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Enfold: Folding World-Generator Computation into Predictive Representations for Efficient Embodied Control

arXiv:2607.26657v1 Announce Type: new Abstract: World generative models are typically used through what they produce: a rendered future, a video-conditioned action, or latent context computed by a cos

researcharxiv-cs-ro

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Enhancing Automated Machine Learning via Homogeneous Train-Test Splitting Methods

DGX agent

arXiv:2607.26625v1 Announce Type: new Abstract: Accurate model evaluation in machine learning depends critically on how datasets are split into training and testing subsets. Standard random splitting

model-releasesarxiv-cs-lg

30 Jul 2026

Enhancing Generative Information Extraction with Two-step Validation: A Product Attribute Use Case

arXiv:2607.26780v1 Announce Type: new Abstract: The ability of large language models (LLMs) to process and generate text has introduced potential for applications in information extraction (IE). While

Entity Resolution in Practice: Lessons from a Self-Serve Pipeline

arXiv:2607.26298v1 Announce Type: new Abstract: We built and evaluated a self-serve entity resolution (ER) system on six benchmarks spanning 864 to 5M records, and three lessons emerged that are absen

Equilibrium Training of Energy-Based Models with Parallel Trajectory Tempering

arXiv:2607.27077v1 Announce Type: new Abstract: Energy-Based Models (EBMs) provide an interpretable framework for generative modeling of scientific data, but poor Markov Chain Monte Carlo mixing often

Equivariant Eikonal Neural Networks: Grid-Free, Scalable Travel-Time Prediction on Homogeneous Spaces

arXiv:2505.16035v3 Announce Type: replace Abstract: We introduce Equivariant Neural Eikonal Solvers, a novel framework that integrates Equivariant Neural Fields (ENFs) with Neural Eikonal Solvers. Our

Evaluating Prompt Scope and Demonstration Similarity in Local LLM Machine Translation

arXiv:2607.26286v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as general-purpose translation systems, but their behavior is usually evaluated under a single prompt

Evaluating Regional Bias in LLMs From Abstract Stereotype to Concrete Social Decision-Making

arXiv:2607.27022v1 Announce Type: new Abstract: Regional bias in large language models (LLMs) may shape both perceptions of regional groups and decisions about individuals from different regions. Yet

safetyarxiv-cs-cl

EvoPINN: Agentic Discovery of Executable Algorithms for Physics-Informed Neural Networks

arXiv:2607.26490v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful paradigm for solving partial differential equations (PDEs), yet their performance

Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts

arXiv:2607.26404v1 Announce Type: new Abstract: Graph Neural Networks (GNN) facilitate effective prediction on graph data such as molecules, media networks and neural network blueprints. GNNs facilita

Existence-Field Diffusion Model for Spatial Point Processes with Variable Cardinality

arXiv:2607.26428v1 Announce Type: new Abstract: We study generative modeling of spatial point processes (SPP), where both the number of points and their spatial configuration are governed by a joint d

Explainable and Resource-Efficient Spatial Reasoning in Multimodal LLMs for Decision-Critical Applications

arXiv:2607.27145v1 Announce Type: new Abstract: As Multimodal Large Language Models (MLLMs) are increasingly deployed in decision-critical pipelines such as robotics, embodied AI, and safety monitorin

safetyarxiv-cs-cv

Tutorials

Explicit Kinematic Guidance from Analytic Concepts for Vision-Language-Action Models

arXiv:2607.26513v1 Announce Type: new Abstract: Current Vision-Language-Action (VLA) models rely mainly on 2D inputs, neglecting the rich object structural information and commonsense knowledge inhere

tutorialsarxiv-cs-ro

exttt{AMEND++}: Benchmarking Eligibility Criteria Amendments in Clinical Trials

arXiv:2601.06300v2 Announce Type: replace Abstract: Clinical trial amendments frequently introduce delays, increased costs, and administrative burden, with eligibility criteria being the most commonly

FakeIDet3-DB: Refining Digital Attacks and Patch Extraction for Secure ID Benchmarking

arXiv:2607.26641v1 Announce Type: new Abstract: Identity document (ID) authentication relies on the structural integrity of complex, high-frequency security patterns. However, advanced Generative AI m

local-aiarxiv-cs-cv

FAS-R1: A Unified Multi-Task MLLM for Reasoning Face Anti-Spoofing

model-releasesarxiv-cs-cv

arXiv:2607.26432v1 Announce Type: new Abstract: Face anti-spoofing (FAS) is increasingly expected to provide not only bona fide/spoof decisions, but also attack semantics and image-grounded evidence f

Feature Bagging Provides Stability

arXiv:2607.26964v1 Announce Type: cross Abstract: We study feature bagging through the lens of algorithmic stability. Feature bagging is an ensemble strategy that aggregates base learners trained on r

FedTopo: Relation-Level Topology Sharing for Model-Heterogeneous Federated Learning

arXiv:2607.26801v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative learning over decentralized data silos without centralizing raw data. However, heterogeneous local archite

FedWeave: Rethinking the Unit of Specialization in Heterogeneous Federated MoE-LoRA

arXiv:2607.26618v1 Announce Type: cross Abstract: Federated PEFT enables LLMs to collaboratively adapt to decentralized private data without sharing raw examples. However, task heterogeneity across cl

Few-Shot Open-Set Audio Classification via Transductive Prototype Refinement and Class Logit Enhancement

arXiv:2607.26607v1 Announce Type: cross Abstract: Few-shot Open-set audio classification requires classifying query samples from known classes with a few labeled support samples while rejecting query

Field Codes for Distributed Coupling Samplers and Certified Empirical Transport

arXiv:2607.27078v1 Announce Type: cross Abstract: In this paper, we formulate three communication tasks for empirical optimal transport: distributed coupling sampling, cost-evaluable coupling output,

Agents

Filesystem-Based Memory for LLM Agents: Organization, Evolution, and Sustainability

arXiv:2607.26637v1 Announce Type: new Abstract: Deployed LLM agents increasingly keep their long-term memory as a filesystem: a directory tree of markdown files that the agent itself reads, writes, an

agentsarxiv-cs-cl

Financial Volatility and Risk Forecasting Incorporating a Larger Number of Realized Measures

arXiv:2411.17136v2 Announce Type: replace-cross Abstract: Realised volatility has become increasingly prominent in volatility forecasting due to its ability to capture intraday price fluctuations. Wit

FleetScape: A Mixed Reality Sandtable for Spatial Supervision and Control of Scalable Drone Fleets

arXiv:2607.26423v1 Announce Type: cross Abstract: As autonomous drone deployments scale from individual units to coordinated swarms, the human operator's role shifts from direct piloting to high-level

safetyarxiv-cs-ro

FloDR: An invertible dimensionality reduction method based on a normalising flow

arXiv:2607.26278v1 Announce Type: new Abstract: It is common for two-dimensional embeddings of high-dimensional data to be read far beyond what they can support. Distances in and between clusters, the

Flow Map Learning via Nongradient Vector Flow

arXiv:2607.26398v1 Announce Type: new Abstract: Diffusion and flow-based models benefit from simple regression losses, but inference incurs significant overhead because sampling requires integration.

Forecasting Trajectory-Level Safety Risks in Black-Box Multi-Turn Interactions

arXiv:2607.26820v1 Announce Type: new Abstract: As large language models (LLMs) evolve from standalone assistants into autonomous agents, ensuring their safety requires shifting beyond pointwise risk

safetyarxiv-cs-lg

ForgetBench: Benchmarking Forgetting Dynamics of Long-Term Parametric Memory in Language Models

arXiv:2607.26455v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong capabilities in knowledge acquisition and reasoning, yet their ability to retain previously acquir

Foundation Models for Face Presentation Attack Detection: A Unified Linear-Probing Benchmark

arXiv:2607.26993v1 Announce Type: new Abstract: Face presentation attack detection (PAD) remains challenging under cross-dataset evaluation, where domain shift degrades models trained on a single data

FPSGen: Flexible Point Cloud Scene Generation with BEV-Supported Transport Flows

arXiv:2607.26645v1 Announce Type: new Abstract: Existing point-based generative methods for outdoor scenes primarily focus on LiDAR-conditioned completion. During training, noisy point clouds are cons

safetyarxiv-cs-cv

FreeShadow: Training-Free Shadow Removal via Illumination Transfer and Selective Content Preservation in Diffusion Models

arXiv:2607.26715v1 Announce Type: new Abstract: Existing supervised and unsupervised shadow removal methods often suffer from limited generalization due to the insufficient diversity of available trai

researcharxiv-cs-cv

FreqForcing: Autoregressive Long Video Generation via Spectral Self-Anchoring

arXiv:2607.27110v1 Announce Type: new Abstract: Autoregressive video diffusion models enable real-time streaming video generation. However, errors introduced during self-rollout accumulate over long h

researcharxiv-cs-cv

From Classification to Regression: Using a Fruitfly to Solve Equations

arXiv:2607.27196v1 Announce Type: new Abstract: We present a novel approach to regression tasks using classification which is motivated by the mechanism used by fruitflies to sense their environment.

local-aiarxiv-cs-lg

Agents

From Conceptual Hydrologic Models to Conceptually Interpretable Neural Networks: A Snow-Water Mass-Conserving-Perceptron Framework for Discovering Catchment-Scale Precipitation-Storage-Runoff Representations

arXiv:2607.26492v1 Announce Type: new Abstract: The Mass-Conserving Perceptron (MCP) establishes a modeling paradigm in which conceptual hydrologic models can be reformulated as physically constrained

agentsarxiv-cs-lg

From Found to Designed: Concepts as a Design Axis for Large Language Models

arXiv:2607.26825v1 Announce Type: new Abstract: Large language models (LLMs) encode rich concept-like information, but represent it implicitly through distributed statistical associations rather than

safetyarxiv-cs-cl

From Interface to Inference: Eliciting Any-Order Inference from Any-Order Models

arXiv:2607.26504v1 Announce Type: new Abstract: Many discrete reasoning tasks, such as code generation, are inherently non-causal: programmers move between high-level structure and local details, a pr

local-aiarxiv-cs-lg

From Keypoints to Predictive Distributions: Post-Hoc Uncertainty for YOLO-Pose Models

arXiv:2607.26921v1 Announce Type: new Abstract: YOLO-Pose models provide efficient keypoint localization, but do not quantify the associated spatial uncertainty. We introduce a lightweight post-hoc pr

local-aiarxiv-cs-cv

Tutorials

From Passive Video to Editable Experience: Physically Grounded Experience Synthesis for Embodied Intelligence

arXiv:2607.26903v1 Announce Type: cross Abstract: The key bottleneck in embodied AI is not model architecture but data. Although billions of human manipulation videos exist online, robots cannot direc

tutorialsarxiv-cs-ro

From Representations to Behaviors: Exploring the Person-Situation-Behavior Triad in LLMs

arXiv:2607.26853v1 Announce Type: new Abstract: Human personality theories characterize traits not as isolated attributes captured by a single score, but as stable individual tendencies expressed thro

researcharxiv-cs-cl

From Spatial Semantics to Temporal Context: Leveraging Gaze Trajectory for Weakly Supervised Medical Image Segmentation

arXiv:2607.26542v1 Announce Type: new Abstract: Medical image segmentation heavily depends on labor-intensive and time-consuming pixel-level annotations. Eye tracking offers a cost-effective solution

researcharxiv-cs-cv

From Tokens to Watt-hours: Analytical Energy Estimation for LLM Inference on Modern GPUs

arXiv:2607.26571v1 Announce Type: new Abstract: The operational energy consumption of large language model (LLM) inference is becoming an increasingly important component of the environmental footprin

From Uncertainty to Determinism: Coarse-to-Fine Visual Floorplan Localization without Ray Matching

arXiv:2607.26817v1 Announce Type: cross Abstract: Visual Floorplan Localization (FLoc) has emerged as a promising solution for indoor localization by matching egocentric images against minimalist stru

local-aiarxiv-cs-cv

From Unsupervised Subgroups to Hypothetical State-Intervention Policies: An Evaluation of Selected Subgrouping Methods in Observational Health Data

arXiv:2607.26521v1 Announce Type: new Abstract: Conventional subgroup analyses can yield unstable and difficult-to-interpret conclusions, especially in observational biomedical data where each individ

safetyarxiv-cs-lg

Gated Adaptation for Continual Learning in Human Activity Recognition

arXiv:2603.10046v2 Announce Type: replace Abstract: Wearable sensors in Internet of Things (IoT) ecosystems increasingly support applications such as remote health monitoring, elderly care, and smart

GBPP: Grasp-Aware Base Placement Prediction for Robots via Two-Stage Learning

arXiv:2509.11594v3 Announce Type: replace Abstract: GBPP is a fast learning based scorer that selects a robot base pose for grasping from a single RGB-D snapshot. The method uses a two stage curriculu

researcharxiv-cs-ro

Generation or Judgement? A Paradigm Perspective on LLM-Based Emotion-Cause Pair Extraction in Conversation

arXiv:2607.26967v1 Announce Type: new Abstract: Emotion-cause pair extraction in conversation (ECPEC) identifies utterance pairs in which one utterance causes an emotion expressed in another. Recent L

researcharxiv-cs-cl

Hardware

Genie Sim PanoWorld: An Infinite Indoor 3D World Generation Pipeline via Panoramic Scene Modeling and Simulation

arXiv:2607.26646v1 Announce Type: new Abstract: We address the problem of reconstructing a high-fidelity, freely navigable 3D scene from a single 360^irc panorama, without per-scene optimization or mu

hardwarearxiv-cs-cv

GEqTrain: A Configuration-Driven Framework for Retargeting Equivariant Graph Neural Networks Across 3D Scientific Tasks

arXiv:2607.19083v2 Announce Type: replace Abstract: Equivariant graph neural networks provide a powerful modeling language for three-dimensional scientific data, but their reuse is often limited by im