Every story across every category, newest first. Each card links to the original publisher; daily-brief posts open as editorial pages.
arXiv:2504.07459v2 Announce Type: replace Abstract: We propose a novel framework for generating causal graphs from narrative texts, bridging high-level causality and detailed event-specific relationships. Our method…
arXiv:2607.09328v1 Announce Type: new Abstract: Answering complex questions over long documents frequently requires integrating evidence that the source itself disperses naturally across distant passages. In an…
arXiv:2311.02564v2 Announce Type: replace Abstract: Relational extraction is one of the basic tasks related to information extraction in the field of natural language processing, and is…
arXiv:2607.09324v1 Announce Type: new Abstract: Identifying and assigning keywords at scale is a technical, practical, and ethical challenge for crowdsourced collections. This article reports the findings…
arXiv:2605.28215v2 Announce Type: replace-cross Abstract: In-context learning (ICL) enables multimodal large language models (MLLMs) to classify images from a few labelled examples. Yet, how these models…
arXiv:2607.09316v1 Announce Type: new Abstract: Thematic indexing -- the practice of assigning structured conceptual labels to sections of text -- is essential to scholarly access in…
arXiv:2505.14300v2 Announce Type: replace-cross Abstract: White-box monitoring is increasingly adopted as an auditing tool as Large Language Models (LLMs) are deployed in daily operations to ensure…
arXiv:2607.09092v1 Announce Type: new Abstract: Knowledge graphs (KGs) are often automatically constructed from large-scale corpora, but they inevitably contain factual errors due to noisy sources and…
arXiv:2607.09094v1 Announce Type: new Abstract: Legal precedent retrieval is a fundamental task in legal case preparation, planning, litigation strategy, and legal research. Current approaches for automatic…
arXiv:2607.09121v1 Announce Type: new Abstract: In this study, we examine the opportunities brought by Large Language Models (LLMs) to various aspects of fundamental analysis of companies…
arXiv:2607.09291v1 Announce Type: new Abstract: Medieval document transcribers have very different practices; on top of that, heterogeneous digitization policies have resulted in corpora where the character-set…
arXiv:2607.09204v1 Announce Type: new Abstract: Pretrained language models often exhibit structured weight spectra, suggesting that training may repeatedly produce similar layerwise and component-wise organization. We ask…
arXiv:2607.09600v1 Announce Type: cross Abstract: Enhancing the reasoning capabilities of large language model (LLM) agents requires effective orchestration of diverse expert models and tools. However, existing…
arXiv:2607.09306v1 Announce Type: new Abstract: Language models are optimised for scale, yet remain functional rather than companionable, and as an assistant personalises into a companion, accumulating…
arXiv:2508.14817v2 Announce Type: replace Abstract: Objective: To evaluate whether retrieval-augmented generation (RAG) can serve as an efficient alternative to long-context prompting for clinical reasoning over electronic…
arXiv:2407.21082v3 Announce Type: replace Abstract: This paper presents a modular approach to accelerate inference in large language models (LLMs) by adding early exit heads at intermediate…
arXiv:2603.00620v2 Announce Type: replace Abstract: Multilingual NLP research increasingly involves hundreds or thousands of languages across different datasets. Managing, discovering, and reporting language metadata becomes a…
arXiv:2511.04476v2 Announce Type: replace Abstract: Accurate and interpretable predictions of depression severity are essential for clinical decision support, yet existing models often lack uncertainty estimates and…
arXiv:2607.03882v2 Announce Type: replace Abstract: LLMs are increasingly deployed as post-hoc explainers of AI-generated outputs, yet it remains unclear whether they can reliably communicate probabilistic information…
arXiv:2604.22294v2 Announce Type: replace Abstract: Systematic reviews -- which requires comprehensive evidence collection and synthesis from large document corpora in response to targeted research questions --…
arXiv:2607.09322v1 Announce Type: new Abstract: In this work, we introduce LongMedBench, a real-world EHR-based benchmark for long-horizon clinical decision-making. Prior evaluations of LLM-based medical agents have…
arXiv:2607.09502v1 Announce Type: cross Abstract: Explaining machine-learning models is increasingly important for decision-making and consumer trust, yet it is widely believed to come at a cost:…
arXiv:2607.09217v1 Announce Type: new Abstract: In this system paper, we present OpenProver, an open-source system for LLM-driven automated theorem proving (ATP) with integrated Lean 4 formal…
arXiv:2607.09400v1 Announce Type: cross Abstract: Adaptive power management in Electric Vehicles (EVs) requires accurate power prediction. Although deep learning models have emerged as highly effective for…