Knowledge catalogue
arXiv:2610.18516v1 Announce Type: new Abstract: Post-training pruning enables efficient deployment, but conventional methods do not explicitly preserve internal-gradient alignment between sparse and d
arXiv:2610.15909v1 Announce Type: cross Abstract: The extension complexity operatorname{xc}(P) of a polytope P is the minimum number of facets of a polytope that projects onto it. Motivated by the com
arXiv:2610.14077v1 Announce Type: cross Abstract: Embodied Audio-Visual Navigation (AVN) requires an agent to locate sound targets by jointly leveraging visual and auditory cues. While prior work has
arXiv:2603.18572v2 Announce Type: replace-cross Abstract: Deep unrolled models (DUM) have become the state-of-the-art for accelerated MRI reconstruction, yet their robustness under domain shift remain
arXiv:2610.14184v1 Announce Type: new Abstract: Studying model training requires checkpoint trajectories, whose storage cost limits temporal resolution. Although adjacent checkpoints are numerically c
arXiv:2610.15533v1 Announce Type: new Abstract: 3D editing aims to modify an existing 3D asset according to user intent while preserving geometry and appearance outside the intended edit region. Exist
arXiv:2512.00724v3 Announce Type: replace Abstract: Reward models (RMs) are critical to reinforcement learning from human feedback (RLHF). However, policy models can exploit biases or spurious correla
arXiv:2610.18552v1 Announce Type: cross Abstract: Teacher-free one-step generative models such as W-Flow learn directly from distributional objectives, with the Sinkhorn divergence providing the stron
arXiv:2610.16595v1 Announce Type: cross Abstract: Surface-based cortical analysis is useful in many applications as it is a natural way to model the highly folded structure of the cerebral cortex, all
arXiv:2610.17996v1 Announce Type: new Abstract: Sugarcane leaf diseases severely impact crop yield and quality, making accurate and timely detection critical for sustainable agriculture. Conventional
arXiv:2610.18066v1 Announce Type: new Abstract: Reliable uncertainty evaluation is essential for assessing the trustworthiness of machine learning (ML) predictions, particularly in safety-critical app
arXiv:2610.15724v1 Announce Type: new Abstract: Denoising Diffusion Probabilistic Models have emerged as powerful generative priors across domains ranging from molecular design to high-resolution imag
arXiv:2610.14394v1 Announce Type: cross Abstract: Training large-scale recommender systems on dynamic industrial platforms, such as Short-Form Video (SFV) services with billions of daily interactions,
arXiv:2610.16070v1 Announce Type: cross Abstract: Frontier models are now trained for complex multi-agent tasks, from running a company to engaging in negotiations. These tasks necessitate training mo
arXiv:2610.18551v1 Announce Type: cross Abstract: CMinHash promises a new direction for designing MinHash algorithms with reduced variance and storage space via circulant permutations. However, the or
arXiv:2610.17699v1 Announce Type: cross Abstract: Single-cell RNA sequencing (scRNA-seq) enables cell-level analysis of biological heterogeneity, but learning effective representations from high-dimen
arXiv:2606.13209v2 Announce Type: replace Abstract: Reward models (RMs) are a key component of reinforcement learning from human feedback, enabling the alignment of large language models with human va
arXiv:2610.14696v1 Announce Type: new Abstract: Bayesian optimization uses posterior uncertainty to decide where to explore, but this uncertainty is not spatially neutral. On bounded domains, Gaussian
arXiv:2610.17019v1 Announce Type: cross Abstract: Whisper-style ASR models provide strong zero-shot transcription, but their residual errors are not evenly distributed across datasets, recording condi
arXiv:2507.14777v2 Announce Type: replace Abstract: We investigate foundational questions about the interplay between memorization and learning in large language models (LLMs). When an LLM generates a
arXiv:2610.15961v1 Announce Type: cross Abstract: Emergent misalignment (EM) occurs when fine-tuning a language model to a narrow task makes it misaligned on unrelated ones. Because organizations now
arXiv:2601.17673v2 Announce Type: replace Abstract: Unified remote sensing multimodal models exhibit a pronounced spatial reversal curse: Although they can accurately recognize and describe object loc
arXiv:2610.12941v2 Announce Type: replace Abstract: Image deblurring is a fundamental task in computer vision, yet existing methods mainly specialize in isolated scenarios. Consequently, the deblurrin
arXiv:2610.15357v1 Announce Type: new Abstract: Generating complete 3D assets from sparse, unposed images requires preserving observed geometry while completing unseen regions. Joint reconstruction an
arXiv:2610.16969v1 Announce Type: cross Abstract: Latent Action Models (LAMs) can capture manipulation priors from action-free videos, but discrete latents usually impose a granularity bottleneck, lim
arXiv:2610.16945v1 Announce Type: new Abstract: Recent advances in multimodal foundation models, exemplified by GPT-6 Astra, raise the prospect of general-purpose embodied reasoning and planning. Usin
arXiv:2610.15564v1 Announce Type: new Abstract: We introduce UNISON, a unified framework that bridges multiple instance learning and recommendation systems by treating both as instances of learning fr
arXiv:2610.16707v1 Announce Type: cross Abstract: This paper considers a distributed estimation system where edge nodes transmit averaged features to a center node for parameter estimation. A key chal
arXiv:2503.07035v3 Announce Type: replace Abstract: Existing incremental learning (IL) methods rely on strong assumptions: incoming tasks either introduce exclusively new classes or new domains (i.e.,
arXiv:2105.10759v4 Announce Type: replace-cross Abstract: We show how a pair of points can uniquely represent a left-infinite sequence obtained from observations of an underlying dynamical system thro
arXiv:2610.18697v1 Announce Type: cross Abstract: World models (WMs) and world-action models (WAMs) focus on future world prediction and executable action generation, respectively. Although these mode
arXiv:2610.16146v1 Announce Type: new Abstract: We present Unravel, a training-free framework that turns a single image into an interactive 3D scene, where every object is an independent and movable a
arXiv:2601.17274v2 Announce Type: replace Abstract: We develop unrolled neural networks to solve constrained optimization problems, offering accelerated, learnable counterparts to dual ascent (DA) alg
arXiv:2511.13714v2 Announce Type: replace Abstract: The Segment Anything Model (SAM) family has become a widely adopted vision foundation model, but its ability to control segmentation granularity rem
arXiv:2610.18291v1 Announce Type: new Abstract: Since the rise of supervised deep learning modelet dets, researchers has attempt to solve infrared small targection using various models like convention
arXiv:2610.16826v1 Announce Type: cross Abstract: Deepfake speech detectors are typically evaluated as binary classifiers on fixed benchmarks, but deployed systems face evolving attacks, codecs, speak
arXiv:2204.09334v4 Announce Type: replace-cross Abstract: Unsupervised domain adaptation approaches have recently succeeded in various medical image segmentation tasks. The reported works often tackle
arXiv:2610.18356v1 Announce Type: new Abstract: City-scale 3D grounding is commonly formulated as candidate ranking, which requires precomputed instance proposals and becomes inefficient in large outd
arXiv:2610.15049v1 Announce Type: new Abstract: This study aims to evaluate and compare convolutional neural network (CNN) architectures, specifically AlexNet, Inception, LeNet, VGG16, and MobileNet,
[UPDATE / RETRACTION: The '100% GTO' claim had a prompt-leakage bug] We patched the repo to pass strictly neutral states, here are the real, honest numbers: • Latency: Still ~2ms (ONNX + Absorbing Mar
arXiv:2610.15963v1 Announce Type: cross Abstract: High-pileup conditions in CMS during the HL-LHC era make charged-particle tracking increasingly challenging as detector occupancy and combinatorics gr
Agentic artificial intelligence startup Uvi said today it has raised 7.5 million in seed funding to tackle an expensive problem around business context that’s draining millions of dollars annually fro
What's Changed mlx: port the Metal custom kernels to CUDA by @dhiltgen in #18657 docs: model recs and language standardization by @anra8571 in #18981 openai: accept custom tool calls in Responses hist
arXiv:2610.17252v1 Announce Type: cross Abstract: Search-based policy learning, such as Monte Carlo tree search (MCTS), offers the flexibility to trade runtime computation for data efficiency and adap
arXiv:2610.18189v1 Announce Type: new Abstract: Modern text-to-video (T2V) models generate visually compelling videos, yet meaning-preserving changes in prompt wording can substantially alter semantic
arXiv:2610.17383v1 Announce Type: new Abstract: Large-scale repositories of wearable sensor data have enabled the development of foundation models for human activities and behavior. However, existing
arXiv:2610.14260v1 Announce Type: new Abstract: Augmentation-based self-supervised learning can recover the content shared across views without separating its underlying factors into individual coordi
arXiv:2610.15888v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards spends much of its rollout budget on states that yield no learning signal. We recast this as a question a
arXiv:2610.16578v1 Announce Type: cross Abstract: Modern deep classifiers trained by gradient descent need many passes over the data to become accurate, and even then are not always well calibrated; p
Data sovereignty is becoming an architectural problem as enterprises push artificial intelligence inference toward their most sensitive information. Pipelines assembled from separate tools struggle to
arXiv:2610.16735v1 Announce Type: new Abstract: Multimodal reinforcement learning with verifiable rewards commonly assigns one sequence advantage to an entire response. This outcome-level signal disti
arXiv:2610.14634v1 Announce Type: new Abstract: Continuous indoor-outdoor navigation connects destination-level reasoning with motion decisions that must respond to current observations. We present Vb
arXiv:2610.14885v1 Announce Type: new Abstract: Test-time compute (TTC), which scores multiple candidate actions at inference time, is a powerful way to improve the execution performance of vision-lan
Very cool to see Embedding Gemma 2 by @Google, number on trending on HF! The media and silicon valley bubble only talks about reasoning LLMs when there is a much bigger variety of AI models that are s
arXiv:2602.16681v2 Announce Type: replace Abstract: Time-series anomaly detection (TSAD) requires identifying both immediate Point Anomalies and long-range Context Anomalies. However, existing foundat
arXiv:2610.16204v1 Announce Type: new Abstract: Autoencoders are the core component of latent video generative models, compressing high-dimensional videos into compact latent spaces for downstream gen
arXiv:2610.14000v1 Announce Type: new Abstract: Group-relative reinforcement learning methods such as GRPO train large language model (LLM) agents by comparing rollouts for the same task. When every t
arXiv:2610.14158v1 Announce Type: new Abstract: Understanding and tracking a dynamic world are fundamental to visual intelligence. Existing video benchmarks provide limited insight into whether models
arXiv:2610.15354v1 Announce Type: new Abstract: Sexual harassment represents a pervasive societal challenge, with a substantial proportion of incidents remaining unreported due to insufficient evidenc
arXiv:2610.15389v1 Announce Type: new Abstract: Video understanding agents rely on a harness to acquire evidence, use tools, and reason, but improving it requires substantial effort. Recent self evolu