Knowledge catalogue
arXiv:2610.11628v1 Announce Type: cross Abstract: Machine unlearning updates a trained model after records are deleted, aiming to match exact retraining without repeating the full training procedure.
arXiv:2610.11233v1 Announce Type: cross Abstract: A core challenge in short-video fact-checking is identifying which evidence is sufficient to support a verification conclusion. Existing approaches ei
arXiv:2610.12194v1 Announce Type: new Abstract: World modeling has emerged as an effective co-training objective for robot policies, giving rise to World Action Models (WAMs) that jointly predict acti
arXiv:2610.11419v1 Announce Type: cross Abstract: Multimodal brain tumor segmentation typically leverages multiple MRI modalities, yet incomplete modality acquisition is common in clinical practice du
arXiv:2602.00811v2 Announce Type: replace Abstract: Current Multimodal Affective Computing (MAC) systems heavily rely on the completeness of multiple modalities to accurately understand human's affect
arXiv:2512.14014v2 Announce Type: replace Abstract: World models have shown great utility in improving the task performance of embodied agents. While prior work largely focuses on pixel-space world mo
arXiv:2407.00079v5 Announce Type: replace-cross Abstract: Mooncake is the serving platform for Kimi, a leading LLM service provided by Moonshot AI. It features a KVCache-centric disaggregated architec
arXiv:2510.04532v2 Announce Type: replace Abstract: Vision-Language Model (VLM) driving agents promise explainable end-to-end autonomy by first producing natural-language reasoning and then predicting
arXiv:2610.10832v1 Announce Type: new Abstract: Conventional decision tree algorithms produce effective, transparent models that can be audited, communicated, and deployed independently of the trainin
arXiv:2610.11442v1 Announce Type: new Abstract: Human mobility generation, an important task in urban research, synthesizes trajectory data for urban planning and transportation management. Human mobi
arXiv:2610.12039v1 Announce Type: cross Abstract: Graph neural networks (GNNs) predict molecular properties from chemical graph data, but predictive accuracy does not explain how graph information sup
arXiv:2610.10617v1 Announce Type: cross Abstract: In post-deployment time, inputs to deep learning models may or may not be adversarially patched. Patch robustness certification on such inputs within
arXiv:2610.11526v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) offer an energy-efficient computing paradigm through sparse event-driven computation, showing great potential for effic
arXiv:2610.12299v1 Announce Type: cross Abstract: Egocentric world models predict first-person observations conditioned on an agent's actions, but most focus on a single agent. Real embodied settings
arXiv:2610.10989v1 Announce Type: cross Abstract: Researchers are exploring effective one-step generative model continuously, and, Drifting Models (Deng et al., 2026), demonstrate great potential in o
arXiv:2610.11228v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated strong potential for sequential recommendation through their ability to reason over complex m
arXiv:2603.06578v3 Announce Type: replace Abstract: Multimodal Large Language Model (MLLM) classification performance depends critically on evaluation protocol and ground truth quality. Studies compar
arXiv:2610.11176v1 Announce Type: new Abstract: Multimodal remote sensing image registration is a crucial prerequisite for the collaborative processing and downstream application of remote sensing dat
arXiv:2610.11723v1 Announce Type: new Abstract: Multiplayer world models must ensure that independently controlled views remain consistent with one shared and persistent world. We introduce MultiWorld
arXiv:2610.11605v1 Announce Type: cross Abstract: We introduce NanoProof, to our knowledge the first factorized execution-guided theorem prover in Lean 4 whose training data, extraction tooling, train
arXiv:2610.11768v1 Announce Type: cross Abstract: Large language model (LLM) agents are beginning to operate industrial energy equipment, and what they get right depends on what they are told about th
arXiv:2610.12243v1 Announce Type: cross Abstract: Dense retrieval usually ranks text chunks by their semantic similarity to a question. This ignores structure that many data systems already store, inc
arXiv:2610.12030v1 Announce Type: new Abstract: Large Language Models (LLMs) have renewed interest in autoformalization. Yet, when First-Order Logic (FOL) is considered as the target formalism, the fi
arXiv:2610.10787v1 Announce Type: cross Abstract: Language models trained with long-horizon agentic reinforcement learning can generalize knowledge through reasoning, express precise actions, and purs
arXiv:2610.10776v1 Announce Type: new Abstract: Equivariant graph neural networks have emerged as foundational architectures for machine-learned interatomic potentials, approaching quantum-chemical ac
arXiv:2502.02679v3 Announce Type: replace-cross Abstract: Approximation and learning of classifiers of large data sets by neural networks in terms of high-dimensional geometry and statistical learning
arXiv:2507.18074v2 Announce Type: replace Abstract: Recent progress in LLM agents has advanced the prospect of autonomous research. Yet whether AI can complete difficult long-horizon tasks, especially
arXiv:2610.11923v1 Announce Type: cross Abstract: The brain maintains stable cognition despite continuously changing neural activity. How to extract stable cognitive states from variable neural observ
arXiv:2610.11994v1 Announce Type: cross Abstract: Deep joint source-channel coding (DeepJSCC) transmits data end-to-end over wireless channels using a neural encoder-decoder, but reconstruction qualit
arXiv:2610.11631v1 Announce Type: cross Abstract: Deploying intelligent robotic systems that interact with humans through gestures requires neural networks capable of recognizing diverse temporal patt
arXiv:2610.11252v1 Announce Type: cross Abstract: This study presents the Neuro-Memory Fuzzy Inference System (NeMeFIS), a hierarchical machine learning architecture that asymmetrically models acceler
arXiv:2610.11551v1 Announce Type: new Abstract: We study online sparse linear regression (OSLR) where any algorithm is restricted to accessing only b out of d attributes per instance for prediction an
arXiv:2610.11070v1 Announce Type: new Abstract: Audio-driven facial animation underpins real-time avatars, telepresence, and embodied virtual agents. And it must run online: each frame emitted from au
arXiv:2610.10949v1 Announce Type: cross Abstract: In this note, we study the problem of designing a feedback law that globally steers a system to a prescribed target configuration. Even if the system
arXiv:2610.11030v1 Announce Type: cross Abstract: Tool-using LLM agents violate the policies they are deployed to enforce, often silently. Prior defenses hand-write rules, query an LLM verifier per ac
arXiv:2610.11915v1 Announce Type: new Abstract: Machine unlearning in large language models aims to remove unwanted knowledge while preserving the model's remaining capabilities. Although existing met
arXiv:2610.11313v1 Announce Type: new Abstract: A fundamental question in neural network architecture optimization is whether the minimum hidden-neuron count required to approximate a target function
arXiv:2610.10655v1 Announce Type: cross Abstract: Large Language Models (LLMs) inevitably internalize substantial amounts of sensitive or private information during pre-training, while LLM unlearning
arXiv:2610.12134v1 Announce Type: new Abstract: Knee osteoarthritis (KOA) progression prediction can support patient monitoring, requiring the integration of multimodal data and multidomain expertise.
arXiv:2601.14994v2 Announce Type: replace-cross Abstract: Data contamination can invalidate benchmark evaluation when a model benefits from memorized evaluation content rather than genuine generalizat
arXiv:2610.10990v1 Announce Type: new Abstract: Unified multimodal diffusion large language models (dLLMs) offer a single architecture for both image generation and multimodal understanding, but their
arXiv:2610.12458v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are rapidly evolving toward continuous audio--visual reasoning, creating an urgent need for evaluations that ex
arXiv:2610.11194v1 Announce Type: cross Abstract: Dexterous grasping is the foundational primitive in embodied AI, demanding massive data to train robust models. As real-world data collection is expen
arXiv:2610.10855v1 Announce Type: new Abstract: Monocular videos of human manipulation provide abundant dexterous demonstrations, yet reconstructing hand-object interaction from a single view and tran
arXiv:2610.10697v1 Announce Type: cross Abstract: Accurately simulating chaotic quantum many-body dynamics remains a major computational challenge for classical methods, due to the rapid buildup and s
arXiv:2610.10833v1 Announce Type: new Abstract: We study whether small LLM agents can operate effectively under explicit wall-clock time budgets by both respecting the allocated runtime and using avai
arXiv:2610.12466v1 Announce Type: new Abstract: METR's 50% time horizon measures the human completion time of software tasks that an AI solves with 50% probability, allowing AI capabilities to be expr
arXiv:2610.11735v1 Announce Type: new Abstract: Onboard Artificial Intelligence can improve responsiveness and bandwidth efficiency of Earth Observation systems by processing data directly on the sate
arXiv:2610.12448v1 Announce Type: new Abstract: In this work, we show that a single Transformer block, applied recurrently, can match the accuracy of a full-depth vision encoder at comparable inferenc
arXiv:2512.13892v3 Announce Type: replace-cross Abstract: Reliable estimation of feature contributions in machine learning models is essential for transparency, algorithmic fairness, and regulatory co
arXiv:2610.11647v1 Announce Type: cross Abstract: Coding agents are extended with agent skills, directories whose SKILL.md tells the model when and how to perform a task. Because skills come from inde
arXiv:2610.12292v1 Announce Type: new Abstract: A typed decision model reads a piece of text and returns a probability over caller-defined options, each with a short written definition, generating no
arXiv:2603.07980v2 Announce Type: replace-cross Abstract: As language models (LMs) evolve from chat assistants to long-horizon agents capable of multi-step reasoning and tool use, existing benchmarks
arXiv:2610.12419v1 Announce Type: new Abstract: Single-image, multi-image, and video deep research require different visual operations but share a workflow of visual grounding, external retrieval, and
arXiv:2610.12375v1 Announce Type: new Abstract: Agents are deployed in applications from trip planners and stock trading to IT incident triage. In most cases, LLM agents work autonomously with minimal
arXiv:2610.11289v1 Announce Type: new Abstract: Autonomous scientific discovery with LLMs requires generating and testing hypotheses adaptively as evidence accumulates while maintaining statistical va
arXiv:2610.11846v1 Announce Type: new Abstract: Open-Vocabulary Audio-Visual Event Localization (OV-AVEL) labels each video segment with an event class, including classes that were never seen during t
arXiv:2610.11118v1 Announce Type: new Abstract: The next frontier for artificial general intelligence is tackling unresolved scientific problems, calling for benchmarks that assess progress beyond est
arXiv:2609.33987v2 Announce Type: replace Abstract: Long-horizon coding agents need timely corrections, yet feedback can be ineffective or even harmful when it misjudges ongoing work or fails to addre
arXiv:2610.11772v1 Announce Type: cross Abstract: Zador's celebrated theorem is a cornerstone of optimal quantisation: it establishes both the weak limit of the empirical distribution of an optimal n-