Search: “meta”

arXiv:2610.11117v1 Announce Type: new Abstract: Dynamic convolution enhances convolutional neural networks (CNNs) by adapting kernels to input content, but it expresses the effective kernel as a linea

arXiv:2511.04000v2 Announce Type: replace-cross Abstract: Decision trees are widely used in high-stakes fields like finance and healthcare due to their interpretability. This work introduces an effici

arXiv:2610.10393v1 Announce Type: cross Abstract: Models can be retrained as new data arrive, but deploying every new version risks replacing a good policy with a worse one. We study how to plan polic

arXiv:2610.01949v1 Announce Type: cross Abstract: Ransomware has emerged as a major cybersecurity threat, with incidents increasing in frequency and impact across critical sectors. These attacks are t

arXiv:2609.38143v1 Announce Type: new Abstract: Agent performance depends on both reasoning ability and the environment in which it acts. We study test-time AI-for-AI, asking how a Builder can learn t

arXiv:2609.38147v1 Announce Type: new Abstract: As agents take on longer and more complex problems, controlling the execution becomes a task in its own right. Each step in the run brings new control c

arXiv:2609.21122v1 Announce Type: new Abstract: Manipulating previously unseen objects remains challenging, as their dynamics depend on latent physical properties, such as friction and mass distributi

arXiv:2609.19832v1 Announce Type: new Abstract: Relational table learning has gained increasing attention with the widespread use of relational databases. Existing methods typically rely on deep GNN o

arXiv:2609.18597v1 Announce Type: new Abstract: Propagation structures provide crucial evidence for fake news detection, yet existing approaches primarily rely on supervised GNN-based models, which re

arXiv:2602.00532v2 Announce Type: replace-cross Abstract: Constraint handling is central to constrained black-box optimization (BBO), where objective improvement and feasibility restoration often prov

arXiv:2609.11041v1 Announce Type: new Abstract: No Free Lunch theorem implies that any performance gains achieved by a classifier on a particular image distribution are necessarily offset by a loss of

arXiv:2609.09511v1 Announce Type: cross Abstract: Physical-layer authentication (PLA) in non-terrestrial networks (NTNs) is challenged by severe Doppler shifts, long delays, and fast channel variation

arXiv:2609.06396v1 Announce Type: new Abstract: Recursive self-improvement (RSI) lets a system improve the model-building machinery from its own failures, so every later model inherits the gain. Yet R

arXiv:2608.30678v1 Announce Type: new Abstract: Text-rich image understanding requires multimodal large language models (MLLMs) to organize OCR (Optical Character Recognition)-grounded evidence across

arXiv:2608.26638v1 Announce Type: new Abstract: Across various non-verifiable tasks, human evaluation is reliable but expensive, while automatic metrics are more scalable but often biased. Building on

arXiv:2608.20513v1 Announce Type: cross Abstract: Continuous deployment to large scale production systems creates a tension between release velocity and reliability. Every change is a potential reliab

arXiv:2608.12389v1 Announce Type: new Abstract: Cross-domain zero- or few-shot personalization aims to generate user-preferred responses in unseen conversational domains from only a handful of target-

arXiv:2607.19322v2 Announce Type: replace Abstract: Rubric-based evaluation of open-ended generation faces a fundamental tension between expressiveness and reliability. Authoring a faithful rubric req

arXiv:2608.00107v1 Announce Type: new Abstract: Agentic systems must repeatedly decide whether to answer directly, decompose a task, invoke a tool, execute code, delegate to a specialist, verify an in

arXiv:2605.05623v2 Announce Type: replace Abstract: Hyperspectral in situ sensing has shown promise in retrieving aquatic biogeochemical (BGC) parameters, such as total suspended solids, dissolved org

arXiv:2607.24749v1 Announce Type: cross Abstract: Although advancements in game character AI aim to enhance player engagement, evidence suggests that perceiving an opponent as artificial can diminish

arXiv:2607.22770v1 Announce Type: cross Abstract: Although artificial intelligence (AI) has shown promising performance in several medical tasks, accurate dementia etiology diagnosis with AI remains c

arXiv:2505.23378v3 Announce Type: replace Abstract: Speaker-dependent modelling can substantially improve performance in speech-based health monitoring applications. While mixed-effect models are comm

arXiv:2607.05297v1 Announce Type: new Abstract: Recent LLM agents tackle increasingly long-horizon, open-ended tasks, and external skills, reusable procedural knowledge supplied to the agent, further

arXiv:2603.27313v2 Announce Type: replace Abstract: Disturbance observer-based control has shown promise in robustifying robotic systems against uncertainties. However, tuning such systems remains cha

arXiv:2607.00926v1 Announce Type: cross Abstract: Generalizing machine learning models to environments that differ from their training distribution remains a critical hurdle, particularly when data fr

arXiv:2606.28399v1 Announce Type: new Abstract: The structure of human visual representations underpins our capacity for adaptive behaviour. While pretrained neural networks model human visual represe

arXiv:2606.23758v1 Announce Type: cross Abstract: Domain generalization learns from multiple source domains to generalize to unseen target domains. However, it often neglects the realistic case of lab

arXiv:2606.20702v1 Announce Type: new Abstract: Vision-language models (VLMs) have sparked growing interest in zero-shot Earth Observation (EO) downstream tasks, with further gains enabled by remote-s

arXiv:2606.21627v1 Announce Type: cross Abstract: As agentic systems tackle increasingly complex multi-step tasks, evaluating their trajectories presents a major bottleneck - human annotation of a sin

arXiv:2606.07217v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are emerging as a promising paradigm for robotic manipulation, enabling general-purpose policies trained from larg

arXiv:2606.04823v1 Announce Type: new Abstract: Large language models (LLMs) are fluent on open-ended tasks, yet in agentic settings, where a system must plan, use tools, and act over extended horizon

arXiv:2605.31271v1 Announce Type: new Abstract: Driving Vision-Language-Action Models (Driving VLAs) aim to use language to improve end-to-end planning, but the language-action gap limits this promise

arXiv:2601.21909v2 Announce Type: replace Abstract: Current LLM post-training methods optimize complete reasoning trajectories through Supervised Fine-Tuning (SFT) followed by outcome-based Reinforcem

arXiv:2602.02417v2 Announce Type: replace Abstract: Continual learning aims to acquire tasks sequentially without catastrophic forgetting, yet standard strategies face a core tradeoff: regularization-

arXiv:2504.04639v4 Announce Type: replace-cross Abstract: It is an open question whether the search and decision versions of promise CSPs are equivalent. Most known algorithms for PCSPs solve only the

arXiv:2605.25233v1 Announce Type: new Abstract: AI agents are increasingly used to solve complex, multi-step tasks, but existing multi-agent frameworks remain brittle as workflows grow in scale and de

arXiv:2601.18685v3 Announce Type: replace-cross Abstract: The capabilities of generative AI in mathematics education are rapidly evolving, posing significant challenges for research to keep pace. Rese

arXiv:2605.20385v1 Announce Type: new Abstract: Recent progress in promptable segmentation has shifted visual perception from object-level localization toward concept-level understanding. However, the

arXiv:2508.13309v3 Announce Type: replace Abstract: Numerous techniques have been proposed for generating adversarial examples in white-box settings under strict Lp-norm constraints. However, such nor

arXiv:2605.18437v1 Announce Type: new Abstract: Vehicular edge computing (VEC) enables latency-sensitive vehicular applications by offloading computation-intensive tasks to nearby edge servers. Howeve

arXiv:2605.13228v1 Announce Type: cross Abstract: Video understanding requires active evidence seeking, motivating tool-augmented video agents for temporal reasoning, cross-modal understanding, and co

arXiv:2605.11388v1 Announce Type: new Abstract: Humans intuitively solve complex problems by flexibly shifting among reasoning modes: they plan, execute, revise intermediate goals, resolve ambiguity t

arXiv:2605.12368v1 Announce Type: new Abstract: Solving partial differential equations (PDEs) with machine learning typically requires training a new neural network for every new equation. This optimi

arXiv:2605.09661v1 Announce Type: cross Abstract: Large language models (LLMs) have saturated standard medical benchmarks that test factual recall, yet their ability to perform higher-order reasoning,

arXiv:2605.08144v1 Announce Type: cross Abstract: Diffusion models have achieved remarkable success across a wide range of generative tasks, yet their training paradigm largely treats injected noise a