Knowledge catalogue
arXiv:2609.06080v1 Announce Type: new Abstract: Deeply phenotyped cohorts combine clinical, imaging, molecular, and wearable observations across timescales from seconds to years. This breadth can reve
arXiv:2609.08215v1 Announce Type: new Abstract: Video generation models have recently attracted substantial attention for their ability to generate visually compelling videos, yet ensuring physically
arXiv:2609.05808v1 Announce Type: cross Abstract: In situ adjoint training extracts parameter gradients directly from measurement, but has so far been limited to reciprocal or restricted systems. Here
arXiv:2609.06629v1 Announce Type: new Abstract: Recent advancements in deep learning allow robotic agents to interact with dynamic and unstructured environments. Of special interest is the integration
arXiv:2609.08992v1 Announce Type: new Abstract: False ventricular tachycardia (VT) alarms are a leading contributor to alarm fatigue in intensive care units. We propose a deep learning framework combi
arXiv:2609.05542v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) provide a mesh-free framework for solving governing equations, but their application to granular avalanche dy
arXiv:2609.07532v1 Announce Type: new Abstract: Learning physically plausible dynamics from visual observations is essential for interactive world models and embodied agents. However, modeling real-wo
arXiv:2609.07061v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) embed PDE residuals into neural network training, but their internal representations remain opaque: it is unkno
arXiv:2507.15824v2 Announce Type: replace Abstract: Text-to-video (T2V) models now produce striking visuals, yet they routinely violate everyday physics; objects float, tools are misused, and causal s
arXiv:2609.06207v1 Announce Type: new Abstract: Image-to-video generators are often credited with absorbing physical dynamics as implicit world models, a claim the community currently checks with plau
arXiv:2609.09020v1 Announce Type: new Abstract: Implicit neural representation (INR) has achieved remarkable progress in novel view synthesis and image/video coding in recent years.Compared to convent
arXiv:2609.07515v1 Announce Type: new Abstract: Infrared small target detection (ISTD) is an important research direction in image processing. However, existing methods are limited by severe backgroun
arXiv:2609.08965v1 Announce Type: cross Abstract: Ensuring the safety of autonomous driving is a critical challenge. Scenario-based testing is a systematic process used to validate Autonomous Driving
arXiv:2609.09059v1 Announce Type: new Abstract: While many video-game environments (VGEs) have played crucial roles in advancing reinforcement learning (RL), developing novel VGEs or modifying existin
arXiv:2609.06436v1 Announce Type: new Abstract: High-resolution 3D asset generation is vital in various 3D applications. Existing state-of-the-art diffusion-based models remain constrained by fixed re
arXiv:2609.08101v1 Announce Type: cross Abstract: Protein-conditioned 3D molecule generation is a central challenge in structure-based drug design, requiring a balance between pocket compatibility, mo
arXiv:2609.09145v1 Announce Type: new Abstract: We introduce Point4D, a feed-forward model for 4D reconstruction of long-range video sequences. Point4D is able to reliably infer dense per-point 3D tra
arXiv:2609.07916v1 Announce Type: new Abstract: Images are important tools in various sciences. Despite the development of photo-taking tools, creating clear and image without noise remains challengin
arXiv:2609.05896v1 Announce Type: cross Abstract: Link sign prediction (LSP) aims to infer the positive or negative polarity of unobserved links in signed networks. Signed Graph Neural Networks (SGNNs
arXiv:2410.02605v5 Announce Type: replace Abstract: We derive a policy gradient theorem for Cumulative Prospect Theory (CPT) objectives in finite-horizon Reinforcement Learning (RL), generalizing the
arXiv:2104.00239v3 Announce Type: replace Abstract: Visual and audio signals often coexist in natural environments, forming audio-visual events (AVEs). Given a video, we aim to localize video segments
arXiv:2609.06489v1 Announce Type: new Abstract: Monte Carlo Tree Search (MCTS) has demonstrated success in online planning for deterministic environments, yet significant challenges remain in adapting
arXiv:2609.06869v1 Announce Type: new Abstract: Three-dimensional bioheat simulation aims to predict transient temperature distributions in biological tissue and is commonly modeled using the Pennes b
arXiv:2609.07762v1 Announce Type: cross Abstract: Recurrent detectors such as bidirectional long short-term memory (Bi-LSTM) networks are low-complexity alternatives to the optimal Bahl-Cocke-Jelinek-
arXiv:2407.03463v2 Announce Type: replace Abstract: In the realm of self-supervised learning (SSL), conventional wisdom has gravitated towards the utility of massive, general domain datasets for pretr
arXiv:2609.06194v1 Announce Type: cross Abstract: Offshore wind turbines are widely used to generate renewable energy, but their maintenance can result in decreased efficiency due to forced shutdowns.
arXiv:2609.06888v1 Announce Type: new Abstract: Predictive coding characterizes adaptive behavior as a dynamic balance between internally generated predictions and external sensory evidence, yet how a
arXiv:2609.07922v1 Announce Type: cross Abstract: Shortcut learning denotes the widespread situation in which a classifier exploits spurious correlations rather than diagnostic features. Existing miti
arXiv:2609.06948v1 Announce Type: new Abstract: Synthesizing photorealistic driving videos along specified trajectories is essential for scalable closed-loop simulation. Reconstruction-based methods l
arXiv:2609.08949v1 Announce Type: new Abstract: Object 6D pose estimation formulations have progressively reduced reliance on object-specific priors, evolving from explicit 3D models to multi-view obj
arXiv:2609.07623v1 Announce Type: cross Abstract: Dot maps, which visualize individual data points as dots over a geographic region, are widely used across diverse domains to represent spatial pattern
arXiv:2609.08080v1 Announce Type: new Abstract: Ensuring safety in reinforcement learning under nonstationarity requires anticipating changes in risk before they lead to unsafe behavior. Existing appr
arXiv:2609.06102v1 Announce Type: cross Abstract: Cellular automata is a local computation paradigm where complex behavior can arise from local interactions between simple functions. This paradigm has
arXiv:2410.23667v2 Announce Type: replace Abstract: Neural differential equations offer a powerful approach for learning dynamical systems from data. However, they do not inherently respect known cons
arXiv:2405.18253v4 Announce Type: replace Abstract: Data plays a central role in advancements in modern artificial intelligence, with high-quality data emerging as a key driver of model performance. T
arXiv:2609.07199v1 Announce Type: new Abstract: Trust-Hub reuses host circuits: several files differ mainly in the inserted Trojan. When gates from sibling variants enter both training and test folds,
arXiv:2609.05953v1 Announce Type: new Abstract: Few-shot fine-grained object detection (FGOD) in remote sensing imagery is challenging because limited annotations must support both object localization
arXiv:2403.05006v2 Announce Type: replace Abstract: Pluralistic alignment requires learning from feedback that reflects persistent and potentially conflicting stakeholder preferences while ultimately
arXiv:2609.07229v1 Announce Type: new Abstract: Vision-language models offer a promising approach for zero-shot anomaly detection (ZSAD). However, due to object-centric bias, normal and anomalous text
arXiv:2609.09148v1 Announce Type: new Abstract: Generalist robot policies carry broad manipulation priors from large-scale data, but specializing them to a new task remains the deployment bottleneck.
arXiv:2609.07328v1 Announce Type: new Abstract: Local pedestrian-vehicle forecasting spans heterogeneous physical scales: pedestrians combine root locomotion with articulated motion, whereas vehicles
arXiv:2507.06625v4 Announce Type: replace-cross Abstract: Model Predictive Control (MPC) enables reliable trajectory optimization under dynamics constraints, but often depends on accurate dynamics mod
Q2D-Web uses agent citations, production web rankings, and a combined set expanded with LLM judgments. These three relevance sets reduce false negatives and reliance on a single labeling pipeline, whi
arXiv:2609.05952v1 Announce Type: new Abstract: Nearest-neighbor classification is widely used in machine learning, yet existing methods often suffer from low computational efficiency and limited robu
Qodo has released the Agentic Toolbox, enabling Claude Code, Codex, Kiro, and Cursor to directly utilize QodoAI’s review engine, codebase knowledge base, and team‑specific rules. The launch was announ
I’ve been using Qwen a lot recently and I’m honestly very impressed with it. For some of my tasks, Qwen seems to understand what I’m actually trying to achieve better than ChatGPT/GPT-5.6. I currently
Its chat template has this line: {%- set resolved_reasoning_effort = reasoning_effort|default('xhigh') %} xhigh is the most expensive of its three settings (low / medium / xhigh). If you never set one
Model available here: Qwen3.8-27B-Uncensored-Genesis-V1-MTP-GGUF This model is a practical realisation of things described in this paper, but adapted by me for machine learning: https://arxiv.org/pdf/
Hi, I'm the co-creator of this Qwen3.8-Flash-Next engine support in MLX-serve. I've been tuning this one to run both fast, efficient and correct up 1m context using kv cache 8 bits in M5 Max 128GB. Qw
arXiv:2609.06343v1 Announce Type: new Abstract: Multimodal image matching is a fundamental task for multi-source information fusion. However, geometric distortions and nonlinear radiometric difference
arXiv:2609.07409v1 Announce Type: cross Abstract: Recent multimodal Speech Emotion Recognition (SER) systems achieve high accuracy through interaction-heavy cross-modal transformers, but their computa
https://github.com/paulknysh/raggy A lightweight CLI tool for Retrieval-Augmented Generation (RAG) over local documents built with Ollama. Hybrid database (vector + BM25 index) and embedding generatio
arXiv:2512.23243v3 Announce Type: replace Abstract: Multimodal vision-language modeling has emerged as a promising paradigm for remote sensing (RS) image understanding. However, existing methods are l
arXiv:2609.06843v1 Announce Type: new Abstract: Continuous sign language recognition (CSLR) is a key technology for accessibility, yet its development remains limited by the high cost of annotating co
arXiv:2609.05770v1 Announce Type: new Abstract: Differentially private (DP) fine-tuning methods treat sparse Mixture-of-Experts (MoE) models as a single dense block, ignoring that shared layers see al
arXiv:2609.06967v1 Announce Type: new Abstract: Ensuring effective transfer learning for vision-language models without compromising their generalization performance is crucial. However, many existing
arXiv:2609.07547v1 Announce Type: new Abstract: Multi-object tracking (MOT) has advanced rapidly in urban surveillance and autonomous driving, yet many trackers rely on ReID- and transformer-based app
arXiv:2606.06295v2 Announce Type: replace Abstract: Path sampling methods generate ensembles of reactive trajectories connecting metastable states, but extracting mechanistic insight from these data r
arXiv:2609.07941v1 Announce Type: new Abstract: In this paper, we propose ReactVAU, a Slow-Fast Decoupled Framework for real-time streaming Video Anomaly Understanding (VAU). Existing VAU methods rely
read the whole thread. although i don’t 100% agree with certain bits it is compelling and informed from the inside perspective that definitely deserves to be heard. I resigned from Anthropic today. I