Knowledge catalogue
arXiv:2610.19640v1 Announce Type: new Abstract: The scarcity of observed biodegradation reactions constrains the training of product and pathway predictors. We present enviGraph, a framework that gene
arXiv:2610.15029v1 Announce Type: cross Abstract: Automated disease classification from patient-reported symptoms requires balancing the speed of lightweight discriminative models against the contextu
arXiv:2610.20175v1 Announce Type: new Abstract: Parallel drafters such as DFlash and DSpark reduce proposal latency, but early mismatches limit how much of each block can be accepted. Retaining altern
ci: shorten Windows Vulkan build path Keep the nested shader-generator compiler probes below Windows path limits in checkouts with longer repository names. mlx: fix Nemotron bugs and support global sc
Overview llama.cpp v0.7.0 adds the LiliCorr speculative decoding technique and MTP for GLM5-Next, a new GPU cache for MoE experts kept in host memory (--moe-cache-mib, up to ~2.2x decode speedup when
arXiv:2610.17959v1 Announce Type: cross Abstract: The design of attention mechanisms in long-context large language models faces an ``impossible triangle'' among KV cache size, training and prefill FL
arXiv:2610.19847v1 Announce Type: new Abstract: We address inverse rendering with 3D Gaussian Splatting (3DGS) by introducing explicit visibility modeling for relighting as well as material and light
arXiv:2610.20604v1 Announce Type: new Abstract: World-action models aim to turn visual anticipation into driving decisions, yet typically train action experts on trajectory representations defined ind
arXiv:2610.19731v1 Announce Type: new Abstract: Goal misgeneralization is a common failure mode in which an agent behaves competently out of distribution but does not pursue the intended goal. However
arXiv:2610.15492v1 Announce Type: new Abstract: Skill internalization aims to convert externally specified procedural skills into persistent model capabilities, allowing language-model agents to execu
arXiv:2610.20067v1 Announce Type: new Abstract: Large Language Models (LLMs) frequently encounter conflicts between internal parametric knowledge and external contextual knowledge, yet the mechanisms
arXiv:2610.15625v1 Announce Type: new Abstract: World Action Models (WAMs) unify visual world modeling and action generation, yet their real-world deployment is heavily bottlenecked by iterative actio
arXiv:2610.18227v1 Announce Type: cross Abstract: Domain-specific curvature is an appealing idea for domain generalization (DG), but curvature learning can denote substantially different computations.
arXiv:2610.15966v1 Announce Type: new Abstract: We present a low-cost miniature Ackermann vehicle for embedded neural-driving research, organized as a hardware-partitioned perceive-decide-act agent: a
arXiv:2610.16352v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a critical phase in the post-training pipeline of large language models (LLMs). As the frontier of LLM capabi
Modal Labs Inc., a provider of artificial intelligence infrastructure, today announced that it has raised 750 million in late-stage funding. Accel led the Series D round. It was joined by Kleiner Perk
arXiv:2610.17463v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) are promising for energy-efficient spatiotemporal pattern recognition at the edge, but efficient deployment and on-devi
arXiv:2610.15328v1 Announce Type: new Abstract: We introduce Anchor-and-Harvest Curriculum (AHC), a domain-informed, rule-based training system for Craftax-Full. AHC combines achievement-oriented task
arXiv:2603.15046v2 Announce Type: replace Abstract: In this study, we address the problem of language-guided robotic manipulation, where a robot is required to manipulate a wide range of objects based
arXiv:2610.16330v1 Announce Type: new Abstract: Feed-forward transformer models have driven remarkable progress in 3D vision,yet their quadratic complexity renders them impractical for long video sequ
arXiv:2610.15077v1 Announce Type: new Abstract: A brain tumor is an unusual growth of cells in the brain that can be cancerous or not, affecting normal brain function and causing serious risk to healt
arXiv:2610.15509v1 Announce Type: new Abstract: KV caches make long-context serving efficient by reusing past computation, but model switching breaks this reuse and forces the receiver to prefill the
arXiv:2610.14941v1 Announce Type: new Abstract: Diffusion-based policies have recently emerged as a powerful paradigm for offline reinforcement learning, but their reliance on multi-step denoising int
arXiv:2610.14365v1 Announce Type: new Abstract: Language model classifiers are increasingly deployed for high-stakes decisions such as phishing screening, content moderation, and fraud triage, yet val
arXiv:2610.14294v1 Announce Type: cross Abstract: Conditional diffusion models can generate high-quality images that align closely with a given prompt. However, samples generated from different random
arXiv:2610.15250v1 Announce Type: new Abstract: Reconstruction-based detectors for multivariate time series anomaly detection typically learn a single entangled representation of normal behavior and f
arXiv:2610.16546v1 Announce Type: new Abstract: Basic probabilistic models form the basis of modern deep learning largely by defining the final activation layer of a network and allowing the networks
arXiv:2610.18556v1 Announce Type: new Abstract: Deep learning for medical image restoration is often constrained by the need for paired training data. Acquiring perfectly registered clean-noisy radiog
arXiv:2610.16411v1 Announce Type: new Abstract: Deploying robots in human-shared environments requires deciding not only how to navigate, but also when dedicated social signals are worth emitting. Cla
arXiv:2610.16667v1 Announce Type: new Abstract: Accurate modelling of soft tactile sensors remains difficult because finite element methods (FEM) are computa- tionally expensive, while real-time simul
arXiv:2610.17639v1 Announce Type: new Abstract: Aesthetic image cropping improves composition by balancing subjects, context, and image boundaries. Large vision-language models bring semantic knowledg
arXiv:2610.17630v1 Announce Type: cross Abstract: In outdoor environments, varying terrain and contact conditions can cause identical action commands to produce different motion outcomes for quadruped
arXiv:2610.15183v1 Announce Type: new Abstract: The rapid proliferation of distributed energy resources (DERs) aggregated by virtual power plants (VPPs) elevates cybersecurity risk by expanding the at
arXiv:2610.16272v1 Announce Type: new Abstract: Feed-forward reconstruction recovers camera poses and dense geometry from image sets whose views are assumed to come from a single scene. In practice, u
arXiv:2610.15856v1 Announce Type: new Abstract: DiffusionNFT (NFT) fine-tunes diffusion models toward task-specific rewards, but optimizing these rewards does not guarantee fidelity to the full prompt
arXiv:2610.17348v1 Announce Type: new Abstract: Modeling 3D hair strands is challenging due to complex hairstyle geometry, scarce high-quality training data, and incomplete or noisy input observations
arXiv:2610.16545v1 Announce Type: new Abstract: Feature caching has become a mainstream training-free strategy for accelerating video diffusion models by reusing intermediate outputs across denoising
FactSet leveraged the Databricks AI Runtime to cut end‑to‑end model training and deployment times from days to hours. By adopting high‑performance GPU clusters, managed data pipelines, and automated n
arXiv:2610.15032v1 Announce Type: new Abstract: Industrial anomaly detection benchmarks have advanced rapidly, yet they remain largely static and therefore underrepresent deployment settings in which
arXiv:2610.17895v1 Announce Type: new Abstract: Coding agents are tasked with producing sophisticated interactive applications such as websites, desktop applications, and games. While humans assess th
arXiv:2610.13239v2 Announce Type: replace Abstract: Visual grounding combines image--language understanding with geometric prediction, yet autoregressive multimodal models often express a box as a coo
arXiv:2610.18001v1 Announce Type: new Abstract: Diffusion language models (DLMs) enable flexible parallel generation by resolving tokens according to the model's evolving predictability rather than a
arXiv:2610.14406v1 Announce Type: cross Abstract: It is widely appreciated that robotic policies require long-term memory to perform complex tasks. However, directly conditioning policies on all past
arXiv:2610.16560v1 Announce Type: new Abstract: PAC-Bayesian generalization bounds are among the strongest distribution-dependent guarantees in statistical learning and are typically expressed in term
arXiv:2610.15020v1 Announce Type: new Abstract: PATTERN, a synthetic node-classification benchmark still used to rank graph networks, plants a random subgraph in a random host graph and asks which nod
arXiv:2610.18670v1 Announce Type: cross Abstract: Reliable robot autonomy requires reasoning about the world from partial and noisy observations. However, many perception systems model only visible sc
arXiv:2610.14012v1 Announce Type: new Abstract: Many systems read the latent space of a variational autoencoder (VAE) as discrete codes, and rely on each latent being read back as the code it was writ
arXiv:2610.15864v1 Announce Type: cross Abstract: Large-scale personalization is shifting from embedding-table retrieval to LLM-based recommenders that generate the Semantic ID (SID) of the item to re
arXiv:2610.14091v1 Announce Type: new Abstract: Training on broad, unfiltered data enables high-quality image generation, but can also teach models concepts that should not be generated; restricting t
arXiv:2610.16967v1 Announce Type: new Abstract: Physical AI is commonly formulated around an explicitly specified task: given a goal or instruction, a system perceives, reasons, and acts. In many real
arXiv:2602.21189v3 Announce Type: replace Abstract: Pass@k is a widely used performance metric for verifiable large language model tasks, including mathematical reasoning, code generation, and short-a
arXiv:2506.19549v2 Announce Type: replace-cross Abstract: Identifying which tokens and activations truly influence model predictions is critical for both the efficiency and interpretability of large a
arXiv:2406.07169v3 Announce Type: replace Abstract: Human motion generation is a challenging task due to its high dimensionality and the difficulty of generating fine-grained motions. Diffusion method
arXiv:2610.16022v1 Announce Type: new Abstract: Neuromorphic sensing and computing promise low latency and high energy efficiency for robotics, yet most event-based visual place recognition (VPR) pipe
arXiv:2610.18374v1 Announce Type: new Abstract: RGB-based category-level 9D pose estimation remains highly challenging because a monocular system must generalize across unseen object instances while j
arXiv:2603.08185v2 Announce Type: replace Abstract: Post-training quantization (PTQ) has emerged as a prevailing technique for deploying large language models (LLMs) efficiently in terms of both memor
arXiv:2610.15854v1 Announce Type: new Abstract: Grounded spatial reasoning about indoor scenes rests on a geometric reconstruction that the multimodal system does not itself produce. Feed-forward mult
arXiv:2512.10581v2 Announce Type: replace Abstract: Current methods for all-in-one image restoration increasingly rely on complex mechanisms, such as Mixture-of-Experts or prompt learning, to handle d
arXiv:2610.15458v1 Announce Type: new Abstract: Existing analyses of interval length in split conformal prediction largely focus on clean data. However, how contamination at the training and calibrati
arXiv:2610.16990v1 Announce Type: new Abstract: Tabular data generation aims to synthesize realistic samples from limited observations while faithfully capturing the underlying data distribution. Tabu