Knowledge catalogue
arXiv:2608.23047v1 Announce Type: cross Abstract: Misinformation that cites legitimate papers can be especially harmful when it distorts what those studies actually report. While existing automatic fa
arXiv:2603.26757v2 Announce Type: replace Abstract: Does multi-view demonstration truly improve robot manipulation, or merely enhance cross-view robustness? We present a systematic study quantifying t
arXiv:2608.21450v1 Announce Type: new Abstract: Knowledge-Based Visual Question Answering (KB-VQA) relies on retrieving external information to answer queries involving long-tail entities. However, ex
arXiv:2608.22232v1 Announce Type: new Abstract: Real-world situation appearances can deviate from their underlying physical states, challenging the reliability of multimodal large language models (MLL
arXiv:2608.21927v1 Announce Type: cross Abstract: Zero-cost proxies enable neural architecture search (NAS) to rank candidate networks from statistics computed at initialization, avoiding repeated tra
arXiv:2508.05206v2 Announce Type: replace Abstract: Online advertising systems typically use a cascaded architecture to manage massive requests and candidate volumes, where the ranking stages allocate
Big step forward for Engine! Some things I'm excited about in this release that we've shipped for our most avid users: - Engine now points more obviously to the issue in a trace. Engine identifies and
arXiv:2608.21447v1 Announce Type: new Abstract: Structured-language models such as SceneScript reconstruct a scene as a short program of parametric commands, an inherently editable and semantically ex
arXiv:2608.21864v1 Announce Type: cross Abstract: The current progress of Clinical Vision Large Language Models (C-VLLMs) has substantially improved digital diagnostics, still these frameworks often e
arXiv:2608.22557v1 Announce Type: cross Abstract: Existing LLM unlearning methods struggle with robustness: unbounded forget losses degrade model coherence, fixed-weight balancing cannot adapt as reta
arXiv:2608.22129v1 Announce Type: new Abstract: Cubic-regularized Newton methods have the optimal O(arepsilon^{-3/2}) global rate and an automatic saddle-escape mechanism, but their subproblem is most
arXiv:2608.21431v1 Announce Type: new Abstract: Knowledge-based Visual Question Answering aims to answer questions about an image by integrating external knowledge with visual and textual information.
arXiv:2608.21653v1 Announce Type: cross Abstract: Multi-task learning often combines losses that span several orders of magnitude, causing homoscedastic uncertainty weighting to degrade severely. We p
arXiv:2608.21895v1 Announce Type: cross Abstract: Locally deployed Large Language Models (LLMs) via inference engines such as Ollama run without the moderation and abuse detection present in API-serve
arXiv:2608.23136v1 Announce Type: new Abstract: Poor illumination obscures small, low-contrast defects in UAV bridge imagery, reducing the reliability and operational flexibility of automated inspecti
arXiv:2408.11827v2 Announce Type: replace Abstract: Understanding how language models compose meaning from linguistic input remains a central problem in interpretability research. Mechanistic studies
The reinforcement learning (RL) ecosystem is rapidly adopting Ray as the unified compute runtime for complex post-training workflows. Across Google Cloud, we see customers using Ray for workloads rang
arXiv:2608.22975v1 Announce Type: new Abstract: Embodied multimodal agents must answer from growing observation streams under a fixed per-decision token budget. We formalize this constraint through fo
arXiv:2608.21950v1 Announce Type: cross Abstract: Arabic automatic speech recognition (ASR) faces unique challenges due to diglossia, extensive regional dialect variation, and limited speech resources
arXiv:2512.05076v2 Announce Type: replace Abstract: Emerging video diffusion models achieve high visual fidelity but fundamentally couple scene dynamics with camera motion, limiting their ability to p
arXiv:2608.22963v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) are increasingly deployed as multi-step agents, where explicit reasoning supports task decomposition and tool c
arXiv:2608.22760v1 Announce Type: new Abstract: Byte-space Action Recognition (BAR) aims to recognize human actions directly from compressed image bitstreams without any pixel decoding. By operating e
arXiv:2608.21937v1 Announce Type: new Abstract: Incremental Object Detection (IOD) aims to enable detectors to continuously learn novel categories while preserving previously acquired knowledge. Howev
arXiv:2608.22708v1 Announce Type: new Abstract: Tool use in LLM systems faces a structural trade-off. Progressive disclosure keeps the prompt small by showing only the tools relevant to the current ta
arXiv:2608.22646v1 Announce Type: new Abstract: Diffusion language models can generate many tokens in parallel, but they still require repeated denoising steps during inference. This makes generation
arXiv:2608.22251v1 Announce Type: cross Abstract: Rising global demand for mental health support creates significant service delivery challenges, with asynchronous email counselling serving as a cruci
arXiv:2608.21748v1 Announce Type: new Abstract: Verifier-guided text-to-image systems increasingly use test-time search to select, refine, or stop among multiple candidates, yet release thresholds are
arXiv:2211.04340v2 Announce Type: replace Abstract: Autonomous driving systems must be capable of making quick decisions based on the perceived environment and specific driving conditions. Perception
arXiv:2602.02824v2 Announce Type: replace Abstract: LLM unlearning aims to remove the influence of undesirable knowledge from pretrained language models, which offers a practical mechanism for address
arXiv:2608.01033v2 Announce Type: replace-cross Abstract: Language models small enough to run on a handset, quantized to a few bits, are increasingly capable of acting on their user's behalf -- which
arXiv:2608.23336v1 Announce Type: new Abstract: Developing competitive deep learning baselines for medical imaging remains a highly iterative process requiring literature review, implementation, exper
arXiv:2608.22376v1 Announce Type: new Abstract: Large language models generate tokens sequentially, but can they execute multiple tasks concurrently while forming each token? Broader attention allocat
arXiv:2608.21606v1 Announce Type: new Abstract: Machine unlearning aims to remove the influence of targeted training data from a model while preserving its remaining capabilities, but evaluating wheth
arXiv:2608.22780v1 Announce Type: new Abstract: Reinforcement learning (RL) enables direct preference optimization for image editing through editing-specific rewards, which remain less developed due t
arXiv:2607.15721v2 Announce Type: replace Abstract: Cardiometabolic diseases remain among the most persistent drivers of preventable morbidity because diabetes, hypertension, and cardiovascular diseas
arXiv:2608.23172v1 Announce Type: new Abstract: Large-scale vision-language models (VLMs) have demonstrated remarkable versatility across a wide range of multimodal tasks. However, understanding humor
arXiv:2502.03696v2 Announce Type: replace-cross Abstract: Recent studies have demonstrated that learned Bloom filters (LBFs), which combine machine learning with the classical Bloom filter, can achiev
arXiv:2608.22808v1 Announce Type: new Abstract: When can an agent failure be caught? An audit is usually limited by the record rather than by the method. CatchBench therefore puts one auditor's questi
arXiv:2608.22577v1 Announce Type: new Abstract: Long-horizon GUI agents can retain a complete interaction trace cheaply as textual action records, but expose only a few past events to the policy in hi
arXiv:2608.21909v1 Announce Type: new Abstract: While Multi-Task Learning (MTL) is essential for adapting Large Language Models (LLMs) to diverse domains, prevailing LoRA-based methods rely on complex
arXiv:2608.22899v1 Announce Type: new Abstract: Unlike static medical question answering, long-horizon diagnosis captures the sequential nature of clinical practice: evidence is progressively acquired
arXiv:2608.21860v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) reasoning has significantly enhanced the multi-step problem-solving capabilities of large language models (LLMs) by introducing
arXiv:2608.22807v1 Announce Type: new Abstract: A rapidly growing range of sequential data tasks, such as identifying trend reversals in financial markets, auto-segmenting video and audio recordings,
arXiv:2608.22992v1 Announce Type: cross Abstract: Dynamic spectrum access must coordinate secondary users under bursty primary-user activity while preserving packet reliability and delay. We present T
arXiv:2608.23446v1 Announce Type: new Abstract: We study the problem of formally explaining why a candidate was not selected by a given tournament rule, by identifying sub-tournaments in which the can
ChatGPT Work can now use its computer and browser to sign in to websites on web and mobile, without ChatGPT ever seeing your username or password. That means you can ask it to: • Set up utilities for
arXiv:2608.23429v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) have shown strong performance in high-fidelity image generation, but their sampling process remains computationally inte
arXiv:2608.21504v1 Announce Type: new Abstract: The rapid advancement of large language models (LLMs) has led to increasing interest in their application to scientific domains such as chemistry. Howev
arXiv:2608.21629v1 Announce Type: new Abstract: Cheque fraud is a material risk in after-hours business deposit operations because funds may be released within one business day, while cheque clearing
arXiv:2608.21497v1 Announce Type: cross Abstract: Biochemical recurrence (BCR), defined as any detectable prostate-specific antigen level after prostatectomy with confirmatory elevation, is widely use
Physical artificial intelligence chip infrastructure startup Embedd Ltd. says it’s ready to fix one of the most critical bottlenecks in the multibillion-dollar robotics industry after raising 2.7 mill
arXiv:2512.15206v3 Announce Type: replace Abstract: A key bottleneck toward scalable IoT sensing is efficiently adapting trained AI models to new deployment conditions. Context shifts, such as changes
arXiv:2608.21899v1 Announce Type: new Abstract: Human-in-the-loop real-world reinforcement learning enables rapid acquisition of effective robotic manipulation policies for individual tasks, often wit
AI factories are moving from ambitious plans toward production, but the path from graphics processing unit acquisition to usable systems remains a race against time. Neoclouds already have customers w
Cisco Systems Inc. today introduced an expansion to its current lineup of rack-scale artificial intelligence solutions for massive AI workloads in secure AI factories, addressing growing needs for mas
arXiv:2608.22281v1 Announce Type: cross Abstract: Medical image segmentation requires high accuracy and robustness, yet practical commercial deployment also demands privacy preservation and computatio
arXiv:2608.22483v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly support decision-making in high-stakes domains, but they often hallucinate and express confidence that is misa
arXiv:2608.22266v1 Announce Type: new Abstract: In the context of information seeking, conversational agents are undergoing an evolution from reactive tools to proactive, personalized assistants. A cr
arXiv:2608.21473v1 Announce Type: new Abstract: Quantification, estimating class prevalences in bags of unlabeled instances is vital in domains where aggregate statistics are more important than indiv
arXiv:2608.22510v1 Announce Type: new Abstract: Agent benchmarks often evaluate only final answers even when agents run on stateful runtimes. We argue this under-specifies what is being evaluated: the