Knowledge catalogue
arXiv:2605.10377v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning often assumes a fixed execution team, yet many decentralized systems must operate with varying numbers of
arXiv:2605.09636v1 Announce Type: new Abstract: PDE-to-solver code generation aims to automatically synthesize executable numerical solvers from partial differential equation (PDE) specifications. Thi
arXiv:2605.09344v1 Announce Type: new Abstract: Most path planners assume fully known, static environments, assumptions that fail when robots navigate in dynamic and partially observable environments.
arXiv:2605.10136v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) train a single neural approximation by minimizing multiple physics- and data-derived losses, but the gradients
arXiv:2605.09422v1 Announce Type: new Abstract: Although Large Multimodal Models (LMMs) have achieved strong performance on general video understanding, their susceptibility to textual prior shortcuts
arXiv:2605.09339v1 Announce Type: cross Abstract: Human color categories are not uniformly distributed in perceptual space, yet most computational color models still assume fixed and evenly structured
arXiv:2605.08121v1 Announce Type: cross Abstract: Early detection of plant diseases is critical for improving crop productivity, while it also facilitates the foundations of precision agriculture. Rec
arXiv:2605.09503v1 Announce Type: new Abstract: Large-scale visual generative models have achieved remarkable performance. However, their high computational and memory costs make deployment challengin
arXiv:2605.10936v1 Announce Type: new Abstract: As wearable devices like smart glasses integrate Large Multimodal Models (LMMs) into the continuous first-person visual streams of individual users, the
arXiv:2605.09119v1 Announce Type: cross Abstract: Personalized alignment aims to adapt large language models to heterogeneous user preferences, yet the precise theoretical conditions for its statistic
arXiv:2605.10043v1 Announce Type: cross Abstract: Large Language Model (LLM) personalization aims to align model behaviors with individual user preferences. Existing methods often focus on isolated us
arXiv:2605.05682v2 Announce Type: replace-cross Abstract: Recent developments in AI safety research have called for red-teaming methods that effectively surface potential risks posed by generative AI
arXiv:2605.10137v1 Announce Type: cross Abstract: Thompson sampling is a widely used strategy for contextual bandits: at each round, it samples a reward function from a Bayesian posterior and acts gre
arXiv:2605.09319v1 Announce Type: new Abstract: With the proliferation of AI-generated images, digital watermarking has become an essential safeguard for protecting intellectual property and mitigatin
arXiv:2605.10073v1 Announce Type: new Abstract: Patent claims form a directed dependency structure in which dependent claims inherit and refine the scope of earlier claims; however, existing patent en
arXiv:2605.09043v1 Announce Type: cross Abstract: One partner says 'Fine' meaning resolution; the other hears surrender. The word is shared; the affective uptake is not. We formalize this as affective
arXiv:2605.09552v1 Announce Type: cross Abstract: Recently, Muon and related spectral optimizers have demonstrated strong empirical performance as scalable stochastic methods, often outperforming Adam
arXiv:2605.08673v1 Announce Type: new Abstract: Online clustering methods that adaptively create and update nodes as data arrive often make node learning explicit, whereas the mapping from the learned
arXiv:2604.01532v2 Announce Type: replace Abstract: LLM agents are beginning to invoke industrial asset-management tools through the Model Context Protocol (MCP), yet whether they can act reliably on
arXiv:2605.10391v1 Announce Type: cross Abstract: We introduce Phoenix-VL 1.5 Medium, a 123B-parameter natively multimodal and multilingual foundation model, adapted to regional languages and the Sing
arXiv:2605.10806v1 Announce Type: cross Abstract: Generative world models are increasingly used for video generation, where learned simulators are expected to capture the physical rules that govern re
arXiv:2605.10547v1 Announce Type: new Abstract: Electronic design automation (EDA) addresses placement, routing, timing analysis, and power-integrity verification for integrated circuits. Learning met
arXiv:2605.09538v1 Announce Type: cross Abstract: While existing methods for reconstructing hand-object interactions have made impressive progress, they either focus on rigid or part-wise rigid object
arXiv:2605.10429v1 Announce Type: cross Abstract: Nuclear magnetic resonance (NMR) spectroscopy provides an experimental readout of local chemical environments, but its use in molecular representation
arXiv:2605.08915v1 Announce Type: new Abstract: Deep learning paradigms, such as PINNs and neural operators, have significantly advanced the solving of PDEs. However, they often struggle to capture th
arXiv:2605.08176v1 Announce Type: new Abstract: We introduce Dynamical Physics-Modeled Neural Networks (DynPMNNs), a continuous-time deep learning architecture in which each hidden layer is defined as
arXiv:2605.09287v1 Announce Type: new Abstract: Large Language Model (LLM)-based search agents trained with reinforcement learning (RL) have significantly improved the performance of knowledge-intensi
arXiv:2605.08945v1 Announce Type: new Abstract: Action quality assessment (AQA) aims to automatically quantify the execution quality of human actions in videos and is valuable for applications such as
arXiv:2502.03749v2 Announce Type: replace Abstract: Optimal transport (OT) is a widely used tool in machine learning, but computing high-accuracy solutions for large instances remains costly. Entropic
arXiv:2602.00593v2 Announce Type: replace Abstract: Despite progress on general tasks, vision-language models (VLMs) still struggle with challenges that demand both fine-grained visual grounding and e
arXiv:2605.10922v1 Announce Type: new Abstract: Recent advances in 3D generative models have rapidly improved image-to-3D synthesis quality, enabling higher-resolution geometry and more realistic appe
arXiv:2605.10046v1 Announce Type: new Abstract: Precipitation nowcasting aims to forecast short-term radar echo sequences for extreme weather warning, where both prediction fidelity and inference effi
arXiv:2605.08388v1 Announce Type: new Abstract: Human-AI teams play a pivotal role in improving overall system performance when neither the human nor the model can achieve such performance on their ow
arXiv:2605.10118v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have demonstrated exceptional general reasoning capabilities. However, their performance in embodied navigation remains hi
arXiv:2605.09638v1 Announce Type: new Abstract: Ensuring the security of reinforcement learning (RL) models is critical, particularly when they are trained by third parties and deployed in real-world
arXiv:2605.10032v1 Announce Type: new Abstract: Cell-type-specific marker genes are fundamental to plant biology, yet existing resources primarily rely on curated databases or high-throughput studies
arXiv:2605.08409v1 Announce Type: new Abstract: Conversational AI has a fundamental flaw as a knowledge interface: sycophantic chatbots induce epistemic entrenchment and delusional belief spirals even
arXiv:2605.08093v1 Announce Type: cross Abstract: The use of chatbots for various forms of companionship is growing rapidly, raising a myriad of questions about simulated relationships, emotional depe
arXiv:2605.08982v1 Announce Type: new Abstract: Monte Carlo Tree Search (MCTS) is a widely used approach for policy improvement through search with increasing popularity for real world applications. D
arXiv:2605.08935v1 Announce Type: new Abstract: Coupled spatiotemporal forecasting is important for predicting the future evolution of multiple interacting dynamical systems, such as in climate models
This post describes a proof-of-concept implementation of a multi-agent AI development studio with architect, senior, and junior role personas, built entirely locally using AirLLM and Ollama without re
arXiv:2605.10084v1 Announce Type: cross Abstract: The performance of audio latent diffusion models is primarily governed by generator expressivity and the modelability of the underlying latent space.
arXiv:2605.09434v1 Announce Type: cross Abstract: Low-cost air quality sensors are becoming ubiquitous in our daily lives as public awareness of air pollution continues to grow, and people take measur
arXiv:2605.10275v1 Announce Type: new Abstract: Polarimetric imaging captures surface polarization characteristics, such as the Degree of Linear Polarization (DoLP) and the Angle of Polarization (AoP)
arXiv:2605.10816v1 Announce Type: cross Abstract: We study policy gradient methods for reinforcement learning in non-Markovian decision processes (NMDPs), where observations and rewards depend on the
arXiv:2605.08415v1 Announce Type: new Abstract: Since the advent of Large Language Models (LLMs), a significant area of research has focused on their intrinsic biases, particularly in political discou
arXiv:2605.10581v1 Announce Type: new Abstract: Retinal vessel segmentation is crucial for diagnosis and assessment of ocular diseases. Notably, segmentation of small retinal vessels has been consiste
arXiv:2410.14702v2 Announce Type: replace Abstract: Multi-modal Large Language Models (MLLMs) exhibit impressive problem-solving abilities in various domains, but their visual comprehension and abstra
arXiv:2605.10349v1 Announce Type: cross Abstract: Annotating bounding boxes is costly and limits the scalability of object detection. This challenge is compounded by the need to preserve high accuracy
arXiv:2605.09915v1 Announce Type: cross Abstract: The implicit policy of maintaining relatively stable acceptance rates at top AI conferences, despite exponentially growing submissions, introduces a c
arXiv:2605.09504v1 Announce Type: cross Abstract: We present swarm-attack, an open-source adversarial testing framework in which multiple lightweight LLM agents coordinate through shared memory, paral
arXiv:2605.09365v1 Announce Type: new Abstract: Enterprise workloads are dominated by deterministic, structured, and knowledge-dependent tasks operating under strict cost, latency, and reliability con
arXiv:2605.10404v1 Announce Type: new Abstract: With the growing prevalence of always-on hardware such as smart glasses, body cameras, and home security systems, life-logging visual sensing is becomin
arXiv:2507.23009v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved remarkable results on a range of standardized tests originally designed to assess human cognitive a
arXiv:2509.12635v3 Announce Type: replace-cross Abstract: We prove under practical assumptions that Rotary Positional Embedding (RoPE) introduces an intrinsic distance-dependent bias in attention scor
arXiv:2605.09472v1 Announce Type: new Abstract: Positional encoding in transformers is commonly implemented through positional embeddings, attention masks, or bias terms, but formal connections betwee
arXiv:2605.10310v1 Announce Type: new Abstract: Existing alignment research is dominated by concerns about safety and preventing harm: safeguards, controllability, and compliance. This paradigm of ali
arXiv:2605.08574v1 Announce Type: new Abstract: As synthetic images become increasingly realistic, reliable synthetic image detection techniques are of pressing need to prevent their misuse. Despite s
arXiv:2605.10937v1 Announce Type: new Abstract: Recently, post-training methods based on reinforcement learning, with a particular focus on Group Relative Policy Optimization (GRPO), have emerged as t
arXiv:2605.10335v1 Announce Type: cross Abstract: Adaptive optimizers, most notably Adam, have become the default standard for training large-scale neural networks such as Transformers. These methods