arXiv AI August 25, 2026 By Kethmi Hirushini Hettige, Jiahao Ji, Cheng Long, Shili Xiang, Gao Cong, Jingyuan Wang

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs

The paper introduces STReason, a modular multitask reasoning framework that combines large language models with spatio‑temporal models to handle complex natural language queries without task‑specific fine‑tuning. STReason decomposes queries into interpretable programs, executes them to produce numerical results and detailed, computation‑grounded explanations, thereby reducing hallucinations. The authors evaluate the system on a new benchmark and show it outperforms advanced LLM baselines, with human studies confirming its credibility and practical utility.

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arXiv Computation and Language

Sep 25

STRAND: Benchmarking and Improving Object-Centric Spatio-Temporal Monitoring in Video Large Language Models

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llms multimodal benchmarks safety

arXiv AI

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llms rag fine-tuning safety

arXiv Machine Learning

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llms reinforcement-learning efficiency

arXiv Computer Vision

1d ago

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llms multimodal benchmarks

arXiv Machine Learning

Jul 2

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llms fine-tuning multimodal benchmarks safety

arXiv AI

Aug 18

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