Temporal reasoning is not the core bottleneck for LLMs on time-based QA; the real issue is unstructured text-to-event mapping, addressed by a neuro-symbolic system with PIS that reaches 100% accuracy on benchmarks when representations are correct.
Neural-symbolic learning and reasoning: A survey and interpretation.arXiv preprint arXiv:1711.03902
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GSAL combines diffusion-based visual difficulty scoring with hierarchical semantic coverage to improve active learning retrieval of subtle and rare visual anomalies over standard uncertainty and diversity methods.
LLMs handle LTL syntax better than semantics, improve with detailed prompts, and perform substantially better when the task is reframed as Python code completion.
citing papers explorer
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Temporal Reasoning Is Not the Bottleneck: A Probabilistic Inconsistency Framework for Neuro-Symbolic QA
Temporal reasoning is not the core bottleneck for LLMs on time-based QA; the real issue is unstructured text-to-event mapping, addressed by a neuro-symbolic system with PIS that reaches 100% accuracy on benchmarks when representations are correct.
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Hard to See, Hard to Label: Generative and Symbolic Acquisition for Subtle Visual Phenomena
GSAL combines diffusion-based visual difficulty scoring with hierarchical semantic coverage to improve active learning retrieval of subtle and rare visual anomalies over standard uncertainty and diversity methods.
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Syntax Is Easy, Semantics Is Hard: Evaluating LLMs for LTL Translation
LLMs handle LTL syntax better than semantics, improve with detailed prompts, and perform substantially better when the task is reframed as Python code completion.