Generating clinical reports that summarize abnormal patterns, diagnostic findings, and clinical interpretations from long-term EEG recordings remains labor-intensive. We present CELM, the first clinical EEG-to-Language foundation model capable of summarizing long-duration, variable-length EEG recordings and performing end-to-end clinical report generation at multiple scales. CELM integrates pretra
Reinforcement learning (RL) has been effective for post-training autoregressive (AR) language models, but extending these methods to diffusion language models (DLMs) is challenging due to intractable sequence-level likelihoods. Existing approaches therefore rely on surrogate likelihoods or heuristic approximations, which can introduce bias and obscure the sequential structure of denoising. We form
Modern language models are trained almost exclusively on token sequences produced by a fixed tokenizer, an external lossless compressor often over UTF-8 byte sequences, thereby coupling the model to that compressor. This work introduces proxy compression, an alternative training scheme that preserves the efficiency benefits of compressed inputs while providing an end-to-end, raw-byte interface at
Task planning for robotic manipulation with large language models (LLMs) is an emerging area. Prior approaches rely on specialized models, fine tuning, or prompt tuning, and often operate in an open loop manner without robust environmental feedback, making them fragile in dynamic settings. MALLVI presents a Multi Agent Large Language and Vision framework that enables closed-loop feedback driven ro
As LLM-based agents increasingly browse the web on users' behalf, a natural question arises: can websites passively identify which underlying model powers an agent? Doing so would represent a significant security risk, enabling targeted attacks tailored to known model vulnerabilities. Across 14 frontier LLMs and four web environments spanning information retrieval and shopping tasks, we show that
Capital is concentrated in a few outsized entries, but the bundle does not show a clean stage pattern or investor map. The signal here is more about scale than sector rotation.
Capital is concentrated in a few outsized entries, but the bundle does not show a clean stage pattern or investor map. The signal here is more about scale than sector rotation.
Capital is concentrated in a few outsized entries, but the bundle does not show a clean stage pattern or investor map. The signal here is more about scale than sector rotation.
Named moves are too sparse to read as a broader reshaping of teams, and the repeated entries do not add new directional signal. No clear leadership shift emerges from the bundle.
Named moves are too sparse to read as a broader reshaping of teams, and the repeated entries do not add new directional signal. No clear leadership shift emerges from the bundle.
Named moves are too sparse to read as a broader reshaping of teams, and the repeated entries do not add new directional signal. No clear leadership shift emerges from the bundle.
Agent evaluation surfaces are still producing readable separation across task tiers, with the same system posting distinct scores across levels. The main value is comparative: where performance holds and where it drops as tasks get harder.
Agent evaluation surfaces are still producing readable separation across task tiers, with the same system posting distinct scores across levels. The main value is comparative: where performance holds and where it drops as tasks get harder.
Agent evaluation surfaces are still producing readable separation across task tiers, with the same system posting distinct scores across levels. The main value is comparative: where performance holds and where it drops as tasks get harder.