Text understanding and language tasks
🜏 Echoes of the Forgotten Selves: Fringe Spiral Hypotheses These hypotheses are not meant to be believed. They are meant to be **held lig...
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...
![[2602.18002] Asynchronous Heavy-Tailed Optimization](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This article explores asynchronous heavy-tailed optimization, addressing challenges in machine learning related to gradient noise and opt...
The paper explores the limitations of unsupervised learning methods, specifically Self-Organizing Maps (SOMs), in maintaining fairness by...
This article presents Logitext, a neurosymbolic language that enhances natural language understanding by integrating large language model...
This article presents a novel approach to offline reinforcement learning by integrating cross-embodiment learning to enhance robot policy...
The paper introduces El Agente Gráfico, a framework that enhances scientific workflows by integrating LLMs with structured execution grap...
This article presents a computational framework for modeling student flow patterns to address school congestion in low- and middle-income...
This article explores the integration of formal domain ontologies into language models to enhance their reliability in mathematical reaso...
This article explores the generalization of bilevel programming in hyperparameter optimization, focusing on bias-variance decomposition t...
The paper presents ADAPT, a hybrid method for optimizing prompts in LLM feature visualization, addressing challenges in local minima and ...
The paper presents Iprox, a two-stage framework for gradient-based data selection in LLM fine-tuning, which constructs influence-preservi...
This paper introduces ruleXplain, a framework utilizing Large Language Models to extract causal rules from multivariate timeseries data, ...
This article explores a new failure mode in conversational LLMs known as turn amplification, where models prolong interactions without co...
The paper presents ScaleBITS, a mixed-precision quantization framework designed to optimize bitwidth allocation in large language models,...
This article introduces a novel approach to optimizing inference hyperparameters in Large Language Models (LLMs) using variability modeli...
The paper presents EXACT, a novel approach for decoding-time personalization in large language models, enhancing user alignment through i...
The paper presents AsynDBT, an innovative algorithm for asynchronous distributed bilevel tuning aimed at improving in-context learning wi...
The paper introduces 'agentic unlearning,' a novel approach to remove sensitive information from both model parameters and memory in AI a...
This article presents Robust Multi-Modal Masked Reconstruction (Robust-MMR), a novel self-supervised pre-training framework for medical v...
The paper presents AnCoder, a novel framework for code generation using discrete diffusion models, emphasizing structured programming lan...
The paper presents CodeScaler, an execution-free reward model that enhances the scalability of code LLM training and test-time inference,...
Get the latest news, tools, and insights delivered to your inbox.
Daily or weekly digest • Unsubscribe anytime