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Pulse · forbes.com

forbes.com · Nov 4

Are We Ready For A World Without Managers? Anthropic’s Vision Of The Future

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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Top picks this weekarXiv

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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arXiv

Sep 23

Forecast Workflow Bench: Evaluating Language-Model Decisions with Budgeted Forecast Tools

Time-series foundation models (TSFMs) provide forecasts for operational decisions, but accuracy alone does not determine their value. Evaluating agents that use these models requires measuring decision quality and forecast cost. FWBench evaluates this capability on 1,251…

arXiv

Sep 23

When Entanglement Lower-Bounds Disparity: Auditing and Repairing Demographic Fairness in Audio Understanding Models

Speech technology penalizes some voices: recognition errs nearly twice as often for Black speakers, and accuracy declines for second-language accents and older speakers. We introduce TRIAD, an audit grid crossing 120 texts, 24 rendered demographic voice profiles (gender, age…

arXiv

Sep 23

MORSE: Multi-Context Ordering via Reverse Scoring for Evidence-Preserving Compression

Retrieval-augmented generation often relies on multiple retrieved contexts that contain substantial redundancy, motivating context compression to preserve useful information under limited input budgets. Likelihood-based compressors can account for cross-context redundancy…

arXiv

Sep 23

Psychoacoustically Aligned Latent Smoothing for Adversarial Robustness of Full-Duplex Speech-to-Speech Dialogue Models

End-to-end speech-to-speech dialogue models listen and speak simultaneously, so a continuously open acoustic channel is exposed to adversarial manipulation. We formalize imperceptible attacks on full-duplex agents as optimization over additive perturbations confined beneath the…

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