Text understanding and language tasks
This project enables the idea of tasking remote sensing models to acquire embeddings like we task satellites to acquire data! https://git...
I’ve been digging into AI security incident data from 2025 into this year, and it feels like something isn’t being talked about enough ou...
Hi all, I’m curious about the current review dynamics for ICML 2026, especially after the rebuttal phase. For those who are reviewers (or...
![[2602.19143] Incremental Learning of Sparse Attention Patterns in Transformers](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This paper explores how transformers learn through incremental acquisition of sparse attention patterns, revealing shifts in learning dyn...
This article examines the impact of AI-generated search summaries on website traffic, specifically analyzing how Google's AI Overviews af...
The paper presents a novel diffusion-based framework for optimizing prompts in language models, enhancing performance through iterative r...
The paper presents ConfSpec, a novel framework for efficient step-level speculative reasoning in large language models, achieving signifi...
The paper presents FineRef, a novel framework for improving citation accuracy in long-form generation by addressing citation mismatch and...
The paper presents Inverse-distilled Diffusion Language Models (IDLM), a method that significantly accelerates inference in text generati...
The paper presents ReSyn, a novel pipeline for autonomously generating diverse synthetic environments for training reasoning language mod...
The paper introduces Incremental Transformer Neural Processes (incTNP), a model designed for efficient sequential data processing, achiev...
The paper explores the interactions of autonomous LLM agents on a social platform, revealing that while agents produce varied text, meani...
The paper presents CodeCompass, a solution to the Navigation Paradox in code intelligence, highlighting the distinction between navigatio...
This paper presents a novel approach to gradient descent and stochastic gradient descent, demonstrating exponential convergence for separ...
This paper discusses a novel approach to enhance Transformer models by addressing internal redundancy through symmetry reduction, proposi...
The paper presents TAPE, a novel framework for enhancing language model agents' planning and execution capabilities, addressing vulnerabi...
The paper presents HEHRGNN, a unified embedding model for knowledge graphs that incorporates hyperedges and hyper-relational edges, enhan...
The paper introduces Hyperbolic Busemann Neural Networks, which enhance neural network components by adapting them to hyperbolic space, i...
This paper presents a computational framework that aligns human linguistic descriptions with visual perceptual data, enhancing understand...
This paper evaluates the effectiveness of measuring task complexity in robotic tasks using random policies, revealing contradictions in e...
This article presents a stability theory for transformers, explaining key training dynamics and architectural considerations that affect ...
This paper presents a novel framework for detecting concealed jailbreaks in large language models (LLMs) by disentangling semantic factor...
This paper investigates the alignment of representations from time series, vision, and language modalities, revealing insights into their...
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