Knowledge catalogue
arXiv:2604.11552v1 Announce Type: cross Abstract: Voice imitation aims to transform source speech to match a reference speaker's timbre and speaking style while preserving linguistic content. A straig
arXiv:2506.13331v3 Announce Type: replace Abstract: Human cognitive behavior arises from the interaction of specialized brain networks dedicated to distinct functions, such as language, logic, and soc
arXiv:2604.09587v1 Announce Type: new Abstract: Mobile agents can autonomously complete user-assigned tasks through GUI interactions. However, existing mainstream evaluation benchmarks, such as Androi
arXiv:2604.10593v1 Announce Type: new Abstract: Feed-forward geometric foundation models can infer dense point clouds and camera motion directly from RGB streams, providing priors for monocular SLAM.
arXiv:2604.09734v1 Announce Type: cross Abstract: We study how far structured architectural bias can compensate for the absence of end-to-end gradient-based representation learning in visual recogniti
arXiv:2604.09704v1 Announce Type: new Abstract: Recent advances in reasoning-induced image quality assessment (IQA) have demonstrated the power of reinforcement learning to rank (RL2R) for training vi
arXiv:2604.08324v2 Announce Type: replace-cross Abstract: Symbolic regression (SR) aims to discover mathematical expressions from data, a task traditionally tackled using Genetic Programming (GP) thro
arXiv:2604.11734v1 Announce Type: cross Abstract: Closed-loop cooperative driving requires planners that generate realistic multimodal multi-agent trajectories while improving safety and traffic effic
arXiv:2604.10632v1 Announce Type: cross Abstract: Collecting large, aligned cross-modal datasets for music-flavor research is difficult because perceptual experiments are costly and small by design. W
arXiv:2604.11142v1 Announce Type: new Abstract: Low-light conditions severely hinder 3D restoration and reconstruction by degrading image visibility, introducing color distortions, and contaminating g
arXiv:2604.10401v1 Announce Type: new Abstract: Inferring nationality from personal names is a critical capability for equity and bias monitoring, personalization, and a valuable tool in biomedical an
arXiv:2603.11974v2 Announce Type: replace Abstract: In the late 2010s, the fashion trend NormCore framed sameness as a signal of belonging, illustrating how norms emerge through collective coordinatio
arXiv:2604.10452v1 Announce Type: new Abstract: Olfaction lies at the intersection of chemical structure, neural encoding, and linguistic perception, yet existing representation methods fail to fully
arXiv:2504.13818v4 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as the leading approach for enhancing reasoning capabilities in large langua
arXiv:2604.10647v1 Announce Type: new Abstract: UMI-style interfaces enable scalable robot learning, but existing systems remain largely visuomotor, relying primarily on RGB observations and trajector
arXiv:2512.15564v2 Announce Type: replace Abstract: Remote sensing (RS) image segmentation is constrained by the limited availability of annotated data and a gap between overhead imagery and natural i
arXiv:2604.10412v1 Announce Type: cross Abstract: Conditional effects are commonly used measures for understanding how treatment effects vary across different groups, and are often used to target trea
arXiv:2604.11484v1 Announce Type: new Abstract: On-the-Fly Category Discovery (OCD) requires a model, trained on an offline support set, to recognize known classes while discovering new ones from an o
arXiv:2604.10085v1 Announce Type: new Abstract: We propose a robust alignment technique for Standard Fundus Images (SFIs) and Ultra-Widefield Fundus Images (UWFIs), which are challenging to align due
arXiv:2412.11390v3 Announce Type: replace-cross Abstract: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the brain and external devices. Howeve
arXiv:2506.01201v2 Announce Type: replace Abstract: David Marr's seminal theory of human perception stipulates that visual processing is a multi-stage process, prioritizing the derivation of boundary
arXiv:2603.25620v2 Announce Type: replace Abstract: Large language model (LLM)-based persona agents are rapidly being adopted as scalable proxies for human participants across diverse domains. Yet the
arXiv:2604.11510v1 Announce Type: cross Abstract: To encourage diverse exploration in reinforcement learning (RL) for large language models (LLMs) without compromising accuracy, we propose Policy Spli
arXiv:2509.21882v2 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is a practical, scalable way to improve large language models on math, code, and other s
Carissa Véliz delivered a TED Talk in 2026 arguing that predictions function as instruments of power, shaping behavior and outcomes through the act of belief itself. Her thesis suggests that predictiv
arXiv:2603.20725v2 Announce Type: replace Abstract: Text-to-image generation has advanced rapidly, yet it still struggles to capture the nuanced user preferences. Existing approaches typically rely on
arXiv:2604.10030v1 Announce Type: new Abstract: Video diffusion models have achieved remarkable progress in generating high-quality videos. However, these models struggle to represent the temporal suc
arXiv:2604.10633v1 Announce Type: new Abstract: LLM-based universal information extraction (UIE) methods often rely on additional information beyond the original training data, which increases trainin
arXiv:2508.17784v2 Announce Type: replace-cross Abstract: Supervised fine-tuning (SFT) of foundation models often leads to poor generalization, where prior capabilities deteriorate after tuning on new
arXiv:2604.10687v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate strong performance in text summarization, yet their effectiveness drops significantly across languages with res
arXiv:2511.04776v2 Announce Type: replace-cross Abstract: Generative Artificial Intelligence (GenAI) represents a rapidly expanding digital infrastructure whose energy demand and associated CO2 emissi
arXiv:2604.10960v1 Announce Type: new Abstract: Knowledge Tracing (KT) infers a student's knowledge state from past interactions to predict future performance. Conventional Deep Learning (DL)-based KT
arXiv:2506.16796v4 Announce Type: replace Abstract: Real-World Image Super-Resolution is one of the most challenging task in image restoration. However, existing methods struggle with an accurate unde
arXiv:2604.09916v1 Announce Type: new Abstract: Simultaneous Speech Translation (SimulST) requires balancing high translation quality with low latency. Recent work introduced REINA, a method that trai
arXiv:2406.05358v3 Announce Type: replace Abstract: Intensity control is a class of continuous-time dynamic optimization problems with many important applications in Operations Research including queu
arXiv:2507.11019v4 Announce Type: replace Abstract: Score-function based methods for policy learning, such as REINFORCE and PPO, have delivered strong results in game-playing and robotics, yet their h
arXiv:2508.03306v4 Announce Type: replace-cross Abstract: Lowering the numerical precision of model parameters and computations is widely adopted to improve the efficiency of retrieval systems. Howeve
arXiv:2603.14968v2 Announce Type: replace-cross Abstract: While watermarking serves as a critical mechanism for LLM provenance, existing secret-key schemes tightly couple detection with injection, req
arXiv:2604.11056v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has substantially improved the reasoning ability of Large Language Models (LLMs). However, its s
arXiv:2604.11496v1 Announce Type: cross Abstract: Dual-encoder Vision-Language Models (VLMs) such as CLIP are often characterized as bag-of-words systems due to their poor performance on compositional
arXiv:2505.24778v3 Announce Type: replace Abstract: As large language models (LLMs) are increasingly used in high-stakes domains, accurately assessing their confidence is crucial. Humans typically exp
arXiv:2604.10688v1 Announce Type: cross Abstract: On-policy reinforcement learning has become the dominant paradigm for reasoning alignment in large language models, yet its sparse, outcome-level rewa
arXiv:2604.07791v2 Announce Type: replace Abstract: Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) have demonstrated significant potential in single-turn reasoning tasks. Wit
arXiv:2604.09749v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) frequently hallucinate objects that are absent from the visual input, often because attention during decoding i
arXiv:2510.07972v3 Announce Type: replace Abstract: Query-product relevance prediction is vital for AI-driven e-commerce, yet current LLM-based approaches face a dilemma: SFT and DPO struggle with lon
arXiv:2604.10373v1 Announce Type: cross Abstract: From adversarial robustness to multi-agent learning, many machine learning tasks can be cast as finite-sum min-max optimization or, more generally, as
arXiv:2604.10334v1 Announce Type: new Abstract: Structured Illumination Microscopy (SIM) enables rapid, high-contrast optical sectioning of fresh tissue without staining or physical sectioning, making
arXiv:2604.10674v1 Announce Type: cross Abstract: Reinforcement learning (RL) has been widely used to train LLM agents for multi-turn interactive tasks, but its sample efficiency is severely limited b
arXiv:2604.11466v1 Announce Type: cross Abstract: Large Language Model (LLM) agents offer a potentially-transformative path forward for generative social science but face a critical crisis of validity
Gary Marcus, a prominent AI researcher and critic known for his skepticism of current deep learning approaches, was invited to speak at BugBash, an event hosted by Antithesis. The post, shared by Anti
arXiv:2510.05057v2 Announce Type: replace-cross Abstract: A fundamental challenge in embodied intelligence is developing expressive and compact state representations for efficient world modeling and d
arXiv:2604.05549v2 Announce Type: replace Abstract: With the widespread application of LLM-based agents across various domains, their complexity has introduced new security threats. Existing red-team
arXiv:2604.11479v1 Announce Type: new Abstract: As global political tensions rise and the anticipation of additional tariffs from the United States on international trade increases, the issues of econ
arXiv:2604.10371v1 Announce Type: new Abstract: This study proposes Structural Gating and Effect-aligned Discovery for Temporal Causal Discovery (SGED-TCD), a novel and general framework for lag-resol
arXiv:2604.04415v2 Announce Type: replace Abstract: Human understanding of video dynamics is typically grounded in a structured mental representation of entities, actions, and temporal relations, rath
arXiv:2604.11530v1 Announce Type: cross Abstract: Vision-Language Models (VLM) have revolutionized multimodal learning by jointly processing visual and textual information. Yet, they face significant
arXiv:2604.11184v1 Announce Type: cross Abstract: Context: Software engineering (SE) researchers increasingly study Generative AI (GenAI) while also incorporating it into their own research practices.
arXiv:2604.10849v1 Announce Type: cross Abstract: Federated learning (FL) performance is highly sensitive to heterogeneity across clients, yet practitioners lack reliable methods to anticipate how a f
arXiv:2511.05782v2 Announce Type: replace Abstract: Unsupervised domain adaptation for medical image segmentation remains a significant challenge due to substantial domain shifts across imaging modali
arXiv:2507.22767v3 Announce Type: replace-cross Abstract: Obtaining human-readable symbolic formulas via genetic programming-based symbolic distillation of a deep neural network trained on a target da