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MIRT: Transformers for Truthful Generative Auctions with Whole-feed Permutation Externalities

arXiv:2610.06559v1 Announce Type: cross Abstract: Modern online platforms commonly rank ads and organic content separately before blending them into a feed displayed to the user, overlooking externali

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DGX agent

arXiv:2510.03614v2 Announce Type: replace Abstract: Sequential estimation under partial observability requires tracking beliefs that may be high-dimensional, multimodal, and non-Gaussian. Classical Ba

tutorialsarxiv-cs-lg

6 Oct 2026

OCL-PDE: A Generative Framework for PDE Inverse Problems with Observation-Complementary Latents

arXiv:2610.06259v1 Announce Type: new Abstract: Partial differential equation (PDE) inverse problems are often ill-posed, making fine-scale details difficult to recover. We address this problem by int

OctMesh: A Unified Octree-Hierarchical Framework for Lossless Triangle Mesh Compression

arXiv:2610.04281v1 Announce Type: new Abstract: Lossless triangle mesh compression must preserve both vertex positions and connectivity. Octrees support learned point cloud geometry coding and progres

One Token Can Be Enough: Bridging Prompting and Activation Steering with Prefix Steering

arXiv:2610.04967v1 Announce Type: new Abstract: Prompting guides language model behavior through the initial context, whereas activation steering often intervenes throughout generation. A natural ques

Optimizing Breast Cancer Detection in Mammograms: A Comprehensive Study of Transfer Learning, Resolution Reduction, and Multi-View Classification

arXiv:2503.19945v4 Announce Type: replace-cross Abstract: Mammography, an X-ray-based imaging technique, remains central to the early detection of breast cancer. Recent advances in artificial intellig

Out-of-control Hamiltonian Learning

arXiv:2610.06709v1 Announce Type: cross Abstract: Learning the Hamiltonian of a many-body system from its dynamics is a central task in quantum science, yet the algorithms with the strongest provable

Pay to Learn, Share to Earn: Incentivized Federated Multi-Player Bandits

arXiv:2610.06062v1 Announce Type: new Abstract: Federated multi-player multi-armed bandit problems model collaborative sequential decision-making where multiple players interact with a common bandit e

Prism: Dynamic Sparse Attention for Native 2K Joint Video-Audio Generation Model Training

arXiv:2610.05416v1 Announce Type: new Abstract: Natively training joint video-audio generation models at higher resolutions empowers them to learn richer visual details and sharper motion dynamics. Ho

Progressive Multi-Ancestor Bit-Depth Distillation

arXiv:2610.04100v1 Announce Type: new Abstract: Model compression strategies are widely employed to reduce memory footprint and network complexity, particularly for devices with constrained computatio

Protecting Sensitive Data in Image Synthesis via PAC-Private Adaptation for Diffusion Models

arXiv:2610.04038v1 Announce Type: new Abstract: Synthetic data are increasingly used as an alternative to sharing sensitive records. However, synthetic data generation does not guarantee privacy, as d

Pythia: Toward Foundation World Models for Multimodal Time Series

arXiv:2610.05240v1 Announce Type: new Abstract: Time-series foundation models offer a unified approach to forecasting across heterogeneous domains. Textual context and auxiliary observations provide c

Questioning the Questions: Sustaining Self-Evolution in Reasoning Models

arXiv:2610.04299v1 Announce Type: new Abstract: Self-evolving reasoning models learn from their own generated questions, yet repeated self-training can lead to performance collapse. In this paper, we

Researching from scratch! [R]

tutorialsr-machinelearning

Hey everyone! I am trying to go really deep into Machine Learning and Artificial Intelligence. Not just learning how to use the latest models and frameworks, but understanding how we reached here from

Robotic Long-Horizon Manipulation with Progressive In-Context Code Generation and Episodic Feedback

arXiv:2503.21969v4 Announce Type: replace Abstract: Robotic long-horizon manipulation requires robots to compose perception, reasoning, and action over extended task sequences, yet existing language-c

tutorialsarxiv-cs-ro

S^3N: A Spherical Spiral Scanning Network for Weather Forecasting

arXiv:2610.04338v1 Announce Type: new Abstract: Machine learning-based weather prediction (MLWP) has achieved strong performance in global weather forecasting. Recent Hierarchical Equal Area isoLatitu

SimAuthor: Harnessing Foundation Models for Persistent Scientific Simulator Authoring

arXiv:2610.06257v1 Announce Type: new Abstract: Foundation models can generate scientific code, but authoring a scientific simulator (an executable program encoding hypotheses about how mechanisms gen

TMAML: Temporal Model-Agnostic Meta-Learning for Cold-Start Time Series Forecasting

arXiv:2610.04547v1 Announce Type: new Abstract: Cold-start forecasting, the task of forecasting a time series with little to no historical data, is a common challenge. Addressing it requires approache

To Learn is to Wander: Learning Across Graphs and Tasks with Random Walks

arXiv:2610.06694v1 Announce Type: new Abstract: Graph foundation models aim to transfer across graphs, feature spaces, relational schemas, and prediction tasks, yet existing approaches typically gener

Towards Looped Models Done Right, Part II: Rethinking at Fixed Points

arXiv:2610.06833v1 Announce Type: new Abstract: Every recurrence of a looped language model adds cost in training, decoding, prefill, and reinforcement learning (RL). The closer recurrent states get t

Training Numerical Intelligence via Auto-Diagnosis and Skill Discovery

arXiv:2610.03872v1 Announce Type: cross Abstract: AI agents are becoming increasingly capable of generating scientific code, but generating code is not the same as improving the algorithms behind it.

Ultrasound Operator Guidance Using World Modeling and Retrieval Based Action Planning

arXiv:2610.06008v1 Announce Type: cross Abstract: Ultrasound is widely used, but acquisition quality is heavily dependent on the operator's knowledge and expertise. With demand for examinations outpac

Variance-Optimal Control Variates for Learning with Black-box Feedback

arXiv:2610.04766v1 Announce Type: new Abstract: Modern models increasingly learn through black-box oracles such as humans, optimization solvers, and external tools that provide feedback without exposi

Variational Quantum Attention for Molecular Graph Learning

arXiv:2610.04588v1 Announce Type: cross Abstract: Molecular property prediction is central to computational drug discovery, where graph neural networks learn to weight neighboring atomic environments

What Matters for Latent Reasoning with Flow Matching

arXiv:2610.06666v1 Announce Type: new Abstract: Latent reasoning lets a large language model (LLM) think in a continuous space and verbalize only the answer. We argue that an effective latent thought

When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation

arXiv:2605.05592v2 Announce Type: replace Abstract: Increasing a voting budget can improve or reduce population accuracy, and an initial gain or loss need not persist. We give a complete structural cl

When Does a Diffusion Model Decide What to Draw ?

arXiv:2610.05645v1 Announce Type: new Abstract: A diffusion model starts from pure noise and removes it step by step. Somewhere along the way it stops being able to become 'anything' and becomes commi

Causal Representation Learning with Instantaneous and Lagged Relations via Nonstationarity

arXiv:2610.03452v1 Announce Type: new Abstract: Causal representation learning for time-series data aims to identify latent states and their causal relations from observations. In this setting, an imp

CLEAN: Psychometrically Consistent Incremental Cognitive Diagnosis under Concept-Space Expansion via Architectural Isolation

arXiv:2610.02278v1 Announce Type: new Abstract: Cognitive diagnosis (CD) is a fundamental task in intelligent education that profiles learner proficiency over knowledge concepts. In real-world learnin

Decoding the Functional Roles of Register and High-Norm Patch Tokens in Vision Transformers

arXiv:2610.03698v1 Announce Type: new Abstract: Self-supervised Vision Transformers (ViTs), such as DINOv2, learn rich visual representations, but the functions of their internal tokens remain poorly

Dirac-Interconnected Neural Elements: Discovering Modularity in Physical Systems Without Reduction

arXiv:2610.02960v1 Announce Type: new Abstract: Deep learning has shown remarkable success in the data-driven modeling of dynamical systems. Much of its success is attributed not to the flexibility of

Exploring Subnetwork Interactions in Heterogeneous Brain Network via Prior-Informed Graph Learning

arXiv:2603.19307v2 Announce Type: replace-cross Abstract: Modeling the complex interactions among functional subnetworks is crucial for the diagnosis of mental disorders and the identification of func

Foresight: planning future perception in streaming VLMs without retraining

arXiv:2610.03123v1 Announce Type: cross Abstract: Existing streaming vision-language models (VLMs) continuously perceive and reason over visual streams, but their computational pathways remain fixed t

ForestQuery: Boundary-Aware and Spatially Anchored Query Learning for Unified Forest Point Cloud Segmentation

arXiv:2610.03403v1 Announce Type: cross Abstract: Forest point cloud segmentation is fundamental for fine-grained 3D forest scene understanding, yet remains challenging due to irregular tree structure

Geometry-Aware Time Reparameterization for Flow-Map Distillation

arXiv:2610.02427v1 Announce Type: cross Abstract: Flow-map distillation enables one- and few-step generation by learning finite-time transitions of a pretrained generative ODE. We investigate whether

Geometry Meets Physics: Data-Efficient Pre-Training for Unstructured Neural PDE Solvers

arXiv:2610.03363v1 Announce Type: new Abstract: Neural surrogate models for Partial Differential Equations (PDEs) on unstructured 3D geometries are often limited by poor generalization and the high co

How to Find and Reuse Policies for Continuous Adaptation in Lifelong Reinforcement Learning

arXiv:2610.03119v1 Announce Type: cross Abstract: In lifelong reinforcement learning, retaining previously learned policies is not sufficient for effective transfer to a new task. Useful knowledge may

How to Have a Sensitive Debate: An Instance-Optimal Protocol for AI Debate

arXiv:2610.02557v1 Announce Type: new Abstract: As powerful AI systems reach and sometimes surpass the abilities of human experts across a range of cognitively demanding tasks, the problem of accurate

How To Train Your World Model: Fine-tuning vs RAG for LM-based World Modeling

arXiv:2610.02542v1 Announce Type: new Abstract: World models (WMs) simulate the transition dynamics of environments, enabling agents to plan over the consequences of their actions. In text-based envir

Improving Atomic-Fact Recall via Focused Views in Unstructured Knowledge Editing

arXiv:2610.02772v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly serve as general-purpose interfaces to factual knowledge, but their parameters do not automatically reflect

Infinity Search: Approximate Vector Search with Projections on q-Metric Spaces

arXiv:2506.06557v3 Announce Type: replace-cross Abstract: An ultrametric space or infinity-metric space is defined by a dissimilarity function that satisfies a strong triangle inequality in which ever

Jacob Coxon @hilbertspaess and @DKokotajlo next at the NYC City Council meeting, hosted by @SpeakerMenin. Coxon cpens with “We do not know h…

Jacob Coxon @hilbertspaess and @DKokotajlo next at the NYC City Council meeting, hosted by @SpeakerMenin. Coxon cpens with “We do not know how to control any AI system yet”, which, importantly, isn’t

tutorialsgary-marcus--x

JOVE: Joint Execution and Verification for Resource-Aware LLM Task Graphs

arXiv:2610.03296v1 Announce Type: new Abstract: Complex reasoning queries can be decomposed into directed acyclic task graphs and distributed across heterogeneous LLMs, reducing latency through parall

Learn Feasibility Once, Optimize All Objectives: Derivative-Free Diffusion Models for Chance-Constrained Programming

arXiv:2610.03071v1 Announce Type: new Abstract: Chance-constrained programs (CCPs) optimize decisions under uncertainty by limiting the probability of constraint violation. Despite advances in traditi

Learning a Fact Is Not Learning How to Retrieve It

arXiv:2610.03251v1 Announce Type: new Abstract: A model trained on 'The capital of X is Y' may produce 'Y' after 'The capital of X is' but fail after 'The capital of X:'. We call these different ways

Misinformation Without Triggers: From Factual Answers to Downstream Decisions

arXiv:2610.02886v1 Announce Type: cross Abstract: Language models learn from web documents, some of them false, and false content can reach a model's answer to a factual question and the summaries and

Multi-Task Evolution for Zero-Shot Cross-Problem Generalization using LLMs

arXiv:2610.03316v1 Announce Type: new Abstract: Designing effective heuristics for diverse combinatorial optimization problems requires substantial expertise and repeated search. Large language models

No-Free-Graph: Learning When Multimodal Data Should Be Graphified

arXiv:2610.02768v1 Announce Type: new Abstract: Multimodal graph learning has recently emerged as an effective paradigm for in corporating inter-entity relationships into multimodal representations. E