Knowledge catalogue
arXiv:2609.27446v1 Announce Type: cross Abstract: Quantum cloud computing, delivered through the quantum-as-a-service (QaaS) model, provides access to quantum computing resources. However, applying un
arXiv:2609.28262v1 Announce Type: new Abstract: Deploying deep learning models on edge CPUs is bottlenecked by computational and memory constraints. Mixed-precision quantization promises to reduce inf
arXiv:2609.28080v1 Announce Type: new Abstract: Evaluating open-ended text generation involves understanding how different properties of a continuation relate to its perceived quality. We present a re
arXiv:2609.27035v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) and related policy-gradient methods for training language model agents collapse an entire multi-turn rollout i
Remember, kids, the best argument for soldiering on with an insanely large bet on generative AI is not that it adds all that much productivity to the economy, but that it makes Trump look good as long
arXiv:2609.28145v1 Announce Type: new Abstract: Reinforcement learning (RL) improves reasoning, but its performance depends on the policy from which training begins. We study on-policy distillation (O
arXiv:2609.27413v1 Announce Type: new Abstract: Alignment-free RGB-T salient object detection (RGB-T SOD) aims to identify salient objects from unregistered RGB and thermal image pairs without costly
arXiv:2609.26986v1 Announce Type: new Abstract: For multimodal large language models, when images or speech conflict with accompanying text, measured text reliance can entangle modality preference wit
arXiv:2609.28117v1 Announce Type: cross Abstract: In this paper, we introduce a gradient-based head attribution strategy where the Token-level Max-Margin loss is backpropagated to the attention maps.
arXiv:2609.28064v1 Announce Type: new Abstract: Driving world-action models improve planning by coupling multimodal reasoning with future prediction, but their growing inference cost increasingly conf
arXiv:2604.00241v2 Announce Type: replace-cross Abstract: Algorithms for the Multi-Armed Bandit (MAB) problem play a central role in sequential decision-making and have been extensively explored both
The AWS WhisperX Deep Learning Container packages Whisper, wav2vec2 forced alignment, and speaker diarization into a GPU-ready image. Learn how to deploy it to Amazon SageMaker AI real-time and asynch
arXiv:2609.26989v1 Announce Type: new Abstract: Hexapod robots can achieve static stability with fewer actuated joints than bipeds or quadrupeds, yet many platforms still use 3-DOF legs, increasing we
arXiv:2609.27917v1 Announce Type: cross Abstract: Neural combinatorial optimization solvers generate many candidate solutions per instance and report the best one found, using the same sample budget f
arXiv:2609.27441v1 Announce Type: new Abstract: Achieving stable long-term neural decoding in invasive brain-machine interfaces (BMIs) remains challenging due to variations in recorded neural populati
arXiv:2609.27696v1 Announce Type: new Abstract: The SYNTAX score is an established tool for assessing coronary artery disease and guiding revascularization treatment decisions. However, its manual est
arXiv:2609.26940v1 Announce Type: cross Abstract: Biological sensory neurons have selective receptive fields organized along meaningful stimulus coordinates, such as frequency, motion direction, or re
arXiv:2609.27059v1 Announce Type: new Abstract: We introduce the Illinois Social Attitudes Aggregate Corpus (ISAAC), an open, modular, and accessible corpus of 527 million+ English-language Reddit pos
arXiv:2609.28187v1 Announce Type: new Abstract: Self-supervised vision transformers trained with DINO-style objectives exhibit striking emergent semantic representation quality across visual tasks, ye
arXiv:2604.06039v2 Announce Type: replace-cross Abstract: Value iteration-type methods have been extensively studied for computing a nearly optimal value function in reinforcement learning (RL). Under
arXiv:2609.27577v1 Announce Type: new Abstract: Multimodal joint training often suffers from modality imbalance, where a dominant modality suppresses the optimization of others. Existing methods mainl
arXiv:2609.28429v1 Announce Type: new Abstract: An always-on robot faces an endless stream that never resets: instructions arrive and lapse, the scene changes, and its own past actions reshape what it
We are really fucked. • AI has become dangerous (not because it is brilliant but because it has poor judgement, a constitutional inability to reliably follow rules, a surfeit of brute force and too mu
arXiv:2609.28137v1 Announce Type: cross Abstract: Educational technology (EdTech) platforms collect highly sensitive student data, including behavioral logs, disability records, and academic histories
arXiv:2609.27252v1 Announce Type: cross Abstract: The Platonic Representation Hypothesis suggests that increasingly capable models converge toward shared representations. Recent work narrows this clai
arXiv:2609.28385v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) supervises mathematical reasoning through final-answer correctness, but provides little guidance
arXiv:2609.27382v1 Announce Type: cross Abstract: Speech technology penalizes some voices: recognition errs nearly twice as often for Black speakers, and accuracy declines for second-language accents
arXiv:2609.27560v1 Announce Type: cross Abstract: Avatar-streaming systems are commonly evaluated with image and video quality assessment (IQA/VQA) metrics, implicitly treating visual fidelity as a pr
arXiv:2609.26929v1 Announce Type: new Abstract: People hold diverse, sometimes conflicting values, so no single aligned model can satisfy everyone. Pluralistic alignment therefore calls for steerable
arXiv:2609.26623v1 Announce Type: new Abstract: Diffusion-based synthetic data generation offers a promising route for sharing medical imaging data without releasing sensitive patient records. However
arXiv:2609.26783v1 Announce Type: cross Abstract: We study decentralized partially observable team decision problems with low-rank latent dynamics and unknown system models. The proposed framework com
arXiv:2609.26445v1 Announce Type: cross Abstract: This manuscript formalizes the most popular model validation tools used in general insurance actuarial modeling. These include graphical tools like ca
arXiv:2609.26527v1 Announce Type: new Abstract: When two texts describe the same expression, standard metrics based on lexical overlap or whole-text similarity may fail to detect meaningful difference
arXiv:2609.23989v1 Announce Type: new Abstract: Building general-purpose agents for industrial deployment requires integrating multiple capabilities, each typically acquired at a distinct stage of tra
arXiv:2609.24446v2 Announce Type: cross Abstract: Large language model (LLM) agents increasingly execute long-horizon workflows through external tools, allowing untrusted outputs to influence subseque
arXiv:2504.20106v4 Announce Type: replace Abstract: Ensuring that large language models (LLMs) are both helpful and harmless is a critical challenge, as overly strict constraints can lead to excessive
arXiv:2609.25578v1 Announce Type: new Abstract: Urban-scale 3D reconstruction from satellite imagery supports disaster response, city monitoring, and geospatial digital twins, yet neural rendering met
arXiv:2512.21066v4 Announce Type: replace Abstract: Explainable artificial intelligence (XAI) reveals how explanatory variables relate to a response variable, yet communicating XAI outputs to layperso
arXiv:2609.26274v1 Announce Type: new Abstract: The rapid evolution of generative AI (e.g., Sora, Hunyuan) makes it essential to develop effective detection strategies that can generalize across ever-
arXiv:2609.23640v1 Announce Type: new Abstract: Human-feedback alignment has made language models useful assistants and is commonly described as aligning them with humans. However, the responses peopl
arXiv:2609.25930v1 Announce Type: new Abstract: Detecting and localizing 3D point-cloud defects is essential for industrial inspection. However, existing methods often suffer from imprecise localizati
arXiv:2609.25735v1 Announce Type: new Abstract: Class-balanced replay controls class frequency but does not determine the interval between successive replay appearances of a class. We study this inter
arXiv:2609.26567v1 Announce Type: new Abstract: Robots that store past experiences must select which one to reuse in a new scene. Most systems select by visual similarity, and most evaluations report
arXiv:2609.23421v1 Announce Type: cross Abstract: Standard data center sustainability metrics, including Power Usage Effectiveness (PUE), Water Usage Effectiveness (WUE), and Carbon Usage Effectivenes
arXiv:2609.22747v1 Announce Type: cross Abstract: Understanding the performance of large-scale recommender systems remains an underexplored challenge, especially for content creators and model develop
arXiv:2609.25820v1 Announce Type: cross Abstract: Discrete action tokenization is central to autoregressive vision-language-action (VLA) models, yet action representations are often evaluated primaril
arXiv:2508.09297v4 Announce Type: replace-cross Abstract: Artificial intelligence (AI) increasingly shapes how people think, engage, and evaluate information. To minimize risk, most current systems ar
arXiv:2609.26489v1 Announce Type: new Abstract: Calibration of language models -- the alignment between expressed or implicit confidence and empirical correctness -- is a well-studied subfield within
arXiv:2609.25369v1 Announce Type: new Abstract: Shared control often allocates robot authority based on confidence in inferred human intent, assuming reliable autonomous execution. When this assumptio
arXiv:2609.26056v1 Announce Type: new Abstract: Vision-Language Models (VLMs) offer promising capabilities for automated sports coaching but face a fundamental limitation: they implicitly compare agai
arXiv:2609.24801v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in production systems, raising concerns about their exposure to adversarial manipulation throug
arXiv:2609.25650v1 Announce Type: new Abstract: Accurately isolating disease-related features from confounding covariates (e.g., age, gender, site) and individual variations remains a fundamental chal
arXiv:2609.25017v1 Announce Type: new Abstract: Deepfakes, synthetic audiovisual content produced by deep generative models, have escalated into a critical threat across civilian and military domains,
arXiv:2609.25666v1 Announce Type: new Abstract: We present and tackle two problems associated with deploying Foundation Models (FMs) on Embodied Agents performing navigation: 1) Training bias in FMs l
arXiv:2609.26068v1 Announce Type: cross Abstract: Learning-based control is increasingly central to automating network operations. A learned policy, however, must satisfy cross-layer constraints on in
arXiv:2609.25527v1 Announce Type: new Abstract: Current robot-assisted minimally-invasive surgery (RMIS) platforms provide a fixed console for the surgeon to view stereo endoscopic images and teleoper
arXiv:2609.26392v1 Announce Type: new Abstract: Conventional wisdom in deep learning holds that overparameterization---having more parameters p than training samples n---is benign: larger models gener
arXiv:2609.22981v1 Announce Type: cross Abstract: We present a dual-locking method for securing trained neural networks that combines key-driven index permutation with PIN-based watermarking based on
arXiv:2609.25631v1 Announce Type: new Abstract: Dynamic object manipulation is essential for robots operating in real-world environments, yet methods for generating high-quality demonstrations remain
arXiv:2609.22537v1 Announce Type: new Abstract: Enterprise AI assistants must produce responses that are verifiable and traceable to source evidence. However, retrieval augmented generation (RAG) over