AIBullisharXiv – CS AI · Jun 237/10
🧠NOEM³A is a lightweight neuro-symbolic framework that enhances compact language models with intent ontologies to improve natural language understanding for mobile agents. By injecting structured symbolic knowledge into both input prompts and output decoding, the method achieves better performance on dialogue understanding tasks while maintaining privacy and low-latency requirements suitable for on-device deployment.
🧠 Llama
AINeutralarXiv – CS AI · Jun 97/10
🧠A comprehensive survey examines the evolution of AI systems for mathematical reasoning, from early rule-based solvers to contemporary language models, neuro-symbolic systems, and verified discovery workflows. The research catalogs major benchmarks, identifies critical failure modes like reward hacking and formalization brittleness, and proposes future directions centered on efficiency and usable AI-assisted formalization.
AIBullisharXiv – CS AI · Jun 27/10
🧠AXIOM is a neuro-symbolic architecture that pairs language models with deterministic computer algebra systems to solve mathematical problems with verifiable correctness. The system achieves 94.36% accuracy on MATH benchmarks with 100% confidence (zero incorrect confident answers) and has processed ~30,000 production queries, establishing a framework for trustworthy AI systems that prioritize verifiability over raw performance.
AIBullisharXiv – CS AI · May 297/10
🧠ProtoMedAgent introduces a framework that combines interpretable prototype networks with privacy-aware AI workflows to generate clinically accurate medical reports without the hallucination issues common in standard RAG systems. The approach achieves 91.2% faithfulness in clinical documentation while protecting patient privacy through k-anonymity and ℓ-diversity constraints.
AIBullisharXiv – CS AI · May 277/10
🧠Researchers present a hybrid neuro-symbolic architecture that combines formal logic with neural semantic analysis to verify LLM outputs in high-stakes domains like healthcare. The system achieves over 83% hallucination detection rates for structured data and 72% for semantic fabrications while reducing report creation time by 30%, demonstrating practical safeguards for deploying LLMs in data-sensitive applications.
AIBullisharXiv – CS AI · May 127/10
🧠Researchers introduce GuardAD, a safety framework that enhances autonomous driving systems using multimodal large language models (MLLMs) by incorporating Markovian logic to detect and prevent accidents. The model-agnostic safeguard reduces accident rates by 32% while improving task performance, combining neuro-symbolic logic with dynamic action revision rather than simple action veto mechanisms.
AIBullisharXiv – CS AI · May 97/10
🧠ReaComp introduces a method to compile reasoning traces from large language models into reusable symbolic program synthesizers that eliminate runtime LLM calls. The approach achieves 91.3% accuracy on benchmark tasks while reducing token usage by 78%, demonstrating that neuro-symbolic hybrid systems can outperform pure LLM inference on complex program synthesis problems.
AIBullisharXiv – CS AI · Mar 97/10
🧠Google's Gemini-based AI models, particularly Gemini Deep Think, have demonstrated the ability to collaborate with researchers to solve open problems and generate new proofs across theoretical computer science, economics, optimization, and physics. The research identifies effective techniques for human-AI collaboration including iterative refinement, problem decomposition, and deploying AI as adversarial reviewers to detect flaws in existing proofs.
🧠 Gemini
AIBullisharXiv – CS AI · Mar 57/10
🧠Researchers introduced AI4S-SDS, a neuro-symbolic framework combining multi-agent collaboration with Monte Carlo Tree Search for automated chemical formulation design. The system addresses LLM limitations in materials science applications and successfully identified a novel photoresist developer formulation that matches commercial benchmarks in preliminary lithography experiments.
AIBullisharXiv – CS AI · Mar 56/10
🧠Researchers propose a hybrid AI agent and expert system architecture that uses semantic relations to automatically convert cyber threat intelligence reports into firewall rules. The system leverages hypernym-hyponym textual relations and generates CLIPS code for expert systems to create security controls that block malicious network traffic.
AIBullisharXiv – CS AI · Mar 56/10
🧠Chimera introduces a framework that enables neural network inference directly on programmable network switches by combining attention mechanisms with symbolic constraints. The system achieves line-rate, low-latency traffic analysis while maintaining predictable behavior within hardware limitations of commodity programmable switches.
AIBullisharXiv – CS AI · Mar 46/103
🧠Researchers developed a Neuro-Symbolic Agentic Framework combining machine learning with LLM-based reasoning to predict colorectal cancer drug responses. The system achieved significant predictive accuracy (r=0.504) and introduces 'Inverse Reasoning' for simulating genomic edits to predict drug sensitivity changes.
AIBullisharXiv – CS AI · Mar 37/104
🧠Researchers have developed a new AI architecture that learns high-level symbolic skills from minimal low-level demonstrations, enabling robots to manipulate objects and execute complex tasks in unseen environments. The system combines neural networks for symbol discovery with visual language models for high-level planning and gradient-based methods for low-level execution.
AINeutralarXiv – CS AI · Feb 277/107
🧠Researchers introduced LeanCat, a benchmark comprising 100 category-theory tasks in Lean to test AI's formal theorem proving capabilities. State-of-the-art models achieved only 12% success rates, revealing significant limitations in abstract mathematical reasoning, while a new retrieval-augmented approach doubled performance to 24%.
AINeutralarXiv – CS AI · Jun 46/10
🧠Researchers introduce MechSim, a neuro-symbolic framework that enables large language models to reason transparently about the assumptions and mechanisms underlying scientific simulators. The approach improves explainability and decision-making reliability in high-stakes simulation-driven applications by treating simulators as structured systems rather than black boxes.
AINeutralarXiv – CS AI · Jun 46/10
🧠BiNSGPS introduces a bidirectional neuro-symbolic framework that enables dynamic feedback loops between machine learning models and symbolic solvers for geometry problem-solving. Unlike traditional unidirectional approaches, this system allows the neural component to actively incorporate feedback and correct errors, addressing fundamental limitations in AI's ability to solve complex geometric reasoning tasks.
AIBullisharXiv – CS AI · Jun 16/10
🧠Researchers propose CYKNN, a neural network architecture that directly embeds the CYK parsing algorithm into trainable matrix operations. The approach demonstrates superior performance compared to large language models with 20B+ parameters on grammar parsing tasks, suggesting a viable path for integrating symbolic algorithms into neural architectures.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduce IRIS-14B, a 14-billion-parameter LLM fine-tuned to translate compiler intermediate representations between GCC's GIMPLE and LLVM IR, achieving up to 44 percentage points higher accuracy than existing state-of-the-art models. The approach demonstrates how LLMs can function as interoperability layers in hybrid compiler architectures, enabling cross-toolchain workflows without modifying existing compiler infrastructure.
AINeutralarXiv – CS AI · Apr 146/10
🧠VeriTrans is a machine learning system that converts natural language requirements into formal logic suitable for automated solvers, using a validator-gated pipeline to ensure reliability. Achieving 94.46% correctness on 2,100 specifications, the system combines fine-tuned language models with round-trip verification and deterministic execution, enabling auditable translation for critical applications.
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AIBullisharXiv – CS AI · Apr 66/10
🧠Researchers propose AIVV, a hybrid framework using Large Language Models to automate verification and validation of autonomous systems, replacing manual human oversight. The system uses LLM councils to distinguish between genuine faults and nuisance faults, demonstrated successfully on unmanned underwater vehicle simulations.
AIBullisharXiv – CS AI · Apr 66/10
🧠Researchers propose a new Neuro-Symbolic Dual Memory Framework that addresses key limitations in large language models for long-horizon decision-making tasks. The framework separates semantic progress guidance from logical feasibility verification, significantly improving performance on complex AI tasks while reducing errors and inefficiencies.
AINeutralarXiv – CS AI · Mar 266/10
🧠Researchers propose DUPLEX, a dual-system architecture that restricts LLMs to information extraction rather than end-to-end planning, using symbolic planners for logical synthesis. The system demonstrated superior performance across 12 planning domains by leveraging LLMs for semantic grounding while avoiding their hallucination tendencies in complex reasoning tasks.
AINeutralarXiv – CS AI · Mar 266/10
🧠Researchers investigated whether Vision-Language Models (VLMs) can reason robustly under distribution shifts and found that fine-tuned VLMs achieve high accuracy in-distribution but fail to generalize. They propose VLC, a neuro-symbolic method combining VLM-based concept recognition with circuit-based symbolic reasoning that demonstrates consistent performance under covariate shifts.
AIBullisharXiv – CS AI · Mar 176/10
🧠Researchers introduced NS-Mem, a neuro-symbolic memory framework that combines neural representations with symbolic structures to improve multimodal AI agent reasoning. The system achieved 4.35% average improvement in reasoning accuracy over pure neural systems, with up to 12.5% gains on constrained reasoning tasks.
AIBearisharXiv – CS AI · Mar 176/10
🧠Researchers warn that AI-powered conversational navigation systems using Large Language Models could transform route guidance from verifiable geometric tasks into manipulative dialogues. The study proposes a framework categorizing risks as dark patterns or explainability pitfalls, suggesting neuro-symbolic architectures to maintain trustworthiness.
