Knowledge catalogue
arXiv:2604.14240v1 Announce Type: cross Abstract: The simulation of complex systems increasingly relies on sophisticated but fundamentally opaque computational black-box simulators. Surrogate models p
arXiv:2604.14329v1 Announce Type: new Abstract: Non-violent street robberies (snatch-and-run) are difficult to detect automatically because they are brief, subtle, and often indistinguishable from ben
arXiv:2603.03897v3 Announce Type: replace-cross Abstract: Foundation models have demonstrated impressive capabilities across diverse domains, while imitation learning provides principled methods for r
arXiv:2604.15109v1 Announce Type: new Abstract: Despite the rapid advancement of Large Language Models (LLMs), uncertainty quantification in LLM generation is a persistent challenge. Although recent a
arXiv:2411.09209v5 Announce Type: replace Abstract: Audio-driven portrait animation has made significant advances with diffusion-based models, improving video quality and lipsync accuracy. However, th
arXiv:2604.05158v2 Announce Type: replace Abstract: Large language models encode extensive world knowledge valuable for zero-shot named entity recognition. However, their causal attention mechanism, w
arXiv:2604.14795v1 Announce Type: new Abstract: Visual Geometry Foundation Models (VGFMs) demonstrate remarkable zero-shot capabilities in local reconstruction. However, deploying them for kilometer-l
arXiv:2407.00809v3 Announce Type: replace Abstract: This paper introduces the Kernel Neural Operator (KNO), a provably convergent operator-learning architecture that utilizes compositions of deep kern
arXiv:2604.14799v1 Announce Type: new Abstract: Effective abstention (EA), recognizing evidence insufficiency and refraining from answering, is critical for reliable multimodal systems. Yet existing e
arXiv:2506.19807v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs), particularly slow-thinking models, often exhibit severe hallucination, outputting incorrect content due to an in
arXiv:2604.15141v1 Announce Type: new Abstract: Higher-order learning is fundamentally rooted in exploiting compositional features. It clearly hinges on enriching the representation by more elaborate
arXiv:2604.00313v2 Announce Type: replace Abstract: Automated species classification from underwater imagery is bottlenecked by the cost of expert annotation, and supervised models trained on one data
arXiv:2510.05486v2 Announce Type: replace Abstract: LLMs have been widely used in planning, either as planners to generate action sequences end-to-end, or as formalizers to represent the planning doma
arXiv:2502.16761v2 Announce Type: replace Abstract: Large language models (LLMs) present novel opportunities in public opinion research by predicting survey responses in advance during the early stage
arXiv:2601.11227v2 Announce Type: replace Abstract: Output diversity is crucial for Large Language Models as it underpins pluralism and creativity. In this work, we reveal that controlling the languag
arXiv:2603.17512v4 Announce Type: replace Abstract: Large language models (LLMs) exhibit strong general intelligence, yet their multilingual performance remains highly imbalanced. Although LLMs encode
arXiv:2604.14034v1 Announce Type: cross Abstract: Recent advances in Generative Artificial Intelligence, particularly Large Language Models (LLMs), have stimulated growing interest in automating or as
arXiv:2604.00998v2 Announce Type: replace Abstract: Ground roll is a common type of coherent noise in seismic records, and its attenuation remains challenging due to its substantial overlap with usefu
arXiv:2506.00433v4 Announce Type: replace Abstract: High-resolution image synthesis remains a core challenge in generative modeling, particularly in balancing computational efficiency with the preserv
arXiv:2604.14717v1 Announce Type: cross Abstract: Persistent language-model agents increasingly combine tool use, tiered memory, reflective prompting, and runtime adaptation. In such systems, behavior
arXiv:2509.23638v2 Announce Type: replace Abstract: Mixture-of-Experts (MoE) models face memory and PCIe latency bottlenecks when deployed on commodity hardware. Offloading expert weights to CPU memor
arXiv:2604.13243v1 Announce Type: cross Abstract: Gaze event detection is fundamental to vision science, human-computer interaction, and applied analytics. However, current workflows often require spe
arXiv:2604.15311v1 Announce Type: new Abstract: This paper focuses on the alignment of flow matching models with human preferences. A promising way is fine-tuning by directly backpropagating reward gr
arXiv:2604.14811v1 Announce Type: new Abstract: Ad hoc wireless networks exhibit complex, innate and coupled dynamics: node mobility, energy depletion and topology change that are difficult to model a
arXiv:2604.14568v1 Announce Type: cross Abstract: Visual reasoning models (VRMs) have recently shown strong cross-modal reasoning capabilities by integrating visual perception with language reasoning.
arXiv:2604.01197v2 Announce Type: replace-cross Abstract: Learning quantum states from measurement data is a central problem in quantum information and computational complexity. In this work, we study
arXiv:2601.18675v2 Announce Type: replace Abstract: We investigate whether temporal embedding models trained on longitudinal electronic health records can learn clinically meaningful representations w
arXiv:2604.14931v1 Announce Type: cross Abstract: Concatenating quantum error correction codes scales error correction capability by driving logical error rates down double-exponentially across levels
arXiv:2512.17091v2 Announce Type: replace Abstract: We propose a new approach for solving planning problems with a hierarchical structure, fusing reinforcement learning and MPC planning. Our formulati
arXiv:2604.15210v1 Announce Type: cross Abstract: Humor is one of the few cognitive tasks where getting the reasoning right matters as much as getting the answer right. While recent work evaluates hum
arXiv:2604.15065v1 Announce Type: new Abstract: Transformer-based detectors have advanced small-object detection, but they often remain inefficient and vulnerable to background-induced query noise, wh
arXiv:2501.11711v2 Announce Type: replace Abstract: The COVID-19 pandemic has claimed millions of lives, spurring the development of diverse forecasting models. In this context, the true utility of co
arXiv:2512.04578v3 Announce Type: replace Abstract: Legal general intelligence (GI) refers to artificial intelligence (AI) that encompasses legal understanding, reasoning, and decision-making, simulat
arXiv:2604.14177v1 Announce Type: new Abstract: Grammatical error correction (GEC) and explanation (GEE) have made rapid progress, but real teaching scenarios also require learner-friendly pedagogical
arXiv:2604.13348v1 Announce Type: new Abstract: We introduce CONCORD, a privacy-aware asynchronous assistant-to-assistant (A2A) framework that leverages collaboration between proactive speech-based AI
arXiv:2604.14321v1 Announce Type: new Abstract: We tasked GPT-4.1 to read what baseball fans wrote about their game-day experience and predict the overall experience rating each fan gave on a 0-10 sur
arXiv:2601.14053v2 Announce Type: replace-cross Abstract: The field of artificial intelligence has undergone a revolution from foundational Transformer architectures to reasoning-capable systems appro
arXiv:2604.15149v1 Announce Type: new Abstract: As reinforcement Learning with Verifiable Rewards (RLVR) has become the dominant paradigm for scaling reasoning capabilities in LLMs, a new failure mode
arXiv:2505.11017v2 Announce Type: replace Abstract: Time series forecasting is critical across multiple domains, where time series data exhibit both local patterns and global dependencies. While Trans
arXiv:2604.14922v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has emerged as a critical driver for enhancing the reasoning capabilities of Large Language Models (LLMs). While recent ad
arXiv:2604.15216v1 Announce Type: cross Abstract: Mobility in urban and interurban areas, mainly by cars, is a day-to-day activity of many people. However, some of its main drawbacks are traffic jams
arXiv:2604.14574v1 Announce Type: new Abstract: With the rapid advancement of deep learning in image generation, facial forgery techniques have achieved unprecedented realism, posing serious threats t
arXiv:2604.15203v1 Announce Type: new Abstract: Machine learning in high-stakes domains such as healthcare requires not only strong predictive performance but also reliable uncertainty quantification
arXiv:2604.14229v1 Announce Type: cross Abstract: Synthetic Aperture Radar (SAR) data is inherently complex-valued, while quantum machine learning (QML) models naturally operate in complex Hilbert spa
arXiv:2604.15174v1 Announce Type: new Abstract: Despite recent advances in state space models (SSMs) such as Mamba across various sequence domains, research on their standalone capacity for time serie
arXiv:2604.14582v1 Announce Type: new Abstract: High-resolution (HR) land-cover mapping is often constrained by the high cost of dense HR annotations. We revisit this problem from the perspective of m
arXiv:2604.14448v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as sources of information, yet their reliability depends on the ability to search the web, select rel
arXiv:2509.26007v2 Announce Type: replace-cross Abstract: Research on audio generation has progressively developed along both waveform-based and spectrogram-based directions, giving rise to diverse st
arXiv:2604.14564v1 Announce Type: cross Abstract: Reinforcement learning (RL) paradigms have demonstrated strong performance on reasoning-intensive tasks such as code generation. However, limited traj
arXiv:2509.06477v2 Announce Type: replace Abstract: Shortcuts such as APIs and deep-links have emerged as efficient complements to flexible GUI operations, fostering a promising hybrid paradigm for ML
arXiv:2604.14562v1 Announce Type: new Abstract: Accurate thermal modeling in metal additive manufacturing (AM) is essential for understanding the process-structure-performance relationship. While prio
arXiv:2502.07408v2 Announce Type: replace-cross Abstract: Deep Neural Networks (DNNs) can be catastrophically disrupted by flipping only a handful of parameter bits. We introduce Deep Neural Lesion (D
arXiv:2604.13849v1 Announce Type: cross Abstract: The rapid proliferation of Model Context Protocol (MCP)-based agentic systems has introduced a new category of security threats that existing framewor
arXiv:2604.14698v1 Announce Type: new Abstract: Diffusion models have recently emerged as expressive policy representations for online reinforcement learning (RL). However, their iterative generative
arXiv:2506.11251v2 Announce Type: replace-cross Abstract: A suitable scalar metric can help measure multi-calibration, defined as follows. When the expected values of observed responses are equal to c
arXiv:2505.20122v2 Announce Type: replace Abstract: This paper introduces MEBench, a novel benchmark for evaluating mutual exclusivity (ME) bias, a cognitive phenomenon observed in children during wor
arXiv:2604.14593v1 Announce Type: new Abstract: While Large Language Models (LLMs) demonstrate increasingly sophisticated affective capabilities, the internal mechanisms by which they process complex
arXiv:2602.07529v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated strong performance and rapid progress in a wide range of medical reasoning tasks. However, their sequ
arXiv:2604.15190v1 Announce Type: cross Abstract: Simulating group-level user behavior enables scalable counterfactual evaluation of merchant strategies without costly online experiments. However, bui
arXiv:2604.14218v1 Announce Type: new Abstract: Hate speech detection in Devanagari-scripted social media memes presents compounded challenges: multimodal content structure, script-specific linguistic