Knowledge catalogue
arXiv:2602.04583v2 Announce Type: replace Abstract: Deep neural networks for visual perception are highly susceptible to domain shift, which poses a critical challenge for real-world deployment under
arXiv:2411.04832v3 Announce Type: replace-cross Abstract: Plasticity refers to a network's ability to adapt to changing data distributions, which is crucial for the successful training of deep reinfor
Gary Marcus shared an observation or commentary about poetry encountered on a platform or service referenced as 'B.sky' (likely Bluesky, the decentralized social network). The post appears to document
arXiv:2505.15342v2 Announce Type: replace-cross Abstract: We study the policy testing problem in discounted Markov decision processes (MDPs) in the fixed-confidence setting under a generative model wi
arXiv:2508.05132v2 Announce Type: replace Abstract: As medical LLMs transition to clinical deployment, assessing their ethical reasoning capability becomes critical. While achieving high accuracy on k
arXiv:2604.18444v1 Announce Type: cross Abstract: Zero-shot vision-language models (VLMs) have shown promise for chest radiograph classification, but their performance is often limited by confounding
arXiv:2604.16779v1 Announce Type: cross Abstract: Quantum feature maps offer expressive embeddings for classical learning tasks, and augmenting sparse identification of nonlinear dynamics (SINDy) with
arXiv:2307.08336v2 Announce Type: replace Abstract: Despite the numerous applications of convex constraints in Robotics, enforcing them within learning-based frameworks remains an open challenge. Exis
arXiv:2604.16565v1 Announce Type: new Abstract: While Diffusion Large Language Models (dLLMs) offer structural advantages for global planning, efficiently verifying that they arrive at correct answers
arXiv:2604.17243v1 Announce Type: new Abstract: A robust Multimodal Large Language Model (MLLM) for Earth Observation should maintain consistent interpretation and reasoning under realistic input vari
arXiv:2601.14750v3 Announce Type: replace Abstract: Chain-of-Thought (CoT) prompting has achieved remarkable success in unlocking the reasoning capabilities of Large Language Models (LLMs). Although C
arXiv:2604.17328v1 Announce Type: new Abstract: This paper investigates the length problem in sequence-level relative reinforcement learning. We observe that, although existing methods partially allev
arXiv:2604.18112v1 Announce Type: new Abstract: In recent years, multimodal multidomain fake news detection has garnered increasing attention. Nevertheless, this direction presents two significant cha
arXiv:2604.17415v1 Announce Type: cross Abstract: Reward-based fine-tuning aims to steer a pretrained diffusion or flow-based generative model toward higher-reward samples while remaining close to the
arXiv:2604.17257v1 Announce Type: new Abstract: Recent text embedding models are often adapted to specialized domains via contrastive pre-finetuning (PFT) on a naive collection of scattered, heterogen
arXiv:2601.15625v2 Announce Type: replace Abstract: Large language models (LLMs) can call tools effectively, yet they remain brittle in multi-turn execution: after a tool-call error, smaller models of
arXiv:2604.16557v1 Announce Type: cross Abstract: Current post-training methodologies for adapting Large Vision-Language Models (LVLMs) generally fall into two paradigms: Supervised Fine-Tuning (SFT)
arXiv:2604.18190v1 Announce Type: new Abstract: We propose MADDPG-K, a scalable extension to Multi-Agent Deep Deterministic Policy Gradient (MADDPG) that addresses the computational limitations of cen
arXiv:2604.18438v1 Announce Type: new Abstract: We present a scalable, data-driven simulation framework for large-scale heating, ventilation, and air conditioning (HVAC) systems that couples physics-i
arXiv:2604.16562v1 Announce Type: new Abstract: Generalizable gaze estimation methods have garnered increasing attention due to their critical importance in real-world applications and have achieved s
arXiv:2604.17865v1 Announce Type: new Abstract: Automated polyp segmentation is critical for early colorectal cancer detection and its prevention, yet remains challenging due to weak boundaries, large
arXiv:2604.18135v1 Announce Type: new Abstract: Large-scale dataset distillation requires storing auxiliary soft labels that can be 30-40x larger on ImageNet-1K and 200x larger on ImageNet-21K than th
arXiv:2604.17748v1 Announce Type: new Abstract: Source-Free Domain Adaptation (SFDA) seeks to adapt a source model, which is pre-trained on a supervised source domain, for a target domain, with only a
arXiv:2604.16429v1 Announce Type: cross Abstract: We introduce Mosaic, a probabilistic weather forecasting model that addresses two principal sources of spectral degradation in ML-based weather predic
arXiv:2604.18420v1 Announce Type: cross Abstract: Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this paper, we study a bandit problem where the payoffs
arXiv:2509.24328v2 Announce Type: replace Abstract: LLMs have low GPU efficiency and high latency due to autoregressive decoding. Speculative decoding (SD) mitigates this using a small draft model to
arXiv:2511.08983v2 Announce Type: replace Abstract: Recent advances in large reasoning models have been driven by reinforcement learning and test-time scaling, accompanied by growing interest in laten
arXiv:2604.16995v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a promising paradigm for training reasoning-oriented models by leveraging rule-based reward signals. However,
arXiv:2604.01032v3 Announce Type: replace Abstract: High-resolution digital elevation models (DEMs) of the lunar surface are essential for surface mobility planning, landing site characterization, and
arXiv:2604.16434v1 Announce Type: cross Abstract: When a system commits to a hypothesis, much of the evidential structure behind that commitment is lost to compression. Standard accounts assume that s
// Survey on Multi-Agent Systems // The paper traces the landscape from classical paradigms (consensus, distributed control, swarm intelligence, cooperative learning) to foundation-model-enabled MAS (
arXiv:2604.18557v1 Announce Type: new Abstract: Controllable cooperative humanoid manipulation is a fundamental yet challenging problem for embodied intelligence, due to severe data scarcity, complexi
arXiv:2604.16451v1 Announce Type: new Abstract: Recent advances in visual-language models (VLMs) have led to significant improvements in a plethora of complex multimodal tasks like image captioning, r
arXiv:2507.14922v2 Announce Type: replace Abstract: Persona-driven simulations are increasingly used in computational social science, yet their validity critically depends on the fidelity of the under
arXiv:2506.06485v4 Announce Type: replace Abstract: Large language models (LLMs) draw on both contextual information and parametric memory, yet these sources can conflict. Prior studies have largely e
arXiv:2604.17005v1 Announce Type: new Abstract: Existing music-driven dance generation approaches have achieved strong realism and effective audio-motion alignment. However, they generally lack semant
arXiv:2604.16830v1 Announce Type: new Abstract: On-policy distillation (OPD) is an increasingly important paradigm for post-training language models. However, we identify a pervasive Scaling Law of Mi
arXiv:2506.09885v2 Announce Type: replace Abstract: Recent advances in feed-forward Novel View Synthesis (NVS) have led to a divergence between two design philosophies: bias-driven methods, which rely
arXiv:2604.17960v1 Announce Type: cross Abstract: Recent studies reveal striking representational alignment between artificial neural networks (ANNs) and biological brains, leading to proposals that a
“There is no option to opt out” - Meta’s CTO “There was no option to opt out” - epigraph for life as we knew it? Scoop! Meta staff are outraged over a new program that trains AI off their keystrokes a
arXiv:2604.17407v1 Announce Type: new Abstract: Image-goal navigation steers an agent to a target location specified by an image in unseen environments. Existing methods primarily handle this task by
arXiv:2506.10630v2 Announce Type: replace Abstract: To advance time series forecasting (TSF), various methods have been proposed to improve prediction accuracy, evolving from statistical techniques to
arXiv:2604.06155v2 Announce Type: replace-cross Abstract: Whether Large Language Models (LLMs) develop coherent internal world models remains a core debate. While conventional Next-Token Prediction (N
arXiv:2604.17626v1 Announce Type: cross Abstract: This work addresses the challenge of disseminating reusable artificial intelligence (AI) models accompanied by AI documentation (a.k.a., AI model card
arXiv:2604.18376v1 Announce Type: new Abstract: In text-to-image person retrieval tasks, the diversity of natural language expressions and the implicitness of visual semantics often lead to the proble
arXiv:2604.17013v1 Announce Type: new Abstract: With the development of robotics, skeleton-based action recognition has become increasingly important, as human-robot interaction requires understanding
arXiv:2604.16955v1 Announce Type: new Abstract: Quantitative prediction of future retinal appearance from longitudinal imaging would support clinical decisions in progressive macular disease that curr
arXiv:2604.18539v1 Announce Type: new Abstract: This paper studies how empirical dialogue-flow statistics can be incorporated into Next Dialogue Act Prediction (NDAP). A KL regularization term is prop
arXiv:2604.16678v1 Announce Type: new Abstract: Contrastive objectives power state-of-the-art multimodal models, but their training remains slow, relying on long stochastic optimization. We propose a
arXiv:2604.17850v1 Announce Type: new Abstract: Style transfer must match a target style while preserving content semantics. DiT-based diffusion models often suffer from content-style entanglement, le
arXiv:2604.16875v1 Announce Type: new Abstract: A central question in computational neuroscience is whether the learning rule used to train a neural network determines how well its internal representa
VA law REQUIRES that ballot language be 'a neutral explanation.' This language is CLEARLY illegal. A court ruled as much...and Democrats ignored the court and waited for a different court to punt on t
arXiv:2604.17248v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) are increasingly integrated into daily applications, yet their generative biases remain underexplored. Existing sp
arXiv:2511.11113v2 Announce Type: replace Abstract: Reinforcement fine-tuning (RFT), a two-stage framework consisting of supervised fine-tuning (SFT) and reinforcement learning (RL) has shown promisin
arXiv:2604.17475v1 Announce Type: cross Abstract: Small Vision-Language Models (SVLMs) are efficient task controllers but often suffer from visual brittleness and poor tool orchestration. They typical
arXiv:2604.17797v1 Announce Type: new Abstract: Referring video object segmentation (RVOS) aims to segment the target instance in a video, referred by a text expression. Conventional approaches are mo
arXiv:2604.18249v1 Announce Type: new Abstract: Speech encoder models are known to model members of some speaker groups (SGs) better than others. However, there has been little work in establishing wh
arXiv:2506.00079v2 Announce Type: replace-cross Abstract: The rapid integration of Large Language Models (LLMs) in high-stakes decision-making -- such as allocating scarce resources like donor organs
arXiv:2604.16369v1 Announce Type: cross Abstract: Global corporate AI investment reached $252.3 billion in 2024, yet only 6% of firms report significant earnings impact. This article argues that AI pr
arXiv:2510.04212v3 Announce Type: replace Abstract: The pursuit of computational efficiency has driven the adoption of low-precision formats for training transformer models. However, this progress is