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arXiv:2609.35936v1 Announce Type: cross Abstract: As autonomous systems and embodied intelligence enter the dynamic physical world, multi-agent collaboration calls for a paradigm shift in communicatio

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Evaluation Choices Shape Biomedical ML Claims: A Pediatric Pneumonia Benchmark Case Study

DGX agent

arXiv:2609.37848v1 Announce Type: new Abstract: Biomedical machine learning papers often compress model performance into one headline number. That number can look like a property of the model even whe

model-releasesarxiv-cs-cv

30 Sept 2026

From Sky to Soil: A Morphing Aerial-Ground Robot for Seed Deployment

arXiv:2609.37072v1 Announce Type: new Abstract: Aerial seed broadcasting can reach remote restoration sites, but provides limited control over seed placement within the soil. This paper presents a geo

Generated Query Expansion Still Helps Strong Sparse Retrieval: A Controlled Study with SPLADE-v3

arXiv:2609.37911v1 Announce Type: cross Abstract: Scientific queries are often brief, while relevant papers use specialized vocabulary. Generated query expansion can bridge this mismatch, but earlier

researcharxiv-cs-ai

GenLimitLib: A Formal Library for Language Generation in the Limit and AI-Assisted Mathematical Research

arXiv:2609.36663v1 Announce Type: new Abstract: We present GenLimitLib, a source-aligned Lean 4 library for language generation in the limit. Introduced by Kleinberg and Mullainathan at NeurIPS 2024,

tutorialsarxiv-cs-lg

Hybrid Joint-Selective Optimization: Reduced-Space Levenberg-Marquardt Refinement of Low-Dimensional Parameters of Interest

arXiv:2609.37308v1 Announce Type: cross Abstract: This paper introduces a hybrid joint-selective optimization (HJSO) framework for large-scale numerical problems in which a small subset of trainable q

KernelOnet: An Interpretable Neural Operator Based on Kernel Functions

arXiv:2609.35938v1 Announce Type: new Abstract: This paper proposes an interpretable neural operator framework, the Kernel Operator Network (KernelOnet), which incorporates kernel functions explicitly

tutorialsarxiv-cs-lg

Physics-Aware Machine Unlearning for Cyber-Physical Systems

arXiv:2609.36633v1 Announce Type: new Abstract: This paper proposes a physics-guided gradient-ascent-based machine unlearning method that couples the forgetting signal with the physical residual of th

Agents

Reconstructing Implicit Scientific Knowledge: Evaluating LLM Agents through End-to-End Reproduction of Astronomy

arXiv:2609.35900v1 Announce Type: cross Abstract: The integration of large language models (LLMs) into scientific workflows is accelerating, yet their ability to reconstruct the reasoning underlying p

agentsarxiv-cs-ai

Local Ai

Reconstructing the Vocal Tract with Differentiable Acoustic Simulation

arXiv:2609.36737v1 Announce Type: cross Abstract: The vocal tract is the region of the human body responsible for filtering one's voice to create speech. In this paper, we present a differentiable and

local-aiarxiv-cs-cl

Render Before Reading: Visual Rendering as a Prompt Injection Defense

arXiv:2609.36121v1 Announce Type: cross Abstract: Large language models are vulnerable to prompt injection attacks, where third-party adversarial content can hijack the model's behavior. In this paper

researcharxiv-cs-ai

Stein-based Optimization of Sampling Distributions in Model Predictive Path Integral Control

arXiv:2511.02015v3 Announce Type: replace Abstract: This paper introduces a method for Model Predictive Path Integral (MPPI) control that optimizes sample generation towards an optimal trajectory thro

Two open-weights releases: Victoria (Qwen3.8-Flash-Next with 44% of experts cut, 70% Terminal-Bench 2.1, GGUF included) and Maple (a Canada-first fine-tune)

model-releasesr-localllama

We had a Dell B300 in the lab for a few weeks and used it to create two fine tunes of Qwen Flash Next. Victoria (coding and agents) Qwen3.8-Flash-Next cut down by 44% using a paper / technique called

Wasserstein Causal Forests for Distribution-Valued Outcomes

arXiv:2609.35898v1 Announce Type: cross Abstract: This paper proposes Wasserstein Causal Forests (WCF) for settings in which each unit's outcome is itself a probability distribution. This study also d

3-D Emissions Mapping and Social Cost Estimation for US Domestic Aviation at West Coast Hubs

arXiv:2609.31686v1 Announce Type: new Abstract: Existing aviation emissions inventories lack accurate trajectory data for high-resolution social cost and health impact assessment. This paper develops

A Function-Level Vulnerability Score Measures Flag Rate More Than the Model: Protocol Effects on Paired Benchmarks

arXiv:2609.32890v1 Announce Type: new Abstract: Language models are increasingly evaluated as vulnerability detectors, and scores reported for similar models differ widely between papers. We measured

AD-E2E-JEPA: A Joint-Embedding Predictive Architecture For End-to-End Autonomous Driving

model-releasesarxiv-cs-cv

arXiv:2609.34085v1 Announce Type: cross Abstract: Autonomous driving requires extit{world models} that can understand the physical world, reason and plan, and operate safely. In this paper, we first s

Agents

AD-SAM: Adapting the Segment Anything Model for Semantic Segmentation in Autonomous Driving

arXiv:2510.27047v2 Announce Type: replace Abstract: This paper presents the Autonomous Driving Segment Anything Model (AD-SAM), a foundation-model adaptation framework for semantic segmentation in aut

agentsarxiv-cs-cv

AGRO-SUVIDE: Agentic Robotics for Surgical Viscoelastic Debridement

arXiv:2609.34823v1 Announce Type: new Abstract: Augmented dexterity has the potential to reduce the fatigue experienced by surgeons during repetitive surgical tasks. In this paper, we propose the firs

Local Ai

Analytical and Convolutional Neural Network-Based Motion-Vector Propagation for Efficient Video Object Detection

arXiv:2609.34142v1 Announce Type: new Abstract: Continuous video analytics requires accurate localization at low latency within embedded power budgets. This paper presents a hardware-software design m

local-aiarxiv-cs-cv

Bregman geometry-aware split Gibbs sampling for Bayesian Poisson inverse problems

arXiv:2511.12257v2 Announce Type: replace-cross Abstract: This paper proposes a novel Bayesian framework for solving Poisson inverse problems by devising a Monte Carlo sampling algorithm which account

researcharxiv-cs-cv

Contact-Aware Impedance Controller for Robot-Assisted Ultrasound Imaging

arXiv:2609.34400v1 Announce Type: new Abstract: Safe robot-assisted ultrasound imaging requires a reliable controller able to detect and localize probe--tissue interaction. In this paper, we present a

Convex Optimization Is Free When Accuracy Is Expensive

arXiv:2609.35418v1 Announce Type: cross Abstract: This paper studies convex optimization when the gradient cannot be evaluated exactly, but only approximated by a hierarchy of algorithms whose compute

DecFlowEdit: Self-Localized Flow-based Image Editing via Guidance Decoupling

arXiv:2609.34237v1 Announce Type: new Abstract: Flow-based image editing (FlowEdit) enables inversion-free semantic changes through the difference between source and target velocities. In this paper,

researcharxiv-cs-cv

EFC++: Elastic Feature Consolidation with Prototype Re-balancing for Cold Start Exemplar-free Incremental Learning

arXiv:2503.10439v4 Announce Type: replace Abstract: Exemplar-free Class Incremental Learning (EFCIL) aims to learn from a sequence of tasks without having access to previous task data. In this paper,

safetyarxiv-cs-cv

Efficient Message Passing for Partial Differential Equation Priors

arXiv:2609.32956v1 Announce Type: new Abstract: Prior information for real-world physical quantities is most elegantly expressed via partial differential equations (PDEs). In this paper, we propose a

GAUGE: Planner-Conditioned Active Calibration of Opaque Quadruped Velocity Interfaces

arXiv:2609.32154v1 Announce Type: new Abstract: In this paper, we present a Goal-Aware Uncertainty-Guided Exploration (GAUGE) framework for planner-conditioned active calibration of opaque quadruped v

Applications

GERIS: A Game-Theoretic Framework for Filtering Instance-Dependent Label Noise in License Plate Data Augmentation

arXiv:2609.31731v1 Announce Type: new Abstract: In this paper, we propose GERIS, a game-theoretic framework for instance selection in the data augmentation phase of license plate recognition systems.

applicationsarxiv-cs-cv

Grasp2Twist: Learning Bimanual Dexterous Jar Opening by Reinforcement Learning

arXiv:2609.32064v1 Announce Type: new Abstract: This paper presents Grasp2Twist, a bimanual dexterous manipulation system that learns to grasp and twist open jar lids using reinforcement learning. Lea

Applications

Hierarchical Clustering and Signal Denoising on Digraphs

arXiv:2609.34670v1 Announce Type: cross Abstract: In this paper, we propose a representation of a digraph (directed graph) as a Hermitian matrix derived from its adjacency matrix. This representation

applicationsarxiv-cs-lg

Hierarchical Multi-agent Reinforcement Learning for Warehouse Robot Coordination under Communication Loss

arXiv:2609.33637v1 Announce Type: cross Abstract: In this paper, we propose a hierarchical multi-agent reinforcement learning framework for coordinating robot teams in warehouse environments under com

Human Motion Prediction for Human-Robot Collaboration

arXiv:2609.32346v1 Announce Type: new Abstract: This paper compares three human motion prediction methods - GMM-based clustering, TransFusion, and Graph-Mixer - evaluating their effectiveness in human

Investigating the Effect of k-NN Preprocessing on Developing Graph Neural Networks: A Fairness-Based Perspective

arXiv:2609.33416v1 Announce Type: new Abstract: In this paper, a methodology to design fair graph convolutional neural networks (GCNs) is developed and tested over several application data sets. The g

Learning Shuffle Ideals with Membership Queries and Contrastive Examples

arXiv:2609.32820v1 Announce Type: cross Abstract: This paper studies learning of shuffle ideals with membership queries as well as with contrastive queries---a form of membership query that reveals no

researcharxiv-cs-lg

Managing Self-Learning Experts under Per-Round Budget Constraints

arXiv:2510.22654v3 Announce Type: replace Abstract: This paper addresses the problem of sequential decision-making under learning budget constraints. Such settings naturally arise in applications like

tutorialsarxiv-cs-lg

MASTEST: A LLM-Based Multi-Agent System For Testing RESTful APIs

arXiv:2511.18038v2 Announce Type: replace-cross Abstract: Testing RESTful API is increasingly complicated but indispensable to quality assurance of cloud-native applications. This paper reports a mult

Memory in the Sky: Low-Altitude Question Answering with Multi-Agent Memory Aggregation

model-releasesarxiv-cs-ro

arXiv:2609.35431v1 Announce Type: new Abstract: This paper studies low-altitude question answering (LAQA), in which distributed unmanned aerial vehicle (UAV) memories are aggregated at a ground server

Model Casting and Low-Parameter Gating: Towards More Sparsely Activated FFNs

arXiv:2609.31975v1 Announce Type: new Abstract: This paper introduces model casting, a mid-training recipe that drastically sparsifies the activations within the Feed-Forward Network (FFN) layer. With

Observability-Informed Optimal Sensor Placement for Soft Robots

arXiv:2609.33681v1 Announce Type: new Abstract: This paper presents the application and experimental evaluation of a systematic method for optimal sensor placement in soft robots. Existing methods eit

tutorialsarxiv-cs-ro

Q-learning Penalized Transformer for Safe Offline Reinforcement Learning

arXiv:2609.34426v1 Announce Type: new Abstract: This paper addresses the problem of safe offline reinforcement learning, which involves training a policy to satisfy safety constraints using an offline

Residual Learning-Based Control of Vehicle Platoons with ell_2 Stability Guarantees via Recurrent Equilibrium Networks

arXiv:2609.33892v1 Announce Type: new Abstract: This paper proposes a residual learning-based control framework for heterogeneous vehicle platoons subject to parametric uncertainty and external distur

Revisiting Risky Tackle Detection with Vision Transformers

model-releasesarxiv-cs-cv

arXiv:2609.35562v1 Announce Type: new Abstract: This paper is a Track 2 reproducibility companion to an ICPR 2026 study on risky tackle detection in American football prac- tice videos. The original w

Riemannian Difference-of-Convex Optimization for K-Means Clustering

arXiv:2609.34310v1 Announce Type: new Abstract: K-means is a widely adopted clustering approach in signal processing and machine learning. In this paper, we study K-means clustering through a cardinal

Symmetry-quotient Flatness and Generalization

arXiv:2609.31634v1 Announce Type: new Abstract: This paper develops a theorem-level pipeline in symmetry-quotient settings: quotient linear stability implies quotient flatness, quotient flatness impli

The Text Beside the Image: Detection, Utility and Leakage for Trustworthy Multimodal Medical Data and Beyond

model-releasesarxiv-cs-cv

arXiv:2609.33280v1 Announce Type: cross Abstract: Medical images are released with the reports that describe them, and protecting the image does not protect the report. This paper measures the text co

Training Witnesses: Trusting the Training without Trusting the Trainer

arXiv:2609.33915v1 Announce Type: new Abstract: Progress in machine learning cannot outpace our ability to verify it. With an explosion in papers today, every scientific claim rests initially on trust

researcharxiv-cs-lg

Video-to-Music Generation for Gameplay Videos

arXiv:2609.31810v1 Announce Type: cross Abstract: Video-to-music models have advanced considerably in the last few years, particularly in film and music video applications. In this paper, we investiga

researcharxiv-cs-cv

An End-to-End Pipeline for Causal ML with Continuous Treatments: An Application to Financial Decision Making

arXiv:2609.30396v1 Announce Type: cross Abstract: This paper presents an end-to-end causal machine learning (ML) pipeline designed for real-world applications with continuous treatments. The proposed