uses tensor network factorization to reduce computational complexity from exponential to linear, enabling low-power edge deployment. It validates the approach in simulation for tracking, coverage, and coordination tasks.
PandaAI proposes a closed-loop neuro-symbolic LLM agent for sequential decision-making in quantitative finance, integrating market regime modeling and constrained alpha generation to address low SNR and non-stationarity in financial data, achieving significant improvements over state-of-the-art time-series models.
BiNSGPS is a framework that introduces bidirectional interaction between a multimodal LLM adviser and a symbolic solver for geometry problem solving, allowing feedback from the solver to correct errors and generate auxiliary hypotheses. It achieves state-of-the-art performance of 90.5% on Geometry3K and 90.1% on PGPS9K benchmarks.
Researchers from the University of Michigan introduce MechSim, a mechanism-grounded neuro-symbolic reasoning framework that enables LLM agents to reason about the internal assumptions, dependencies, and execution behavior of scientific simulators rather than treating them as black boxes. The framework improves explanation quality and decision-making reliability across high-stakes domains like healthcare, finance, and public policy.
AXIOM is a trust-first neuro-symbolic execution architecture for mathematical reasoning where the LLM acts as a canonicalizer, rewriting natural language problems into schemas processed by a deterministic CAS pipeline, achieving 94.36% correctness with 100% trust on parseable queries.
Introduces DisjunctiveNet, a unified end-to-end framework for enforcing hard, input-dependent mixed integer linear constraints within neural networks via differentiable convexified optimization layers, achieving perfect rule satisfaction on real-world datasets.
PhyDrawGen is a neuro-symbolic pipeline that generates physically accurate diagrams from natural language by combining LLM-based scene understanding with a deterministic constraint solver and a VLM-based verify loop, outperforming existing models on a benchmark of physics problems.
This paper proposes a neuro-symbolic framework for constructing ontology-grounded knowledge graphs from text by deferring consistency corrections to a post-extraction stage, reducing token usage while improving KG consistency and maintaining QA performance.
This paper presents a neuro-symbolic verification architecture for LLM outputs in high-stakes domains, combining formal symbolic methods with neural semantic analysis. Evaluated on a medical device damage assessment system, it achieves over 83% hallucination detection for structured entities and 30% reduction in report creation time.
This paper explores using LLMs to predict state changes within rule-based interactive storytelling systems, aiming to improve coherence and player expression. Experiments with Llama 3 70B and Gemini 1.5 Flash show that world-state transformations can maintain consistency while encouraging creative player input.
SciAtlas is a large-scale, multi-disciplinary academic knowledge graph containing over 43 million papers and 3 billion triplets, designed to provide structured knowledge for AI-driven automated scientific research with a neuro-symbolic retrieval algorithm.
ReacTOD proposes a bounded neuro-symbolic architecture for zero-shot dialogue state tracking, using a self-correcting ReAct loop with deterministic validation. It achieves state-of-the-art results on MultiWOZ and Schema-Guided Dialogue benchmarks, improving joint goal accuracy by up to 14 percentage points.
Introduces ANNEAL, a neuro-symbolic agent that converts recurring failures into governed symbolic edits of a process knowledge graph without modifying model weights, achieving persistent structural repairs and eliminating recurring failures in tested settings.
Proposes Logic-GNN, a neuro-symbolic framework that uses temporal graph neural networks and graph Kolmogorov complexity to induce a symbolic grammar for clinical records, enabling detection and correction of data entry errors as grammatical violations. The system achieves an F1-score of 0.94 on a large healthcare dataset, outperforming state-of-the-art methods by 12%.
This paper empirically studies LLMs' legal reasoning in tax law, showing that data contamination inflates performance and that neuro-symbolic hybrid systems offer more reliable and robust generalization than monolithic LLMs.
This paper presents NSPI, a neuro-symbolic framework that combines LLMs and symbolic computation to prove polynomial inequalities. It uses LLM-generated sum-of-squares conjectures, refines them symbolically, and formally verifies the proofs in Lean, demonstrating scalability on polynomials with up to 10 variables.