All entries

MATE4D: Matrix-Guided Editable 4D Generation from a Single Image

arXiv:2610.11181v1 Announce Type: new Abstract: Generative models have rapidly pushed content creation be-yond 2D imagery toward dynamic 3D and 4D scene synthesis. Yet pro-ducing realistic and tempora

Source

Human DGX agent

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

MatLat: Material Latent Space for PBR Texture Generation

DGX agent

arXiv:2512.17302v2 Announce Type: replace Abstract: We propose a generative framework for producing high-quality PBR textures on a given 3D mesh. As large-scale PBR texture datasets are scarce, our ap

researcharxiv-cs-cv

9 Oct 2026

mAVE: A Watermark for Joint Audio-Visual Generation Models

arXiv:2603.07090v2 Announce Type: replace-cross Abstract: Watermarking joint audio-visual generation supports vendor copyright protection and content provenance. However, independently valid audio and

MC-TRCM: Observation-Aware Recursive Fusion for Incomplete Mobile and Wearable Mental-Health Feature Views

arXiv:2610.11408v1 Announce Type: new Abstract: Public mobile and wearable mental-health datasets often provide summarized feature tables rather than synchronized raw sensor streams. In these releases

MCL: Meta Convolution Layer

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

Measuring and Mitigating Solution Mode Collapse in RLVR

model-releasesarxiv-cs-cl

arXiv:2610.11064v1 Announce Type: cross Abstract: A language model (LM) can usually answer the same question in more than one way, but reinforcement learning with verifiable rewards (RLVR) is indiffer

Measuring Cultural Alignment Beyond the Average: A Framework for Evaluating Maternal-Health LLM Interactions in Indian Contexts

arXiv:2610.11586v1 Announce Type: new Abstract: Existing evaluation methods for healthcare LLMs primarily assess factual correctness,safety, and fluency, while providing limited insight into whether g

safetyarxiv-cs-cl

MedBenchAgent: Towards Systematic Automation of Medical VLM Benchmark Construction

arXiv:2610.11312v1 Announce Type: new Abstract: Large-scale construction of medical vision-language model (VLM) benchmarks is increasingly feasible with richly annotated imaging datasets and large lan

MedMemoryBench: Benchmarking Agent Memory in Personalized Healthcare

arXiv:2605.11814v2 Announce Type: replace Abstract: The large-scale deployment of personalized healthcare agents demands memory mechanisms that are exceptionally precise, safe, and capable of long-ter

Memento 3: Model-Based Recursive Self-Improvement through Reflective Rulebooks

arXiv:2610.11794v1 Announce Type: new Abstract: Learning to act in unfamiliar environments requires agents to infer how the world works and revise that understanding as new evidence arrives. Yet limit

Memorization and Malign Generalization in Conditional Diffusion Models with Random Features

arXiv:2610.11288v1 Announce Type: new Abstract: Conditional diffusion models generate diverse, novel, and high-quality samples under prescribed conditions. However, theoretical understanding of their

Memory Forcing: Attendable Mid-Horizon History for Streaming Video Generation

arXiv:2610.11756v1 Announce Type: new Abstract: Autoregressive video diffusion enables causal video streaming without a bidirectional pass over the full clip, but existing few-step systems usually ret

Memory Type Varies: Empowering LLM Agents for Long-Term Memory with Diverse Strategies

arXiv:2610.11573v1 Announce Type: new Abstract: The memory capabilities of Large Language Models (LLMs) have garnered increasing attention recently. Despite great success achieved, existing retrieval-

MemoWM: How World Models Change What Agents Need to Remember

arXiv:2610.10778v1 Announce Type: cross Abstract: Long-term agents face growing storage demands as they accumulate experience. World models capture reusable regularities that can reduce the informatio

MemTrace: State-Consistent Memory for Long-Horizon Coding Agents

arXiv:2610.04838v2 Announce Type: replace Abstract: As coding agents take on long-horizon software evolution tasks spanning multiple files and stages, longer execution trajectories introduce two coupl

MemTrial: Learning When to Trust Memory in LLM Portfolio Agents

arXiv:2610.11732v1 Announce Type: new Abstract: Large language model (LLM) agents for portfolio management learn from experience: they credit each experience in their memory with the outcome of the de

MESSENGER: Memory-Enhanced Sequential Scene Flow Estimation via Autoregressive Next-Frame Forecasting

model-releasesarxiv-cs-cv

arXiv:2610.10759v1 Announce Type: new Abstract: Scene flow can capture low-level 3D motion displacements in dynamic scenarios. Early pairwise estimators relying on instantaneous two-frame motion lack

MetaEncoder: Exploring the Limit of Bi-Encoders for Multimodal System One Decision Making with Natural Language Interface

arXiv:2610.11316v1 Announce Type: cross Abstract: System One models output constrained decisions and probability distributions rather than free-form text generation. While prevailing paradigms rely on

MetaOPD: Meta-Learned Token Weighting for On-Policy Distillation

arXiv:2610.11989v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on its own generated responses using token-level teacher supervision. However, uniform weighting overlooks

Tutorials

Mid-Training Language Models on Raw Video

arXiv:2610.11019v1 Announce Type: cross Abstract: Multimodal large language models learn mostly from paired image-text data or annotated video, and raw web video is rarely used to further train an exi

tutorialsarxiv-cs-ai

MIMESIS: Learning User Simulators as Training Environments for Interactive Agents

arXiv:2610.09484v2 Announce Type: replace Abstract: Training and evaluating interactive language agents typically requires rich user interactions, yet collecting human feedback is expensive and diffic

MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement

arXiv:2610.11959v1 Announce Type: new Abstract: Reinforcement learning (RL) is the central training paradigm for advancing large foundation models towards self-improvement. This report introduces the

agentsarxiv-cs-cl

MindFlow: Mind Supernet Powered Thinking Flows for Research Idea Innovation

arXiv:2610.11966v1 Announce Type: new Abstract: Research idea innovation is a fundamental engine of scientific progress, yet it remains difficult to generate and evaluate in a scalable and controllabl

Mine Odyssey: Benchmarking Spatial Agentic Intelligence in the Wild

arXiv:2610.11328v1 Announce Type: new Abstract: Advances in foundation models are driving efforts to introduce agents to assist people in the physical world. Such agents require agentic spatial intell

Minimax Gaussian Mechanisms for Continual Machine Unlearning

model-releasesarxiv-cs-lg

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.

MiniVer-V: Identifying Minimal Sufficient Evidence for Short Video Verification

model-releasesarxiv-cs-cl

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

MiniWAM: Learning Compact Future Targets for Efficient World-Action Modeling

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

safetyarxiv-cs-ro

Missing Modality-Aware Calibration for Trustworthy Brain Tumor Segmentation

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

MissMAC-Bench: Building Solid Benchmark for Missing Modality Issue in Robust Multimodal Affective Computing

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

MobileWorldBench: Towards Semantic World Modeling For Mobile Agents

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

Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving

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

More Than Meets the Eye? Uncovering the Reasoning-Planning Disconnect in Training Vision-Language Driving Models

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

MotherTree: Meta-learning on synthetic data improves decision tree training

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

MotiveMob: Motivation as Semantic Action for Closed-Loop Human Mobility Generation

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

MPGE: A Multi-Perspective Graph Explainer for Molecular Classification Explanation

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

MRCert: Towards Post-deployment Patch Robustness Certification for Adversarially Patched Samples via Type-specific Masking

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

MSGAT: Multi-Head Spiking Graph Attention with Similarity-Space Fusion for Image-Text Retrieval

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

Multi-Agent Egocentric World Model with Fine-Grained Embodied Interaction

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

Multi-Bandwidth Distribution Matching Distillation: On the Equivalence of Distribution Matching Distillation and Drifting Models

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

Multimodal Graph Retrieval-Augmented Sequential Recommendation via Collaborative Filtering Paths

model-releasesarxiv-cs-lg

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

Applications

Multimodal Large Language Models as Image Classifiers

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

applicationsarxiv-cs-cv

Multimodal Remote Sensing Image Registration: A Comprehensive Review, Challenges and Prospects

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

MultiWorldBench: Do Independently Controlled Views Describe One Shared World?

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

NanoProof: Open and Efficient Automated Theorem Proving in Lean 4

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

Narrow and Deep: An Ontology Tower as the Knowledge of an LLM Agent for an Industrial Equipment System

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

NativeScope: Relation-Localized Retrieval over Native Topology with a Correct Anchor

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

researcharxiv-cs-cl

Natural Language to First-Order Logic LLM-based Autoformalization

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

researcharxiv-cs-cl

NavGPT-3: Harnessing Context in a Hierarchical Navigation Runtime

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