This work introduces Aloe-Vision, a family of open-source large vision-language models (7B and 72B) trained on the newly released Aloe-Vision-Data dataset to address data scarcity and robustness issues in healthcare AI. The authors demonstrate that their high-quality training mixture yields significant performance gains over baselines while maintaining general capabilities.
A re-derivation of the activation patching estimand from causal mediation analysis reveals that the natural indirect effect (NIE) captures not only the causal effect through a specific component but also interaction effects (INT). These INT terms measure how much a component's causal effect depends on the state of other components in the model, challenging the assumption that NIE isolates individual contributions.
The authors introduce the context-ready transformer, a recurrent neural network architecture that pre-contextualizes each token before it enters a D-layer transformer block using a correction network.
The authors propose Entropy-guided Multi-Token Prediction (EntMTP), a training-free scheduler that dynamically adjusts speculation depth during LLM inference based on local generation entropy. This approach addresses the inefficiency of static tree-based attention topologies by matching compute requirements to context predictability.
The article introduces Ko-WideSearch, a new benchmark designed to evaluate the breadth-search capabilities of web agents in Korean, addressing the lack of exhaustive set enumeration metrics outside English.
The authors introduce Narrative-UFET, a controlled extension of ultra-fine entity typing that pairs entity mentions with automatically generated short narratives to address limitations in long-tail type disambiguation. The study demonstrates that narrative context yields consistent improvements over sentence-level baselines, particularly when the entity's type shifts within the text.
The authors introduce Masked Language Flow Models (MLFMs), which combine masked diffusion with continuous flows to enable efficient, multi-step reasoning in language generation. This approach bridges the gap between parallel generation efficiency and complex task performance by allowing pretrained models to be adapted into MLFMs.
This paper introduces DysLexLens, a low-resource LLM framework designed to analyze the experiences of dyslexic learners with AI tools through online forum discussions. The system provides an end-to-end, evidence-traceable architecture that transforms noisy social media posts into focused corpora and generates verifiable query responses.
This paper addresses the performance degradation of offensive comment detection models when deployed across different Chinese social media platforms by proposing a dual-threshold hard example mining method.
The Generational Context Architecture (GCA) proposes treating an LLM's context window as a finite lifespan rather than infinite storage to solve "context rot" and attention dilution in multi-agent systems. By enforcing artificial mortality, agents are terminated before performance degrades, passing their state to new generations via a flat-file Markdown vault.
The Yuvion LLM is a new large language model designed to address safety failures by treating adversarial robustness and agentic capability as primary objectives. It utilizes a pipeline combining adversarially aware data construction, knowledge-enhanced continued pretraining, and policy-grounded multi-task safety post-training.
The authors introduce DiscoBench, a benchmark designed to evaluate whether search agents powered by large language models can proactively identify ambiguity and ask effective clarification questions during deep search tasks. Unlike existing benchmarks that assume complete user queries, this framework addresses the reality of vague or underspecified requests in real-world scenarios.
This study conducts a factorised analysis of probe-based uncertainty estimation to determine what drives performance in detecting hallucinations within Large Language Models. The research isolates variables across feature design, training data, and evaluation settings to provide clear insights into effective methodologies.
This article addresses the issue of unidentifiable latent states in LLM-based world models caused by history bypass, proposing strict latent state mediation to resolve this. The authors introduce textual latent states and factorized GRPO (fGRPO), a tree-structured reinforcement learning method that enforces strict mediation during training.
An independent researcher analyzed the evolution of hidden representations during inference across seven open-weight models, including GPT-2, OPT-125M, and Llama-3.2-1B, to identify internal dynamical regimes beyond standard output benchmarks.
This independent research project characterizes the internal dynamics of seven small and medium-sized language models by analyzing how hidden representations evolve during inference rather than relying on standard output benchmarks. The study investigates dynamic behavior, functional organization, and representation geometry to identify reproducible patterns across different architectures.
A developer has created a World Cup 2026 prediction tool that uses historical data to simulate tournament outcomes. The application provides win probabilities and score predictions for any two national teams based on patterns learned from approximately 50,000 international matches spanning over a century.
Carles Marin has released an open-source, bilingual (English and Spanish) guide that bridges the mathematical foundations of Transformer architectures with their practical implementation. The resource focuses on low-level mechanics, providing reproducible code and interactive elements to explain complex topics.
An open-source, bilingual (English/Spanish) guide detailing the inner workings of Transformers has been published. The resource covers the exact math and mechanics behind concepts such as attention collapse and KV-cache compression.
Researchers propose a protocol to mitigate p-hacking in large language model (LLM) research by preregistering experiments and running confirmatory analyses on the first eligible LLM released after the commitment. This approach prevents researchers from tuning prompts or parameters to achieve desired results, as the target model does not exist at the time of preregistration.