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Pulse · nrn.com

nrn.com · Nov 4

Uber Eats sees little traffic from AI agents

The company reported virtually no business from AI despite recent buzz about the technology.

Pulse · msn.com

msn.com · Nov 4

Exclusive: Meta buys AI startup, joins race to own coding in show of outsized ambition

Meta has acquired Conductor, an AI coding startup that lets software engineers manage multiple coding agents simultaneously, sources familiar with the deal tell Axios exclusively. Why it matters: Meta is joining OpenAI and other AI giants in a race to own the tools developers use,

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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

Multitask Regression with Pairwise Fusion

We study multitask regression when coefficient sharing can differ by predictor. For a given predictor, many tasks may have the same coefficient while a few differ, and the exceptional tasks need not be the same for another predictor. We describe this structure by two…

arXiv

Sep 23

EnSIMem: Entity-Structured Indexing for Long-Term Agent Memory

An agent that interacts with users over long periods must recall facts, preferences, events, and changes from a continuously growing interaction history. Existing memory systems often compress interactions into generic summaries or retrieve anonymous text chunks, making it…

arXiv

Sep 23

Graph Learning with Spectral Connectivity Priors for Scarce Data

Learning a sparse graph from scarce data is practically important but challenging. Motivated by the desirable combination of local sparsity and strong global connectivity exhibited by expander-like graphs, we propose spectral connectivity-regularized graph learning (SCoGL), a…

arXiv

Sep 23

TimeEvo: Failure-Driven Self-Evolution of a Time Series Agent

Time series agents answer analytical questions by calling external tools, and which tools they carry is decided by people before the agent runs. However, we identify two failures in this setup. Human-Agent Tool Misalignment: a library of 21 expert-curated tools helps on some…

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