Knowledge catalogue
arXiv:2605.26346v1 Announce Type: new Abstract: Objective: To describe the design and early clinical evaluation of The Daily Dose (TDD), an LLM-driven, automated clinical summarization and clinical-tr
arXiv:2605.26830v1 Announce Type: cross Abstract: State estimation is a fundamental problem in control and signal processing, for which the Kalman Filter provides an optimal solution under linear dyna
arXiv:2605.26670v1 Announce Type: cross Abstract: Sequential editing of structured knowledge in large language models allows targeted factual updates without retraining, yet existing methods often rel
arXiv:2605.26494v1 Announce Type: new Abstract: We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum
arXiv:2602.03238v2 Announce Type: replace Abstract: With the advent of Large Language Models (LLMs), general-purpose agents have seen fundamental advancements. However, evaluating these agents present
arXiv:2605.26711v1 Announce Type: new Abstract: We construct a binary mixed-regime process with one deterministic textual regime and one random regime governed by an unobserved latent state. Even an i
arXiv:2605.26415v1 Announce Type: cross Abstract: Deploying Vision-Language Models on resource-constrained hardware typically requires INT8 quantization, but in joint-embedding architectures such as C
arXiv:2605.27163v1 Announce Type: new Abstract: In strategic classification, an institution (e.g., a bank) anticipates adaptation from users who change their features to increase utility in a classifi
arXiv:2605.26856v1 Announce Type: cross Abstract: Cognitive science remains split between cognitivism - which accounts for recursion and language but cannot ground formal symbols in meaning - and 4E a
arXiv:2605.26489v1 Announce Type: new Abstract: Large language model pre-training typically exhibits a two-phase trajectory: a fast initial loss drop followed by a prolonged slow improvement. We ident
arXiv:2605.26872v1 Announce Type: cross Abstract: LLM training increasingly relies on teacher-generated supervision, from synthetic responses to reasoning traces and tool-use demonstrations. Current p
arXiv:2505.11063v3 Announce Type: replace Abstract: LLM-based agents solve complex tasks through iterative reasoning, tool use, and environment interaction, where each intermediate thought directly sh
arXiv:2605.26759v1 Announce Type: new Abstract: Causal discovery from time series is critical for many real-world applications, such as tracing the root causes of anomalies. Existing approaches typica
arXiv:2605.27003v1 Announce Type: cross Abstract: W4A4 quantization of large video diffusion Transformers offers substantial memory savings but is hindered by two main challenges: sparse large-magnitu
arXiv:2510.05141v2 Announce Type: replace Abstract: When we read, we make predictions about upcoming words; these predictions influence our reading behavior. The success of large language models (LLMs
arXiv:2605.26165v1 Announce Type: cross Abstract: Agentic RAG systems that equip language models with dozens to hundreds of tool definitions face a critical resource conflict: tool schemas consume the
arXiv:2605.27154v1 Announce Type: new Abstract: While rule-based reinforcement learning has recently catalyzed explicit reasoning in multimodal models, tactile reasoning remains largely underexplored.
arXiv:2605.26958v1 Announce Type: cross Abstract: Reinforcement learning in open-ended long-form generation is challenging because reliable reference answers and automatic metrics are often unavailabl
arXiv:2605.27343v1 Announce Type: new Abstract: Diffusion models have emerged as powerful tools for high-quality image generation and editing, but guiding these models to produce specific outputs rema
arXiv:2605.27180v1 Announce Type: new Abstract: Volcanoes emit large amounts of CO2, directly influencing human lives. Mapping volcanic gas emissions helps to forecast eruptions and understand the imp
arXiv:2605.26463v1 Announce Type: cross Abstract: Data consistency between unstructured clinical notes and structured tables in Electronic Health Records (EHRs) is essential for patient safety and cli
arXiv:2605.26720v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong empirical gains as self-evolving agents for CUDA kernel generation, driven by feedback-conditioned planni
arXiv:2605.26776v1 Announce Type: cross Abstract: In recent years, Deep Reinforcement Learning (DRL) has achieved substantial progress on Vehicle Routing Problems (VRPs). However, existing DRL-based m
arXiv:2302.13473v2 Announce Type: replace Abstract: Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data. In
arXiv:2605.26405v1 Announce Type: new Abstract: Educational interventions are effective tools for enhancing student learning. While Large Language Models (LLMs) allow for generating adaptive feedback
arXiv:2605.26151v1 Announce Type: cross Abstract: Contactless diagnosis of musculoskeletal disorders can potentially improve population health as well as robot behaviours in collaborative settings. Ho
arXiv:2605.26991v1 Announce Type: new Abstract: Collaboration is central to human behavior, enabling tasks beyond individual capability. This ability arises from coordinating actions through internal
arXiv:2601.05899v2 Announce Type: replace Abstract: Recent breakthroughs in Large Language Models (LLMs) have positioned them as a promising paradigm for agents, with long-term planning and decision-m
arXiv:2605.27038v1 Announce Type: new Abstract: Vision-Language Models (VLMs) provide a promising foundation for autonomous driving planning, yet bridging semantic reasoning and precise 3D spatial for
arXiv:2605.27071v1 Announce Type: new Abstract: Key knowledge for steel-industry volatile organic compounds (VOCs) governance is scattered across unstructured scientific literature, making it difficul
arXiv:2605.27033v1 Announce Type: cross Abstract: Transformer-based large language models (LLMs) are comprised of billions of parameters arranged in deep and wide computational graphs, but it is not c
arXiv:2605.26576v1 Announce Type: new Abstract: Referring 3D Gaussian Splatting (R3DGS), which utilizes natural language for 3D object segmentation, has emerged as a crucial capability for embodied AI
arXiv:2512.06609v3 Announce Type: replace-cross Abstract: Vector-quantized variational autoencoders (VQ-VAEs) are discrete autoencoders that compress images into discrete tokens. However, they are dif
arXiv:2605.27269v1 Announce Type: new Abstract: Disease forecasting models typically rely on a single data stream, making models brittle when histories are short or noisy. Recent top-performing models
arXiv:2605.26713v1 Announce Type: cross Abstract: Prior-data fitted networks (PFNs) have recently emerged as a powerful approach for Bayesian prediction tasks, approximating the posterior predictive d
arXiv:2511.17852v2 Announce Type: replace Abstract: Transformers can acquire Chain-of-Thought (CoT) capabilities to solve complex reasoning tasks through fine-tuning. Reinforcement learning (RL) and s
arXiv:2605.26470v1 Announce Type: new Abstract: Generative posterior sampling using diffusion models has emerged as a dominant paradigm for solving inverse problems in imaging, which usually consists
arXiv:2605.26974v1 Announce Type: new Abstract: Autonomous navigation of Unmanned Surface Vehicles (USVs) that is safe and compliant with the International Regulations for Preventing Collisions at Sea
arXiv:2605.27079v1 Announce Type: cross Abstract: Off-policy reinforcement learning of pretrained flow policies remains challenging due to the instability of optimization arising from the multi-step s
arXiv:2605.26161v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) are increasingly pretrained on large corpora, raising concerns that evaluation datasets may have been exposed du
arXiv:2605.26156v1 Announce Type: cross Abstract: The known stylistic biases in LLM judges, such as a preference for verbosity or specific sentence structures, present an underexplored security vulner
arXiv:2605.27205v1 Announce Type: cross Abstract: Wireless digital twins require repeated synchronization between a time-evolving physical scene and its digital counterpart under limited and time-vary
arXiv:2605.26285v1 Announce Type: new Abstract: This work addresses the problem of learning the dynamics of high-dimensional probability densities over time using unlabeled samples, without assuming a
arXiv:2605.27078v1 Announce Type: cross Abstract: Training loss and accuracy are the standard signals used to monitor generalization during deep neural network training. Two well-documented phenomena
arXiv:2601.22648v2 Announce Type: replace Abstract: The key to building trustworthy large language models (LLMs) lies in endowing them with inherent uncertainty expression capabilities, thereby mitiga
arXiv:2510.17790v3 Announce Type: replace-cross Abstract: Computer-use agents face a fundamental limitation. They rely exclusively on primitive GUI actions (click, type, scroll), creating brittle exec
arXiv:2605.26849v1 Announce Type: new Abstract: Sampling multiple responses improves language model reasoning, but uniform compute allocation is inefficient: easy questions are over-sampled while hard
arXiv:2605.26503v1 Announce Type: new Abstract: Vision-Language Navigation (VLN) requires an agent to navigate 3D environments following natural language instructions. During navigation, existing agen
arXiv:2603.15500v2 Announce Type: replace Abstract: LLMs often exhibit Aha moments such as self-correction after tokens like 'Wait,' yet the underlying mechanism remains unclear. Standard LLMs collaps
arXiv:2603.23994v2 Announce Type: replace-cross Abstract: Generative optimization uses large language models (LLMs) to iteratively improve artifacts (such as code, workflows or prompts) using executio
arXiv:2605.26447v1 Announce Type: new Abstract: Underwater scene reconstruction is essential for immersive exploration of aquatic environments, yet remains challenging due to complex participating-med
arXiv:2605.26424v1 Announce Type: cross Abstract: With the rapid evolution of internet services, recommendation systems have become indispensable. In particular, the blending (re-ranking) stage plays
arXiv:2605.26248v1 Announce Type: cross Abstract: We present a functional form (that we refer to as a Unified Neural Scaling Law (UNSL)) that accurately models and extrapolates the scaling behaviors o
arXiv:2605.26368v1 Announce Type: cross Abstract: Geometry estimation from perspective images has greatly advanced, maturing to the point where off-the-shelf foundation models are able to reconstruct
arXiv:2512.04085v2 Announce Type: replace Abstract: We introduce the 'single-life' learning paradigm, where we train a distinct vision model exclusively on egocentric videos captured by one individual
arXiv:2605.26646v1 Announce Type: new Abstract: LLM-based multi-agent systems decompose complex tasks into interacting roles, but most remain manually orchestrated by prompts, tools, and control rules
arXiv:2605.27139v1 Announce Type: cross Abstract: Electron tomography (ET) plays an important role in the three-dimensional (3D) characterization of nanomaterials. However, under limited-angle and spa
arXiv:2605.26501v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have transformed multi-modal understanding, excelling in tasks like image captioning and visual question answerin
arXiv:2312.02694v2 Announce Type: replace Abstract: Existing optical character recognition (OCR) methods rely on task-specific designs with divergent paradigms, architectures, and training strategies,
arXiv:2504.07853v2 Announce Type: replace Abstract: Light field microscopy (LFM) has gained significant attention due to its ability to capture snapshot-based, large-scale 3D fluorescence images. Howe