Knowledge catalogue
arXiv:2606.20990v1 Announce Type: new Abstract: Dual-robot collaboration enables tasks that exceed the reach and payload of a single robot, such as collaboratively transporting objects across environm
arXiv:2606.22073v1 Announce Type: new Abstract: This paper proposes an energy-recycling rimless wheel with spring-clutch legs. The proposed mechanism uses a lockable clutch to store part of the impact
arXiv:2606.22918v1 Announce Type: new Abstract: Maintaining physical consistency in video generators and world models increasingly relies on vision-language models (VLMs) as automated judges that prov
arXiv:2606.22167v1 Announce Type: new Abstract: Graph Neural Networks are great for link prediction in various network-like structures; however, the question of their speed/quality tradeoff has been b
arXiv:2606.18239v2 Announce Type: replace Abstract: We present EBench, a simulation benchmark that diagnoses generalist mobile manipulation policies beyond a single success-rate scalar. EBench compris
arXiv:2606.22925v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models increasingly rely on multi-dataset training and evaluation, yet public EEG datasets still lack a shared t
arXiv:2510.26113v2 Announce Type: replace Abstract: Do Video-LLMs have consistent temporal understanding when videos capture the same event from different viewpoints? To study this question, we introd
arXiv:2606.20771v1 Announce Type: new Abstract: We introduce ELADO (Elliptic PDE Assessment Datasets for Operator Learning), a systematic benchmark suite constructed to show and quantify failure modes
arXiv:2606.20924v1 Announce Type: new Abstract: In multi-object text-to-image (T2I) diffusion, ensuring semantic consistency between textual prompts and generated visual content is crucial for image s
arXiv:2606.22307v1 Announce Type: new Abstract: Data augmentation (DA) has been proven to be an effective means for improving protein representation learning (PRL) by generating additional training sa
arXiv:2606.22948v1 Announce Type: cross Abstract: As multimodal agents move from interface understanding to real software control, successful trajectory discovery in live desktop environments becomes
arXiv:2606.19164v2 Announce Type: replace Abstract: Model merging aims to enable multi-task learning by integrating the capabilities of multiple models fine-tuned from the same pre-trained checkpoint
arXiv:2606.20177v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in various Remote Sensing (RS) tasks. However, their ability to compre
arXiv:2606.22269v1 Announce Type: cross Abstract: We investigate the translation quality of current large language models (LLMs) for English-to-Hausa and English-to-Fongbe - two typologically distinct
arXiv:2606.20723v1 Announce Type: new Abstract: Chronic wound assessment remains a clinically challenging task that requires accurate interpretation of wound morphology, tissue composition, vascular c
arXiv:2606.22955v1 Announce Type: new Abstract: Large-scale pretrained foundation models have revolutionized general medical screening, but often falter on rare diseases because such conditions are un
arXiv:2606.22252v1 Announce Type: new Abstract: The integration of multiple thematic data layers into a single composite map, known as the cartographic synthesis problem, is typically addressed throug
Security intelligence and management solutions company Exabeam Inc. today introduced Agent Behavior Verification, a pre-deployment security discipline for artificial intelligence agents, and released
arXiv:2503.21321v2 Announce Type: replace-cross Abstract: With the rapid development of machine learning and deep learning techniques, actuaries and the broader insurance industry face a persistent tr
arXiv:2509.23946v3 Announce Type: replace Abstract: Many LLMs plan before they act, yet planning and execution are often still entangled in one long generation trace, enforced only through prompts, or
arXiv:2606.21100v1 Announce Type: new Abstract: The integration of pretrained encoders with diffusion policies has become a dominant paradigm for visual robotic manipulation. However, it still struggl
arXiv:2606.22768v1 Announce Type: new Abstract: To make large-scale distributed training practical outside high-bandwidth datacenters, we must reduce blocking, high-volume synchronization. While DiLoC
Falcon 9 successfully launched the Starfall Demo mission from a Florida launch facility, marking another operational flight for SpaceX's reusable rocket. The mission demonstrates continued progress in
arXiv:2606.20785v1 Announce Type: cross Abstract: Collecting computer use data from human demonstrations is expensive and slow, motivating the need for scalable generation strategies. This requires tw
arXiv:2606.21075v1 Announce Type: cross Abstract: Standard Transformers use a single self-attention pathway to model both global dependencies and local patterns, creating tension between long-range st
Thariq is announcing a new 'scheduling' channel feature that allows users to tag Claude to help coordinate and find available time across multiple calendars. This tool appears designed to streamline m
arXiv:2606.20246v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models pre-trained on massive video-robot datasets have revolutionized robotic manipulation, yet their multi-billion pa
arXiv:2606.21683v1 Announce Type: cross Abstract: We consider the parameter estimation problem in logistic regression with Gaussian design: the estimation of a fixed unknown parameter heta^*in R^d (|h
arXiv:2606.20769v1 Announce Type: cross Abstract: AI systems for peer review fail on three fronts: they train on Computer Science and Machine Learning venues alone, ignore the iterative dialogue that
arXiv:2606.21030v1 Announce Type: cross Abstract: Diffusion-based image compression methods, leveraging powerful generative priors, have demonstrated remarkable perceptual quality at ultra-low bitrate
arXiv:2606.23420v1 Announce Type: new Abstract: Flow matching has recently become a new standard for behavior cloning in robotic manipulation. However, state-of-the-art flow matching policies suffer f
arXiv:2606.22378v1 Announce Type: new Abstract: Event cameras enable high-frequency visual perception with microsecond latency, offering advantages for dynamic scenes. However, event-based small objec
arXiv:2606.22168v1 Announce Type: new Abstract: Medical image segmentation plays an important role in computer aided diagnosis, treatment planning, and disease monitoring. U-Net has been widely used f
arXiv:2606.22258v1 Announce Type: new Abstract: Electroencephalogram (EEG) analysis remains the clinical gold standard for epilepsy diagnosis and seizure detection. While Deep Learning (DL) has signif
arXiv:2606.20661v1 Announce Type: cross Abstract: The integration of external tools has transitioned LLM agents from passive responders to autonomous systems. However, current benchmarks prioritize ex
arXiv:2606.23005v1 Announce Type: new Abstract: Preterm birth (PTB) prediction can enable targeted surveillance and timely intervention, yet most ultrasound-based models use a single selected transvag
arXiv:2606.22274v1 Announce Type: cross Abstract: Low-resource African languages lack text corpora needed for language model training. We investigate whether ASR pipelines can extend text resources fo
arXiv:2606.22872v1 Announce Type: new Abstract: Global furry conventions produce massive fursuit photographs, while manual sorting brings heavy labor costs and calls for automatic identity retrieval a
arXiv:2606.22993v1 Announce Type: cross Abstract: We develop a comprehensive theory for regularized M-estimation in reproducing kernel Hilbert spaces. Under mild conditions on the loss we establish ex
arXiv:2411.05985v3 Announce Type: replace-cross Abstract: This paper examines potential biases and inconsistencies in the emotions evoked by images produced by generative artificial intelligence (AI)
arXiv:2606.23669v1 Announce Type: new Abstract: Text-to-image models can generate visually plausible city streets, but whether their outputs correspond to a requested road segment rather than a generi
arXiv:2606.20707v1 Announce Type: new Abstract: While humans can identify physically implausible events within milliseconds, machine learning approaches addressing the same problem are extremely slow
arXiv:2606.22776v1 Announce Type: new Abstract: The traveling salesman problem (TSP) is a canonical NP-hard combinatorial optimization benchmark that tests the representational capacity and generaliza
arXiv:2512.20399v3 Announce Type: replace Abstract: We present GeoTransolver, a multiscale geometry-aware physics attention transformer for Computer Aided Engineering (CAE). GeoTransolver extends the
arXiv:2606.14470v2 Announce Type: replace-cross Abstract: Large language model reasoning leaves no trace once it is done. The steps of a chain of thought disappear when the context window closes, a pr
arXiv:2606.22409v1 Announce Type: new Abstract: Robots operating in everyday environments must understand fine-grained human actions, intentions, and contextual cues from broad views where people occu
arXiv:2603.05035v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly served on shared accelerators where an adversary with read access to device memory can observe K
arXiv:2602.04548v2 Announce Type: replace Abstract: We develop a general mathematical framework to analyze scaling regimes and derive explicit analytic solutions for gradient flow (GF) in large learni
arXiv:2606.21097v1 Announce Type: cross Abstract: Deploying highly capable personalized conversational agents in resource-constrained or privacy-sensitive environments remains a significant challenge.
arXiv:2606.20799v1 Announce Type: new Abstract: Generating visually consistent multi-shot videos remains an open challenge. As videos span more shots, inconsistencies can accumulate across shots, caus
arXiv:2606.20945v1 Announce Type: new Abstract: Self-attention is central to Transformer performance and is often the most expensive part of the Transformer at long context lengths because its pairwis
arXiv:2603.14109v2 Announce Type: replace Abstract: Millimeter-wave radar enables robust perception in visually degraded environments, yet radar-inertial estimation remains prone to drift: sparse body
arXiv:2512.24946v2 Announce Type: replace Abstract: Existing film-restoration methods frequently fail under fast motion, producing limb disappearance and structural distortion due to inaccurate motion
HalluHard update: We’ve added GLM-5.2, using adaptive thinking with maximum reasoning effort, to our leaderboard. Despite its impressive performance on other benchmarks, GLM-5.2 still hallucinates fre
arXiv:2606.23615v1 Announce Type: new Abstract: We propose Hedgementation: a new benchmark to evaluate machine learning models for hedgerow mapping from remote sensing data at country scale and 10m^2
arXiv:2501.12191v2 Announce Type: replace-cross Abstract: Training deep neural networks (DNNs) on classification tasks can be performed with a number of different losses, but cross-entropy (CE) loss i
arXiv:2606.22756v1 Announce Type: cross Abstract: We present HERCULES, an open-source simulator and data-collection pipeline for heterogeneous multi-robot autonomy. Built upon the Unreal Engine 5 (UE5
Here's Sonnet 3.7 doing the same thing a year ago. https://x.com/emollick/status/1894441728175677837?s=20 Snake games are a bad test of AI beca- 'Claude 3.7, make a snake game, but the snake is self-a
arXiv:2505.19061v2 Announce Type: replace Abstract: Motivated by Online Configuration Optimization in large, dynamic parameter spaces, this work studies the nonstochastic multi-armed bandit (MAB) prob
arXiv:2601.12879v2 Announce Type: replace Abstract: Extracting sparse circuits from billion-parameter transformers is constrained by O(2^n) search cost and pervasive feature reuse across co-active pat