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Filtered Conformal Ellipsoids for Graph-Native Time Series

arXiv:2606.17014v2 Announce Type: replace Abstract: Joint prediction sets for multivariate time series should control a single event while adapting to cross-coordinate dependence. We study filtered co

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Human DGX agent

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Find Something You Can't Do: Agentic Real-World Reinforcement Learning for Self-Improving VLA Models

DGX agent

arXiv:2609.32069v1 Announce Type: new Abstract: Vision--language--action (VLA) models provide strong priors for robotic manipulation but are typically deployed as frozen policies, unable to improve fr

safetyarxiv-cs-ro

29 Sept 2026

Agents

FINE: Future-Informed Navigation Encoding for Data-Efficient Vision-Language Navigation

arXiv:2609.32855v1 Announce Type: cross Abstract: Adapting vision-language navigation (VLN) policies to new environments is expensive because every additional route and instruction requires an embodie

agentsarxiv-cs-cv

Fine Until Fine-Tuned: Repeated Solutions Make Reasoning Fragile

arXiv:2609.33559v1 Announce Type: new Abstract: Recipes such as s1 and LIMO teach a model to reason with little data by showing it the same thousand or fewer worked solutions many times over. Judged w

FINGR: Learning Dexterous Hand Control for Real-World Rubik's Cube Solving

arXiv:2609.33973v1 Announce Type: new Abstract: Manipulating a Rubik's Cube with a single dexterous hand is a challenging test of sustained, contact-rich control: the hand must execute successive laye

safetyarxiv-cs-ro

Finite Probes Suffice: Identifiability and Universality for Weight-Space Learning

arXiv:2609.33901v1 Announce Type: new Abstract: Learning properties of neural networks has recently attracted growing interest, with existing approaches operating either directly on network parameters

Finite-Time Concentration and Convergence Rates for Projected Two-Time-Scale Stochastic Approximation with Markov Noise

arXiv:2609.34791v1 Announce Type: cross Abstract: We study finite-time concentration and convergence rates for projected two-time-scale stochastic approximation driven by a controlled Markov chain. Th

Fiona: Accelerating FHE Inference with Packing-Aware Ternary Weights

arXiv:2609.35352v1 Announce Type: new Abstract: Fully homomorphic encryption (FHE) enables neural network inference directly on encrypted inputs, but it remains orders of magnitude slower than plainte

First Learn, Then Memorize: The Spectral Bias of Diffusion Models

arXiv:2609.35377v1 Announce Type: new Abstract: Diffusion models trained on a finite dataset first learn to generate novel, high-quality samples and only much later collapse onto their training set. W

Fisher-Informed Recalibration for Feedback-Based On-Policy Self-Distillation of LLMs

arXiv:2609.34009v1 Announce Type: new Abstract: Feedback-based on-policy self-distillation has emerged as a promising approach for enabling foundation models, more specifically Large Language Models (

Fisher Simplicity in Kolmogorov-Arnold Networks and Multilayer Perceptrons

arXiv:2609.32503v1 Announce Type: new Abstract: Kolmogorov-Arnold Networks (KANs) are motivated in part by interpretability: their learned edge functions can be inspected, pruned, and reduced to symbo

Hardware

FLARE: Flow Matching with Local Axis-Angle Representations for Stochastic Micromagnetic Evolution

arXiv:2609.34070v1 Announce Type: new Abstract: Long-horizon micromagnetic simulation remains expensive because conventional and learned solvers typically propagate Landau--Lifshitz--Gilbert (LLG) dyn

hardwarearxiv-cs-lg

Tutorials

Flat-Consensus Diffusion for Robust Data Reshaping under Noisy Evaluator

arXiv:2609.32696v1 Announce Type: cross Abstract: Data shape determines how features are structured, how patterns are separated, and how distributions cover the underlying domain. Poor data shape can

tutorialsarxiv-cs-lg

FlexiWorld: Learning and Planning via Flexible Action Chunks Across Multiple Time Scales

arXiv:2609.35138v1 Announce Type: new Abstract: Latent world models predict future states for goal-directed planning using action chunks spanning multiple primitive steps. Existing methods typically u

FlexLoop: Depth-Elastic Looped Policies for Adaptive Test-Time Computation in Deep RL

arXiv:2609.34488v1 Announce Type: new Abstract: Looped architectures scale computation by reusing the same parameters across recurrent steps, and recent work shows that they substantially improve deep

Applications

FloodDiffusion 2: Efficient and Path Controllable Streaming Motion Generation

arXiv:2609.33167v1 Announce Type: new Abstract: We present FloodDiffusion 2 (FD2), an efficient and controllable framework that builds upon FloodDiffusion (FD1), a state-of-the-art streaming motion ge

applicationsarxiv-cs-cv

Flow-Matching-Based Protein Structure Tokenizer Made Efficient and Easy

arXiv:2609.33129v1 Announce Type: new Abstract: As the bridge between protein modality and discrete modeling, protein structure tokenization still largely relies on heavily engineered training objecti

Agents

FlowAct-R2: Beyond Talking Avatar via Streaming Multimodal References and Proactive Agent Planning

arXiv:2609.35728v1 Announce Type: new Abstract: We present FlowAct-R2, a framework for interactive humanoid video generation that combines continuous multimodal control with proactive agent planning.

agentsarxiv-cs-cv

FlowTool: Controlling Tool Parameter in Image Retouching via Flow Matching

arXiv:2609.35673v1 Announce Type: new Abstract: Tool-based image editing (image retouching) is commonly formulated with autoregressive multimodal large language models (MLLMs) that sequentially genera

Focus and Supplement: Dual-Enhanced Vision Transformer for Multi-Class Anomaly Classification

arXiv:2609.33353v1 Announce Type: new Abstract: Multi-class anomaly classification in industrial vision remains challenging due to noisy/incomplete anomaly representations and the unknown number of an

FOCUS: Benchmarking Retinal Model Generalization from Foundation Vision Encoders to Multimodal LLMs

arXiv:2609.33158v1 Announce Type: new Abstract: Progress in AI-based retinal image analysis has advanced with foundation models, yet evaluating their reliability remains challenging. Performance repor

FOCUS: Fixed-Confidence Online Causal Learning Using Sequential Adaptive Interventions

arXiv:2609.32165v1 Announce Type: cross Abstract: We study fully online fixed-confidence causal discovery without any historical observational data. Starting from zero samples, the learner sequentiall

Focus on Likely Classes for Test-Time Prediction

arXiv:2505.03819v4 Announce Type: replace Abstract: We ask: Can focusing on likely classes of a single, in-domain sample improve accuracy? Prior work argued 'no', we answer 'yes' on average. Standard

Agents

FocusDrive: Reasoning with Visual Focus for Autonomous Driving

arXiv:2609.33190v1 Announce Type: new Abstract: Driving decisions depend on both where to focus and how to act on what is seen. Effective driving reasoning must establish which objects matter, where t

agentsarxiv-cs-cv

FoLD: Force-Informed Learning for Dexterous Articulated Object Manipulation

model-releasesarxiv-cs-ro

arXiv:2609.33551v1 Announce Type: new Abstract: Transferring human demonstrations to dexterous robots remains challenging because differences in hand morphology and contact dynamics often cause retarg

Hardware

FoldAttention: Declared-Reference Softmax for Fast Decode and Deterministic Backward

arXiv:2609.33410v1 Announce Type: new Abstract: Autoregressive decode repeatedly streams a growing KV cache, making attention a major cost at long context. Existing high-performance kernels use online

hardwarearxiv-cs-lg

Forecast-Necessary Causal Discovery for Nonlinear Political Panel Data: Feedback, Functional Form, and the Dynamics of Democratization

arXiv:2609.34115v1 Announce Type: cross Abstract: A non-significant coefficient in a dynamic panel model need not imply the absence of a relationship. It may instead reflect heterogeneous effects aver

Forecasting Bacterial Antimicrobial Resistance Trends Using Machine Learning on WHO GLASS Surveillance Data: A Retrieval-Augmented Generation Approach for Policy Decision Support

arXiv:2602.22673v3 Announce Type: replace Abstract: Background: Antimicrobial resistance (AMR) is a global health threat. While the WHO Global Antimicrobial Resistance and Use Surveillance System (GLA

Forecasting Intraday USD/CAD Exchange Rate with News-Derived Monetary-Policy Signals

arXiv:2609.32773v1 Announce Type: cross Abstract: Monetary-policy announcements and central-bank communications play a central role in foreign exchange markets, yet their qualitative, unstructured for

ForeFly: A Dual-Horizon World Action Model for Aerial Vision-Language Navigation

arXiv:2609.33581v1 Announce Type: new Abstract: Aerial Vision-Language Navigation (AVLN) requires UAVs to maintain reliable instruction following over long trajectories in complex 3D environments. How

ForensicZoom: Adaptive Visual Inspection with Multimodal LLMs for Industrial-Grade Face Forgery Detection

arXiv:2609.31661v1 Announce Type: new Abstract: Reliable face forgery detection is critical to the security of online identity verification systems, where missed attacks compromise security and excess

Forest-Guided Semantic Transport for Label-Supervised Manifold Alignment

arXiv:2602.00974v2 Announce Type: replace Abstract: Label-supervised manifold alignment bridges the gap between unsupervised and correspondence-based paradigms by leveraging shared label information t

FORGE: Form-Optimal Routing of Grounded Evidence for Frozen LLM Agents

arXiv:2609.34358v1 Announce Type: new Abstract: In agentic AI systems, frozen foundation models are increasingly deployed as closed-weight API endpoints, making downstream adaptation possible only thr

ForVis: An In-Field Dataset and Benchmark for VIO Using Under-Canopy UAV Flights in Forests

model-releasesarxiv-cs-ro

arXiv:2609.35482v1 Announce Type: new Abstract: Visual-inertial Simultaneous Localization and Mapping (VI-SLAM) for UAVs remains difficult to evaluate in real forest environments, where motion, illumi

FoundDSR: A Generalizable Foundation Model with Guided 2D Gaussian Splatting for Depth Super-Resolution

arXiv:2609.32323v1 Announce Type: new Abstract: We introduce FoundDSR, a generalizable foundation model for robust depth reconstruction across unseen data distributions using RGB-D pairs. FoundDSR beg

safetyarxiv-cs-cv

Frequency-Domain AI-Generated Image Detection: Exploring Decoder and Channel Attention for Feature Refinement

arXiv:2609.31723v1 Announce Type: new Abstract: With the rapid progress of AI, the number of AI-generated images has increased significantly in recent years. However, the increasing variety of image g

From Attention Sensitivity to Layer Role: Revisiting Mixed-Precision Quantization of Transformers

arXiv:2609.34866v1 Announce Type: new Abstract: Most post-training quantization pipelines fit each weight matrix to its pretrained counterpart, one matrix at a time. Whether that proxy tracks what an

From Constitutions to Control: Interpretable Rewards for Aligning Language Models

arXiv:2609.33086v1 Announce Type: new Abstract: Current approaches to aligning language models often make it hard to know what behavior is being rewarded or to change that reward in a targeted way. In

From Data to Program: Fast & Direct Generative Program Inference from Empirical Data

arXiv:2609.35348v1 Announce Type: new Abstract: Estimating probability densities from a finite set of samples typically requires dataset-specific model fitting. We introduce PRODiGI, a pretrained data

From Distributions to Stochastic Processes: Neural Approximation of Measure-Valued Maps

arXiv:2609.33649v1 Announce Type: new Abstract: Learning mappings between probability distributions arises naturally when inputs and outputs are represented by populations of samples rather than indiv

From Dual Tracking to Clipping: Provably Faster Distributionally Robust Multi-Objective Optimization

arXiv:2605.05660v2 Announce Type: replace Abstract: Multi-objective optimization (MOO) has received growing attention in applications that require learning under multiple criteria. However, most exist

Hardware

From Experience to Expertise: Adoption-Aware Memory Learning for Data-Scarce NPU Kernel Synthesis

arXiv:2609.35568v1 Announce Type: new Abstract: High-performance kernels underpin efficient accelerator execution but require expert tuning and lengthy manual optimization cycles. LLM coding agents pr

hardwarearxiv-cs-lg

From Feed-Forward to Flow: Unifying Reconstruction and Generation Is Easier Than You Think

arXiv:2609.32761v1 Announce Type: new Abstract: Reconstruct where the images provide evidence, and generate where they do not: recent success of spatial world models such as Atlas (World Labs Team, 20

From Grey-Box to Green-Box: When can Physics-Informed Machine Learning Reduce Carbon Footprints in Structural Health Monitoring?

arXiv:2609.33387v1 Announce Type: new Abstract: Machine learning plays an increasingly vital role in engineering, but the corresponding increase in compute time is not without environmental cost. Phys

From HL to H+L-1 Parameters: A Hankel-Toeplitz Forecaster for Long-Term Time Series Forecasting

arXiv:2609.33984v1 Announce Type: new Abstract: Linear forecasters have shown competitive accuracy against Transformer-based models in long-term time series forecasting. We study how classical station

Tutorials

From internal representations to model improvement through prediction errors

arXiv:2609.35449v1 Announce Type: new Abstract: With limited annotation budgets, choosing which images to label determines how much a model improves. Data-selection methods that use features from a se

tutorialsarxiv-cs-cv

From Knowing to Abstaining: Bridging the Representation-Action Gap in Vision-Language Models

arXiv:2609.32653v1 Announce Type: cross Abstract: The ability of vision-language models (VLMs) to abstain from unanswerable questions is as important as their ability to answer answerable ones accurat

From Language to Task Maps: Compiling Semantic Relations While Preserving Task-Relevant Freedom

arXiv:2609.34412v1 Announce Type: new Abstract: Natural-language manipulation instructions specify qualitative relations, whereas continuous controllers require state-evaluable task quantities, differ

researcharxiv-cs-ro