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...
![[2512.01865] Cross-Lingual Interleaving for Speech Language Models](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
The paper presents a novel cross-lingual interleaving method for Speech Language Models (SLMs), enhancing multilingual understanding and ...
The paper introduces GRAPE (Group Representational Position Encoding), a framework for positional encoding that integrates multiplicative...
The paper introduces the Conductor model, which utilizes reinforcement learning to optimize coordination strategies among large language ...
The paper introduces Consistency Diffusion Language Models (CDLM), a method that accelerates inference in diffusion language models by re...
This study presents TraDy, a novel transfer learning scheme for memory-constrained fine-tuning of deep neural networks, achieving state-o...
This paper explores the linearity of neural networks by introducing a framework that identifies non-standard vector spaces where neural n...
The paper discusses the concept of anthropomimetic uncertainty in language models, emphasizing the need for these models to express confi...
PonderLM introduces a novel approach to language model training by incorporating a 'pondering' phase, enhancing cognitive processing duri...
The paper presents ConformalNL2LTL, a novel method for translating natural language instructions into Linear Temporal Logic (LTL) formula...
This paper discusses how classification errors in automated speech processing can distort findings in child-development research, proposi...
FLUKE introduces a novel framework for evaluating the robustness of NLP models through controlled linguistic variations, revealing task-d...
The paper introduces CAE, a novel approach in deep reinforcement learning that repurposes value networks to enhance exploration efficienc...
This article presents a novel approach to knowledge distillation for interactive AI, emphasizing contextual guidance over simple output i...
The paper introduces Generative Distribution Embeddings (GDE), a novel framework that enhances autoencoders for multiscale representation...
The paper presents Assimilative Causal Inference (ACI), a novel framework that utilizes Bayesian data assimilation to identify dynamic ca...
This article discusses advancements in model diffing using crosscoders to better interpret changes in AI models during chat-tuning, addre...
This paper presents a novel semantic scheduling paradigm for cluster workload allocation using Natural Language Processing, enhancing usa...
The paper presents SPARK, a framework for personalized search using agent-driven retrieval and knowledge-sharing, enhancing user experien...
This paper presents two innovative constraint compilation methods for lifted planning in AI, addressing scalability issues in existing co...
This paper presents a novel perspective on in-context learning in large language models (LLMs) through the lens of quantum mechanics, pro...
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