Search: “tutorials”

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

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Variational Quantum Attention for Molecular Graph Learning

DGX agent

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

tutorialsarxiv-cs-lg

6 Oct 2026

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

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

PAPER2LLM++: Continual Self-Evolution of LLMs from Research Papers

arXiv:2610.02793v1 Announce Type: new Abstract: Research on LLMs continually uncovers model limitations, their causes, and potential solutions. Yet these human discoveries remain largely disconnected

PointWAM: 3D World Action Modeling for Dexterous Robotic Manipulation

arXiv:2610.02840v1 Announce Type: cross Abstract: World action models jointly learn to forecast world dynamics and predict robot actions, such that the learned internal world dynamics guide accurate a

Post-Training Frontier Text-to-Image Models by Composing Preference and Rubric Rewards

arXiv:2610.02967v1 Announce Type: cross Abstract: Recent text-to-image generation models have achieved remarkable visual quality, but improving them through post-training remains challenging because n

Reliable Self-Evolution with Imperfect Proxy Rewards

arXiv:2610.02975v1 Announce Type: new Abstract: Large language model (LLM)-based self-evolving search is a promising approach to scientific discovery. However, high-fidelity evaluation of every candid

SDECast: Probabilistic Weather Forecasting in Continuous Time with Neural SDEs

arXiv:2610.03313v1 Announce Type: cross Abstract: Existing machine learning weather forecasting models typically generate forecasts through autoregressive rollouts at a fixed temporal resolution. Whil

The AI Risk Observatory: What Can We Learn from AI Disclosures in Annual Reports About Societal Resilience?

arXiv:2610.02281v1 Announce Type: new Abstract: Societal resilience research relies on access to useful and actionable data, which motivates our main research question: Can annual reports, processed a

The Fragility of Trigger-Tag Mechanisms for Misuse Detection in Open-Weight LLMs

arXiv:2610.03124v1 Announce Type: cross Abstract: Open-weight language models can be downloaded, modified, and deployed beyond their developers' control, limiting the effectiveness of centrally enforc

Toward Controlling Biology with Language:Offline Learning of Prompt-Conditioned Interventions for Cells, Organoids, and Biobots

arXiv:2610.02247v1 Announce Type: cross Abstract: Artificial intelligence increasingly serves as a natural-language interface to complex technical systems, letting people accomplish sophisticated task

Toward Omni Multimodal Graph Foundation Model: A Topology-Driven Binding Approach

arXiv:2610.02881v1 Announce Type: new Abstract: Multimodal graph foundation models (MGFMs) seek to learn generalizable representations from large-scale graphs with heterogeneous node modalities. Howev

Validated Data Onboarding for AI Demand Forecasting on U.S. Building Meter Data: Design, Controlled Evaluation, and a Corrected Negative Result

arXiv:2610.02397v1 Announce Type: new Abstract: Electric utilities and grid operators increasingly rely on machine-learning models to forecast next-day demand, and those models learn from meter data t

When History Fails to Become Experience: Action Calibration in Language Agents

arXiv:2610.02769v1 Announce Type: cross Abstract: Language agents should draw on prior attempts and environmental feedback to improve subsequent decisions within the same task. However, providing addi

Where to Look Is Not How to Fix: Pre-Denoising Diagnostics and Modality-Dependent Control in Diffusion Composition

arXiv:2610.03068v1 Announce Type: new Abstract: Understanding compositional failures in text-to-image diffusion requires identifying both where stress is detectable and how intervention changes the ou

World Action Learning via Interaction-Centric Spectral Latent Guidance

arXiv:2610.03607v1 Announce Type: new Abstract: Learning general-purpose robot policies requires large-scale real-world interaction data, yet collecting robot demonstrations remains expensive and diff

tutorialsarxiv-cs-ro

XGenAct: Geometry-Enhanced World Action Models through Cross-Task Generation

arXiv:2610.03516v1 Announce Type: cross Abstract: World action models (WAMs) have advanced robot control by predicting how observations and actions evolve over time. Despite this progress, RGB and act

A Large Scale Investigation of Scaling Limits in Chemical Language Models

arXiv:2508.13408v3 Announce Type: replace Abstract: Chemical Language Models (CLMs) are increasingly used in de novo drug design, driven by recent growth in model scale, compute, and dataset size. How

ABDA-NL: A Natural-Language Scenario Explorer for Argument-Based Reasoning

arXiv:2610.00947v1 Announce Type: new Abstract: ABDA-NL adds a natural-language interface to ABDA, a system for argument-based discussion using ASPIC- knowledge bases under grounded semantics. Users s

Backdoor Containment via Expert Quarantine and Shutdown in LLMs

arXiv:2610.00663v1 Announce Type: new Abstract: Backdoored large language models (LLMs) can behave normally on benign inputs while producing attacker-specified outputs under hidden triggers. Existing

Distillation of Tabular Foundation Models into Efficient Predictors

arXiv:2610.01435v1 Announce Type: new Abstract: Tabular foundation models (TFMs) achieve strong predictive performance through in-context learning, yet repeatedly conditioning on labeled data makes in

Efficient Generative Modeling beyond Memoryless Diffusion via Adjoint Schrodinger Bridge Matching

arXiv:2602.15396v2 Announce Type: replace Abstract: Diffusion models often yield highly curved trajectories and noisy score targets due to an uninformative, memoryless forward process that induces ind

EndoLive: Real-Time Style Transfer for Endoscopic Endonasal Skull Base Surgical Video

arXiv:2610.01956v1 Announce Type: new Abstract: Complex surgical procedures around critical anatomy, such as the endoscopic endonasal skull base surgery, requires significant practice and training on

EvoGen-Harness: Learning Where and How to Evolve Image-Generation Harnesses

arXiv:2610.00383v1 Announce Type: new Abstract: Modern text-to-image (T2I) systems can be improved without modifying generator parameters by adapting the external system around frozen generators. Howe

Exploring More, Reasoning Better: Stepwise Risk-Sensitive GRPO for Diffusion Language Models

arXiv:2610.00661v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) generate text by denoising a sequence or successive blocks, allowing several tokens to be revealed in parallel

FlashBack: Knowing When to Remember in Streaming Vision-Language Models

arXiv:2610.01192v1 Announce Type: new Abstract: Streaming vision-language models must process continuously growing video streams under a bounded compute budget, creating a persistent tension between r