State-of-the-art automatic event detection struggles with interpretability and adaptability to evolving large-scale key events-unlike episodic structures, which excel in these areas.Often overlooked, episodes represent cohesive clusters of core entities performing actions at a specific time and location; a partially ordered sequence of episodes can represent a key event.This paper introduces a novel task, episode detection, which identifies episodes within a news corpus of key event articles.Detecting episodes poses unique challenges, as they lack explicit temporal or locational markers and cannot be merged using semantic similarity alone.While large language models (LLMs) can aid with these reasoning difficulties, they suffer with long contexts typical of news corpora.To address these challenges, we introduce EpiMine, an unsupervised framework that identifies a key event's candidate episodes by leveraging natural episodic partitions in articles, estimated through shifts in discriminative term combinations.These candidate episodes are more cohesive and representative of true episodes, synergizing with LLMs to better interpret and refine them into final episodes.We apply EpiMine to our three diverse, real-world event datasets annotated at the episode level, where it achieves a 59.2% average gain across all metrics compared to baselines.

With the exponential growth of research facilitated by modern technology and improved accessibility, scientific discoveries have become increasingly fragmented within and across fields.This makes it challenging to assess the significance, novelty, incremental findings, and equivalent ideas between related works, particularly those from different research communities.Large language models (LLMs) have recently demonstrated strong quantitative and qualitative reasoning abilities, and multi-agent LLM debates have shown promise in handling complex reasoning tasks by exploring diverse perspectives and reasoning paths.Inspired by this, we introduce Tree-of-Debate (ToD), a framework which converts scientific papers into LLM personas that debate their respective novelties.To emphasize structured, critical reasoning rather than focusing solely on outcomes, ToD dynamically constructs a debate tree, enabling fine-grained analysis of independent novelty arguments within scholarly articles.Through experiments on scientific literature across various domains, evaluated by expert researchers, we demonstrate that ToD generates informative arguments, effectively contrasts papers, and supports researchers in their literature review.

Claims made by individuals or entities are oftentimes nuanced and cannot be clearly labeled as entirely "true" or false"-as is frequently the case with scientific and political claims.However, a claim (e.g., "vaccine A is better than vaccine B") can be dissected into its integral aspects and sub-aspects (e.g., efficacy, safety, distribution), which are individually easier to validate.This enables a more comprehensive, structured response that provides a well-rounded perspective on a given problem while also allowing the reader to prioritize specific angles of interest within the claim (e.g., safety towards children).Thus, we propose CLAIMSPECT, a retrieval-augmented generation-based framework for automatically constructing a hierarchy of aspects typically considered when addressing a claim and enriching them with corpusspecific perspectives.This structure hierarchically partitions an input corpus to retrieve relevant segments, which assist in discovering new sub-aspects.Moreover, these segments enable the discovery of varying perspectives towards an aspect of the claim (e.g., support, neutral, or oppose) and their respective prevalence (e.g., "how many biomedical papers believe vaccine A is more transportable than B?").We apply CLAIMSPECT to a wide variety of real-world scientific and political claims featured in our constructed dataset, showcasing its robustness and accuracy in deconstructing a nuanced claim and representing perspectives within a corpus.Through real-world case studies and human evaluation, we validate its effectiveness over multiple baselines.

Priyanka Kargupta, Nan Zhang, Yunyi Zhang, Rui Zhang, Prasenjit Mitra, Jiawei Han. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Connected and autonomous vehicles are changing the future of mobility by providing vehicles connectivity and intelligence for smarter performance. Driver behavior analysis plays an important role in most of these applications. This paper provides a case study using A Distributed Analytics Platform for Telematics (ADAPT) that powers many popular connected car products in the market today. It provides an overview of the platform and some of its supported products. The paper focuses on driver safety data analysis using several novel techniques. It explores image analysis for advanced driver safety using a novel randomized contractive mappings-based technique for efficient onboard driver data analysis. In addition, it explains how social collective incentive mechanisms can be integrated with the driving scoring functions for improving driver safety. The paper discusses market adoption and presents experimental results.