temporal-reasoning

This paper proposes a composable trust infrastructure for manufacturing knowledge graphs, focusing on cross-system provenance, temporal reasoning, and decision traceability.

This paper investigates whether language models consistently encode the current year through associative and declarative tasks, finding that the current year is not consistently encoded due to differing mechanisms between tasks.

The paper introduces temporal dependency graphs extracted from legal documents to compute filing deadlines, showing a pipeline approach outperforms direct language model answering in accuracy.

The paper constructs a benchmark to evaluate LLMs on temporal legal reasoning, revealing biases towards applying the most recently enacted laws and an inverse relationship between general reasoning ability and temporal performance.

CRAFT is a new LLM framework for iterative refinement of temporal reasoning over clinical narratives, introducing a verifier-based feedback mechanism and the MedTempo benchmark for vaccine adverse-event reports.

MobileMem introduces a benchmark and framework for on-device AI systems to learn from year-long mobile experiences, focusing on temporal reasoning, knowledge updating, and preference inference.

ChronoVision is a multimodal framework that improves temporal reasoning in vision-language models by aligning visual logic with latent imagery, using a Reconstructive Visual Head, ROI Attention Locating module, and reinforcement learning. It introduces the Vbvr-VQA dataset and achieves SOTA accuracy on temporal tracking benchmarks.

GROVE is a training-free framework that grows a temporally stratified memory from continuous video streams, supporting both reactive QA and proactive assistance. It achieves state-of-the-art results on benchmarks like MM-lifelong and EgoServe.

ReMem introduces a dual-level memory-augmented keyframe selection framework for training-free long video understanding, achieving state-of-the-art zero-shot performance on multiple benchmarks.

MA-DAR is a plug-and-play framework that addresses representation conflicts in replay-based continual temporal knowledge graph reasoning by aligning replayed and current representations on a shared manifold and using a dynamic gating mechanism for adaptive fusion.

This article discusses the persistent difficulty AI chatbots have with correctly handling time-related queries, exploring the underlying reasons and user frustrations.

OpenCoF introduces a reasoning video dataset and a fine-tuned video generation model that improves temporal reasoning through diverse supervision and explicit reasoning tokens, showing significant gains on four video reasoning benchmarks.

This paper presents LingBot-VLA 2.0, which enhances VLA foundation models for robotics by improving generalization across tasks and embodiments, expanding action space to whole-body degrees of freedom, and incorporating predictive dynamics modeling for better temporal reasoning.

This paper systematically studies how temporal metadata can be structurally embedded into named entity recognition (NER) models for historical texts. Experiments with absolute and relative temporal representations injected via early or late fusion mechanisms show that late fusion strategies yield more robust performance on French and German historical datasets.

This paper presents the results of HIPE-2026, the third edition of the HIPE evaluation series, which focuses on temporally grounded person-place relation extraction from multilingual historical documents in French, German, and English. Seventeen participating teams were evaluated on predictive accuracy, computational efficiency, and cross-domain generalization.

The article identifies four key flaws in current AI agent memory systems—brittleness, lack of temporal reasoning, forgetting dilemma, and evaluation gap—and presents a novel memory architecture inspired by code agents, achieving high benchmark scores while emphasizing context learning as the next challenge.

This paper proposes recall-based prompting strategies (Self-Recall and Question-Recall) to improve LLM knowledge cutoff adherence, outperforming existing methods on counterfactual questions and introducing a Multi-cutoff Historical Event Benchmark (MHEB) for robustness evaluation.

Researchers introduce DoseBench, a benchmark of 81 OTC dosing scenarios to evaluate LLM decision-making under temporal uncertainty for acetaminophen and ibuprofen use. Results show LLMs frequently struggle with rolling-window reasoning and can produce confident but medically unsupported responses.

This paper introduces the readout-mediator angle to demonstrate that linear probes can decode information from language model activations that is orthogonal to the model's actual causal computation, undermining probe-based interpretability. The finding replicates across model scales and families, revealing a fundamental failure mode in using probes for mechanistic understanding or safety monitoring.

This paper introduces AsyncTool, a benchmark for evaluating LLM-based agents' asynchronous function calling abilities in multi-task scenarios with delayed tool responses. It proposes efficiency-oriented metrics and identifies key failure modes of current tool-using agents.