Knowledge catalogue
arXiv:2603.17784v2 Announce Type: replace Abstract: We developed a multi-label gastrointestinal video analysis pipeline based on a ResNet-50 frame classifier followed by anatomy-guided temporal event
arXiv:2508.09001v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly deployed in long-context tasks such as reasoning, code generation, and multi-turn dialogue. However, i
arXiv:2605.20592v1 Announce Type: new Abstract: We study model-free Q-learning in finite-horizon episodic Markov Decision Processes (MDPs) with stationary dynamics across episodes. We identify a centr
arXiv:2605.21088v1 Announce Type: new Abstract: Modern deep-learning models have achieved remarkable success in time-series forecasting. Yet, their performance degrades in long-term prediction due to
arXiv:2603.10718v2 Announce Type: replace Abstract: Flow Matching enables simulation-free training of generative models on Riemannian manifolds, yet sampling typically still relies on numerically inte
arXiv:2605.20914v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved strong multimodal reasoning capabilities, but further improving them still relies heavily on large-scale hum
arXiv:2605.21411v1 Announce Type: new Abstract: Existing video-language models can generate factual descriptions of road events but lack control over how these events are expressed: their tone, urgenc
arXiv:2605.21121v1 Announce Type: new Abstract: Single-image-to-3D generative models can now produce high-quality geometry, yet conditioning on a single view inevitably introduces ambiguity about unse
arXiv:2605.20300v1 Announce Type: new Abstract: In this paper, we propose a robust subspace-constrained quadratic model (SCQM) for learning low-dimensional structure from high-dimensional data. Buildi
arXiv:2605.20659v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have revolutionized high-fidelity video generation, yet their O(L^2) attention complexity poses a formidable bottleneck fo
arXiv:2605.21429v1 Announce Type: cross Abstract: Tactile-based reinforcement learning (RL) is currently hindered by fragmented research and a focus on over-saturated orientation tasks. We introduce v
arXiv:2605.20642v1 Announce Type: new Abstract: When annotators disagree, that disagreement can reflect epistemic uncertainty rather than simple label noise. We study hard-label delivery as an alterna
arXiv:2605.20545v1 Announce Type: cross Abstract: Transfer learning is an essential technique for many machine learning/AI models of complex structures such as large language models and generative AI.
arXiv:2605.20713v1 Announce Type: new Abstract: Multimodal IE in social media is difficult because a post may attach multiple images that are weakly related, redundant, or even misleading with respect
arXiv:2605.20395v1 Announce Type: new Abstract: A fundamental challenge in multi-robot motion planning is achieving sufficient coordination to avoid inter-robot conflicts without incurring the large c
Storytelling is core to humanity’s DNA, stemming from our impulse to express ideals, warnings, hopes, and experiences. Technology has always been woven through the medium and the distribution: from ea
arXiv:2605.20396v1 Announce Type: new Abstract: Identifying latent variables and the causal structure involving them is essential across various scientific fields. While many existing works fall under
arXiv:2605.20308v1 Announce Type: new Abstract: Gradient-based attacks are important methods for evaluating model robustness. However, since the proposal of APGD, it has been difficult for such method
arXiv:2502.03752v5 Announce Type: replace Abstract: Meta-reinforcement learning (Meta-RL) facilitates rapid adaptation to unseen tasks but faces challenges in long-horizon environments. Skill-based ap
arXiv:2605.20602v1 Announce Type: new Abstract: Successive self-training on a language model's own outputs is widely characterized as a process of flattening: diversity drops, distributions narrow, an
arXiv:2605.20998v1 Announce Type: new Abstract: Aspect-Term Sentiment Analysis (ATSA) in multi-aspect sentences faces a fundamental tradeoff between efficiency and expressiveness. Existing models eith
arXiv:2605.20733v1 Announce Type: new Abstract: Converting hand-drawn sketches into structured 3D geometries remains challenging due to the difficulty of representing non-Euclidean surfaces and mainta
arXiv:2605.21135v1 Announce Type: new Abstract: As MT quality increases, interest in enhanced post-editing features such as QE-derived error highlights is growing, yet evidence for their usefulness re
arXiv:2605.20850v1 Announce Type: new Abstract: Objective: Stage-wise workflows that separate model scaling and inverse kinematics can induce morphology-posture compensation, resulting in anatomically
arXiv:2603.02845v3 Announce Type: replace Abstract: Efficient communication is critical for decentralized Multi-Robot Path Planning (MRPP), yet existing learned communication methods treat all neighbo
arXiv:2605.21075v1 Announce Type: new Abstract: Earth observation (EO) foundation models (FMs) are increasingly trained on multisensor data, spanning multispectral imagery (MSI), synthetic aperture ra
arXiv:2605.21244v1 Announce Type: new Abstract: Super-Resolution (SR) has advanced rapidly in recent years, with diffusion-based models achieving unprecedented fidelity at the cost of introducing new
arXiv:2605.21324v1 Announce Type: cross Abstract: What can representational similarity matrices (RSMs) tell us about a neural code? As the popularity of these summary statistics grows, so too does the
arXiv:2605.20924v1 Announce Type: new Abstract: Designing effective task-level prompts is crucial for improving the performance of Large Language Models (LLMs). While prior work on instruction inducti
arXiv:2605.21466v1 Announce Type: new Abstract: Although existing video editing methods are generally feasible, they often require many costly iterations and still struggle to deliver high-quality yet
arXiv:2605.20919v1 Announce Type: new Abstract: Sutra is a typed, purely functional programming language whose compiled forward pass is a PyTorch neural network. The compiler beta-reduces the whole pr
arXiv:2605.20347v1 Announce Type: new Abstract: Labeling a training set is often expensive and susceptible to errors, making the design of robust loss functions for label noise an important problem. T
arXiv:2605.20234v1 Announce Type: new Abstract: Prior-Data Fitted networks (PFNs) have been very successful in tabular contexts, handling prediction tasks in context. However, they are designed for si
arXiv:2509.07674v2 Announce Type: replace Abstract: Explainability, in particular, the ability for robots to explain why they have made a decision or behaved in a certain way, is a critical tool in he
arXiv:2410.18915v4 Announce Type: replace-cross Abstract: Consider two problems about an unknown probability distribution p: 1. How many samples from p are required to test if p is supported on n elem
arXiv:2605.21318v1 Announce Type: new Abstract: Large language models (LLMs) are highly sensitive to the prompts used to specify task objectives and behavioral constraints. Many recent prompt optimiza
arXiv:2605.20749v1 Announce Type: new Abstract: Gated Linear Units (GLU) and their variants are widely adopted in modern open-source large language model architectures and consistently outperform thei
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Tech researchers are suing the Trump administration over the f
arXiv:2605.20745v1 Announce Type: cross Abstract: Generative verifiers have emerged as a promising paradigm for step-wise verification, but their verification behavior is often poorly calibrated: they
The new issue of Daedalus, the open-access Journal of the American Academy of Arts and Sciences, is chock full of good material on AI. w/@demishassabis @ylecun @alondra @pushmeet and so many others I
arXiv:2605.20946v1 Announce Type: new Abstract: The thinking-while-speaking paradigm aims to make AI communication more human. A key challenge is maintaining fluent speech while performing deep reason
This New York Times piece is worth your time. Here’s what is happening, as simply as I can put it. Back in January, Trump sued the IRS, an agency he controls, demanding 10 billion over the leak of his
This New York Times piece on the January 6th slush fund is worth your time. Here’s what is happening, as simply as I can put it. Back in January, Trump sued the IRS, an agency he controls, demanding 1
arXiv:2605.20309v1 Announce Type: new Abstract: Current personalization methods for generative vision models typically encode new concepts through continuous adapters or weight updates, yet provide li
arXiv:2605.20973v1 Announce Type: new Abstract: The effectiveness of rock support in underground mines depends on the interaction between installed rock bolts and the structural fabric of the surround
arXiv:2605.21299v1 Announce Type: new Abstract: Humans effortlessly go beyond literal meanings: If you mow the lawn, I will give you fifty dollars, is typically understood as implying that the speaker
arXiv:2605.20292v1 Announce Type: new Abstract: Numerical time-series models can effectively process irregular electronic health record (EHR) trajectories, but they do not naturally expose the measure
arXiv:2605.20581v1 Announce Type: new Abstract: Machine learning interatomic potentials (MLIPs) achieve excellent accuracy when trained on large Density Functional Theory (DFT) data. To be useful in p
Trump’s latest corruption seems to breach the constitution, creating a $1.8 billion slush fund for his friends without congressional authorization and effectively pardoning himself by stopping IRS aud
Trump’s use of taxpayer money to create a $1.8 billion political slush fund may be the episode of presidential corruption that is the most blatant and threatening to the constitutional order in modern
arXiv:2605.20543v1 Announce Type: new Abstract: Accurate vessel segmentation is essential for medical image analysis, yet remains challenging due to complex vascular patterns and imaging ambiguity. Mo
arXiv:2605.20461v1 Announce Type: new Abstract: Accurate polyp size stratification guides surveillance decisions, with lesions larger than 5 mm typically requiring closer follow-up. However, monocular
arXiv:2605.21487v1 Announce Type: new Abstract: Currently, enhancing Unified Multimodal Models (UMMs) with image understanding, generation, and editing capabilities mainly relies on mixed multi-task t
arXiv:2504.13109v2 Announce Type: replace Abstract: Flow matching models have emerged as a strong alternative to diffusion models, but existing inversion and editing methods designed for diffusion are
arXiv:2605.21131v1 Announce Type: new Abstract: Recent feed-forward models have significantly advanced geometry perception for inferring dense 3D structure from sensor observations. However, its essen
arXiv:2605.20599v1 Announce Type: new Abstract: This study presents a comprehensive approach for the clustering and classification of upper-limb surface electromyography (sEMG) signals during function
arXiv:2605.21094v1 Announce Type: new Abstract: We investigate unpaired image inverse problems, a challenging setting where only independent, non-paired sets of noisy measurements and clean target sig
arXiv:2605.21489v1 Announce Type: cross Abstract: Pretrained diffusion models serve as frozen teachers feeding downstream pipelines such as text-to-3D, single-step distillation, and data attribution.
arXiv:2605.20772v1 Announce Type: new Abstract: While medical Multimodal Large Language Models (MLLMs) have shown promise in assisting diagnosis, they still frequently generate hallucinated responses
arXiv:2605.20291v1 Announce Type: new Abstract: Large language models (LLMs) have enabled web agents that follow natural language goals through multi-step browser interactions. However, agents fine-tu