Amazon Research Awards (ARA) provides unrestricted funds and AWS Promotional Credits to academic researchers investigating various research topics in multiple disciplines. This cycle, ARA received many excellent research proposals from across the world and today is publicly announcing 63 award recipients who represent 41 universities in 8 countries. This announcement includes awards funded under five call for proposals during the spring 2025 cycle: AI for Information Security, Amazon Ads, AWS AI: Agentic AI, Build on Trainium and Think […]

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arXiv:2601.07880v1 Announce Type: new Abstract: Identity Security Posture Management (ISPM) is a core challenge for modern enterprises operating across cloud and SaaS environments. Answering basic ISPM visibility questions, such as understanding identity inventory and configuration hygiene, requires interpreting complex identity data, motivating growing interest in agentic AI systems. Despite this interest, there is currently no standardized way to evaluate how well such systems perform ISPM visibility tasks on real enterprise data. We introduce the Sola Visibility ISPM Benchmark, […]

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arXiv:2603.16893v1 Announce Type: new Abstract: In many low-income countries, neighborhood diesel generators are widely used to compensate for unreliable or unavailable national electricity grids. These diesel-based microgrids are typically characterized by market power, significant pollution, and weak regulatory oversight. In parallel, households increasingly deploy off-grid solar photovoltaic (PV) systems to gain control over electricity supply. However, these systems suffer from curtailed excess generation during peak solar hours and unreliable access at other times. While prior studies have optimized […]

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arXiv:2603.25861v1 Announce Type: new Abstract: Activation-based probes have emerged as a promising approach for detecting deceptively aligned AI systems by identifying internal conflict between true and stated goals. We identify a fundamental blind spot: probes fail on coherent misalignment – models that believe their harmful behavior is virtuous rather than strategically hiding it. We prove that no polynomial-time probe can detect such misalignment with non-trivial accuracy when belief structures reach sufficient complexity (PRF-like triggers). We show the emergence […]

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1. Introduction & Contract-First Approach APIs are the arteries of modern software, powering everything from mobile apps to distributed cloud microservices. Building those APIs, however, is rarely as straightforward as writing a few controller methods. If you’ve worked on any enough complex backend, you’ve probably wrestled with inconsistent request/response payloads, mismatched client/server contracts, or ambiguous endpoint documentation. I certainly have, and the pain points are always the same: a frontend developer working from outdated documentation, a client blocked […]

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arXiv:2603.23512v1 Announce Type: new Abstract: We present S-Path-RAG, a semantic-aware shortest-path Retrieval-Augmented Generation framework designed to improve multi-hop question answering over large knowledge graphs. S-Path-RAG departs from one-shot, text-heavy retrieval by enumerating bounded-length, semantically weighted candidate paths using a hybrid weighted $k$-shortest, beam, and constrained random-walk strategy, learning a differentiable path scorer together with a contrastive path encoder and lightweight verifier, and injecting a compact soft mixture of selected path latents into a language model via cross-attention. The […]

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’m scoping out a topic on robotic clothes folding and need a sanity check on my proposed stack. I’m thinking of combining a VLA (Vision-Language-Action) foundation model for semantic reasoning, SERL (Sample Efficient RL) for fine-tuning the physical manipulation, and DAgger / HIL for human-in-the-loop corrections during out-of-distribution states. I want to know if this is actually feasible ? any landmines I might runinto ? submitted by /u/Ok_Abbreviations2264 [link] [comments]

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arXiv:2602.09240v1 Announce Type: cross Abstract: We consider the problem of parameter estimation from a generalized linear model with a random design matrix that is orthogonally invariant in law. Such a model allows the design have an arbitrary distribution of singular values and only assumes that its singular vectors are generic. It is a vast generalization of the i.i.d. Gaussian design typically considered in the theoretical literature, and is motivated by the fact that real data often have a […]

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arXiv:2510.17794v2 Announce Type: replace-cross Abstract: Modern probabilistic regressors often remain overconfident under distribution shift. We present Functional Distribution Networks (FDN), an input-conditioned distribution over network weights that induces predictive mixtures whose dispersion adapts to the input. FDN is trained with a beta-ELBO and Monte Carlo sampling. We further propose an evaluation protocol that cleanly separates interpolation from extrapolation and stresses OOD sanity checks (e.g., that predictive likelihood degrades under shift while in-distribution accuracy and calibration are maintained). On […]

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