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Flash-CNNCap: Capacitance Extraction via Image Mapping

model-releasesarxiv-cs-lg

arXiv:2607.23877v1 Announce Type: new Abstract: We present Flash-CNNCap, a CNN-based capacitance extractor that reformulates full-matrix capacitance prediction as image-to-image regression over spatia

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Flick: Few Labels Text Classification using K-Aware Intermediate Learning in Multi-Task Low-Resource Languages

DGX agent

arXiv:2506.10292v2 Announce Type: replace-cross Abstract: Training deep learning networks with minimal supervision has gained significant research attention due to its potential to reduce reliance on

researcharxiv-cs-ai

28 Jul 2026

FloAff-Kitchen: Bridging Navigation and Manipulation via Canonical and Progressive Floor Affordance Learning

model-releasesarxiv-cs-ro

arXiv:2607.24207v1 Announce Type: new Abstract: Mobile manipulation requires robots to identify Floor Affordance (FloAff) that maximizes downstream manipulation success rather than merely ensuring nav

Flow4R: Unifying 4D Reconstruction and Tracking with Scene Flow

arXiv:2602.14021v2 Announce Type: replace Abstract: Reconstructing and tracking dynamic 3D scenes is a fundamental challenge in computer vision. Existing methods typically decouple geometry from motio

FlowCTS: On-policy Continuous Trajectory Supervision of Flow Models

arXiv:2607.24522v1 Announce Type: cross Abstract: While on-policy distillation (OPD) effectively addresses sparse rewards and exposure bias in large language model post-training, its extension to flow

safetyarxiv-cs-cv

FMOPF: Latent Flow Matching with Constraint-Aware Interaction Priors for AC Optimal Power Flow

arXiv:2607.22788v1 Announce Type: cross Abstract: AC optimal power flow determines the minimum-cost generation dispatch under nonlinear power balance constraints and is solved thousands of times daily

Agents

Focus Is All You Need: Adaptive Goal-aware Attention Orchestration for Multi-Agent Graph Systems

arXiv:2607.23678v1 Announce Type: new Abstract: Large language models (LLMs) enable autonomous agents for reasoning, planning, and tool use. Recent systems increasingly organize these agents as graphs

agentsarxiv-cs-ai

FogDrive: A Multi-Modal Synthetic Driving Dataset for Perception under Graded Fog

arXiv:2607.22698v1 Announce Type: new Abstract: Perception under adverse weather remains a critical bottleneck for reliable autonomous driving, yet existing benchmarks lack the systematic multi-modal

Forecasting the Emergence and Evolution of Crash Hotspots: A Unified Deep Learning Framework for Proactive Traffic Safety

arXiv:2607.24168v1 Announce Type: new Abstract: Road crashes remain among the gravest threats to public safety, and preventing them is a defining task of transportation systems worldwide. Much of that

safetyarxiv-cs-lg

Forgetting is Everywhere

arXiv:2511.04666v4 Announce Type: replace Abstract: A fundamental challenge in developing general learning algorithms is their tendency to forget past knowledge as they adapt to new data. Addressing t

Formalizing Flag Algebras in Lean

arXiv:2607.23500v1 Announce Type: cross Abstract: Razborov's flag algebra method is a powerful tool for proving asymptotic inequalities in extremal graph theory, often reducing the task to finding a f

Formally Verified Synthesizable Floating-Point Data Types in ARCH HDL

arXiv:2607.23715v1 Announce Type: new Abstract: We report the design and end-to-end verification of first-class IEEE-754 binary32 (FP32) and bfloat16 (BF16) arithmetic for ARCH, a hardware description

researcharxiv-cs-cl

Foundation Models and Fine-Tuning: Toward a New Generation of Models for Time Series Forecasting

arXiv:2607.23146v1 Announce Type: new Abstract: Inspired by recent breakthroughs in large language models for natural language processing, foundation models have emerged as a promising paradigm for ze

Frequency-Aware Dual-Stream Learning for Balanced Realism and Fidelity in Electron Microscopy Imaging

arXiv:2607.22765v1 Announce Type: cross Abstract: Electron microscopy enables nanoscale cellular visualization but faces a trade-off between imaging resolution and acquisition speed. Existing learning

Frequency-Based Reservoir computing

arXiv:2607.24420v1 Announce Type: cross Abstract: Reservoir computing has emerged as an efficient machine learning framework for predicting time series generated by dynamical systems. In contrast to o

From Camera-Based Sensing to Reasoning: A Comprehensive Review Toward Proactive Vulnerable Road User Safety

arXiv:2510.03314v2 Announce Type: replace-cross Abstract: Ensuring the safety of vulnerable road users (VRUs), such as pedestrians and cyclists, remains a critical challenge, as conventional infrastru

Agents

From Cognitive Architectures to Language Agents: A Mechanism-Level Review of Lineage, Convergence, and Migration Gaps

arXiv:2607.23942v1 Announce Type: new Abstract: Memory, planning, reflection, and tool use are often compared as feature labels, obscuring the control semantics that determine how an agent actually ru

agentsarxiv-cs-ai

From Data to Device: ELMOD An Efficient German-First 2.7B Language Model for Mobile Inference

model-releasesarxiv-cs-cl

arXiv:2607.24585v1 Announce Type: new Abstract: We present ELMOD - Efficient Language Model for On-Device Deployment - a compact (2.7B) German language model designed for efficient inference on resour

From Execution to Capability: Scientific Experience Consolidation via Procedural Knowledge Synthesis

arXiv:2607.24459v1 Announce Type: new Abstract: Large language models increasingly solve scientific-computing tasks, but executable feedback from one problem rarely becomes durable capability on subse

From 'Help' to Helpful: A Hierarchical Assessment of LLMs in Mental e-Health Applications

arXiv:2602.18443v2 Announce Type: replace-cross Abstract: Psychosocial online counselling frequently encounters generic subject lines that impede efficient case prioritisation. This study evaluates el

Applications

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How

arXiv:2607.22811v1 Announce Type: cross Abstract: Hybrid mechanistic/data-driven models, which combine first-principles with learned components, are increasingly used in process engineering and scient

applicationsarxiv-cs-ai

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps

arXiv:2607.24532v1 Announce Type: new Abstract: Recent years have seen a rapid expansion in the production of large-scale geospatial maps derived from Earth observation (EO) data, driven largely by ad

tutorialsarxiv-cs-lg

From peer review nuances to best practices

arXiv:2607.22681v1 Announce Type: cross Abstract: This report studies three nuances in peer review data: paper version, score version, and input format. We characterize how the variants differ, and me

researcharxiv-cs-cl

From Physics to Surrogate Intelligence: A Unified Electro-Thermo-Optimization Framework for TSV Networks

arXiv:2603.29268v2 Announce Type: replace Abstract: High-density through-substrate vias (TSVs) enable 2.5D/3D heterogeneous integration but introduce significant signal-integrity and thermal-reliabili

From Proprietary to Open-Source: Bridging the Distribution Gap via Multi-Agent Protocol Distillation in Agentic Search

arXiv:2607.24280v1 Announce Type: new Abstract: Agentic search enables large language models to solve knowledge-intensive tasks by interleaving multi-step reasoning with retrieval, yet optimizing this

From RLVR to RLSVR: Task Transformation Induces Self-Verifiable Rewards for Open-Ended LLM Self-Improvement

model-releasesarxiv-cs-ai

arXiv:2607.23802v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has driven recent progress in reasoning-oriented large language models (LLMs) by enabling large-sc

From Score Learning to Discretized Sampling: An End-to-End Generalization Analysis of Diffusion Models

arXiv:2607.23226v1 Announce Type: new Abstract: Despite the empirical success of score-based diffusion models, a complete theoretical understanding of how finite-sample learning, network parameterizat

From transcription to semantic corpus analysis: unsupervised learning of sentence representations for ancient languages

model-releasesarxiv-cs-cl

arXiv:2607.24542v1 Announce Type: new Abstract: Automatic Text Recognition (ATR) now supplies digital humanities with large volumes of unstructured, heterogeneous, and often noisy text in ancient lang

From Vibe to Code -- and Back: Lexical Oscillation in the Formation of Design Intent with Generative AI

arXiv:2607.23126v1 Announce Type: cross Abstract: Generative AI design tools make natural-language prompts a starting point for design, placing new articulation demands on designers. Rather than treat

Frustratingly Simple Black-Box Adaptation of Language Models via Logit Bias

arXiv:2607.22837v1 Announce Type: cross Abstract: Many organizations aim to adapt language models for internal use, both to improve performance on domain-specific tasks and to address privacy concerns

FusionML: Prefill, Not Decode - Mechanism and Boundaries of CPU+GPU Co-Execution on Unified-Memory Apple Silicon

model-releasesarxiv-cs-lg

arXiv:2607.22785v1 Announce Type: cross Abstract: Apple-Silicon SoCs share CPU, GPU, and Neural Engine over one unified memory system, raising the question of whether transformer inference can be acce

FutureRTC: Real-Time Robot Execution with Anticipatory-Conditioned Action Chunking

arXiv:2607.24008v1 Announce Type: new Abstract: Real-time deployment of Vision-Language-Action (VLA) policies necessitates asynchronous execution, wherein subsequent action chunks are computed concurr

safetyarxiv-cs-ro

GaitFace: A Multimodal Dataset for Long-Range Person Identification

arXiv:2607.23542v1 Announce Type: new Abstract: Efficient border control is becoming a significant global challenge, mainly due to severe congestion and extended passenger waiting times. To mitigate t

gamma-Bridge: A Look-Parametric Diffusion Bridge

arXiv:2607.22719v1 Announce Type: new Abstract: Multiplicative Gamma noise is a signal-dependent degradation in coherent imaging; synthetic aperture radar (SAR) despeckling is its most prominent real-

Gaze-Anchored Social Net: Decoding Implicit Relations via Joint Modeling

arXiv:2607.22847v1 Announce Type: new Abstract: Human gaze does more than point to visual targets; it serves as a subtle indicator of social intent within static images, whereas standard models typica

Gaze-to-text Generation: Beyond Categorical Decoding of Human Attention

arXiv:2607.23917v1 Announce Type: new Abstract: We introduce a novel learning problem: decoding gaze into natural language descriptions of human goals across diverse visual tasks. Unlike prior work, w

tutorialsarxiv-cs-cv

GazeLT: Visual attention-guided long-tailed disease classification in chest radiographs

arXiv:2508.09478v2 Announce Type: replace Abstract: In this work, we present GazeLT, a human visual attention integration-disintegration approach for long-tailed disease classification. A radiologist'

tutorialsarxiv-cs-cv

GCR: Geometry-Consistent Routing for Task-Agnostic Continual Anomaly Detection

arXiv:2601.01856v3 Announce Type: replace Abstract: Feature-based anomaly detection is widely adopted in industrial inspection due to the strong representational power of large pre-trained vision enco

GEMCo: A Validated, Ethically Releasable Proxy for Inaccessible Counselling Data

model-releasesarxiv-cs-cl

arXiv:2607.23621v1 Announce Type: new Abstract: This paper presents GEMCo, a releasable, human-written proxy for inaccessible counselling data: 86 complete German e-mail counselling conversations (728

Generalization bounds and sample complexity for remaining useful life prediction from complete degradation trajectories

arXiv:2607.23454v1 Announce Type: new Abstract: Data-driven remaining useful life (RUL) prediction requires complete degradation trajectories for training, yet such run-to-failure data are scarce and

safetyarxiv-cs-lg

Generative Artificial Intelligence (GenAI) to convert images of queuing networks into verifiable simulation models: an open-weight LLM workflow approach

arXiv:2607.24259v1 Announce Type: new Abstract: Recent work has explored the use of Large Language Models (LLMs) to automate simulation model building, typically by generating executable code directly

Generative Augmentation for EEG Motor Imagery Classification: A Class-Conditional VAE with Cycle-Consistent Decoder Refinement

arXiv:2607.22733v1 Announce Type: new Abstract: We investigate whether a generative model can supply useful synthetic motor-imagery (MI) electroencephalography (EEG) trials that improve the accuracy o

Local Ai

Generative Video Compression with Adaptive Score Distillation

arXiv:2607.22772v1 Announce Type: cross Abstract: Diffusion models provide strong generative capabilities for video compression at ultra-low bitrates. Existing diffusion-based video codecs adapt base

local-aiarxiv-cs-cv

GenSplatCodec: Feed-Forward Gaussian Splatting Compression via One-Step Diffusion

arXiv:2607.24403v1 Announce Type: new Abstract: Feed-forward 3D Gaussian Splatting (3DGS) enables scalable scene reconstruction without per-scene optimization, yet produces dense Gaussians that are co

Geometry Meets Semantics: Fractional Gradient Stabilization for Semantic-Driven Bounding Box Optimization in Visual Detection Tasks

arXiv:2607.23530v1 Announce Type: new Abstract: Bounding boxes are fundamental for object localization in visual detection tasks. Among them, oriented bounding boxes are widely used in visual detectio

tutorialsarxiv-cs-cv

GFLAN: Generative Functional Layouts

arXiv:2512.16275v2 Announce Type: replace-cross Abstract: Automated floor plan generation lies at the intersection of combinatorial search, geometric constraint satisfaction, and functional design req

Local Ai

Gleam: Adaptive Network-Efficient CUDA API Remoting for Cross-Device GPU Sharing over LANs

arXiv:2607.23115v1 Announce Type: cross Abstract: This paper aims to enable computation- and communication-efficient GPU sharing across devices within local area networks (LANs), facilitating ubiquito

local-aiarxiv-cs-lg

Global Convergence of DGM and PINN Algorithms for Solving Nonlinear PDEs

arXiv:2607.24726v1 Announce Type: new Abstract: The Deep Galerkin Method (DGM) and Physics Informed Neural Networks (PINNs) have become widely-used methods for solving partial differential equations (