Knowledge catalogue
arXiv:2609.18836v1 Announce Type: new Abstract: A surveillance system that detects an anomaly often has to repair the footage as well, yet the two tasks are studied in isolation: training-free anomaly
arXiv:2609.18639v1 Announce Type: cross Abstract: Emergency management assistance programs, such as relief distribution, are essential for delivering necessary supplies to affected communities. Howeve
arXiv:2602.01898v2 Announce Type: replace Abstract: In many experimental settings, active learning can improve sample efficiency by sequentially selecting where to measure, which is particularly valua
arXiv:2609.18041v1 Announce Type: cross Abstract: Multi-encoder fusion extends Large Audio-Language Models (LALMs) beyond speech-centric recognition, but selecting encoders via intuition or exhaustive
arXiv:2609.18216v1 Announce Type: cross Abstract: Prompt-conditioned piano MIDI-to-Music rendering aims to faithfully render target notes while reproducing the timbre of a reference recording. Existin
arXiv:2609.18462v1 Announce Type: cross Abstract: FastWAM-style world action models enable efficient action-only inference, but generalize poorly under visual distribution shifts. Their reconstruction
arXiv:2609.18394v1 Announce Type: new Abstract: We report the results of a Turing Test conducted in Finland in the Finnish language. Because languages and cultural contexts are unevenly represented in
One thing we realized about AI is that when you ask it a question and have a conversation, the AI may give you a real answer, but it doesn't connect you to the real community behind your question! sub
arXiv:2603.26339v2 Announce Type: replace Abstract: We propose an Expected Free Energy-based acquisition function for Bayesian optimization to solve the joint learning and optimization problem, i.e.,
A new creature-catching adventure is ready to stream from the cloud this week. Pawprint Studio’s Aniimo arrives on GeForce NOW at launch, inviting gamers to explore the vibrant continent of Idyll acro
arXiv:2603.03884v2 Announce Type: replace-cross Abstract: Topic localization aims to identify spans of text that express a given topic defined by a name and description. To study this task, we introdu
arXiv:2609.17535v1 Announce Type: new Abstract: Language generation research increasingly spans three paradigms: autoregressive decoding, discrete masked diffusion, and continuous flow-matching. Compa
arXiv:2609.17886v1 Announce Type: new Abstract: EEG decoding tasks can rely on different temporal dynamics and cross-channel relationships. We test whether specialized modules improve a fully fine-tun
arXiv:2609.17601v1 Announce Type: cross Abstract: This paper studies decentralized learning of socially optimal equilibria in finite normal-form games over dynamic communication networks. Each agent o
arXiv:2609.18080v1 Announce Type: new Abstract: Linear probes can decode safety-relevant concepts such as truthfulness from language-model activations, but probe accuracy may show only decodability, n
arXiv:2609.18860v1 Announce Type: cross Abstract: When a large vision-language model misclassifies a harmful meme, the failure may reflect missing internal evidence or an inability to route represente
arXiv:2609.18279v1 Announce Type: new Abstract: Vision Transformers (ViTs) have achieved state-of-the-art performance across a range of computer vision tasks, mainly thanks to the self-attention mecha
arXiv:2609.17721v1 Announce Type: cross Abstract: Lipid nanoparticles (LNPs) have transformed RNA medicine, yet their clinical utility remains constrained by predominant hepatic accumulation after sys
arXiv:2609.18374v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models attach an action module to a Vision-Language Model (VLM) with billions of parameters and pay for that backbone at ev
arXiv:2609.18566v1 Announce Type: cross Abstract: We present a physics-informed machine learning framework based on Neural Ordinary Differential Equations that solves the holographic inverse problem:
deepseek v4.1 flash on together ai is leading on ttft for pre-warmed queries for agent workflows, that startup time shows up again on every model turn, so shaving it down matters more as the loop gets
I think these models are fairly comparable in performance but which uses more compute in the old subscription model? The old subscription model is at least still working in compute usage instead of co
arXiv:2609.18620v1 Announce Type: new Abstract: Creating deformable assets for robot manipulation requires jointly specifying their geometry, appearance, and physical properties. This is especially ch
arXiv:2609.17620v1 Announce Type: cross Abstract: Whole genome sequencing (WGS) is essential for precision oncology, yet its clinical adoption remains limited by prohibitive computational costs and mu
arXiv:2609.18417v1 Announce Type: new Abstract: Multi-turn agent trajectories often contain redundant rounds (failed tool calls, parallel sub-queries, verification-only steps) that inflate both traini
arXiv:2609.17759v1 Announce Type: cross Abstract: Muon updates matrix-valued neural-network parameters by orthogonalizing a gradient-based momentum matrix. Its reliance on derivatives limits its use w
arXiv:2609.18126v1 Announce Type: new Abstract: Agentic AI systems often approach the same task through multiple workflows that differ in reasoning strategy, verification structure, and compute cost.
arXiv:2609.18395v1 Announce Type: new Abstract: Policies for robotic manipulation are produced by training on large teleoperated datasets. These datasets typically consist of free-space trajectories,
arXiv:2506.00214v2 Announce Type: replace-cross Abstract: Rapid urbanization demands efficient monitoring of turbulent wind and pollutant dispersion, yet existing reconstruction and sensor placement s
arXiv:2609.17572v1 Announce Type: new Abstract: Auditing vision-language models (VLMs) for societal bias requires distinguishing direct algorithmic valuation disparities from confounders embedded with
arXiv:2609.18461v1 Announce Type: new Abstract: Personalized agents are required to reason over long-term history interactions to infer both explicit preferences and implicit behavioral evidence. Whil
arXiv:2609.18773v1 Announce Type: new Abstract: Long-range infrared imaging frequently confronts dense target clusters whose diffraction-limited signatures merge into a single indistinguishable blob,
arXiv:2609.18392v1 Announce Type: new Abstract: Vision-language-action models (VLAs) have trans- formed the field of robotic manipulation in recent years by combining the semantic understanding of LLM
arXiv:2609.18510v1 Announce Type: new Abstract: We present DiT-Garment to model dynamic 3D clothing over human body models in arbitrary motion. Unlike existing methods, DiT-Garment can animate garment
arXiv:2412.16555v4 Announce Type: replace Abstract: Large language models (LLMs) are widely applied in various fields of society due to their powerful reasoning, understanding, and generation capabili
arXiv:2609.18470v1 Announce Type: cross Abstract: Video-based Multimodal sentiment analysis (MSA) must handle information from text, audio, and image sequence in human speaking videos, yet current met
arXiv:2609.17865v1 Announce Type: new Abstract: Frontier models are often evaluated on how they respond to safety information once it is already in context. We study an earlier decision point: whether
arXiv:2609.17549v1 Announce Type: new Abstract: Cyberbullying (CB) is a complex social phenomenon characterized by repeated aggression, power imbalance, and multi-party interaction. Although large lan
arXiv:2609.17883v1 Announce Type: cross Abstract: The rapid adoption of generative AI has made final artifacts unreliable evidence of student learning, and AI detectors that examine only the finished
arXiv:2609.17552v1 Announce Type: new Abstract: Moral-reward RL can make language-model agents more cooperative, but whether that alignment survives adversarial persona pressure is unknown. Such attac
arXiv:2505.24427v2 Announce Type: replace Abstract: Accurate modeling of subjective phenomena such as emotion expression requires data annotated with authors' intentions. Commonly such data is collect
arXiv:2609.18943v1 Announce Type: cross Abstract: Reducing radiation dose in computed tomography significantly degrades image quality and poses challenges for accurate and clinically reliable reconstr
arXiv:2609.19076v1 Announce Type: cross Abstract: The test error of a model plotted against its number of parameters d falls, peaks when the model can just fit the training data, and falls again, exhi
arXiv:2506.18773v2 Announce Type: replace-cross Abstract: We develop, analyze, and experimentally explore residual-based loss functions for machine learning of parameter-to-solution maps in the contex
arXiv:2609.19137v1 Announce Type: cross Abstract: Recent advances in video generation allow robots to learn manipulation trajectories from generated videos. However, these approaches produce purely ki
arXiv:2407.06868v3 Announce Type: replace-cross Abstract: In this work, we propose a method for efficient resource utilization of simultaneously transmitting and reflecting reconfigurable intelligent
arXiv:2609.17766v1 Announce Type: new Abstract: Radar-based navigation is appealing for its robustness to adverse conditions involving airborne particles, such as precipitation, dust, fog, and smoke,
arXiv:2609.18444v1 Announce Type: new Abstract: Weakly supervised class-incremental semantic segmentation (WILSS) aims to train a segmentation model over multiple steps, each introducing new concepts
arXiv:2609.17682v1 Announce Type: new Abstract: Humans efficiently learn new tasks by reusing a rich repertoire of motor skills across different goals and contexts. A similar strategy can also be used
arXiv:2609.17613v1 Announce Type: new Abstract: Zero-shot object counting aims to estimate the number of objects specified by a text query without category-specific training. Recent approaches primari
arXiv:2609.18135v1 Announce Type: cross Abstract: State-of-the-art Text-to-SQL systems are typically multi-agent pipelines centered around two fundamental tasks: schema linking and SQL generation. How
arXiv:2609.18649v1 Announce Type: new Abstract: Warning: This paper contains examples of stereotypes and social bias. LLMs are increasingly used in interactive settings by the general public, making t
arXiv:2609.18549v1 Announce Type: new Abstract: Robots that leave structured, static environments must plan motions that are kinodynamically feasible and safe among moving obstacles. However, there ar
arXiv:2609.18706v1 Announce Type: cross Abstract: Neural decompilers can recover readable and recompilable source code from binaries, but their predictions remain difficult to trust. Matching decompil
arXiv:2609.17953v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) can produce Natural Language Explanations (NLEs) that sound plausible yet remain inconsistent with the visual evidence t
arXiv:2609.17916v1 Announce Type: new Abstract: Temporal link prediction on the Temporal Graph Benchmark 2.0 (TGB 2.0) faces a scalability ceiling: on the benchmark's three largest datasets, every exi
arXiv:2609.17655v1 Announce Type: cross Abstract: In an important recent work, Blanc (2026) gave an algorithm for robustly learning Boolean concept classes with respect to a fixed distribution that ou
arXiv:2609.19080v1 Announce Type: new Abstract: As robot capabilities increase, quadratic programming (QP)-based controllers must account for a similarly increasing number of constraints to ensure saf
arXiv:2603.05757v2 Announce Type: replace Abstract: Video generative models (VGMs) pretrained on large-scale internet data can produce temporally coherent rollout videos that capture rich object dynam
arXiv:2609.18385v1 Announce Type: new Abstract: Emotions are an essential aspect of human communication, particularly on social media, where authors frequently combine text and images to convey their