Knowledge catalogue
arXiv:2605.13996v1 Announce Type: new Abstract: In robotics, a common challenge in imitation learning is the mismatch between training and deployment conditions, caused, for example, by environmental
arXiv:2605.15042v1 Announce Type: cross Abstract: We propose EverAnimate, an efficient post-training method for long-horizon animated video generation that preserves visual quality and character ident
arXiv:2605.14855v1 Announce Type: cross Abstract: Forecasting within signal processing pipelines is crucial for mitigating delays, particularly in predicting the dynamic movements of objects such as N
arXiv:2605.14801v1 Announce Type: new Abstract: Zero-shot vision-and-language navigation (VLN) has gained significant attention due to its minimal data collection costs and inherent generalization. Th
arXiv:2605.13853v1 Announce Type: cross Abstract: Facial editing is an important task with applications in entertainment, virtual reality, and digital avatars. Most existing approaches rely on generat
arXiv:2602.03417v2 Announce Type: replace Abstract: Large language models hallucinate factual claims and struggle to ground their outputs in retrievable evidence, particularly in non-English languages
arXiv:2605.14305v1 Announce Type: new Abstract: Discrete diffusion language models improve generation efficiency through parallel token prediction, but standard X_0 prediction methods introduce factor
arXiv:2605.14854v1 Announce Type: cross Abstract: Human Mesh Recovery (HMR) is fundamentally ambiguous: under occlusion or weak depth cues, multiple 3D bodies can explain the same image evidence. This
arXiv:2605.14868v1 Announce Type: new Abstract: Generating adversarial examples at scale is a core primitive for robustness evaluation, adversarial training, and red-teaming, yet even 'fast' attacks s
arXiv:2605.13904v1 Announce Type: cross Abstract: Brain encoder models predict cortical fMRI responses from the internal activations of pretrained vision and language networks, and are typically evalu
arXiv:2605.13974v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) and related flow-based architectures are now among the strongest text-to-image generators, yet the internal mechanisms t
arXiv:2508.15198v2 Announce Type: replace Abstract: Tensor neural networks (TNNs) have demonstrated their superiority in solving high-dimensional problems. However, similar to conventional neural netw
arXiv:2605.15085v1 Announce Type: cross Abstract: Nowadays refinery optimization utilizes sheer amounts of data, which can be handled with modern Linear Programming (LP) software, but the interpreting
arXiv:2605.14465v1 Announce Type: new Abstract: Multi-step LLM reasoning over structured tables fails because planning and execution share no explicit cell-grounding contract. Existing methods constra
arXiv:2602.14674v4 Announce Type: replace Abstract: Gradual argumentation is a field of symbolic AI which is attracting attention for its ability to support transparent and contestable AI systems. It
arXiv:2605.14920v1 Announce Type: new Abstract: Efficient UAV exploration in unknown environments requires rapid coverage expansion while maintaining accurate and reliable localization, since safe nav
Fun interview with Jacob Effron on the Unsupervised Learning podcast. It’s hard to imagine more of a dream Unsupervised Learning guest than @ylecun. Yann is one of the godfathers of AI, and he has som
arXiv:2410.06431v5 Announce Type: replace Abstract: Accurate uncertainty quantification in large language models (LLMs) is essential for reliable confidence estimation, yet fine-tuned LLMs often becom
arXiv:2605.15018v1 Announce Type: cross Abstract: Shapley value and its priority-aware extensions are widely used for valuation in machine learning, but existing methods require pairwise priority to b
arXiv:2603.00772v2 Announce Type: replace-cross Abstract: Score-based generative models (SGMs) have achieved remarkable empirical success, motivating their application to a broad range of data distrib
arXiv:2605.14703v1 Announce Type: new Abstract: The high dynamic range (HDR) video ecosystem is approaching maturity, but the problem of upconverting legacy standard dynamic range (SDR) videos persist
arXiv:2510.25240v3 Announce Type: replace-cross Abstract: We present a general strategy for turning generative models into candidate solution samplers for batch Bayesian optimization (BO). The use of
arXiv:2605.14117v1 Announce Type: cross Abstract: An AI system for professional floor plan design must precisely control room dimensions and areas while respecting the desired connectivity between roo
arXiv:2602.10346v2 Announce Type: replace Abstract: Large language models (LLMs) must balance diversity and creativity against logical coherence in open-ended generation. Existing truncation-based sam
arXiv:2602.00992v2 Announce Type: replace Abstract: In many robot motion planning problems, task objectives and physical constraints induce non-Euclidean geometry on the configuration space, yet many
arXiv:2605.14317v1 Announce Type: new Abstract: Extreme precipitation causes severe societal and economic damage, and weather control has long been discussed as a potential mitigation strategy. Howeve
arXiv:2605.14513v1 Announce Type: cross Abstract: Diffusion-based video generation has advanced substantially in visual fidelity and temporal coherence, but practical deployment remains limited by the
arXiv:2605.14487v1 Announce Type: cross Abstract: Autoregressive video diffusion models support real-time synthesis but suffer from error accumulation and context loss over long horizons. We discover
arXiv:2508.17588v2 Announce Type: replace Abstract: Generation-driven world models create immersive virtual environments but suffer slow inference due to the iterative nature of diffusion models. Whil
arXiv:2605.13997v1 Announce Type: cross Abstract: Sparse Mixture-of-Experts (MoE) layers route tokens through a handful of experts, and learning-free compression of these layers reduces inference cost
In a dimly lit bedroom, a frightened young woman is thrown onto a bed by a tall, muscular man. He grabs her hand, and flame-like vines crawl across her body, fusing with her flesh. She levitates, then
arXiv:2412.03992v3 Announce Type: replace-cross Abstract: Under a set of assumptions on a family of submanifolds subset {mathbb R}^D, we derive a series of geometric properties that remain valid after
If you have this weird gut feeling that the rich pay little tax in the US, your gut is spot on... Source: https://www.nytimes.com/interactive/2019/10/06/opinion/income-tax-rate-wealthy.html?action=cli
arXiv:2605.14267v1 Announce Type: cross Abstract: Diffusion models (DMs) have exhibited remarkable efficacy in various image restoration tasks. However, existing approaches typically operate within th
arXiv:2605.14239v1 Announce Type: new Abstract: Hyperspectral image (HSI) classification is challenging in complex scenes due to spectral ambiguity, spatial heterogeneity, and the strong coupling betw
arXiv:2605.15102v1 Announce Type: cross Abstract: Large language model (LLM) based multi-turn dialogue systems often struggle to track dependencies across non-adjacent turns, undermining both consiste
arXiv:2605.14333v1 Announce Type: new Abstract: Text and faces are among the most perceptually salient and practically important patterns in visual generation, yet they remain challenging for autoregr
arXiv:2605.14831v1 Announce Type: new Abstract: One of the bottlenecks on the way towards recursively self-improving systems is the challenge of interestingness: the ability to prospectively identify
arXiv:2602.12105v2 Announce Type: replace-cross Abstract: We propose a system for differentiating through solutions to geometry processing problems. Our system differentiates a broad class of geometri
arXiv:2605.14736v1 Announce Type: cross Abstract: Target speech extraction remains difficult for compact devices because monaural neural models lack spatial evidence and classical beamformers lose res
arXiv:2510.00977v3 Announce Type: replace-cross Abstract: GRPO has emerged as a prominent reinforcement learning algorithm for post-training LLMs. Unlike critic-based methods, GRPO computes advantages
arXiv:2605.14123v1 Announce Type: cross Abstract: Feature sharing via split inference offers a lightweight alternative to federated learning for resource-constrained hospitals, but transmitted feature
arXiv:2605.14404v1 Announce Type: new Abstract: While LLMs are increasingly used in commercial services, they pose privacy risks such as leakage of sensitive personally identifiable information (PII).
arXiv:2605.14524v1 Announce Type: cross Abstract: Recent studies have reported extit{saturation effects} and extit{multiple descent behavior} in large dimensional kernel ridge regression (KRR). Howeve
arXiv:2602.21302v2 Announce Type: replace Abstract: We introduce a Task-Level Iterative Learning Control method for dynamic manipulation of ropes. We demonstrate this method on a non-planar rope manip
arXiv:2512.02920v3 Announce Type: replace-cross Abstract: We consider analyzing traffic accident patterns using both road network data and satellite images aligned to road graph nodes. Previous work f
arXiv:2605.14494v1 Announce Type: new Abstract: Two-Stage Robust Optimization (2RO) with discrete uncertainty is challenging, often rendering exact solutions prohibitive. Scenario reduction alleviates
arXiv:2605.12970v2 Announce Type: replace Abstract: Many problems seem to require a flash of insight to solve. What form do these sudden insights take, and what impact do they have on how people appro
arXiv:2605.14354v1 Announce Type: new Abstract: We present a new computational framework for detecting and structuring manipulative political narratives. A task that became more important due to the s
arXiv:2601.21929v2 Announce Type: replace Abstract: Training data attribution (TDA) identifies which training examples most influenced a model's prediction. Influence function methods are a theoretica
arXiv:2505.23912v2 Announce Type: replace-cross Abstract: Hallucination remains a major challenge for the safe and trustworthy deployment of large language models (LLMs) in factual content generation.
arXiv:2603.14360v2 Announce Type: replace-cross Abstract: Transformers are highly parallel but are limited to computations in the TC^0 complexity class, excluding tasks such as entity tracking and cod
arXiv:2605.14606v1 Announce Type: new Abstract: Accurate precipitation nowcasting over extended horizons (0-3 hours) is essential for disaster mitigation and operational decision-making, yet remains a
arXiv:2601.20173v2 Announce Type: replace Abstract: We present a new nonlinear dimensionality reduction method, MAPLE, that enhances UMAP by improving manifold modeling. MAPLE employs a self-supervise
arXiv:2605.14021v1 Announce Type: cross Abstract: Google AI Overviews (AIOs) are arguably the most widely encountered deployment of generative AI, reaching over 2 billion users who may not realize the
arXiv:2605.14579v1 Announce Type: new Abstract: Accurate medical image segmentation is fundamental to precision medicine, yet robust delineation remains challenging under heterogeneous appearances, am
arXiv:2605.14960v1 Announce Type: cross Abstract: Impossible objects, geometric constructions that humans can perceive but that cannot exist in real life, have been a topic of intrigue in visual arts,
arXiv:2605.14289v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models scale capacity by combining specialized experts, but most existing approaches assume centralized access to training da
arXiv:2605.14407v1 Announce Type: new Abstract: The dominant discourse on AI limitations frames the boundary of AI capability as a divide between digital tasks (where AI excels) and physical tasks (wh
arXiv:2605.14380v1 Announce Type: new Abstract: Psychological defense mechanisms (PDMs) are unconscious cognitive processes that modulate how individuals perceive and respond to emotional distress. Au