Knowledge catalogue
arXiv:2604.27308v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) methods face a tradeoff between adapter size and expressivity: ultra-low-parameter adapters are confined to fix
arXiv:2604.27277v1 Announce Type: cross Abstract: Brain MRI underpins a wide range of neuroscientific and clinical applications, yet most learning-based methods remain task-specific and require substa
arXiv:2604.27699v1 Announce Type: new Abstract: Current embodied agents are often limited to passive instruction-following or reactive need-satisfaction, lacking a stable, high-order value framework e
arXiv:2604.26981v1 Announce Type: cross Abstract: Large Language Models (LLMs) have revolutionized the field of natural language processing. However, they exhibit some limitations, including a lack of
arXiv:2604.27882v1 Announce Type: new Abstract: Recent advances in agentic AI are shifting automation from discrete tools to proactive multi-agent systems that coordinate multi-specialized capabilitie
arXiv:2604.27944v1 Announce Type: new Abstract: Large-scale IoT weather sensing networks require incentive mechanisms to sustain participation, yet determining how much value individual data contribut
arXiv:2604.27924v1 Announce Type: cross Abstract: Peer review is a multi-stage process involving reviews, rebuttals, meta-reviews, final decisions, and subsequent manuscript revisions. Recent advances
arXiv:2604.26956v1 Announce Type: cross Abstract: The growing use of generative AI raises ethical concerns about authorship and plagiarism. This study examines how people judge the reuse of AI-generat
arXiv:2602.10140v2 Announce Type: replace-cross Abstract: Large language models (LLMs) can now synthesize non-trivial executable code from textual descriptions, raising an important question: can LLMs
arXiv:2604.27667v1 Announce Type: cross Abstract: Policy optimization in high-dimensional continuous control for robotics remains a challenging problem. Predominant methods are inherently local and of
arXiv:2604.26959v1 Announce Type: cross Abstract: Integrating large language models (LLMs) into patient-facing healthcare systems offers significant potential to improve access to medical information.
arXiv:2604.27361v1 Announce Type: new Abstract: Synthesizing realistic 3D indoor scenes remains challenging due to data scarcity and the difficulty of simultaneously enforcing global architectural con
arXiv:2604.27840v1 Announce Type: cross Abstract: Recently, large language models (LLMs) have shown great promise in time series forecasting. However, most existing LLM-based forecasting methods still
arXiv:2602.07915v2 Announce Type: replace-cross Abstract: Causal discovery from time series is a fundamental task in machine learning. However, its widespread adoption is hindered by a reliance on unt
arXiv:2602.12652v2 Announce Type: replace Abstract: Clouds are a common phenomenon that distorts optical satellite imagery, which poses a challenge for remote sensing. However, in the literature cloud
arXiv:2604.28082v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) on narrowly misaligned data generalizes to broadly misaligned behavior, a phenomenon termed emergent misalignme
arXiv:2604.27415v1 Announce Type: new Abstract: With the rapid advancement of semiconductor technology, Electronic Design Automation (EDA) has become an increasingly knowledge-intensive and document-d
arXiv:2504.12612v2 Announce Type: replace Abstract: Provenance is the chronological history of things, resonating with the fundamental pursuit to uncover origins, trace connections, and situate entiti
arXiv:2604.27043v1 Announce Type: new Abstract: Today's AI assistants such as OpenClaw are designed to handle context effectively, making context learning an increasingly important capability for mode
arXiv:2602.00937v2 Announce Type: replace-cross Abstract: Leveraging pre-trained 2D image representations in behavior cloning policies has achieved great success and has become a standard approach for
arXiv:2604.28139v1 Announce Type: cross Abstract: LLM agents are expected to complete end-to-end units of work across software tools, business services, and local workspaces. Yet many agent benchmarks
arXiv:2604.27968v1 Announce Type: new Abstract: The pervasive growth of digital content, specifically short videos on social media platforms, has significantly altered how topics are discussed and und
arXiv:2604.27591v1 Announce Type: cross Abstract: Video moment retrieval is the task of retrieving specific segments of a video corresponding to a given text query. Recent studies have been conducted
arXiv:2604.27083v1 Announce Type: new Abstract: RLVR and OPD have become standard paradigms for post-training. We provide a unified analysis of these two paradigms in consolidating multiple expert cap
arXiv:2604.27354v1 Announce Type: new Abstract: Explainable AI (XAI) aims to improve user understanding and decisions when using AI models. However, despite innovations in XAI, recent user evaluations
arXiv:2506.09110v3 Announce Type: replace Abstract: Electroencephalography (EEG) provides real-time insights into brain activity and supports diverse applications in neuroscience. While EEG foundation
arXiv:2604.27389v1 Announce Type: cross Abstract: In recent years, Multimodal Large Language Models (MLLMs) have achieved remarkable progress on a wide range of multimodal benchmarks. Despite these ad
arXiv:2604.28043v1 Announce Type: new Abstract: We present Collaborative Agent Reasoning Engineering (CARE), a disciplined methodology for engineering Large Language Model (LLM) agents in scientific d
arXiv:2604.27251v1 Announce Type: cross Abstract: Large Language Models (LLMs) are known to acquire reasoning capabilities through shared inference patterns in pre-training data, which are further eli
arXiv:2604.26999v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) approximate solutions of partial differential equations (PDEs) by embedding physical laws into the loss functio
arXiv:2604.28186v1 Announce Type: cross Abstract: Most familiar equilibrium concepts, such as Nash and correlated equilibrium, guarantee only that no single player can improve their utility by deviati
arXiv:2604.27149v1 Announce Type: cross Abstract: Conformal Prediction provides distribution-free prediction intervals with guaranteed coverage, but its reliance on a single global calibration thresho
arXiv:2604.27728v1 Announce Type: new Abstract: The advancement of automated vehicles introduces complex safety challenges, particularly in dynamic and unpredictable environments where AI-enabled perc
arXiv:2601.22993v3 Announce Type: replace Abstract: We introduce the Value-at-Risk Constrained Policy Optimization algorithm (VaR-CPO), a sample efficient and conservative method designed to optimize
arXiv:2508.13316v2 Announce Type: replace Abstract: We consider the problem of designing constraint-aware flow matching (FM) models that address the issue of constraint violations commonly observed in
arXiv:2604.27744v1 Announce Type: new Abstract: AI applications are increasingly being introduced into digital health. While technical performance has advanced rapidly, successful deployment mainly de
arXiv:2604.27445v1 Announce Type: new Abstract: Many agents in real-world environments cannot reliably communicate their goals through language, including household pets, pre-verbal infants, and other
arXiv:2604.27172v1 Announce Type: new Abstract: We propose C-MTAD-GAT, an unsupervised, context-aware graph-attention model for anomaly detection in multivariate time series from mobile networks. C-MT
arXiv:2604.27707v1 Announce Type: new Abstract: Current agentic memory systems (vector stores, retrieval-augmented generation, scratchpads, and context-window management) do not implement memory: they
arXiv:2504.19342v3 Announce Type: replace-cross Abstract: Reinforcement Learning from Human Feedback (RLHF) has become a pivotal paradigm in artificial intelligence to align large models with human pr
arXiv:2604.27372v1 Announce Type: cross Abstract: This paper investigates the continuous-time counterpart of the Q-function for entropy-regularized mean-field control (MFC) with controlled common nois
arXiv:2604.28159v1 Announce Type: new Abstract: Topological features play an essential role in ensuring geometric plausibility and structural consistency in image analysis tasks such as segmentation a
arXiv:2604.28020v1 Announce Type: new Abstract: We consider the problem of Cost-Aware Learning, where sampling different component functions of a finite-sum objective incurs different costs. The objec
arXiv:2602.24262v4 Announce Type: replace Abstract: Identifying the full landscape of small and medium-sized enterprises (SMEs) in specialized industry sectors is critical for supply-chain resilience,
arXiv:2604.28138v1 Announce Type: cross Abstract: Autonomous agents act through sandboxed containers and microVMs whose state spans filesystems, processes, and runtime artifacts. Checkpoint and restor
arXiv:2604.27137v1 Announce Type: new Abstract: This paper introduces a systematic evaluation framework grounded in the Interagency Language Roundtable (ILR) Skill Level Descriptions and applies it to
arXiv:2602.17469v2 Announce Type: replace Abstract: Recent advances in multilingual representation learning aim to bridge the performance gap between high- and low-resource languages, yet their abilit
arXiv:2604.27033v1 Announce Type: new Abstract: Deep learning for cross-subject EEG decoding is hindered by high inter-subject variability, which introduces a severe domain shift between training and
arXiv:2509.05291v2 Announce Type: replace-cross Abstract: Large language models (LLMs) learn non-trivial abstractions during pretraining, such as detecting irregular plural noun subjects. However, bec
arXiv:2102.05231v1 Announce Type: cross Abstract: Color is an essential component of graphic design, acting not only as a visual factor but also carrying cultural implications. However, existing resea
arXiv:2604.27977v1 Announce Type: new Abstract: Despite recent progress in language models and agents for scientific data-driven discovery, further advancing their capabilities is held back by the abs
arXiv:2604.27775v1 Announce Type: cross Abstract: Shallow nanoindentation enables mechanical characterization of thin films, individual phases and other volume-constrained materials, but measured hard
arXiv:2604.27495v1 Announce Type: cross Abstract: Reward models (RMs) play a central role in aligning large language models (LLMs) with human preferences. However, RMs are often sensitive to spurious
arXiv:2604.27604v1 Announce Type: new Abstract: We introduce SPUR, a comprehensive benchmark for scientific experimental image perception, understanding, and reasoning, comprising 4,264 question-answe
arXiv:2604.27883v1 Announce Type: cross Abstract: In modern parametric model training, full-batch gradient descent (and its variants) suffers due to progressively stronger biasing towards the exact re
arXiv:2603.09117v2 Announce Type: replace-cross Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) significantly enhances large language models (LLMs) reasoning but severely suffers from
arXiv:2604.27263v1 Announce Type: new Abstract: Subword tokenization is an essential part of modern large language models (LLMs), yet its specific contributions to training efficiency and model perfor
arXiv:2604.27697v1 Announce Type: cross Abstract: Peritoneal metastases are currently assessed using diagnostic laparoscopy to determine Sugarbaker's Peritoneal Cancer Index (sPCI), which works by div
arXiv:2604.26962v1 Announce Type: cross Abstract: Education represents one of the most promising real-world applications for Large Language Models (LLMs). However, conventional tutoring systems rely o
arXiv:2601.05052v2 Announce Type: replace Abstract: Building efficient and effective generative models for neural network weights has been a research focus of significant interest that faces challenge