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FlowWise: Stateful Fast-Slow Model Serving for Streaming Traffic Intelligence

arXiv:2402.03694v3 Announce Type: replace-cross Abstract: Network traffic analysis in cloud and internet service provider (ISP) networks increasingly relies on machine learning (ML) models, as encrypt

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Human DGX agent

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Focus, Align, and Diffuse: Time-Series-Aware Keyframe Diffusion for Cardiac Dynamic Synthesis from Sparse Observations

DGX agent

arXiv:2610.19050v1 Announce Type: cross Abstract: Dense cardiac dynamics are essential for assessing cardiac function, yet in practice clinicians often observe only extremely sparse measurements. Howe

safetyarxiv-cs-cv

15 Oct 2026

FOLD: Position-Free Far Memory with Local Order for Long-Context Speculative Decoding

arXiv:2610.20392v1 Announce Type: new Abstract: Speculative decoding accelerates LLM generation through lightweight drafting and parallel verification. It is particularly promising for long inputs, wh

Following the Flow the Law Requires: Training Voice Agents to Transfer to Unseen Regulated Procedures in Debt Collection

arXiv:2610.18881v1 Announce Type: new Abstract: Voice agents built on large language models (LLMs) now handle customer calls, but in regulated applications completing the task is not enough: the agent

FOMI: A Force-Feedback Manipulation Interface for Teaching Contact-Rich Manipulation

arXiv:2610.19839v1 Announce Type: new Abstract: Robot-free collection with handheld interfaces enables scalable acquisition of human demonstrations for policy learning. However, existing handheld inte

safetyarxiv-cs-ro

FON: A Draft-Revise Dual-Organ Language Model without Sequence-Level Quadratic Attention

arXiv:2610.16301v1 Announce Type: new Abstract: Self-attention assigns two functions to one mechanism-where to read and how to mix-at the price of O(L^2) training, an O(T) decode state, and position s

ForceBench: Benchmarking Force Integration for Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2610.19604v1 Announce Type: new Abstract: Force feedback provides vision-language-action (VLA) policies with physical observations that can guide action selection and execution. However, differe

Forget About It: Mitigating Forgetting Without Replay or Auxiliary Objectives

arXiv:2501.05559v2 Announce Type: replace Abstract: Have we made mitigating catastrophic forgetting more complicated than it needs to be? Continual adaptation often degrades performance on earlier tas

Tutorials

ForkGRPO: Efficient Exploration and Fine-Grained Credit Assignment for Diffusion Language Models

arXiv:2610.19408v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) have advanced rapidly, yet strengthening their reasoning through reinforcement learning (RL) remains an open cha

tutorialsarxiv-cs-cl

FoveaVLA: Foveation Actions Replace the Wrist Camera in VLAs

arXiv:2610.20064v1 Announce Type: new Abstract: We present FoveaVLA, a Vision-Language-Action (VLA) model that replaces the wrist camera with learned foveation actions. Wrist cameras provide close-up

safetyarxiv-cs-ro

Agents

FPGA-Based Neural Network Accelerators for Space Applications: A Survey

arXiv:2504.16173v4 Announce Type: replace-cross Abstract: Space missions are becoming increasingly ambitious, necessitating high-performance onboard spacecraft computing systems. In response, field-pr

agentsarxiv-cs-ai

FPTAS for Polygonal Multi-Robot Motion Planning

arXiv:2610.20244v1 Announce Type: cross Abstract: We study multi-robot motion planning in a polygonal environment with polygonal robots. The setting of the problem consists of a polygonal domain W wit

researcharxiv-cs-ro

Local Ai

FPV-CRASH: A Dataset for Offline Crash Moment Localization in Drone First-Person-View Videos

arXiv:2610.19785v1 Announce Type: new Abstract: Crash Moment Localization aims to localize the major physical impact that interrupts controlled flight in an onboard first-person-view (FPV) video recor

local-aiarxiv-cs-cv

Fractional is Better: Learnable Derivative Orders in Neural Operator Learning

arXiv:2610.20363v1 Announce Type: new Abstract: Neural operators learn mappings between function spaces, enabling fast surrogate solutions to partial differential equations. Despite remarkable archite

Frame Evidence:A Benchmark for Reference Frames and Visual Evidence in Multi-Image Spatial Reasoning

model-releasesarxiv-cs-cv

arXiv:2610.19564v1 Announce Type: new Abstract: A spatial answer depends on the reference frame specified in language and its supporting visual evidence. Item accuracy alone obscures whether correctne

Framework for an AI-Ready ARPES Community Database

arXiv:2610.15236v1 Announce Type: cross Abstract: Condensed matter stands to gain profoundly from widespread adoption of artificial intelligence (AI). However, its current means of data organization i

Fresh at Decision, Stale at Action: Routing New Information to LLM Decisions Already Made

arXiv:2610.18126v1 Announce Type: new Abstract: Freshness shapes an LLM decision at the moment it is made; the action it commits to is latent, carried out later, in a changing world. We therefore intr

From Brewing to Resolution: Tracing the Internal Lifecycle of Code Reasoning in LLMs

arXiv:2606.17648v3 Announce Type: replace Abstract: Standard accuracy metrics cannot explain why LLMs handle variable tracking but fail on semantically equivalent loops. We study an internal lifecycle

From Caricature to Collective Artifact: How Persona Construction Drives Polarization in Generative Social Simulations

arXiv:2610.16498v1 Announce Type: cross Abstract: Generative Social Simulation (GSS) leverages Large Language Models (LLMs) to create sophisticated personas that define agents with demographics, emoti

Applications

From Chasing Ghosts to Missed Attacks: Perspectives and Perceptions of SOC Practitioners on LLM Integration, Risks, and Readiness

arXiv:2608.00672v2 Announce Type: replace-cross Abstract: Security Operations Centers (SOCs) process large volumes of security events, requiring analysts to accurately detect and assess ongoing cybera

applicationsarxiv-cs-ai

From Conceptual EneMonIE to an Executable Backend: A Modular Architecture for Energy Information Extractionand Grounded Question Answering

arXiv:2610.17243v1 Announce Type: cross Abstract: The original EneMonIE proposal describes a Web-based semantic system for continuously monitoring energy trends through targeted information extraction

From Copying to Composition: How Language Models Develop In-Context Learning

arXiv:2610.19113v1 Announce Type: new Abstract: In-context learning (ICL) allows language models to adapt their behavior from instructions and demonstrations in a prompt, but accuracy alone does not r

From Detection To Anticipation: Continuous Onset Forecasting For Stuttered Speech

arXiv:2610.19235v1 Announce Type: cross Abstract: Automatic stuttering systems primarily detect dysfluencies after they have occurred, limiting their use for anticipatory intervention. We address this

researcharxiv-cs-lg

From Efficiency to Sufficiency: The Cost-Aware Measurement Design Framework for Automated Essay Scoring

arXiv:2610.19287v1 Announce Type: cross Abstract: Compute cost in automated essay scoring (AES) is usually treated as a technical efficiency issue rather than a measurement design concern. Yet deploym

From Existing Methods to New Research Ideas: Two Perspectives on Recursive Self-Improvement

arXiv:2610.14625v1 Announce Type: new Abstract: Recursive self-improvement (RSI) offers a route to AI systems that participate in their own continued development. Recent methods allow language agents

From Failures to Learnable Worlds: Policy-Conditioned Environment Generation for Navigation Agents

model-releasesarxiv-cs-cv

arXiv:2610.19420v1 Announce Type: cross Abstract: Simulated environments enable scalable training data generation for embodied agents, but more data does not necessarily provide what a particular agen

From Global to Tail Convergence: Dynamics of alpha-Stable Score-Based Generative Models

arXiv:2610.19773v1 Announce Type: new Abstract: Score-based generative models driven by alpha-stable L{evy processes have been proposed as an alternative to Brownian diffusion models, with empirical e

researcharxiv-cs-lg

From Human Corrections to Learned Segmentations: A Critical Mathematical Review of Information, Learning, and Human Effort

arXiv:2610.20396v1 Announce Type: new Abstract: When does a human correction become useful learning, and what establishes better segmentation at matched human effort? This critical narrative review fo

safetyarxiv-cs-lg

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship

arXiv:2506.23101v2 Announce Type: replace-cross Abstract: Ensuring the trustworthiness of Multimodal Large Language Models (MLLMs) requires rigorous evaluation of gender bias, particularly in socially

Local Ai

From Plato to Aristotle: Toward a General Theory of Representation Hypotheses

arXiv:2610.20286v1 Announce Type: new Abstract: The Platonic Representation Hypothesis (PRH) holds that increasingly capable models converge toward a common representation. However, recent studies hav

local-aiarxiv-cs-lg

From Qualitative Text to Integrated Evidence: The Parallel-Then-Integrated (PTI) Procedure

arXiv:2610.19164v1 Announce Type: new Abstract: Mixed methods research offers established strategies for integrating qualitative and quantitative evidence, but less guidance for integrating two comput

From Relation-Specific Heads to Shared Interventions: Contextual Entrainment in Language Models

arXiv:2610.18674v1 Announce Type: cross Abstract: Large language models rely on prompt context to perform in-context learning, follow formats, and adapt to local evidence. This ability, however, also

From Spectral to Frobenius: When Muon Loses Its Spectral Implicit Bias

arXiv:2610.20112v1 Announce Type: new Abstract: Prior work on implicit bias shows that gradient descent selects the Frobenius max-margin direction on linearly separable problems, whereas Fan et al. (2

From Stages to States: Unified Iterative Reasoning for MLLMs

arXiv:2610.15620v1 Announce Type: new Abstract: Multimodal Chain-of-Thought (MCoT) substantially enhances the complex reasoning capabilities of Multimodal Large Language Models (MLLMs) by explicitly m

From Triage to Impact: A Multi-Hop Reasoning Benchmark for Explainable Persian Hate Speech

arXiv:2610.20370v1 Announce Type: new Abstract: Hate speech has become increasingly prevalent on social media, raising concerns about its detection, interpretation, and moderation. Persian, a relative

From Unreliable to Usable: Decomposing Failure Modes and Confidence Signals in LLM Entity Extraction - Evidence from Cyber Threat Intelligence

arXiv:2610.20417v1 Announce Type: new Abstract: Audits of large language model (LLM) extraction from full-length documents report severe unreliability, leaving two questions open: what the failures ac

Frustratingly Easy Agentic RAG via Portable Skills: A Case Study in Customer Support

arXiv:2610.14428v1 Announce Type: new Abstract: Enterprise workflow automation typically requires retrieving a complete and accurate set of relevant information from large operational document corpora

Frustratingly Easy Majority Voting Using Length Conditioning

arXiv:2610.19890v1 Announce Type: new Abstract: Majority Voting (MV) is a simple and widely used strategy which improves the reasoning accuracy of Large Language Models (LLMs) by allocating additional

safetyarxiv-cs-cl

Full-Order Safety for Nonlinear Reduced-Order MPC

model-releasesarxiv-cs-ro

arXiv:2610.19250v1 Announce Type: new Abstract: Reduced-order models (ROMs) make model predictive control (MPC) of high-dimensional nonlinear systems tractable, but the resulting modeling error can le

Fully sequential Gaussian process regression for preference learning

arXiv:2610.19391v1 Announce Type: new Abstract: We study continuous global optimization problems in which the objective function cannot be directly evaluated; rather, we observe only which of two sele

safetyarxiv-cs-lg

Function-Space Temporal Self-Distillation for Policy Optimization

arXiv:2610.19687v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) improves reasoning in language models, but scalar outcomes provide coarse supervision over respo

safetyarxiv-cs-cl

FUSE: Fairness-aware Update Screening against Byzantine Exploitation in q-Fair Federated Learning

arXiv:2610.20264v1 Announce Type: new Abstract: Client-level fair federated learning, in the form of q-fair federated learning (q-FFL), agnostic federated learning, or tilted empirical risk minimizati

Game-Creation-Agents: Autonomous Game Production

arXiv:2610.16776v1 Announce Type: new Abstract: Game-Creation-Agents (GCA) turns a natural-language request into a runnable Phaser 4 browser game. The pipeline first builds a multimodal game design do

GAMW: Generation-Aware Mel-Domain Watermarking for TTS Traceability

arXiv:2610.15082v1 Announce Type: cross Abstract: As text-to-speech (TTS) systems continue to advance, provenance tracking and security verification of synthesized speech have become increasingly impo

Gap Detection: Mitigating Homogenization through Demand and Defensibility

arXiv:2610.20162v1 Announce Type: new Abstract: Language models often produce semantically similar answers to open-ended questions, even when many plausible alternatives exist. Generating useful alter

GateSight: Recurrent Memory Anticipates Attention Memory for Query-Agnostic KV Eviction in Hybrid LLMs

arXiv:2610.14754v1 Announce Type: new Abstract: Hybrid language models interleave Gated DeltaNet (GDN) layers, whose recurrent memory has a fixed size, with full-attention layers, whose key-value (KV)

Applications

GBTSVR: Twin Support Vector Regression via Granular Ball Computing

arXiv:2610.20053v1 Announce Type: new Abstract: Regression models used in real-world applications are often challenged by noise, outliers, data heterogeneity, and asymmetric error characteristics, whi

applicationsarxiv-cs-lg

GDBA-Net: A Gated Dual-Branch Adaptation of SAM2 for Robust Cross-Domain Nuclei Segmentation

model-releasesarxiv-cs-cv

arXiv:2610.20073v1 Announce Type: new Abstract: Accurate nuclei segmentation underpins quantitative computational pathology, yet remains challenging due to morphological variability, weak boundaries,