Knowledge catalogue
arXiv:2606.04399v1 Announce Type: new Abstract: In the paradigm of decentralized learning, a group of agents collaborate to train a global model using distributed datasets without a central server. Al
arXiv:2605.06637v2 Announce Type: replace Abstract: Although person re-identification has made impressive progress, occlusion caused by obstacles remains an unsettled issue in real applications. The d
arXiv:2606.04811v1 Announce Type: new Abstract: Video generation models have made impressive strides in synthesizing visually compelling content, yet their outputs remain confined to the virtual domai
arXiv:2606.04844v1 Announce Type: cross Abstract: Contrastive audio-language models such as CLAP enable zero-shot audio classification: a sound is labelled by matching its embedding to text prompt emb
arXiv:2602.14885v2 Announce Type: replace-cross Abstract: Recurrent neural networks (RNNs) provide a theoretical framework for understanding computation in biological neural circuits, yet classical re
arXiv:2606.04432v1 Announce Type: new Abstract: Video diffusion transformers have achieved state-of-the-art visual quality, but their high inference cost remains a major bottleneck for real-time appli
arXiv:2606.04374v1 Announce Type: cross Abstract: Despite rapid progress of continuous embeddings for e-commerce search relevance, a long-standing open problem is the difficulty in capturing fine-grai
arXiv:2602.17907v2 Announce Type: replace-cross Abstract: Traditional neural topic models are typically optimized by reconstructing the document's Bag-of-Words (BoW) representations, overlooking conte
arXiv:2606.04188v1 Announce Type: cross Abstract: Offline goal-conditioned reinforcement learning requires both long-horizon reachability estimates and local action comparisons. Dual goal representati
arXiv:2606.04694v1 Announce Type: new Abstract: Small language models (SLMs) are efficient and scalable, but their multilingual capabilities degrade severely at sub-billion scales, especially for Sout
arXiv:2512.16919v2 Announce Type: replace-cross Abstract: Perceiving and reconstructing 3D scene geometry from visual inputs is crucial for autonomous driving. However, there still lacks a driving-tar
arXiv:2512.03553v3 Announce Type: replace-cross Abstract: Content moderation remains a critical yet challenging task for large-scale user-generated video platforms, especially in livestreaming environ
arXiv:2606.04535v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) offer bidirectional attention and parallel generation, enabling them to exploit global context and naturally s
arXiv:2606.04574v1 Announce Type: new Abstract: This study aims to determine whether the application of Deep Reinforcement Learning (DRL) as a specialized execution overlay can enhance pair trading in
arXiv:2512.24698v2 Announce Type: replace Abstract: Generating dynamic motions for legged robots remains a challenging problem. While reinforcement learning has achieved notable success in various leg
arXiv:2606.03995v1 Announce Type: cross Abstract: Background: Alzheimer's disease (AD) affects over 55 million people worldwide. Accurate, interpretable detection of normal cognition (NC), mild cognit
arXiv:2606.04527v1 Announce Type: cross Abstract: We present Echo Infinity, an autoregressive (AR) framework towards real-time infinite video generation that employs a learnable evolving memory to dyn
arXiv:2606.04212v1 Announce Type: new Abstract: Existing analyses of the edge of stability (EoS) treat it as a global property of optimization. We show that it is also selective: the stability constra
arXiv:2602.09388v2 Announce Type: replace Abstract: Multilingual pre-trained language models(mPLMs) offer significant benefits for many low-resource languages. To further expand the range of languages
arXiv:2602.01658v2 Announce Type: replace-cross Abstract: Bandit algorithms have recently emerged as a powerful tool for evaluating machine learning models, including generative image models and large
arXiv:2606.04299v1 Announce Type: new Abstract: We consider the problem of generating images whose internal structure -- defined by the distribution of patches across multiple scales -- matches that o
arXiv:2603.16652v2 Announce Type: replace Abstract: Monitoring cavity-nesting wild bees and wasps is vital for biodiversity research and conservation. Layer trap nests (LTNs) are emerging as a valuabl
arXiv:2603.16867v2 Announce Type: replace-cross Abstract: Large language models (LLMs) with chain-of-thought reasoning achieve state-of-the-art performance across complex problem-solving tasks, but th
arXiv:2606.04594v1 Announce Type: cross Abstract: LLM serving frameworks are quickly evolving with a complex software stack and a vast number of optimizations. The rapid development process can introd
arXiv:2604.00819v2 Announce Type: replace-cross Abstract: Understanding emotions in natural language is inherently a multi-dimensional reasoning problem, where multiple affective signals interact thro
arXiv:2606.04166v1 Announce Type: new Abstract: Practical text-recognition pipelines for historical documents typically decompose layout analysis into line detection followed by a separate reading-ord
arXiv:2502.03799v4 Announce Type: replace Abstract: Large Language Models (LLMs) are prone to generating plausible yet incorrect responses, known as hallucinations. Effectively detecting hallucination
arXiv:2606.04705v1 Announce Type: cross Abstract: Semantic segmentation in medical imaging is a critical yet challenging task due to data scarcity and high variability across modalities. While foundat
arXiv:2606.05021v1 Announce Type: new Abstract: We investigate multi-agent deep reinforcement learning and propose two enhancements to the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algor
arXiv:2606.04474v1 Announce Type: new Abstract: Speech Large Language Models (SLLMs) underperform their text counterparts on complex reasoning. We reveal that this modality gap is not a uniform cognit
arXiv:2606.04154v1 Announce Type: cross Abstract: Antibodies neutralize foreign antigens by binding to specific surface regions called epitopes. Computational epitope prediction is critical for unders
arXiv:2606.04492v1 Announce Type: new Abstract: Cooperative Multi-Agent Reinforcement Learning (MARL) frequently suffers from severe reward sparsity and exploration bottlenecks. While episodic memory
arXiv:2606.04145v1 Announce Type: cross Abstract: Cloud LLM fine-tuning platforms increasingly serve RLHF workloads, where a learned reward model is optimized as a proxy for human quality. As Gao et a
arXiv:2511.12784v3 Announce Type: replace Abstract: Large Language Models (LLMs) have recently emerged as powerful tools for autoformalization. Despite their impressive performance, these models can s
arXiv:2606.05112v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly proposed as clinical agents, yet static, single-turn benchmarks cannot capture how a model dynamically del
arXiv:2606.04479v1 Announce Type: cross Abstract: Recent text-to-image (T2I) models can render highly legible and well-structured text within images, enabling applications including document generatio
arXiv:2602.23312v3 Announce Type: replace-cross Abstract: Leader-follower interaction is an important paradigm in human-robot interaction (HRI). Yet, assigning roles in real time remains challenging f
arXiv:2603.09493v2 Announce Type: replace-cross Abstract: The adaptation of large-scale vision-language models (VLMs) to downstream tasks with limited labeled data remains a significant challenge. Whi
arXiv:2606.04182v1 Announce Type: cross Abstract: We formulate the problem of exact unlearning in reinforcement learning, where the goal is to design an efficient framework that enables the removal of
arXiv:2606.04342v1 Announce Type: cross Abstract: Multi-step time series forecasting (MSF) is commonly evaluated using point-wise error metrics such as mean squared error (MSE), implicitly treating th
arXiv:2606.04160v1 Announce Type: new Abstract: Safety alignment in instruction-tuned large language models (LLMs) depends on a model's ability to reliably refuse to respond to harmful or disallowed r
arXiv:2606.04634v1 Announce Type: new Abstract: Trust in a decision-making system requires both safety guarantees and the ability to interpret and understand its behavior. This is particularly importa
arXiv:2408.01382v3 Announce Type: replace Abstract: Originating in game theory, Shapley values are widely used for explaining a machine learning model's prediction by quantifying the contribution of e
arXiv:2606.04315v1 Announce Type: new Abstract: LLM agents accumulate histories that outgrow their context windows, motivating a growing literature on memory systems. Yet most existing designs are tun
arXiv:2606.04197v1 Announce Type: cross Abstract: How much should an LLM agent remember, and how should multi-agent systems be connected when trying to reach consensus? We show these two design choice
arXiv:2510.20042v3 Announce Type: replace Abstract: Generative image models produce striking visuals yet often misrepresent culture. Prior work has examined cultural bias mainly in text-to-image (T2I)
arXiv:2511.03304v2 Announce Type: replace-cross Abstract: With the on-going integration of machine learning systems into the everyday social life of millions the notion of fairness becomes an ever inc
arXiv:2606.05145v1 Announce Type: cross Abstract: When post-trained language models fail on reasoning problems, the common test-time-scaling response is to spend more compute on additional attempts, a
arXiv:2606.04751v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents in scientific tasks. Yet whether these systems can effectively engage in for
arXiv:2606.04801v1 Announce Type: new Abstract: We present Flash Cubical, a highly efficient computation of cubical persistence on a V-filtration for 2D and 3D images over F_2. The implementation is b
arXiv:2606.05079v1 Announce Type: new Abstract: Function vectors (FVs) are task representations elicited during in-context learning that can be used to steer Large Language Models (LLMs). However, des
arXiv:2606.04338v1 Announce Type: new Abstract: Privacy-sensitive and distributed characteristics of multi-center medical data bring severe obstacles to centralized modeling for accurate early predict
arXiv:2603.07445v2 Announce Type: replace Abstract: Large language models (LLMs) often require fine-tuning (FT) to perform well on downstream tasks, but FT can induce safety-alignment drift even when
arXiv:2606.04282v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are predominantly evaluated on free-form vision-language tasks such as visual question answering, captioning, a
arXiv:2606.04591v1 Announce Type: new Abstract: With the widespread adoption of multi-modal communication platforms, long-form dialogues interleaving text and images have become increasingly common. U
arXiv:2606.04065v1 Announce Type: cross Abstract: We study simultaneous alternating power iteration for fixed-order asymmetric rank-one spiked tensor models. Our main contribution is a finite-iteratio
arXiv:2603.19225v3 Announce Type: replace-cross Abstract: Real-world financial decision-making is a challenging problem that requires reasoning over heterogeneous signals, including company fundamenta
arXiv:2606.04670v1 Announce Type: cross Abstract: This paper presents a method of multivariate scattered data interpolation and approximation that produces optimal Lipschitz-continuous approximation,
arXiv:2601.19449v2 Announce Type: replace Abstract: Graph neural networks (GNNs) are widely believed to excel at node representation learning through trainable neighborhood aggregations. We challenge
arXiv:2606.05067v1 Announce Type: new Abstract: The Deep Graph Generation's panorama spans two extremes: one-shot and sequential models. The former generates nodes and edges jointly, while the latter