arXiv:2601.16367v1 Announce Type: new Abstract: Game-theoretic models and solution concepts provide rigorous tools for predicting collective behavior in multi-agent systems. In practice, however, different agents may rely on different game-theoretic models to design their strategies. As a result, when these heterogeneous models interact, the realized outcome can deviate substantially from the outcome each agent expects based on its own local model. In this work, we introduce the game-to-real gap, a new metric that quantifies the impact of such […]

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Federated learning (FL) encounters substantial challenges due to heterogeneity, leading to gradient noise, client drift, and partial client participation errors, the last of which is the most pervasive but remains insufficiently addressed in current literature. In this paper, we propose FedAdaVR, a novel FL algorithm aimed at solving heterogeneity issues caused by sporadic client participation by incorporating an adaptive optimiser with a variance reduction technique. This method takes advantage of the most recent stored updates from clients, even […]

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The Subscription Creep Problem Nobody Talks About I pulled my bank statements last November and counted 14 recurring subscriptions. Google One for storage. iCloud for family photos. 1Password. Three streaming services. A VPN. Backblaze for backups. Two productivity tools I barely used. Total monthly damage: $103. According to Deloitte’s 2025 Digital Media Trends report, the average U.S. streaming household pays $69/month across four video services alone, a 13% year-over-year increase. Add cloud storage, password management, and backup services, […]

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paper: https://arxiv.org/pdf/1811.02553 I checked connected papers and didn’t find any recent papers on the questions/issues raised in this paper. They seem pretty insightful to me, so I’m debating at looking at whether more recent methods have alleviated the issues, and if so, why. submitted by /u/icantclosemytub [link] [comments]

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arXiv:2603.03378v2 Announce Type: new Abstract: Large language model (LLM) agents offer a promising data-driven approach to automating Site Reliability Engineering (SRE), yet their enterprise deployment is constrained by three challenges: restricted access to proprietary data, unsafe action execution under permission-governed environments, and the inability of closed systems to improve from failures. We present AOI (Autonomous Operations Intelligence), a trainable multi-agent framework formulating automated operations as a structured trajectory learning problem under security constraints. Our approach integrates three key […]

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arXiv:2604.08718v2 Announce Type: new Abstract: Geometric Foundation Models (GFMs) have recently advanced monocular SLAM by providing robust, calibration-free 3D priors. However, deploying these models on dense video streams introduces significant computational redundancy. Current GFM-based SLAM systems typically rely on post hoc keyframe selection. Because of this, they must perform expensive dense geometric decoding simply to determine whether a frame contains novel geometry, resulting in late rejection and wasted computation. To mitigate this inefficiency, we propose LeanGate, a lightweight […]

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arXiv:2602.22265v1 Announce Type: new Abstract: Modern vision generators transport a base distribution to data through time-indexed measures, implemented as deterministic flows (ODEs) or stochastic diffusions (SDEs). Despite strong empirical performance, standard flow-matching objectives do not directly control the information geometry of the trajectory, allowing low-entropy bottlenecks that can transiently deplete semantic modes. We propose Entropy-Controlled Flow Matching (ECFM): a constrained variational principle over continuity-equation paths enforcing a global entropy-rate budget d/dt H(mu_t) >= -lambda. ECFM is a convex […]

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arXiv:2602.10146v1 Announce Type: new Abstract: While Vision-Language Models (VLMs) have shown promise in textual understanding, they face significant challenges when handling long context and complex reasoning tasks. In this paper, we dissect the internal mechanisms governing long-context processing in VLMs to understand their performance bottlenecks. Through the lens of attention analysis, we identify specific Visual Evidence Retrieval (VER) Heads – a sparse, dynamic set of attention heads critical for locating visual cues during reasoning, distinct from static OCR […]

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