arXiv AI
TRACTA: Benchmarking Temporal Reasoning over Semantic Trajectories
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arXiv AI
Aug 18
STAIR: Semantic-Temporal Automaton for Interpretable Reasoning in Temporal Question Answering
arXiv:2608. 16224v1 Announce Type: cross Abstract: By leveraging large-scale pretraining, LLMs can interpret diverse temporal expressions and question formulations without task-specific training.
arXiv AI
Aug 25
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.
arXiv AI
Jun 2
TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning
arXiv:2606. 01498v1 Announce Type: cross Abstract: Time series data inform critical decisions across many real-world domains.
arXiv AI
Aug 11
TempoBench: Reasoning Execution Without Causal Attribution Is Just Simulation
arXiv:2510. 27544v3 Announce Type: replace Abstract: Current training paradigms, optimized for long-horizon reasoning trace execution, have made Large Language Models (LLMs) excel at pattern matching and forward simulation of reasoning, but underperform at counterfactual causal understanding and reasoning.
arXiv AI
Sep 25
Planning Takes More Than Token Prediction: Causal Plan for Benchmarking and Building Physically Grounded Embodied Reasoners
The paper argues that current embodied vision‑language planning benchmarks favor linguistic next‑token prediction over physically grounded next‑state reasoning, leading models to rely on language priors rather than true causal dependencies. To address this, the authors introduce Causal‑Plan‑Bench, a diagnostic suite covering four causal dimensions, and Causal‑Plan‑1M, a million‑scale corpus of explicit causal reasoning traces extracted from egocentric videos. Extensive experiments show that existing models perform poorly on these tasks, while a new model trained with a tailored recipe—Causal Planner based on Qwen3‑VL‑8B—achieves significant gains, demonstrating the feasibility of physically grounded causal reasoning.
arXiv AI
Jun 9
Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models
arXiv:2606. 08633v1 Announce Type: new Abstract: Long-horizon maritime trajectory prediction is important for shipping management, logistics planning, and maritime risk analysis, yet month-level forecasting remains insufficiently studied.