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Explainability from Training with Applications to TCR-Epitope Prediction

arXiv:2609.36354v1 Announce Type: cross Abstract: Deep learning models have achieved strong performance in artificial intelligence for science, yet their black-box nature limits our understanding of h

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Explainable Machine Learning for Multilayer Planar Winding Inductance Estimation

DGX agent

arXiv:2609.37211v1 Announce Type: new Abstract: Rapid and accurate self-inductance estimation for multilayer rectangle-shaped planar windings is essential for modern high-frequency power converters, y

model-releasesarxiv-cs-lg

30 Sept 2026

Explore Broadly, Reason Sharply: Push Small Models toward the Frontier via Sampling

model-releasesarxiv-cs-lg

arXiv:2609.38104v1 Announce Type: new Abstract: Power-sharpened sampling is an inference-time alternative to reinforcement-learning (RL) post-training for enhancing reasoning in large language models

Agents

Explore, Execute, Evolve: A Skill Acquisition and Reuse Loop for Embodied Agents

arXiv:2609.37810v1 Announce Type: cross Abstract: Vision-language-action and world-action models have demonstrated impressive capabilities in robotics, yet generalization to unseen tasks remains chall

agentsarxiv-cs-ai

Exploring In-Context Learning for Handwritten Text Recognition

model-releasesarxiv-cs-cv

arXiv:2609.37195v1 Announce Type: new Abstract: Handwritten Text Recognition (HTR) systems have become an indispensable tool for the digitization of historical documents. Not only do they cut down tim

Exploring Learning Models for Topological Relationship Recognition from Image Data

arXiv:2609.36172v1 Announce Type: new Abstract: Figuring out how objects relate to each other, like whether they touch, overlap, stay completely separate or one sits inside another, matters a lot in f

Exponential Convergence of Deep Operator Networks for Elliptic Partial Differential Equations

arXiv:2112.08125v3 Announce Type: replace-cross Abstract: We construct and analyze approximation rates of deep operator networks (ONets) between infinite-dimensional spaces that emulate with an expone

researcharxiv-cs-lg

FACT: Fidelity-Aware Construction of Articulated Twins

arXiv:2609.37067v1 Announce Type: cross Abstract: Visually plausible articulated assets may still fail during contact interactions or exhibit inaccurate motion. We present FACT (Fidelity-Aware Constru

Factorized Scheduling Principle: Learning Interpretable and Transferable Policies via Structured Additive Functions

arXiv:2609.36578v1 Announce Type: cross Abstract: Scheduling problems arise from repeatedly selecting one item from a set of candidates based on their states. These problems often reduce to assigning

Fair Policy Optimization in Major-Minor Weakly Coupled Markov Decision Processes

arXiv:2609.36174v1 Announce Type: new Abstract: We consider fair resource allocation in sequential decision-making environments modeled as major-minor weakly coupled Markov decision processes (M2WCMDP

FairDiff: Mitigating the Self-Reinforcing Matthew Effect in Diffusion Recommender Models

arXiv:2609.36671v1 Announce Type: new Abstract: While the 'Matthew Effect' and filter bubbles are widely recognized outcome-level biases in recommender systems, we reveal that Diffusion Recommender Mo

Faster and Better? Benchmark Bugs and Design Limitations Distort the Evaluation of Vision-Language-Action Acceleration

model-releasesarxiv-cs-ro

arXiv:2609.37771v1 Announce Type: new Abstract: Simulated manipulation benchmarks are the standard tool for evaluating vision-language-action (VLA) policies and the acceleration methods that reduce th

FastGuide: Accelerating Reward Guidance for Diffusion Large Language Models

arXiv:2609.36202v1 Announce Type: new Abstract: Gradient-based reward guidance provides a flexible way to use downstream reward models to control masked diffusion language models at inference time. Ho

researcharxiv-cs-cl

Hardware

FastVR: Efficient Streaming Video Restoration with One-Step Diffusion

arXiv:2609.36757v1 Announce Type: new Abstract: Diffusion-based video restoration recovers realistic details, but its practical deployment is limited by two efficiency bottlenecks: costly VAE encoding

hardwarearxiv-cs-cv

FD-AA: A Lightweight Focal-Diffuse And Attenuation-Aware Head for Incidental Abdominal Abnormality Detection in Chest CT

arXiv:2609.36189v1 Announce Type: new Abstract: Routine chest CT captures upper-abdominal structures that may contain clinically relevant incidental abnormalities. Detecting these findings requires fe

FD-VAD: Semantic Endpoint Detection for Streaming Full-Duplex Speech

model-releasesarxiv-cs-cl

arXiv:2609.35791v1 Announce Type: new Abstract: Natural turn-taking in full-duplex voice interaction requires determining from partial speech whether a pause reflects hesitation or a completed convers

Federated Clustering with Unknown Local and Global Cluster Cardinalities

arXiv:2609.36762v1 Announce Type: new Abstract: Federated clustering methods that do not require the global number of clusters K still assume that each client knows its local number K_g. This assumpti

researcharxiv-cs-lg

Hardware

Federation of Experts: Communication Efficient Distributed Inference for Large Language Models

arXiv:2605.06206v2 Announce Type: replace Abstract: Mixture of experts has emerged as the primary mechanism for making Large Language Models (LLMs) computationally efficient. However, in distributed s

hardwarearxiv-cs-lg

FedLAFP: Low-Rank Aggregation Meets Full-Rank Personalization in Federated Fine-Tuning

arXiv:2609.37033v1 Announce Type: new Abstract: Federated parameter-efficient fine-tuning enables clients to adapt pre-trained models without sharing raw data or communicating the full model, but stat

Local Ai

FedSocket: Recipient-Executable Knowledge Exchange for Heterogeneous Multimodal Federated Learning

arXiv:2609.37582v1 Announce Type: cross Abstract: Federated knowledge must remain usable by recipients with different modalities, private architectures, and tasks. We present FedSocket, which makes re

local-aiarxiv-cs-ai

Feedback-Calibrated Protein Optimization with Batch-Aligned Tail Arbitration

arXiv:2609.37808v1 Announce Type: new Abstract: Protein optimization aims to discover high-fitness sequences under a limited experimental budget. Existing machine-learning methods use task-specific pr

FigAct: Turning Scientific Figures into Active Canvases for Explanation

arXiv:2609.36190v1 Announce Type: new Abstract: Scientific figures are designed to communicate information visually, yet MLLMs typically explain them by translating their visual content back into text

Fine-Tuning on Self-Generated and Reward-Weighted Data: Learning Dynamics, Convergence Rates, and Benefits of Off-Policyness

arXiv:2609.36945v1 Announce Type: new Abstract: We study the learning dynamics of fine-tuning a policy model on self-generated and reward-weighted data, with particular focus on a generalized version

FineART: Fine-grained Annotated Robotic Trajectory Dataset and Vision-Language-Action Model for Bimanual Manipulation

arXiv:2609.36416v1 Announce Type: cross Abstract: Robots operating in real-world environments must execute complex, multi-step bimanual tasks over long horizons rather than single, isolated actions. C

FineSID: Scalable and Efficient Semantic Identifier Learning for Generative Recommendation

arXiv:2609.36670v1 Announce Type: new Abstract: A critical prerequisite of generative recommendation is designing semantic identifiers (SIDs) that are both scalable to large item sets and efficiently

Finite-Sample Theory for Fitted Q-Iteration When Actions Are Functions

arXiv:2609.36390v1 Announce Type: cross Abstract: Offline reinforcement learning seeks optimal decision rules from previously collected data. In some applications, a decision can be an entire function

FinRT: Distilling Adaptive Red-Teaming Strategies into Reusable Adversarial Generators in Consumer Finance

arXiv:2609.36474v1 Announce Type: new Abstract: In regulated industries like consumer finance, seemingly harmless user queries can exploit large language model vulnerabilities, triggering safety failu

safetyarxiv-cs-cl

Fisher-IRG: Fisher-Induced Local Invariant Representation Geometry across Language and Vision Models

arXiv:2609.36458v1 Announce Type: cross Abstract: Semantic-preserving transformations can induce substantial motion in learned representations, while small changes may strongly affect model prediction

researcharxiv-cs-cl

FLASH: A 'Generate Once, Synthesize Many' Framework for Synthetic Anomaly Generation in Industrial Anomaly Detection

arXiv:2609.37314v1 Announce Type: new Abstract: Synthetic anomaly generation helps expand industrial anomaly datasets when real defects are scarce or unavailable. Existing approaches lie at two extrem

Flattening the Connectome Spectrum: A Spectral Filter for FC Induces a Pretraining Target for fMRI Encoders

arXiv:2609.37642v1 Announce Type: new Abstract: Self-supervised pretraining reshaped prediction in language and vision, and brain foundation models (BFMs) inherited its promise. Representations learne

researcharxiv-cs-ai

Agents

FloCA: Towards Faithful and Logically Consistent Flowchart Reasoning

arXiv:2602.14035v2 Announce Type: replace Abstract: Flowchart-oriented dialogue (FOD) systems aim to guide users through multi-turn decision-making or operational procedures by following a domain-spec

agentsarxiv-cs-ai

FLOORA: A Human-Aligned Domain-Specific Language Model for Architectural Design

arXiv:2609.36064v1 Announce Type: cross Abstract: Foundation models are powerful generators, but many engineering domains require structured representations that general-purpose systems handle poorly.

FlowMap-OPD: Rollout--Kernel Separation for On-Policy Distillation of Few-Step Flow-Map Generators

arXiv:2609.37851v1 Announce Type: new Abstract: Few-step flow-map generators, including MeanFlow and consistency models, enable efficient sampling through long-range transport, yet their on-policy dis

safetyarxiv-cs-cv

FluxLite: Inference-Time Proposal Control for Discrete Diffusion Models

model-releasesarxiv-cs-lg

arXiv:2609.35947v1 Announce Type: new Abstract: Many inference-time tasks for pretrained discrete diffusion models and diffusion language models reduce to drawing samples from a tilted version of the

Local Ai

FM-ReID: Selective Competitive Token Routing for Object Re-Identification

arXiv:2609.36560v1 Announce Type: cross Abstract: Object re-identification (ReID) faces a recurring challenge: different identities can share highly similar global appearances, while the cues that dis

local-aiarxiv-cs-ai

FOCUS: Training-Free Decision-Preserving Context Compression for LLM Agents

arXiv:2609.37590v1 Announce Type: new Abstract: LLM agents accumulate interaction histories that grow linearly with task length, causing quadratic inference cost scaling and performance degradation fr

Local Ai

FocusVTC: Efficient and High-Performance Visual Text Compression with Adaptive Resolution

arXiv:2609.36651v1 Announce Type: cross Abstract: Long-context reasoning in large language models incurs substantial computation and memory costs. Visual text compression (VTC) reduces input length by

local-aiarxiv-cs-ai

Follow the Entities: A Corpus Map for Agentic Search

arXiv:2609.37226v1 Announce Type: cross Abstract: Answering questions and completing tasks over large document collections often requires connecting evidence spread across multiple documents, such as

Fooling Algorithms in Non-Stationary Bandits using Belief Inertia

arXiv:2511.05620v2 Announce Type: replace Abstract: We study worst-case dynamic regret of specific multi-armed bandit algorithms on piecewise-stationary instances with at most one breakpoint. Our cons

researcharxiv-cs-lg

Applications

Foresight at the Event Boundary: Evaluating Physical Prediction in Video World Models

arXiv:2609.36531v1 Announce Type: new Abstract: Video world models are largely regarded as predictive models of the physical world and are therefore expected to anticipate the consequences of observed

applicationsarxiv-cs-cv

FORM: Robot Manipulation through Direct Material Law Identification

arXiv:2609.38105v1 Announce Type: new Abstract: When interacting with an unfamiliar deformable material, a robot lacks prior knowledge of its physical properties and how it will respond to applied for

researcharxiv-cs-ro

Formalizing the Sampling Design Space of Diffusion-Based Generative Models via Adaptive Solvers and Wasserstein-Bounded Timesteps

arXiv:2602.12624v2 Announce Type: replace-cross Abstract: Diffusion-based generative models have achieved remarkable performance across various domains, yet their practical deployment is often limited

FORUM: Frozen Outputs Reconciled Using Model Agreement for Visual Grounding

model-releasesarxiv-cs-cl

arXiv:2609.37488v1 Announce Type: cross Abstract: Frozen multimodal large language models (MLLMs) now solve standard referring expression comprehension with a single prompted call, yet on adversarial

Foundation-Model-Guided Topology-Aware Semantic Risk Fields for Manipulation

arXiv:2609.36640v1 Announce Type: new Abstract: Robot motion planning in everyday environments must satisfy hard geometric constraints while accounting for context-dependent semantic risk. We present

safetyarxiv-cs-ro

Foundation Neural-Network Quantum States for Molecular Potential Energy Surfaces in Second Quantization

arXiv:2609.37733v1 Announce Type: cross Abstract: Second-quantized neural-network quantum states have achieved accurate molecular energies, but extending them across molecular geometries requires a sh

Agents

Foundations of Proactive Agents: Principles, Technical Layers, and Proactivity-Gym

arXiv:2609.37267v1 Announce Type: new Abstract: Proactive LLM agents can turn idle compute into useful support before users ask. Yet even correct work can misread user context, impose review costs, or

agentsarxiv-cs-ai

FP2: Equipping Robotic Foundation Models with Force Control

arXiv:2609.37433v1 Announce Type: new Abstract: Robotic foundation models (RFMs) are increasingly capable of general-purpose manipulation, yet reliable physical interaction remains challenging in cont

safetyarxiv-cs-ro

FracGen: Learning How Objects Stretch and Tear with Physics-Informed Video Generation

model-releasesarxiv-cs-cv

arXiv:2609.38152v1 Announce Type: new Abstract: We introduce FracGen, a fracture-aware video generation model that produces plausible, controllable fracture dynamics from a single image of an intact o