arXiv:2602.05029v1 Announce Type: new Abstract: Operating effectively in novel real-world environments requires robotic systems to estimate and interact with previously unseen objects. Current state-of-the-art models address this challenge by using large amounts of training data and test-time samples to build black-box scene representations. In this work, we introduce a differentiable neuro-graphics model that combines neural foundation models with physics-based differentiable rendering to perform zero-shot scene reconstruction and robot grasping without relying on any additional 3D data or test-time […]

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Key Highlights: Google appears to be all set for a massive push into native audio, and AI Studio is where the first signs are starting to show. Over the past few days, users digging through AI Studio have spotted an interesting new option in the interface. A hidden “Create Your Voice” option has been spotted in AI Studio The news comes via Testing Catalog, which reports that when you select the Flash native audio preview model, powered by […]

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arXiv:2602.02505v1 Announce Type: new Abstract: This paper investigates learning-augmented algorithms for smooth integer programs, covering canonical problems such as MAX-CUT and MAX-k-SAT. We introduce a framework that incorporates a predictive oracle to construct a linear surrogate of the objective, which is then solved via linear programming followed by a rounding procedure. Crucially, our framework ensures that the solution quality is both consistent and smooth against prediction errors. We demonstrate that this approach effectively extends tractable approximations from the […]

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AGI is here! When exactly it arrived, we’ll never know; whether it was one company’s Pro or another company’s Pro Max (Eddie Bauer Edition) that tip-toed first across the line … you may debate. But generality has been achieved, & now we can proceed to new questions. […] The key word in Artificial General Intelligence is General. That’s the word that makes this AI unlike every other AI: because every other AI was trained for a particular purpose. Consider landmark models across the decades: […]

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Explosive demands for LLMs often cause user queries to accumulate in server queues, requiring efficient routing (query-LLM matching) and scheduling (query prioritization) mechanisms. Several online algorithms are being deployed, but they overlook the following two key challenges inherent to conversational LLM services: (1) unsatisfied users may retry queries, increasing the server backlog, and (2) requests for “explicit" feedback, such as ratings, degrade user experiences. In this paper, we develop a joint routing and scheduling algorithm that leverages “implicit" […]

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arXiv:2602.01863v1 Announce Type: new Abstract: Transformers excel through content-addressable retrieval and the ability to exploit contexts of, in principle, unbounded length. We recast associative memory at the level of probability measures, treating a context as a distribution over tokens and viewing attention as an integral operator on measures. Concretely, for mixture contexts $nu = I^{-1} sum_{i=1}^I mu^{(i^*)}$ and a query $x_{mathrm{q}}(i^*)$, the task decomposes into (i) recall of the relevant component $mu^{(i^*)}$ and (ii) prediction from $(mu_{i^*},x_mathrm{q})$. We […]

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arXiv:2601.00219v2 Announce Type: new Abstract: Agent communication remains a foundational problem in multi-agent systems: protocols such as FIPA-ACL guarantee semantic richness but are intractable for constrained environments, while lightweight IoT protocols achieve efficiency at the expense of expressiveness. This paper presents $mu$ACP, a formal calculus for expressive agent communication under explicit resource bounds. We formalize the Resource-Constrained Agent Communication (RCAC) model, prove that a minimal four-verb basis textit{{PING, TELL, ASK, OBSERVE}} is suffices to encode finite-state FIPA protocols, […]

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