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FestDPO: Few-step Generator Alignment with Direct Preference Optimization

arXiv:2609.34673v1 Announce Type: new Abstract: Few-step generative models can generate high-fidelity samples within a few function evaluations. Despite this efficiency, generated samples may not exhi

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Fewer Tokens, More Self-Teaching: On-Policy Self-Distillation for Extreme Visual Token Reduction

DGX agent

arXiv:2609.32353v1 Announce Type: new Abstract: Visual token reduction is an effective way to accelerate multimodal large language models (MLLMs), but performance deteriorates rapidly under extremely

safetyarxiv-cs-cv

29 Sept 2026

Fiber-Normalized Manipulability and Determinant Proxies: Intrinsic Redundancy Optimization Across and Within Task Fibers

arXiv:2609.32079v1 Announce Type: new Abstract: This work establishes that determinant-based manipulability is an exact objective for fixed-task redundancy optimization, despite its dependence on task

researcharxiv-cs-ro

FIDAL: Diversity-Aware Federated Active Learning Under Real-World Distribution Shifts

arXiv:2609.31637v1 Announce Type: cross Abstract: Federated learning enables collaborative model training across institutions without centralizing data, yet high annotation costs, domain shifts, and c

FILIGREE3D: Scaling Sparse Latent Flow Matching for Ultra-High-Resolution Image-to-3D Generation

arXiv:2609.34900v1 Announce Type: new Abstract: Scaling image-to-3D generation to ultra-high resolutions requires controlling rapidly growing computational costs without sacrificing fine geometric det

hardwarearxiv-cs-cv

Fill2SR: Repurposing Inpainting Diffusion Transformers for Real-World Super-Resolution

arXiv:2609.33582v1 Announce Type: new Abstract: Recent real-world image super-resolution (SR) methods often adapt text-to-image (T2I) backbones with ControlNet-style branches or spatial conditioning t

hardwarearxiv-cs-cv

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

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

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

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

Fingerprint expands device intelligence for the AI-driven web

Artificial intelligence is changing the makeup of internet traffic, making AI identity an emerging concern for websites and applications. They are no longer interacting only with humans and traditiona

agentssiliconangle

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 few days of qwen3.8-flash-next on 4x R9700 - it's been really interesting so far

Here's a metric dashboard giving an idea of the last few days. Been testing with a variety of different agentic coding use-cases, mostly using a pi harness. qwen3.8-flash-next has seriously exceeded m

agentsr-localllama

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

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

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

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

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

researcharxiv-cs-cv

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

researcharxiv-cs-cv

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

Local Ai

Forlinx 20-TOPS M.2 AI accelerator supports PCIe cascading for local LLM inference

Forlinx Embedded has listed an M.2 AI accelerator card based on Rockchip’s RK1820 and RK1828 processors, providing 20 TOPS of INT8 computing performance and up to 5GB of integrated DRAM. The module us

local-air-localllama

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

researcharxiv-cs-cv

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