Knowledge catalogue
arXiv:2510.27114v2 Announce Type: replace-cross Abstract: Generative BC policies model expert action distributions well, but they do not explicitly reason about what scene a candidate action would pro
arXiv:2606.12475v2 Announce Type: replace Abstract: Human-robot collaboration (HRC) combines the complementary strengths of humans and robots to improve task efficiency. However, many existing collabo
arXiv:2610.15267v1 Announce Type: new Abstract: Joint-embedding predictive architectures (JEPAs) learn world models directly in latent space, but unconstrained autoregressive predictors may contract t
arXiv:2610.18072v1 Announce Type: new Abstract: We introduce LensErase, a general-purpose mask-based Erase Anything foundation model designed to cover a broad spectrum of real-world erase scenarios. L
arXiv:2601.12243v3 Announce Type: replace Abstract: Video summarization transforms long videos into concise representations that are easier to document and analyze, especially in high-stakes domains s
arXiv:2610.16313v1 Announce Type: new Abstract: Vision-language-action (VLA) models have advanced robotic manipulation by connecting visual perception and language instructions to action generation. H
arXiv:2610.18604v1 Announce Type: new Abstract: 3D part assembly aims to reconstruct a complete shape from a set of unassembled parts by predicting a rigid SE(3) transformation for each part. Because
arXiv:2610.17930v1 Announce Type: new Abstract: Unknown payloads alter robot dynamics and make precise contact execution difficult when foothold margins are small. We present LOAD, a perceptive locomo
arXiv:2610.15572v1 Announce Type: new Abstract: Open-world mobile manipulation requires a robot to find an object, approach it, and move it to a target receptacle from a language instruction. Training
arXiv:2508.11624v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has become a widely adopted technique in text-to-image diffusion models, enabling the personalisation of visual concepts
arXiv:2610.17384v1 Announce Type: new Abstract: We introduce and define measure neural mappings (MNMs), which combine measure-dependent neural transport with ensemble consistency for learning operator
arXiv:2610.17640v1 Announce Type: new Abstract: Spatial understanding is essential for embodied agents navigating and interacting with the physical world. As observations arrive incrementally, agents
arXiv:2610.18535v1 Announce Type: new Abstract: Video understanding benchmarks are the primary yardstick for measuring progress in video-language modeling, and a growing body of work relies on them to
https://preview.redd.it/gwb4of5vmivh1.png?width=2400&format=png&auto=webp&s=853f4cf25b4140ccf8b151b4124547788a544b33 UserRepGen-4B is a fine-tuned Qwen3.5-4B language model that generates a structured
arXiv:2508.21044v2 Announce Type: replace Abstract: Video Large Language Models (VLLMs) excel in video understanding, but their excessive visual tokens pose a significant computational challenge for r
arXiv:2610.17856v1 Announce Type: new Abstract: Time-series analysis is increasingly studied in multimodal settings, yet most benchmarks pair temporal signals with text or with visualizations derived
arXiv:2610.14871v1 Announce Type: new Abstract: By predicting future states, latent world models provide rich self-supervision for end-to-end driving, but often inherit dense features from pretrained
arXiv:2610.17201v1 Announce Type: new Abstract: Multi-player interactive world models aim to simulate shared environments from multiple players' perspectives, but achieving robust generation while sca
arXiv:2511.19548v2 Announce Type: replace Abstract: Neuroeconomics appears to offer welfare economics an evidential supplement, since neural data may help distinguish choices that express genuine pref
arXiv:2610.14162v1 Announce Type: cross Abstract: Diffusion language models (DLMs) offer parallel sampling for faster generation than autoregressive models, an increasingly important advantage as infe
arXiv:2610.16548v1 Announce Type: new Abstract: Low dose Computed Tomography (CT) is the standard imaging modality for lung cancer screening. On CT scans, lung cancer typically manifests as pulmonary
arXiv:2610.16135v1 Announce Type: cross Abstract: Omni-modal large language models (Omni-LLMs) extend LLMs to process heterogeneous modalities (e.g., vision, audio, and IMU signals) alongside language
arXiv:2607.11432v2 Announce Type: replace Abstract: We study reinforcement learning (RL) from pairwise human-generated trajectory comparisons. We generalize preference-based RL by formalizing a novel
arXiv:2610.14687v1 Announce Type: new Abstract: Many physical systems exhibit one-way coupling: exogenous fields such as medium density or obstacle geometry influence the dynamics without being affect
arXiv:2610.17203v1 Announce Type: new Abstract: Large language models can answer the same question in different styles, but users' preferred styles are hidden and vary across users and questions. We s
arXiv:2610.15177v1 Announce Type: new Abstract: As agentic AI systems take on increasingly complex and consequential tasks, securing them against prompt injection becomes critical. Existing defenses o
arXiv:2610.15833v1 Announce Type: new Abstract: Behavioral foundation models (BFMs) provide humanoids with reusable repertoires of whole-body behaviors; in real-world humanoid control, the same behavi
arXiv:2509.07552v3 Announce Type: replace Abstract: We present a feed-forward framework for static Gaussian full-head reconstruction and novel-view rendering from a single unposed image. Unlike previo
arXiv:2610.17066v1 Announce Type: new Abstract: Virtual cell models should capture phenotypes induced by specific perturbations. However, image-based models are typically trained and evaluated to matc
arXiv:2610.15428v1 Announce Type: new Abstract: Context-based offline meta-reinforcement learning (COMRL) has advanced almost entirely through better task representations, which separate task identity
arXiv:2610.17260v1 Announce Type: new Abstract: To reconstruct a predictive 4D representation of the physical world from monocular video captured by a moving camera, we jointly reconstruct dynamic Gau
arXiv:2610.14844v1 Announce Type: cross Abstract: We introduce stochastic physics-informed neural networks (SPINNs) for stochastic boundary-value problems (SBVPs) with random operators. The method enf
arXiv:2610.16355v1 Announce Type: new Abstract: Generalist robot manipulation can benefit from pretrained vision-language models (VLMs) for semantic reasoning and video generation models (VGMs) for wo
arXiv:2610.14713v1 Announce Type: new Abstract: While diffusion models achieve impressive image generation capabilities, they suffer from high computational costs, implausible artifacts, and limited a
arXiv:2610.16444v1 Announce Type: new Abstract: Visual artifacts such as charts, technical diagrams, webpages, and document pages underpin scientific, commercial, and engineering workflows. Editing th
arXiv:2601.19060v2 Announce Type: replace Abstract: Multimodal Retrieval-Augmented Generation (MM-RAG) augments visual question answering (VQA) with external knowledge, but many systems rely on modula
arXiv:2610.18865v1 Announce Type: new Abstract: Generating editable graphic designs requires arranging visual and textual elements into a coherent composition while preserving their content and editab
arXiv:2605.24273v2 Announce Type: replace Abstract: Automated detection and masking of individual methane plumes from satellite imagery is important for operational emission attribution and quantifica
arXiv:2610.17788v1 Announce Type: new Abstract: Distributional critics model the full return distribution, providing richer signals for exploration and risk-sensitive control. Recent work has shown th
arXiv:2610.14001v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) uses a self-teacher conditioned on privileged information to turn the sparse outcome signal of reinforcement learning
arXiv:2610.18389v1 Announce Type: new Abstract: Large language models are increasingly used as judges to reduce reliance on costly human evaluation, yet their scores can systematically disagree with h
arXiv:2610.14704v1 Announce Type: new Abstract: Coupling a pretrained vision-language model (VLM) with a diffusion or flow-matching action expert has become the mainstream vision-language-action (VLA)
arXiv:2610.16327v1 Announce Type: new Abstract: Bubbly flows involve strongly coupled bubble motion, deformation, and hydrodynamic interactions, making high-fidelity simulation computationally expensi
arXiv:2610.14654v1 Announce Type: new Abstract: Autoregressive video generators synthesize long videos through chunk-wise rollouts, yet they are typically trained to predict only the immediate future.
arXiv:2610.18481v1 Announce Type: new Abstract: Protein sequence, structure, and functional annotations each capture complementary aspects of protein biology, yet existing tokenizers encode each modal
arXiv:2507.21437v3 Announce Type: replace Abstract: Physics-informed neural networks and physics-informed DeepONet excel at solving partial differential equations but often struggle with singularly pe
arXiv:2601.12936v2 Announce Type: replace Abstract: Slot Attention, an approach that binds different objects in a scene to a set of 'slots', has become a leading method in unsupervised object-centric
arXiv:2610.17829v1 Announce Type: new Abstract: Multimodal Diffusion Transformers (MM-DiTs) have recently emerged as a powerful framework for unified text-vision synthesis, surpassing traditional U-Ne
MacStudio 128GB M1 ultra I'm curious how people who can run both models are deciding which one to work with. DeepSeekv4 benchmarks right below GPT 5.5 high. Flash Next benchmarks right above 5.4/5.5 x
arXiv:2604.02892v2 Announce Type: replace Abstract: Real-time estimation of vehicle-tire-road friction is critical for allowing autonomous race cars to safely and effectively operate at their physical
arXiv:2610.14326v1 Announce Type: new Abstract: Traffic density estimation from camera feeds requires connecting visual perception with short-horizon forecasting. We present a two-stage pipeline that
arXiv:2610.18388v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown strong potential for remote sensing understanding, yet reliable reasoning remains challenging when t
arXiv:2610.14043v1 Announce Type: cross Abstract: Frontier language models reliably refuse to explain how to cause physical harm. We ask whether this refusal survives when the same models control a ro
arXiv:2610.16082v1 Announce Type: new Abstract: Heterogeneous medical image data motivate domain-specialized diffusion experts, but expert specialization alone does not determine how their predictions
arXiv:2610.16763v1 Announce Type: new Abstract: Relational learning typically predicts from a database state, but decision making asks a different question: how will that state change under an action?
arXiv:2610.14583v1 Announce Type: cross Abstract: Autoregressive robot video world models based on discrete visual tokens remain unreliable over long rollout horizons. Retaining the entire generated h
arXiv:2610.14035v1 Announce Type: new Abstract: Contact-rich manipulation requires a robot to be precise in free space to establish the intended contacts, and compliant once in contact to avoid excess
arXiv:2610.15814v1 Announce Type: new Abstract: General-purpose multimodal foundation models are increasingly capable of directly controlling robots, yet improving their manipulation capabilities thro
arXiv:2610.15005v1 Announce Type: new Abstract: Transformer architectures have achieved strong results in natural language processing and computer vision, but their deployment is limited by high compu
arXiv:2606.15734v2 Announce Type: replace-cross Abstract: Continual post-training enables models to absorb emerging knowledge after deployment, but repeatedly updating shared parameters can accumulate