Knowledge catalogue
arXiv:2609.04500v1 Announce Type: cross Abstract: Supporting a wide variety of motion styles is critical for creating diverse virtual characters, but current methods either require large stylized data
arXiv:2609.05232v1 Announce Type: new Abstract: Autonomous AI agents increasingly select actions in environments whose memory, execution-time, runtime, compute, and operational constraints determine w
arXiv:2609.04705v1 Announce Type: cross Abstract: Passive cooling eliminates the energy overhead and mechanical failure modes of fans, making it attractive for edge deployment, yet sustained Deep Neur
arXiv:2609.05266v1 Announce Type: new Abstract: World-action models guide action generation with predicted future observations, but vision-centric predictions miss the local contact cues that decide c
arXiv:2609.05333v1 Announce Type: new Abstract: A transformer language model assigns a single, context-independent vector to a word type at its embedding layer, yet is widely believed to individuate t
arXiv:2601.04202v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly applied to telecom engineering tasks, yet perform poorly on 3GPP specifications. These standards
arXiv:2609.04984v1 Announce Type: new Abstract: In the field of computer vision and graphics, high-quality reconstruction of the human body in static scenes has been achieved in recent years by a sing
arXiv:2609.05282v1 Announce Type: new Abstract: Reliable robot-to-human handover requires the robot to infer when the person is ready to receive the object, and release it safely, comfortably, and at
arXiv:2603.21309v4 Announce Type: replace Abstract: Facial expression recognition (FER) in videos requires model personalization to capture considerable variation across subjects. Vision-language mode
arXiv:2609.05279v1 Announce Type: new Abstract: Production multi-agent systems replace agents constantly, on the assumption that an agent filling a role is interchangeable with any other agent that ca
arXiv:2609.04404v1 Announce Type: new Abstract: ASR systems sometimes produce fluent text that is unrelated to the speech they receive. We view these hallucinations as one possible consequence of a br
arXiv:2603.29566v2 Announce Type: replace Abstract: We study polynomial group convolutional neural networks (PGCNNs) for an arbitrary finite group G. In particular, we introduce a new mathematical fra
arXiv:2609.05335v1 Announce Type: cross Abstract: Public vulnerability databases collect rich information about known software flaws, including their weakness types, affected components, and related p
arXiv:2609.04325v1 Announce Type: new Abstract: We explore the use of simulated data for training a model for protein annotation in crowded cryo-electron tomography volumes reconstructed from images c
arXiv:2609.05190v1 Announce Type: new Abstract: In this paper we illustrate a novel architecture generating interpretable behavior and explanations. We refer to this architecture as the Mirror Agent M
arXiv:2410.23440v4 Announce Type: replace Abstract: Operator learning, the approximation of mappings between infinite-dimensional function spaces using machine learning, has gained increasing research
arXiv:2603.08234v2 Announce Type: replace Abstract: With the rapid advancement of large language models (LLMs), the safety of LLMs has become a critical concern. Despite significant efforts in safety
arXiv:2609.05388v1 Announce Type: new Abstract: Visual reasoning tasks require a system to jointly perceive visual content and apply formal relational constraints---a combination that neither pure neu
arXiv:2609.05117v1 Announce Type: cross Abstract: Current LLM safety benchmarks largely rely on binary metrics, overlooking how models respond to harmful prompts with varying threat implicitness. We i
arXiv:2609.04305v1 Announce Type: cross Abstract: We present TNFlow, a transformer and normalizing flow architecture for inferring the surface composition of Trans-Neptunian Objects (TNOs) from their
arXiv:2603.27520v2 Announce Type: replace Abstract: In video diffusion transformers, visual patch tokens maintain explicit correspondence to space and time. We hypothesize that their channel dimension
arXiv:2609.04635v1 Announce Type: new Abstract: Neural ocean emulators are being proposed for regional forecasting in cyclone-exposed coastal seas, and a natural design choice is to hand the network t
arXiv:2609.04454v1 Announce Type: new Abstract: Anatomic tracer studies reveal how axon bundles project from an injection site, branch into smaller groups of axons, and course through the brain to rea
arXiv:2609.05084v1 Announce Type: new Abstract: Reliable autonomous mapping, environmental sampling, last-mile logistics, and infrastructure deployment depend on the optimal surface area-balanced dist
arXiv:2609.04911v1 Announce Type: new Abstract: Interactive visual world models must distinguish observation from physical intervention. Camera motion reveals new surfaces, whereas intervention change
arXiv:2508.06207v3 Announce Type: replace Abstract: Back-support exoskeletons mitigate musculoskeletal strain, yet current systems rely on reactive sensing and lack context-aware assistance modulation
arXiv:2609.04528v1 Announce Type: new Abstract: Humans can learn and generalize novel concepts from sparse data because they express knowledge in rich structural formats. In this paper, we propose tha
arXiv:2609.05040v1 Announce Type: new Abstract: As evolutionary transfer optimization (ETO) scales to larger collections of related tasks, problem evaluation can become a major source of runtime growt
arXiv:2503.08904v3 Announce Type: replace Abstract: The recent developments in data-driven methods have paved the way to new methodologies to provide accurate state reconstruction of engineering syste
arXiv:2609.05369v1 Announce Type: cross Abstract: Vision-language-action (VLA) models can execute short manipulation skills, but remain brittle in long-horizon procedures requiring persistent task sta
arXiv:2609.04489v1 Announce Type: cross Abstract: Pause-token methods improve LLM reasoning by inserting special tokens into sequences. Prior work explains these gains through computational expressivi
arXiv:2609.05261v1 Announce Type: new Abstract: Large language model agents increasingly rely on execution traces to master complex interactive tasks. However, current paradigms are bottlenecked by sh
arXiv:2609.04637v1 Announce Type: cross Abstract: Audio Large Language Models (Audio LLMs) have advanced in audio understanding, yet they can still predict the answer by reasoning from textual cues or
arXiv:2609.04678v1 Announce Type: new Abstract: Existing post-training pipelines for coding and terminal agents suffer severe token and control fidelity errors: simplified training environments mismat
arXiv:2609.04575v1 Announce Type: cross Abstract: Modern fine-grained Mixture-of-Experts (MoE) models route each token to a small number of experts and renormalize their router probabilities. We show
arXiv:2609.05091v1 Announce Type: new Abstract: Industrial anomaly detection must handle two distinct defect families: structural anomalies, which manifest as local texture corruptions, and logical an
arXiv:2609.04242v1 Announce Type: cross Abstract: Audio-visual understanding remains challenging because models must jointly interpret spoken content, visual events, and their temporal relationships.
arXiv:2609.04735v1 Announce Type: new Abstract: Designing viable drug candidates requires searching a combinatorially large and rugged chemical space for molecules that satisfy multiple, often competi
arXiv:2609.04912v1 Announce Type: cross Abstract: Reliability evaluation of deep neural networks under hardware faults commonly relies on fault injection, but exhaustive campaigns are intractable for
arXiv:2609.04452v1 Announce Type: new Abstract: Modern misinformation is often heard before it is read, yet fact-checking systems are still evaluated mainly on clean written claims. Spoken dialogue re
arXiv:2609.05019v1 Announce Type: new Abstract: Agents tend to optimize, select, or constrain execution structures before decisive runtime outcomes are observed. However, such pre-execution commitment
arXiv:2609.05079v1 Announce Type: new Abstract: Autonomous coding agents are increasingly proposed as AI-scientist systems that conduct analyses and write research reports, but executing a prescribed
arXiv:2502.00285v3 Announce Type: replace-cross Abstract: Trajectory similarity is fundamental to many spatio-temporal data mining applications. Recent studies propose deep learning models to approxim
arXiv:2609.04417v1 Announce Type: cross Abstract: Distributed AI training involves recurring rounds of data exchange between multiple pairs of GPU nodes. Slowdown in even one flow due to congestion ca
arXiv:2609.04365v1 Announce Type: cross Abstract: Decompensation represents a critical transition in the course of cirrhosis, yet clinicians have limited non-invasive tools to reliably predict its ons
arXiv:2609.05241v1 Announce Type: new Abstract: A rapidly expanding ecosystem of actors is removing built-in safety guardrails from open-weight AI models. We profile this ecosystem by identifying key
arXiv:2609.04486v1 Announce Type: cross Abstract: Intent-based networking realization starts by translating high-level intents into low-level network configurations. Recent approaches have shifted tow
arXiv:2609.05111v1 Announce Type: new Abstract: Large language models are now trained and evaluated under a diverse set of paradigms: supervised fine-tuning (SFT), few-shot in-context learning (ICL),
arXiv:2609.05415v1 Announce Type: new Abstract: Recent advances in automatic rigging now deliver animation-ready 3D assets at scale, yet generating the motion to drive them remains a bottleneck. Exist
arXiv:2609.05337v1 Announce Type: new Abstract: We present a Hessian-based approach to numerically continue periodic orbits in dynamical systems. A loop (periodic orbit candidate) is parametrized as a
arXiv:2609.04755v1 Announce Type: new Abstract: We construct a corpus of 1,262 verse-commentary (urai) pairs from five Classical Tamil source sections, ranging from technical grammatical prose to mode
arXiv:2609.04366v1 Announce Type: new Abstract: Early-onset colorectal cancer is increasing among younger adults, yet red-flag symptoms in this age group have no evidence-based guidelines for follow-u
arXiv:2609.04948v1 Announce Type: cross Abstract: Multi-expert models have become the dominant paradigm for long-tailed learning, largely attributed to their presumed ability to benefit from expert di
arXiv:2609.04550v1 Announce Type: new Abstract: Video-language benchmarks are usually constructed by the dataset authors without published reliability statistics, leaving the noise floor of the constr
arXiv:2609.04355v1 Announce Type: cross Abstract: Pretrained vision-language-action (VLA) models enable broad manipulation but remain unreliable in tasks demanding precision and repeatability. Applyin
arXiv:2609.05114v1 Announce Type: new Abstract: Semantic 3D maps are increasingly constructed automatically for aerial robotics by integrating learned semantic predictions into 3D representations. Whi
arXiv:2607.10749v2 Announce Type: replace Abstract: Reflections of water pose a significant challenge for computer vision systems, as standard deep learning models frequently confuse objects with thei
arXiv:2510.11839v3 Announce Type: replace Abstract: Time series are ubiquitous in many applications that involve forecasting, classification and causal inference tasks, such as healthcare, finance, au
arXiv:2609.05171v1 Announce Type: new Abstract: Image generation and editing models have advanced rapidly, yet remain unreliable when prompts require external world knowledge. Bounded and long-tail pa
arXiv:2505.18686v3 Announce Type: replace Abstract: Weakly supervised referring expression comprehension(WREC) and segmentation(WRES) aim to learn object grounding based on a given expression using we