Knowledge catalogue
arXiv:2512.02328v2 Announce Type: replace-cross Abstract: Selecting an effective docking algorithm is highly context-dependent, and no single method performs reliably across structural, chemical, and
arXiv:2606.02522v1 Announce Type: cross Abstract: Video multimodal large language models (MLLMs) have made rapid progress on general and long-form video understanding, yet their ability to preserve br
arXiv:2606.01563v1 Announce Type: new Abstract: Autoregressive decoding in Transformer-based language models relies on the KV cache, whose memory footprint grows linearly with sequence length and beco
arXiv:2606.00832v1 Announce Type: new Abstract: Recent advances in agentic AI have enabled agents to complete complex tasks through tool use, reasoning, and multi-step planning. Yet existing benchmark
arXiv:2602.23204v3 Announce Type: replace Abstract: In this work, we introduce the first framework for Motion-aware Event Suppression, which learns to filter events triggered by IMOs and ego-motion in
arXiv:2606.01518v1 Announce Type: new Abstract: Motion generation for rigged shapes is vital for scalable 4D asset production. However, template-based methods are limited by specific topologies and fa
arXiv:2606.01502v1 Announce Type: cross Abstract: Frontier LLMs increasingly decide what a query attends to with a sparse-attention indexer that picks a few KV-cache blocks per query: attention's unit
arXiv:2606.01985v1 Announce Type: new Abstract: Recent breakthroughs in instruction-based image editing have captured significant attention, as models are now capable of handling real-world editing de
arXiv:2606.01533v1 Announce Type: cross Abstract: Computer use agents (CUAs) today are primarily deployed as single serial agents. This setup is suboptimal for complex long-horizon tasks that benefit
arXiv:2606.00146v1 Announce Type: cross Abstract: Motion artifacts in magnetic resonance imaging (MRI) degrade diagnostic reliability. Existing deep learning methods are typically contrast-specific an
arXiv:2606.02105v1 Announce Type: new Abstract: End-to-End Autonomous Driving (E2E-AD) systems have largely converged on predicting intermediate trajectory waypoints, delegating final control to hand-
arXiv:2606.02162v1 Announce Type: cross Abstract: Document type classification in visually rich documents remains challenging, as relevant information is distributed across textual, visual, and layout
arXiv:2606.00125v1 Announce Type: cross Abstract: Music recommendation systems typically treat songs as opaque tokens, relying on collaborative interaction histories which overlooks semantic or acoust
arXiv:2602.02547v2 Announce Type: replace-cross Abstract: Physics-Informed Neural Networks (PINNs) are effective methods for solving inverse problems and discovering governing equations from observati
arXiv:2512.16401v5 Announce Type: replace Abstract: Automatic Speech Recognition (ASR) can significantly reduce documentation burden in clinical workflows, but standard models degrade sharply in real-
arXiv:2606.01402v1 Announce Type: cross Abstract: Neural network compression is commonly achieved by pruning parameters based on local importance scores, e.g., magnitude-based pruning. We propose a co
new in deepagents: agent rubrics! you define a rubric, and the agent self-evaluates and iterates until it satisfies every rubric criterion. this is similar to /goal in claude code or codex, but more f
arXiv:2511.05913v2 Announce Type: replace-cross Abstract: New intent discovery (NID) seeks to recognize both new and known intents from unlabeled user utterances, which finds prevalent use in practica
arXiv:2606.00379v1 Announce Type: new Abstract: We present a non-learning stereo framework for disparity estimation from severely noisy images. Using the Field of Junctions (FoJ), it retains coarse vi
arXiv:2601.00389v2 Announce Type: replace-cross Abstract: Timing and burst patterns can leak through encryption, and an adaptive adversary can exploit them. This undermines metadata-only detection in
arXiv:2405.01930v2 Announce Type: replace Abstract: This paper introduces OARelatedWork: a dataset for related work generation from open-access sources. It is the first large-scale multi-document summ
arXiv:2606.01682v1 Announce Type: cross Abstract: Selecting the best response from multiple small-model samples using a stronger scorer is a simple inference-time strategy, but fails when the small mo
arXiv:2606.00815v1 Announce Type: new Abstract: Electroencephalography (EEG) supports a variety of brain-computer interface (BCI) tasks ranging from brain-state monitoring to human-LLM interactions. E
arXiv:2606.01476v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) trains a student model on its own generative trajectories under dense token-level feedback from a stronger teacher, mitig
arXiv:2606.01442v1 Announce Type: cross Abstract: Network intrusion detection is a core component of modern cybersecurity infrastructure, yet the deep learning models that dominate the field are compu
arXiv:2606.01075v1 Announce Type: new Abstract: Recent work suggests that large language models (LLMs) can improve through self-evolution (SE), using supervision signals generated by the model itself.
arXiv:2606.02179v1 Announce Type: cross Abstract: Surrogate models for topology optimization (TO) exhibit highly variable out-of-distribution (OOD) generalization under distribution shifts such as cha
arXiv:2606.01503v1 Announce Type: cross Abstract: Unified vision-language models (VLMs) integrate visual understanding and visual generation within a single autoregressive backbone, but their joint tr
arXiv:2606.02437v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) is usually treated as a cheaper alternative to full fine-tuning. We study a broader role: small trainable adapt
arXiv:2506.22271v3 Announce Type: replace Abstract: Neural networks often map low-dimensional embeddings to high-dimensional output spaces. Usually, the output layer is linear, which can create a 'ran
arXiv:2606.00272v1 Announce Type: new Abstract: The FETCH classifier generates follow-up questions to help refine the best match for the applicant's legal problem, using a low-cost ensemble of LLMs. I
arXiv:2510.17532v2 Announce Type: replace Abstract: Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinica
arXiv:2606.01241v1 Announce Type: new Abstract: Navigation and manipulation are fundamental capabilities of embodied intelligence, enabling robots to interpret natural language commands and interact p
arXiv:2606.00117v1 Announce Type: new Abstract: This paper proposes ontology-guided reasoning for affordance-based explanations of robot navigation. In human environments, it is not sufficient for a r
OpenAI Group PBC today released a set of features that will make it easier for nontechnical people to use its Codex automation tool. The update comes five months after Anthropic PBC added similar capa
OpenAI: OpenAI releases a new report on knowledge work: Codex now has 5M+ weekly active users, up 6x+ since February, and knowledge workers are ~20% of Codex users — OpenAI today released a new report
arXiv:2603.27645v2 Announce Type: replace Abstract: Open-vocabulary change detection (OVCD) seeks to recognize arbitrary changes of interest by enabling generalization beyond a fixed set of predefined
arXiv:2606.02031v1 Announce Type: cross Abstract: Building capable visual web agents requires long-horizon reasoning, precise grounding, and robust interaction with dynamic real-world websites. Despit
arXiv:2510.12249v2 Announce Type: replace Abstract: In performative learning, the data distribution reacts to the deployed model - for example, because strategic users adapt their features to game it
arXiv:2606.02178v1 Announce Type: cross Abstract: Recent advancements in generative AI have led to image editing models capable of producing realistic forgeries that evade traditional image forgery lo
arXiv:2606.00188v1 Announce Type: cross Abstract: While current multimodal models are proficient at open-ended visual editing, executing precise single-answer edits remains an important obstacle. To p
arXiv:2603.22999v3 Announce Type: replace Abstract: Recent advances in visual language models have enabled autonomous agents for complex reasoning, tool use, and document understanding. However, exist
arXiv:2606.00284v1 Announce Type: new Abstract: While continual pretraining~(CPT) is a practical way to extend large language models to new languages, naive finetuning on targeted data erodes existing
arXiv:2606.02341v1 Announce Type: cross Abstract: Underwater acoustic classification has a wide array of oceanic applications, but faces challenges due to an increasingly complex acoustic environment.
arXiv:2606.01947v1 Announce Type: cross Abstract: Research and applications in artificial intelligence have recently shifted with the rise of large pretrained models, which deliver state-of-the-art re
arXiv:2606.00419v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) is critical for the deployment of machine learning predictors in real-world scenarios where the data distribution may
arXiv:2606.02443v1 Announce Type: cross Abstract: Between the first visible sign of danger and the moment an accident occurs, there is often a window where intervention remains possible. Video-capable
Tool: Pasted File Editor I really like how you can paste a large volume of text into claude.ai (or the Claude desktop/mobile apps) and it will detect it as a large paste and turn it into a file attach
arXiv:2606.00616v1 Announce Type: cross Abstract: Recent Vision-Language Models (VLMs) struggle with grounded reasoning, temporal consistency, and context aware planning in videos. We introduce pause-
People who say this kind of thing are completely lost about what I actually said about deep learning, and I would strongly encourage them to read “Deep learning is hitting a wall” (2022). What I said
arXiv:2606.01485v1 Announce Type: new Abstract: We describe our submission to the VRR Challenge @ CVPR 2026, built on the ImplicitQA / VRR-QA benchmark~ite{implicitqa}: multiple-choice video question
arXiv:2606.02562v1 Announce Type: cross Abstract: Autonomous robots that interact with people must make safe and efficient decisions under human-induced uncertainty, such as their preferences, goals,
arXiv:2606.00150v1 Announce Type: cross Abstract: As Large Language Models evolve for user convenience, vulnerability to jailbreak attacks continues to be reported despite ongoing efforts in safety tr
arXiv:2606.01808v1 Announce Type: new Abstract: Accurate 3D geometric characterization of myocardial infarction (MI) is essential for building cardiac digital twins (CDTs) to precisely simulate infarc
arXiv:2606.00322v1 Announce Type: new Abstract: We introduce a perturbative approach for nonparametric instrumental variable (NPIV) estimation. By drawing inspiration from perturbation theory in physi
arXiv:2601.07742v4 Announce Type: replace-cross Abstract: Many materials properties depend on higher-order derivatives of the potential energy surface, yet machine learned interatomic potentials (MLIP
arXiv:2601.17074v4 Announce Type: replace-cross Abstract: Accurate estimation in time-varying inverse problems under limited and sparse observations remains a fundamental challenge across scientific d
arXiv:2606.00115v1 Announce Type: new Abstract: Bridging the gap between visual realism and physical understanding is a core challenge for video-based world models. We study the structural identifiabi
arXiv:2606.01834v1 Announce Type: cross Abstract: Human Action Recognition (HAR) using WiFi Channel State Information (CSI) has gained increasing attention due to its non-contact, low-cost, and privac
arXiv:2505.17659v4 Announce Type: replace-cross Abstract: Safe and feasible trajectory planning is critical for real-world autonomous driving systems. However, existing learning-based planners rely he