Knowledge catalogue
arXiv:2605.08374v1 Announce Type: new Abstract: Episodic memory allows LLM agents to accumulate and retrieve experience, but current methods treat each memory independently, i.e., evaluating retrieval
arXiv:2605.10268v1 Announce Type: cross Abstract: To tackle long-context reasoning tasks without the quadratic complexity of standard attention mechanisms, approaches based on agent memory have emerge
arXiv:2605.08827v1 Announce Type: new Abstract: The safety of mental health AI is often judged at the wrong temporal scale. Current evaluations typically score isolated responses, endpoint outcomes, o
arXiv:2605.09152v1 Announce Type: new Abstract: Deciphering animal intent is a fundamental challenge in computational ethology, largely because of semantic aliasing, the phenomenon where identical ext
arXiv:2602.07940v3 Announce Type: replace Abstract: To cope with uncertain changes of the external world, intelligent systems must continually learn from complex, evolving environments and respond in
arXiv:2605.09990v1 Announce Type: new Abstract: Data-intensive applications, ranging from large-scale retrieval systems to advanced data pipelines, are increasingly bottlenecked by the processing of h
arXiv:2603.13452v2 Announce Type: replace Abstract: Fairness in machine learning is predominantly evaluated through outcome-oriented metrics, such as Demographic parity, which measure whether predicti
arXiv:2605.08744v1 Announce Type: cross Abstract: Autoregressive (AR) models can generate high-quality low-poly meshes from point clouds, but they still operate in an all-or-nothing manner: when a loc
arXiv:2505.16741v4 Announce Type: replace Abstract: Minimum attention applies the least action principle to changes of control concerning state and time, first proposed by Brockett. The involved regul
arXiv:2602.22508v2 Announce Type: replace Abstract: Large Language Models (LLMs) often produce incorrect answers on multi-hop question answering even when the reasoning trace already contains a correc
arXiv:2605.09708v1 Announce Type: cross Abstract: We present Metal-Sci, a 10-task benchmark of scientific Apple Silicon Metal compute kernels spanning six optimization regimes (stencils, all-pairs in
arXiv:2605.08701v1 Announce Type: new Abstract: This data paper describes METBRA25Y, a harmonized archive of hourly surface meteorological observations from Brazil derived from public historical recor
arXiv:2605.10067v1 Announce Type: cross Abstract: Red teaming is critical for uncovering vulnerabilities in Large Language Models (LLMs). While automated methods have improved scalability, existing ap
arXiv:2605.09654v1 Announce Type: cross Abstract: Sampling from score-based diffusion models incurs bias due to both time discretisation and the approximation of the score function. A common strategy
arXiv:2605.10071v1 Announce Type: new Abstract: The swift advancement in photo-realistic face generation technology has sparked considerable concerns across society and academia, emphasizing the requi
arXiv:2605.08300v1 Announce Type: cross Abstract: Manifold-Constrained Hyper-Connections (mHC) introduce a stability-motivated variant of multi stream residual mixing by constraining residual stream m
arXiv:2605.09296v1 Announce Type: cross Abstract: Recent generative models can produce images that appear highly realistic, raising challenges in distinguishing real and AI-generated images. Yet exist
arXiv:2605.08566v1 Announce Type: new Abstract: Chemical imaging enables label-free visualization of cells, tissues and living systems while providing direct biochemical information that is difficult
arXiv:2605.08815v1 Announce Type: new Abstract: Predicting microbial operon co-membership requires integrating two complementary biological signals: protein-scale molecular identity and genome-context
arXiv:2605.10148v1 Announce Type: new Abstract: The Vision Transformer (ViT) achieves remarkable accuracy across visual tasks but remains computationally expensive for edge deployment. This paper pres
arXiv:2605.10120v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) show remarkable potential for scientific reasoning, yet their performance in specialized domains such as micr
arXiv:2605.08472v1 Announce Type: new Abstract: The effectiveness of Reinforcement Learning (RL) in Large Language Models (LLMs) depends on the nature and diversity of the data used before and during
arXiv:2512.13751v2 Announce Type: replace-cross Abstract: Expanding pre-trained language models offers a practical way to increase capacity without training larger models from scratch. Depth Up-Scalin
arXiv:2510.09592v2 Announce Type: replace Abstract: Real-time Spoken Language Models (SLMs) struggle to leverage Chain-of-Thought (CoT) reasoning due to the prohibitive latency of generating the entir
arXiv:2605.08670v1 Announce Type: new Abstract: Large language model (LLM) powered AI agents have emerged as a promising paradigm for autonomous problem-solving, yet they continue to struggle with com
arXiv:2602.12286v2 Announce Type: replace-cross Abstract: A central challenge in developing Multimodal Large Language Models (MLLMs) is effectively integrating heterogeneous inputs into a cohesive rea
arXiv:2603.13131v3 Announce Type: replace Abstract: Long-horizon embodied intelligence requires agents to improve through interaction, not merely to execute plans generated from static goals. A centra
arXiv:2605.09609v1 Announce Type: new Abstract: We provide a counterexample to the minimal unimodal conjecture for polynomial neural networks (PNNs) with power activation functions. Fixing the input a
arXiv:2605.09633v1 Announce Type: new Abstract: We study multi-robot persistent monitoring on weighted graphs, where node weights encode monitoring priorities and edge weights encode travel distances.
arXiv:2605.08816v1 Announce Type: new Abstract: In the animal kingdom, mirror self-recognition is a canonical probe of higher-order cognition, emerging only in some species. We ask whether an analogou
arXiv:2605.09376v1 Announce Type: new Abstract: Trajectory optimization for autonomous vehicles usually relies on the kinematic bicycle model because of its computational simplicity. However, when the
arXiv:2605.10809v1 Announce Type: new Abstract: We investigate the learning task of language generation in the limit, but shift focus from the traditional time-of-last-mistake metric of a generator's
arXiv:2604.02438v2 Announce Type: replace Abstract: The deployment of reinforcement learning (RL)-based controllers on physical systems is often limited by poor generalization to real-world scenarios,
arXiv:2605.08277v1 Announce Type: cross Abstract: Many-shot jailbreaking (MSJ) causes safety-aligned language models to answer harmful queries by preceding them with many harmful question-answer demon
arXiv:2602.22611v2 Announce Type: replace Abstract: In Embedding-as-an-Interface (EaaI) settings, pre-trained models are queried for Intermediate Representations (IRs). The distributional properties o
arXiv:2507.07871v4 Announce Type: replace-cross Abstract: Watermarking enables GenAI providers to verify whether content was generated by their models. A watermark is a hidden signal in the content, w
arXiv:2502.20213v2 Announce Type: replace Abstract: Depression is a mental disorder and can cause a variety of symptoms, including psychological, physical, and social. Speech has been proved an object
arXiv:2605.09516v1 Announce Type: cross Abstract: Standard Mixture-of-Experts (MoE) transformers route tokens to expert subnetworks within each layer, but the layer structure itself remains monolithic
arXiv:2605.09479v1 Announce Type: cross Abstract: We study full-reference image quality assessment from a machine-centric perspective, where images are evaluated by how well they preserve information
arXiv:2605.08678v1 Announce Type: new Abstract: Modern AI progress has been driven by ML methods that are generalizable across settings and scalable to larger regimes. As large language models demonst
arXiv:2605.10833v1 Announce Type: cross Abstract: Industrial anomaly detection is critical for manufacturing quality control, yet existing datasets mainly focus on static images or sparse views, which
arXiv:2511.01774v3 Announce Type: replace Abstract: This paper presents the MOBIUS platform, a bipedal robot capable of walking, crawling, climbing, and rolling. MOBIUS features four limbs, two 6-DoF
arXiv:2510.21954v2 Announce Type: replace Abstract: Large Language Models (LLMs) are trained to support an increasing number of languages, yet their predefined tokenizers remain a bottleneck for adapt
arXiv:2605.08275v1 Announce Type: cross Abstract: Conventional MRI reconstruction methods treat images and coil sensitivities as discrete objects, leading to high memory demands and limited structural
arXiv:2605.09724v1 Announce Type: new Abstract: Existing accounts of grokking explain the phenomena in terms of mechanistic frameworks such as circuit efficiency or lazy-to-rich transitions. However,
arXiv:2601.21266v3 Announce Type: replace Abstract: Neural network models are increasingly used for state estimation in control and decision-making, yet it remains unclear to what extent they behave a
arXiv:2509.24244v4 Announce Type: replace Abstract: We study empirical scaling laws for language model merging measured by cross-entropy. Despite its wide practical use, merging lacks a quantitative r
arXiv:2605.08525v1 Announce Type: new Abstract: We introduce a model-reference adaptive control (MRAC) architecture for high-performance positional tracking of the Bee++, a 95-mg insect-scale flapping
arXiv:2605.09832v1 Announce Type: new Abstract: Knowledge of a protein's atomic conformational ensemble is critical to determining its function, yet state-of-the-art ensemble prediction models are lim
arXiv:2605.08117v1 Announce Type: cross Abstract: Inertial Measurement Unit (IMU)-based Human Activity Recognition (HAR) aims to interpret and classify user behaviors from temporal motion signals. Rec
arXiv:2603.18256v2 Announce Type: replace-cross Abstract: Recent reasoning-based large language models have shown strong performance on tasks with verifiable outcomes, but their use in de novo molecul
arXiv:2605.10157v1 Announce Type: cross Abstract: Every molecule ever synthesised can be drawn as a 2D skeletal diagram, yet in modern property prediction this universally available representation has
arXiv:2605.08954v1 Announce Type: cross Abstract: Molecular optimization in drug discovery aims to discover molecules with improved target properties, but practical lead optimization often requires mo
arXiv:2605.08512v1 Announce Type: new Abstract: Temporally contrastive representation learning induces a latent structure capable of reducing long-horizon planning to inference in a low-dimensional li
arXiv:2605.09684v1 Announce Type: cross Abstract: We introduce a red-teaming methodology that exposes harder-to-catch attacks for coding-agent monitors, suggesting that current practices may under-eli
arXiv:2605.09258v1 Announce Type: cross Abstract: Accurate hand and finger tracking from video has significant clinical applications for monitoring activities of daily living and measuring range of mo
arXiv:2605.09856v1 Announce Type: cross Abstract: Although recent studies have made remarkable progress in human mesh recovery, they still exhibit limited robustness to occlusions and often produce in
arXiv:2510.26067v2 Announce Type: replace Abstract: Tensegrity robots combine rigid rods and elastic cables, offering high resilience and deployability but at the same time posing major challenges for
arXiv:2605.09703v1 Announce Type: new Abstract: Understanding human mental states from natural behavior is crucial for intelligent systems in the real world. However, most current research focuses on
arXiv:2605.10769v1 Announce Type: cross Abstract: The multimodal fusion of images and scene captions has been extensively explored and applied in various fields. However, when dealing with complex rem