Knowledge catalogue
arXiv:2610.11447v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as coding agents that edit files, run builds and tests, inspect execution results, and repair s
arXiv:2610.12362v1 Announce Type: new Abstract: We consider policy optimization for online episodic tabular Markov decision processes (MDPs) with adversarial losses and bandit feedback. Policy optimiz
arXiv:2610.11577v1 Announce Type: cross Abstract: Autonomous vehicles often suffer from limited perception due to occlusions, blind spots, limited sensor range, and the complex nature of surrounding e
arXiv:2610.10610v1 Announce Type: cross Abstract: Repository benchmarks (e.g., SWE-bench) for coding agents often assume that lines of code edited can predict task difficulty, but such datasets' poor
arXiv:2610.10931v1 Announce Type: new Abstract: In memetic symbolic regression, candidate structures are compared after coefficient calibration, so the calibration protocol itself contributes to evolu
arXiv:2610.11410v1 Announce Type: new Abstract: Affective computing has progressed from categorical emotion recognition to open-ended affective analysis with large multimodal models. Yet affective sci
arXiv:2610.10724v1 Announce Type: new Abstract: How does the human mind represent semantic categories? Why do natural languages favor certain meanings over others? Prior explanations have relied on lo
arXiv:2610.11306v1 Announce Type: new Abstract: Adversarial distillation transfers robustness from high-capacity teachers to compact students. Existing adversarial distillation methods mainly use teac
arXiv:2610.11506v1 Announce Type: cross Abstract: Graph clustering is a fundamental task in data analysis, aiming at grouping nodes with similar characteristics in the graph into clusters. This proble
arXiv:2512.22454v2 Announce Type: replace Abstract: Electrical substations are a significant component of an electrical grid. Indeed, the assets at these substations (e.g., transformers) are vulnerabl
arXiv:2610.11784v1 Announce Type: new Abstract: Tabular in-context learning (ICL) has emerged as a training-free and accurate paradigm for tabular prediction, but current approaches to compressing its
arXiv:2610.12008v1 Announce Type: new Abstract: Abstract argumentation frameworks (AFs) introduced by Dung provide a formal foundation for non-monotonic reasoning in artificial intelligence. While dec
arXiv:2610.12328v1 Announce Type: new Abstract: We consider stochastic nonsmooth nonconvex composite optimization, which includes several important problems such as constrained optimization and the re
arXiv:2610.11863v1 Announce Type: cross Abstract: Kernel Stein discrepancies (KSDs) provide a versatile tool for comparing distributions. One of their main applications is in quantifying the goodness-
arXiv:2610.11479v1 Announce Type: cross Abstract: Causal video diffusion models generate video autoregressively, which suits streaming, interactive, and long-video generation. Under standard training,
arXiv:2610.11548v1 Announce Type: new Abstract: Synthetic tasks are increasingly used both as probes of language-model capability and as pretraining data. Both uses are often justified by loss reducti
arXiv:2507.13835v2 Announce Type: replace-cross Abstract: The amount of quality data in many machine learning tasks is limited to what is available locally to data owners. The set of quality data can
arXiv:2610.10829v1 Announce Type: cross Abstract: Conformal prediction provides prediction sets with finite-sample guarantees, but the label verification required for calibration can be expensive. We
arXiv:2610.12156v1 Announce Type: new Abstract: To stream long videos while maintaining visual quality and temporal consistency, Self Forcing mitigates exposure bias through self-rollout training on s
arXiv:2610.11542v1 Announce Type: cross Abstract: Multi-agent LLM systems negotiating with a stateful counterpart waste model calls in three ways: polite loops that never meet the counterpart's hidden
arXiv:2610.11663v1 Announce Type: new Abstract: Deep reinforcement learning agents reach strong performance in real-time strategy games but can be brittle against opponents outside their training dist
arXiv:2602.03538v2 Announce Type: replace Abstract: While Dynamic Gaussian Splatting enables high-fidelity 4D reconstruction, its deployment is severely hindered by a fundamental dilemma: unconstraine
arXiv:2610.11621v1 Announce Type: new Abstract: Consensus can make pseudo-label learning more reliable, but only when its predictors contribute genuinely different evidence. Multiple models that repea
arXiv:2610.12337v1 Announce Type: cross Abstract: Anatomical continuity provides complementary information for medical image restoration, but its use requires accounting for local anatomy and variatio
arXiv:2610.11096v1 Announce Type: cross Abstract: Visual tracking across the air-water interface is challenged by splashes, bubbles, refraction, reflections, and abrupt appearance changes that can tem
arXiv:2610.12107v1 Announce Type: new Abstract: Generalized referring expression segmentation (GRES) requires dynamically balancing high-level semantics for identifying a variable number of language-s
arXiv:2610.11086v1 Announce Type: new Abstract: Purpose: Cervical cancer is one of the leading causes of mortality worldwide. Deep learning has shown promising performance in medical image classificat
arXiv:2610.12431v1 Announce Type: new Abstract: Diffusion models can represent complex, multimodal trajectory distributions, but extracting uncertainty from them typically requires costly Monte Carlo
arXiv:2610.12316v1 Announce Type: new Abstract: Recent motion generative models have demonstrated strong capabilities in synthesizing physically plausible character motion, but often overlook establis
arXiv:2610.10808v1 Announce Type: new Abstract: When audio, video, and text disagree, accuracy alone does not show whether to acquire another source or abstain. We study these choices in a controlled
arXiv:2610.10740v1 Announce Type: cross Abstract: Conversational task disambiguation over tabular data uses dialogue to resolve missing information about a user's intended task before producing a solu
arXiv:2610.10868v1 Announce Type: cross Abstract: We introduce Conversational Voice Aesthetic Model, a speech large language model for describing the voice aesthetics of real or synthetic speech respo
arXiv:2610.11394v1 Announce Type: cross Abstract: Multimodal Alzheimer's disease (AD) diagnosis benefits from integrating heterogeneous clinical, imaging, genomic, and biomarker evidence, but clinical
AI inference is fast becoming the workload that decides the economics of the AI boom. Training built the first wave of GPU clouds, but serving models faster and cheaper will define the next. That shif
arXiv:2610.11740v1 Announce Type: new Abstract: Neural networks are typically trained using first-order methods and back-propagation. It is unclear whether this approach is optimal for morphological l
arXiv:2610.11908v1 Announce Type: cross Abstract: Existing model marketplaces typically trade and select individual models as indivisible units, limiting their ability to exploit complementarities amo
arXiv:2610.12106v1 Announce Type: cross Abstract: Watermarking large language models is popular for tracing chatbot and agentic outputs, yet detectors remain unreleased since exposing them could let a
arXiv:2610.11108v1 Announce Type: new Abstract: Multi-domain physics-informed neural networks (PINNs) flexibly model medium-specific representations to solve fluid--solid conjugate heat transfer (CHT)
arXiv:2610.10641v1 Announce Type: cross Abstract: Large language models (LLMs) offer promising potential for next-visit diagnosis prediction, owing to their ability to integrate longitudinal clinical
arXiv:2610.10594v1 Announce Type: new Abstract: Activation probes that monitor deployed language models are trained on synthetic conversations, and how many a probe needs is open. We trace learning cu
arXiv:2610.10766v1 Announce Type: cross Abstract: Lack of effective authentication has resulted in numerous security and privacy breaches, including unauthorized access to protected information, ident
arXiv:2610.12115v1 Announce Type: new Abstract: In many machine learning applications, it is necessary to guard against worst-case scenarios and predictions that could result in substantial losses. In
arXiv:2610.11376v1 Announce Type: cross Abstract: Latent LiDAR pipelines suffer from flying pixels: convolutional VAEs blur sharp radial depth discontinuities, yielding edge depths that back-project t
arXiv:2610.10846v1 Announce Type: new Abstract: The diversity of robot embodiments and action spaces makes it challenging to build robot world models that generalize across different embodiments. We i
arXiv:2610.10961v1 Announce Type: cross Abstract: Coding agents increasingly share a workstation while drawing on separate providers and subscription allowances. A second agent can inspect a completed
arXiv:2610.11222v1 Announce Type: cross Abstract: Preclinical models poorly predict human drug efficacy, particularly in neurological disorders. Neural activity offers a uniquely rich source of transl
arXiv:2610.12467v1 Announce Type: cross Abstract: Text-conditioned motion generators produce trackable whole-body motion, but they have no notion of scene-dependent safety: the same action may target
arXiv:2610.11007v1 Announce Type: new Abstract: At the start of every session, LLM agents load a fixed context file, such as exttt{AGENTS.md}. Each loaded token in the file is charged again in every l
arXiv:2610.10636v1 Announce Type: new Abstract: Compressing a matrix for reconstruction still defaults to the truncated SVD, approximating the data with a single low-rank structure. It is common to re
arXiv:2610.11085v1 Announce Type: new Abstract: Offline multi-task reinforcement learning (Offline MTRL) heavily depends on the quality and distribution of pre-collected data. However, existing method
arXiv:2610.11853v1 Announce Type: new Abstract: Modern neural networks are heavily over-parameterized. This redundancy incurs substantial compute and memory overhead during training and inference. Exi
arXiv:2610.11505v1 Announce Type: new Abstract: Humanoid robots possess the structural capability to traverse complex terrains. However, achieving stable t raversal without relying on perceived inform
arXiv:2610.12190v1 Announce Type: new Abstract: As claims about recursive self-improvement (RSI) and artificial general intelligence (AGI) proliferate, we ask a simple question: do frontier AI models
arXiv:2610.11993v1 Announce Type: cross Abstract: Data video is a media form that integrates data visualization with video narrative, widely adopted in news reporting and business analysis. Compared w
arXiv:2605.11015v2 Announce Type: replace-cross Abstract: Software vulnerability detection plays a critical role in ensuring system security, where real-world auditing requires not only determining wh
arXiv:2506.23292v3 Announce Type: replace Abstract: Recent advances in AIGC have exacerbated the misuse of malicious deepfake content, making the development of reliable deepfake detection methods an
arXiv:2610.11351v1 Announce Type: new Abstract: Large language models (LLMs) increasingly act as agents with little human oversight, so potential mistakes they make can go unnoticed. Users then depend
arXiv:2610.10727v1 Announce Type: cross Abstract: Forecasting resale prices of used electronics is critical for subscription-based platforms where pricing errors translate directly into risk. Unlike s
arXiv:2610.09892v2 Announce Type: replace-cross Abstract: Feedback from automated testing, human red teaming, and incident response can strengthen an AI system's defenses when discovered failures lead
arXiv:2610.11315v1 Announce Type: cross Abstract: In diffusion transformers, low-rank branches can mitigate 4-bit weight--activation (W4A4) post-training quantization (PTQ) loss by decomposing each we