Text understanding and language tasks
From the Macintosh to the iPhone to the iMac to the iPod, it’s hard to pick a best Apple product ever. But we tried to do so anyway.
Been thinking about this and want to know if others have hit the same issue. The dominant approach for agent memory (Mem0, Zep, most RAG ...
![[2601.11016] Contextual Distributionally Robust Optimization with Causal and Continuous Structure: An Interpretable and Tractable Approach](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
Abstract page for arXiv paper 2601.11016: Contextual Distributionally Robust Optimization with Causal and Continuous Structure: An Interp...
The paper introduces KNIGHT, a framework for generating multiple-choice questions using knowledge graphs, enhancing efficiency and adapta...
The paper explores how Test-Time Training (TTT) enhances transformer models as in-context learners, demonstrating significant efficiency ...
This article critiques traditional metrics for measuring oversmoothing in Graph Neural Networks (GNNs) and proposes a rank-based approach...
The paper presents vCache, a verified semantic prompt caching system that enhances LLM inference efficiency by dynamically adjusting simi...
The paper presents StructXLIP, a novel approach that enhances vision-language models by integrating multimodal structural cues, improving...
This article examines the performance of multilingual large language models (LLMs) across various languages, revealing that comprehension...
AgenticSum presents a novel framework for improving clinical text summarization using large language models, focusing on reducing factual...
This article presents a novel approach to conformal risk control for non-monotonic losses, extending traditional methods to multidimensio...
The article discusses NanoKnow, a benchmark dataset designed to understand how large language models (LLMs) acquire knowledge, using the ...
The paper presents ReAttn, a novel strategy to enhance attention-based re-ranking in large language models by reducing lexical bias and i...
This paper addresses the modality gap in multimodal medical representation alignment, proposing a framework to enhance alignment between ...
The paper presents MAS-FIRE, a framework for evaluating the reliability of LLM-based Multi-Agent Systems through fault injection, address...
The paper proposes Carbon-Aware Governance Gates (CAGG) to integrate sustainability into Generative AI development, addressing the increa...
The paper presents a novel approach to rescale ReLU neural networks through path-conditioned training, enhancing training dynamics and ef...
DReX is a novel multimodal recommendation framework that enhances user and item representation through explainable deep learning, address...
The paper presents a novel two-stage training approach for Automatic Chord Recognition (ACR), utilizing pseudo-labeling and knowledge dis...
This paper explores the limitations of using a single extractor for HTML-to-text conversion in LLM pretraining, proposing a union of mult...
The paper presents a Tri-Subspace Disentanglement (TSD) framework for Multimodal Sentiment Analysis, enhancing representation by factorin...
The paper presents Laplacian Multi-scale Flow Matching (LapFlow), a new framework for image generative modeling that enhances flow matchi...
This paper presents a novel framework for Temporal Question Answering over Temporal Knowledge Graphs, addressing limitations in temporal ...
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