Knowledge catalogue
arXiv:2608.29831v1 Announce Type: new Abstract: Localizing issue-relevant code regions is a critical step in automated software engineering. However, due to their reliance on sparse trajectory-level s
arXiv:2608.28676v1 Announce Type: new Abstract: Digitized herbarium collections, now comprising over 100 million freely accessible specimen images, have become a critical resource for addressing funda
arXiv:2608.29232v1 Announce Type: cross Abstract: Object detectors are typically trained under closed-set supervision, where unlabeled regions are implicitly treated as background. Under incomplete an
arXiv:2608.30467v1 Announce Type: new Abstract: Deep-learning models can achieve strong chest X-ray (CXR) classification performance without establishing whether their predictions predominantly rely o
arXiv:2608.30897v1 Announce Type: new Abstract: World models are becoming core infrastructure for embodied intelligence, with action-conditioned video generation providing controllable predictions of
arXiv:2608.30242v1 Announce Type: new Abstract: While visual navigation has advanced through imitation learning from cross-platform demonstrations, fully leveraging such data remains challenging. Firs
arXiv:2604.01113v2 Announce Type: replace Abstract: Large language model (LLM) systems are increasingly used to support high-stakes decision-making, but they typically perform worse when the available
arXiv:2608.30758v1 Announce Type: new Abstract: Recent radiology multi-modal language models have made substantial progress in chest X-ray report generation, visual question answering, and temporal re
arXiv:2608.29621v1 Announce Type: cross Abstract: Long-horizon story-driven video generation requires a production agent to coordinate narrative decomposition, state tracking, shot design, prompt cons
arXiv:2608.28677v1 Announce Type: new Abstract: This paper presents l{CogRun}, a framework that enables safety-critical ground robots to perform cognitively-grounded runtime learning entirely on edge-
arXiv:2608.30974v1 Announce Type: cross Abstract: Joint-Embedding Predictive Architecture (JEPA) has shown strong performance in learning rich representations through self-supervised prediction in lat
arXiv:2608.30674v1 Announce Type: cross Abstract: Accurate molecular property prediction requires both statistical reliability and chemical reasoning. Graph neural networks can be calibrated directly
arXiv:2608.29223v1 Announce Type: new Abstract: Accurate through-thickness measurement of subsurface delamination depth in Carbon Fiber Reinforced Polymer (CFRP) is important for structural assessment
arXiv:2603.26509v2 Announce Type: replace Abstract: Computed tomography (CT) provides rich 3D anatomical detail but is often constrained by high radiation exposure, substantial costs, and limited avai
arXiv:2608.29675v1 Announce Type: cross Abstract: Repository exploration is a distinct and costly stage of coding-agent pipelines: before generating a patch, an agent must identify which repository fi
arXiv:2608.29674v1 Announce Type: new Abstract: Sharing tabular data in high-stakes domains is constrained by privacy regulations. Synthetic data offer a promising alternative, but deep generative mod
arXiv:2607.11577v2 Announce Type: replace-cross Abstract: Conventional Graph Neural Networks (GNNs) couple feature transformation and neighborhood aggregation, which often renders them vulnerable to t
arXiv:2608.30543v1 Announce Type: new Abstract: Large Language Models (LLMs) offer new possibilities for scaling qualitative analysis, but existing applications often provide limited methodological tr
arXiv:2608.28970v1 Announce Type: cross Abstract: Current TTS systems typically rely on open-loop, single-pass generation and can produce sporadic local prosodic defects, such as misplaced stress, unn
arXiv:2608.29264v1 Announce Type: new Abstract: Diffusion-based visual generative models deliver strong image and video synthesis quality but incur high inference costs because sequential samplers rep
arXiv:2608.29027v1 Announce Type: new Abstract: Shrimp disease classification has become an urgent issue due to its significant impact on the import-export output of producing countries, particularly
arXiv:2608.28777v1 Announce Type: new Abstract: Although recent deepfake detectors achieve high overall accuracy, their errors remain unevenly distributed across demographic subgroups, with real faces
arXiv:2608.28672v1 Announce Type: new Abstract: Autonomous vehicles operate in dynamic, ever-changing environments where new scenarios and edge cases constantly emerge. As a result, static learning mo
arXiv:2510.23515v3 Announce Type: replace Abstract: This paper proposes FreeFuse, a training-free framework for multi-subject text-to-image generation through automatic fusion of multiple subject LoRA
arXiv:2608.28619v1 Announce Type: cross Abstract: Medical history taking is a dialogue-based clinical reasoning task in which learners must gather, organise, and integrate patient information while th
arXiv:2608.30550v1 Announce Type: new Abstract: Multimodal Sewing pattern generation aims to infer executable sewing patterns from design cues such as sketches and textual descriptions. As an interpre
arXiv:2608.29211v1 Announce Type: new Abstract: Ground image localization with respect to satellite imagery is a key enabler for metrically-accurate, geo-localized 3D scene reconstruction from unconst
arXiv:2608.29313v1 Announce Type: cross Abstract: CLIP-like vision-language models (VLMs) trained with contrastive objectives learn strong global image-text representations, but their Euclidean embedd
Yoyo I'm back with updates to the fastest inference engine with minimal quality loss for Qwen3.8-27B. The last few weeks I've been optimizing decode speed and I don't think it can be pushed further, u
arXiv:2608.29349v1 Announce Type: new Abstract: Gaussian process (GP) regression with a single global GP (GP-glo) incurs cubic computational cost, limiting scalability to large datasets. Product-of-ex
arXiv:2608.30034v1 Announce Type: new Abstract: Two-stage vision pipelines often place an enhancement network before a task network, on the assumption that a cleaner input produces a better output. We
arXiv:2508.08966v2 Announce Type: replace Abstract: The attention mechanism lies at the core of the transformer architecture, providing an interpretable model-internal signal that has motivated a grow
arXiv:2608.29901v1 Announce Type: cross Abstract: Electronic Health Record (EHR) prediction models in the intensive care unit must learn from sparse and irregular measurements while preserving the cli
arXiv:2512.17452v4 Announce Type: replace-cross Abstract: Long-context LLM inference is bottlenecked by the quadratic attention complexity and linear Key-Value (KV) cache growth. Prior approaches miti
arXiv:2607.10851v2 Announce Type: replace Abstract: Medical image classification models are ideally expected to identify diagnostically relevant regions while making predictions, yet standard classifi
arXiv:2608.29268v1 Announce Type: new Abstract: PCB engineering drawings mix sparse graphics, dense tables, and text whose meaning depends on page position. Localizing the regions and sending crops to
arXiv:2608.30773v1 Announce Type: new Abstract: In animals such as elephants and octopuses, acquiring non-visual information about an object and physically engaging with it are inseparable processes m
arXiv:2608.29612v1 Announce Type: new Abstract: Sustained scientific work requires a knowledge substrate that carries interpretation across tasks and preserves paths to source evidence. We call this p
arXiv:2502.10577v2 Announce Type: replace-cross Abstract: Instruct-based large language models (LLMs) have been shown to propagate and even amplify gender bias when prompted with contextually constrai
arXiv:2608.28724v1 Announce Type: new Abstract: Planetary surface exploration missions rely increasingly on autonomous robotic platforms capable of interpreting complex terrain to ensure safe navigati
arXiv:2608.28670v1 Announce Type: new Abstract: Diffusion Transformers achieve high-fidelity image and video generation, but their iterative sampling remains expensive, for each denoising step require
arXiv:2510.06113v2 Announce Type: replace Abstract: Survival analysis plays a vital role in making clinical decisions. However, the models currently in use are often difficult to interpret, which redu
Ollama already support the Muse Code harness out of the box? You can try it with models via Ollama (local and cloud): ollama launch muse Muse Code is out of beta and now built to handle bigger, more c
arXiv:2608.29881v1 Announce Type: new Abstract: Monocular depth estimation has achieved strong open-domain generalization, yet reliable robotic deployment remains difficult in transparent, reflective,
arXiv:2608.28633v1 Announce Type: cross Abstract: Generative AI systems increasingly mediate cultural adaptation, but their cultural decisions are often hidden inside prompts, transient model plans, o
arXiv:2608.29663v1 Announce Type: new Abstract: Remote photoplethysmography (rPPG) enables contactless physiological measurement from facial videos, yet its subtle pulse-related variations are easily
arXiv:2608.30400v1 Announce Type: new Abstract: High-dynamic-range (HDR) images, with their rich tone and detail reproduction, hold significant potential to enhance computer vision systems, particular
arXiv:2608.29604v1 Announce Type: cross Abstract: Vision-language retrieval with CLIP-style dual encoders achieves strong cross-modal performance, yet practical accuracy often hinges on localized sema
arXiv:2608.29748v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive language model inference by having a lightweight draft model propose multiple candidate tokens, which ar
arXiv:2606.16490v2 Announce Type: replace Abstract: Collaborative mobile manipulation requires robots to coordinate with a partially observed partner while physically interacting through shared object
arXiv:2606.20122v2 Announce Type: replace Abstract: Open-ended deep research (OEDR) requires systems to acquire knowledge through multi-round retrieval and generate coherent long-form reports. The out
arXiv:2608.30654v1 Announce Type: new Abstract: Antarctic sea ice concentration (SIC) forecasting is an important yet challenging task due to the coexistence of complex spatial structure, long-range t
arXiv:2608.30005v1 Announce Type: new Abstract: Rubric-based reinforcement learning extends RL beyond tasks with exact answers or rule-based verifiers by scoring responses against instance-specific cr
arXiv:2608.29974v1 Announce Type: cross Abstract: Detecting hallucinations in Large Vision-Language Models (LVLMs) requires both accurate span localization and well-calibrated confidence scores. Fine-
arXiv:2608.29448v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) often face ill-conditioned objectives that limit high-accuracy training. Dense quasi-Newton methods improve l
arXiv:2608.28614v1 Announce Type: cross Abstract: Sequence-to-edit approaches make grammatical error correction (GEC) efficient and locally interpretable by predicting edit labels over the input rathe
arXiv:2608.29326v1 Announce Type: cross Abstract: Positive psychology dialogue aims to support emotional distress and positive resource building, requiring models to produce not only empathetic replie
arXiv:2608.28698v1 Announce Type: new Abstract: Compared with typical vision-language tasks, document parsing places stronger demands on fine-grained visual perception. Existing vision-language model
arXiv:2608.28708v1 Announce Type: cross Abstract: Prospective silent trials provide an important bridge between retrospective validation of artificial intelligence (AI) models and their use in clinica
arXiv:2608.29759v1 Announce Type: cross Abstract: We present SynCrash, a multi-stage pipeline for zero-shot accident detection, spatial localization, and collision-type classification in fixed-view CC