9 stories tagged Neuro Symbolic
Researchers have enhanced neuro-symbolic AI by adding probability to logical reasoning, enabling AI to make educated guesses about unknown information while preserving its logical structure. This could lead to robots that learn and reason like humans, but with more transparency and structure.
Researchers introduced SD-GPS, a solver-driven framework that addresses key bottlenecks in geometry problem solving: autoformalization and theorem prediction. By treating the symbolic solver as an execution oracle, SD-GPS improves accuracy and flexibility, with potential applications in math education.
Researchers developed a neuro-symbolic framework that trains vision-language-action driving models using rule-grounded reasoning traces from classical planners, making autonomous driving decisions more transparent and causally connected to planned motion.
Researchers developed PhyDrawGen, an AI system that generates physics diagrams from text while respecting physical laws. Unlike previous tools, it doesn't make common mistakes about forces or geometry. This could help students and teachers create precise diagrams for learning and teaching physics.
Researchers have developed ANDRE, a new AI system that extracts logical rules from data more effectively than previous methods. This could make AI systems more interpretable and reliable in real-world, uncertain situations.
A new study challenges the idea that AI struggles with time-based questions because of poor reasoning. Instead, it points to how the AI converts text into events as the real problem. This could lead to better AI assistants that handle schedules and timelines more accurately.
A new study questions the assumption that symbol grounding automatically leads to compositional reasoning in neuro-symbolic systems. The research introduces a novel framework to disentangle these two critical capabilities.
Researchers introduce AGEL-Comp, a neuro-symbolic AI architecture that enhances compositional generalization in interactive agents. The framework combines a dynamic Causal Program Graph and an Inductive Logic Programming engine to improve robustness in complex environments.
Researchers introduce a framework to translate natural language into executable formal logic, addressing LLMs' limitations in symbolic reasoning. The NARS-Reasoning-v0.1 benchmark enables evaluation of this neuro-symbolic approach.