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Pulse · dig.watch

dig.watch · Nov 5

Apple includes AI Agent risk factors to SEC Form 10‑K filing

Apple Inc. added new risk factors concerning autonomous AI agents to its SEC filing, warning that such agents could act unpredictably.

Pulse · hoodline.com

hoodline.com · Nov 4

NYC-Tel Aviv Startup Sentra Lands $150M as Rogue AI Agents Spook Corporations

Sentra says the past year brought more than 300% year-over-year growth, a surge it attributes to enterprises racing to deploy AI agents without fully understanding what those agents can access. The company's pitch centers on a platform that finds and classifies data across cloud,

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arXiv

Nov 3

DreamX: Video Diffusion Model is a Universal Latent Action Learner

Learning a universal action representation from video requires modeling dynamics that are consistent across different executions of the same action yet invariant to changes in embodiment and appearance. Existing latent action models infer actions from visual transitions, but their representations often entangle the underlying action with irrelevant visual variations, limiting transfer across embodiments. We introduce DreamX, which repurposes a pretrained video diffusion model as a universal latent action learner. Our approach extracts compact latent actions from the video model's rich spatiotemporal features, compressing each latent video frame by $\boldsymbol{300\times}$ ($\boldsymbol{1{,}200 \rightarrow 4}$ tokens). In addition, by leveraging the video model's generative prior, these latent actions capture action-relevant dynamics disentangled from visual appearance. The resulting latent space serves as a shared action interface across embodiments (human and robot), environments, and action parameterizations. On LARY-Bench, DreamX achieves state-of-the-art performance among latent action models. Moreover, actions encoded in one setting remain effective in another: the latent…

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Hugging Face

Sep 23

A 9B-class vision-language model with ternary transformer weights, for llama.cpp (CUDA, Metal, CPU).

Forget who you Forgot: Speaker Unlearning to Prevent Re-Identification in Zero-Shot Text-to-Speech

Recent zero-shot text-to-speech (ZS-TTS) systems can reproduce a speaker's voice with high fidelity from only a few seconds of reference speech, raising concerns over unauthorized voice cloning and impersonation. Speaker identity unlearning has recently emerged as an approach…

arXiv

Sep 23

When Parallel Drafter Meets Parallel Speculative Decoding

DSpark-style parallel drafters have made speculative decoding highly effective, yet their draft phase remains serialized on the critical path of every round. Parallel speculative decoding (PSD) overlaps drafting with verification, yet existing methods must guess the accepted…

arXiv

Sep 23

PRISM-VLM: A Multi-Axis Discriminative Benchmark for Compact Vision-Language Models

Compact vision-language models (VLMs) now power a growing share of multimodal applications. The benchmarks used to compare them, however, inherit a frontier-centric design: each model is reduced to a single accuracy number, narrowing the inter-model gap on saturated suites and…

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