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Fractional-Order Adaptive Motion Magnification: Phase-Reliability Weighting for Noise-Constrained Video Amplification

arXiv:2609.04502v1 Announce Type: cross Abstract: Eulerian video amplification boosts sub-pixel motion by band-pass filtering per-pixel intensity traces and applying a uniform gain. That gain ignores

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From 80x to 385x: A Best-Matching-Unit Search at the L2 Roof, Measured Against a Symmetrically Tuned Baseline

DGX agent

arXiv:2609.05138v1 Announce Type: new Abstract: Comparisons between GPU implementations are usually asymmetric: one side is tuned by its author, the other is run as found. I report a programme that tu

hardwarearxiv-cs-lg

7 Sept 2026

From Answers to Interpretations: Rethinking Ambiguity-Induced Aleatoric Uncertainty Estimation in LLMs

arXiv:2609.04543v1 Announce Type: new Abstract: A key challenge in reliable LLM deployment is recognizing when uncertainty reflects irreducible variability in the task rather than limitations in the m

From Deep to Shallow: Unconstrained and Efficient Layer Merging Strategy

arXiv:2609.04881v1 Announce Type: new Abstract: Although Deep Neural Networks have become foundational in many areas of Machine Learning, high computational demands limit their application in resource

From Interaction Traces to Persistent Skills: Online Evolution for Computer-Use Agents

arXiv:2609.04869v1 Announce Type: new Abstract: Computer-use agents can execute increasingly complex tasks in graphical interfaces, but their interaction experience is typically transient: procedural

From Interpretability Methods to Interpretable Models

arXiv:2609.05399v1 Announce Type: new Abstract: More than a decade in, explainable AI (XAI) for computer vision has assembled a mature toolbox: attribution, feature visualization, concept-based, and c

From Language Models to World-Acting Systems: Progress and Limits of Agentic AI across Digital, Social, Virtual, and Physical Environments

arXiv:2609.04894v1 Announce Type: new Abstract: Large language models become consequential agents when surrounding systems let outputs change external state. Models now call tools, operate interfaces,

From Matching Models to Recruiting Agents: A Systematized Narrative Review of AI Recruitment Systems, Evaluation, and Governance

arXiv:2609.04286v1 Announce Type: new Abstract: Artificial intelligence in recruitment has shifted the object being automated from profile pairs and ranked lists to multi-stage workflows that retrieve

safetyarxiv-cs-ai

From Plausible to Actionable: A Position on LLM Self-Explanations

arXiv:2607.15957v2 Announce Type: replace Abstract: Large Language Models (LLMs) can generate natural language explanations that rationalize their own decisions, a phenomenon commonly referred to as s

From Vision to Language: Investigating Causal Information Flow in Multimodal Decision-Making

arXiv:2609.05149v1 Announce Type: new Abstract: Vision-Language Models are commonly evaluated through their final predictions, but understanding whether these decisions are grounded in visual evidence

FSPGD: Rethinking Black-box Attacks on Semantic Segmentation

arXiv:2502.01262v3 Announce Type: replace Abstract: Black-box adversarial attacks on semantic segmentation remain a challenging problem, particularly in the black-box transfer attack setting where per

safetyarxiv-cs-cv

Game-Theoretic Drone Swarm Defense: A Case Study in Applied Differential Game Theory

arXiv:2609.04394v1 Announce Type: cross Abstract: This technical report is a study of the use of differential game (DG) theory to solve the target-assignment and midcourse guidance problems of drone s

agentsarxiv-cs-ro

Generating Constructive Feedback on Stories via Reinforcement Learning

model-releasesarxiv-cs-cl

arXiv:2609.04824v1 Announce Type: new Abstract: Constructive feedback is crucial for creative writers to refine their storytelling abilities. Since receiving feedback from human experts is often costl

Hardware

GEPARD - Generative, Prosody-aware, Autoregressive text-to-speech model for Realtime Dialogue

arXiv:2609.04222v1 Announce Type: cross Abstract: We present GEPARD (Generative, Prosody-aware, Autoregressive text-to-speech model for Realtime Dialogue), a streaming text-to-speech model for real-ti

hardwarearxiv-cs-cl

Local Ai

GLASS: Graph-Language Alignment with Spherical Scoring for Transferable Graph-Level Anomaly Detection

arXiv:2609.05253v1 Announce Type: new Abstract: We introduce GLASS, a framework for graph-level anomaly detection (GLAD) that achieves robust cross-domain transferability through graph-language alignm

local-aiarxiv-cs-lg

Global to Local: Topology-Preserving Adaptive Graph Pooling via Granular-Ball

arXiv:2609.04978v1 Announce Type: new Abstract: Graph pooling aims to compress the graph, including both node embeddings and their underlying topological patterns, into a more compact representation.

GLOW: Graph-Language Co-Encoding for Agentic Workflow Performance Prediction

arXiv:2512.15751v2 Announce Type: replace-cross Abstract: Agentic Workflows (AWs) have emerged as a promising paradigm for solving complex tasks. However, automatically generating high-quality AWs rem

GNN-Guided Graph Coarsening and Adaptive QUBO Penalties for the Capacitated Vehicle Routing Problem with Time Windows on a Quantum Annealer

arXiv:2609.04593v1 Announce Type: new Abstract: Graph coarsening reduces the large Quadratic Unconstrained Binary Optimization (QUBO) formulations arising when vehicle-routing problems are solved by q

GRACE: Graph-Grounded Reflective Agent Copilot Engine for Expert-in-the-Loop Knowledge Expansion

arXiv:2609.04442v1 Announce Type: cross Abstract: Large language models deployed in high-stakes settings frequently generate plausible but ungrounded claims. Standard retrieval-augmented generation (R

Gradient-based Model Shortcut Detection for Time Series Classification

arXiv:2510.10075v2 Announce Type: replace-cross Abstract: Deep learning models have attracted lots of research attention in time series classification (TSC) task in the past two decades. Recently, dee

safetyarxiv-cs-ai

Graph Foundation Models for Recommendation: A Comprehensive Survey

arXiv:2502.08346v4 Announce Type: replace-cross Abstract: Recommender systems (RS) serve as a fundamental tool for navigating the vast expanse of online information, with deep learning advancements pl

GUT: Quantifying and Optimizing the Reasoning Uncertainty of LLMs via Graph Complexity

arXiv:2609.05284v1 Announce Type: new Abstract: Recent years have witnessed great advances in the reasoning ability of Large Language Models (LLMs). However, the reasoning processes of LLMs often exhi

Applications

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations

arXiv:2510.07314v4 Announce Type: replace-cross Abstract: Nuclear fusion plays a pivotal role in the quest for reliable and sustainable energy production. A major roadblock to viable fusion power is u

applicationsarxiv-cs-ai

Hakken: Predicting future discoveries to fill the gaps in today's knowledge

arXiv:2609.04494v1 Announce Type: cross Abstract: We present Hakken, a domain-agnostic prediction and explanation system performing knowledge prediction, i.e., growing scientific knowledge by establis

HaptiNet: Networked Haptic Robots Enable Physical Co-presence in Geographically-Unconstrained Rehabilitation

arXiv:2609.04799v1 Announce Type: new Abstract: Cooperative rehabilitation enhances engagement, task performance, and social-motor interaction, yet it demands physical co-presence: users must transmit

researcharxiv-cs-ro

Harbor Adapters and Harbor-Index: Infrastructure and a Curated Meta-Dataset for Large-Scale Agentic Evaluation

arXiv:2609.04298v1 Announce Type: new Abstract: Evaluating agents on the growing number of agentic benchmarks is challenging because they often require complex environments and agent integrations. We

Harness-agnostic detection and immunization of reward hacking in self-evolving language models

arXiv:2609.04665v1 Announce Type: new Abstract: Self-evolving language models improve by proposing candidate updates and keeping whatever raises a visible score. When that score is an imperfect proxy

Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models

arXiv:2503.24377v2 Announce Type: replace-cross Abstract: Recent advancements in Large Language Models (LLMs) have significantly enhanced their ability to perform complex reasoning tasks, transitionin

HarvestBench: Measuring Whether LLM Agents Will Pay to Avoid Killing Animals

arXiv:2609.04444v1 Announce Type: new Abstract: Benchmarks for the side effects an agent causes on the way to a goal already exist, but HarvestBench is the first to put a price on avoiding the side ef

Hessian-based molecular conformation augmentation for a scalable and efficient strategy of machine learning interatomic potentials

arXiv:2609.05233v1 Announce Type: new Abstract: While machine-learning interatomic potentials (MLIPs) have successfully learned potential energy surfaces (PES) and atomic forces, many practical applic

researcharxiv-cs-lg

Hidden In Plain Gaze: Gaze Representations as Privacy Controls for Utility and Re-identification Risk in XR

arXiv:2609.04592v1 Announce Type: new Abstract: Intelligent extended reality (XR) systems increasingly use eye and head tracking to infer user intent, task, and attention, but the same signals can als

Hierarchical Possession-Aware Graph Pointer Network for Pass Receiver Selection

arXiv:2609.04803v1 Announce Type: new Abstract: Pass receiver selection is a fundamental task in football analytics, aiming to predict the intended receiver under a given game state. This task is chal

HiSfM: Disambiguating Structure-from-Motion via Scaffold-Anchored Hierarchical Reconstruction

arXiv:2609.04718v1 Announce Type: new Abstract: Structure-from-Motion (SfM) is a fundamental tool for sparse 3D reconstruction with broad impact in robotics and vision, supporting mapping, localizatio

How a Chatbot's Response Style Shapes a Classroom: A Multi-Agent Simulation of Students Consulting AI

arXiv:2609.05018v1 Announce Type: cross Abstract: LLM-based chatbots are increasingly used as everyday confidants. Because they are designed to maximize user satisfaction, they can respond with excess

How Do Language Models Represent and Use Phonological Information for Allomorph Selection?

arXiv:2609.04708v1 Announce Type: new Abstract: Language models are trained on tokenized text that obscures the sound structure of words, yet they reliably produce morphemes whose form is phonological

How do LLMs Evaluate Perceived Moral Agency? Investigating Moral Decision-Making in Human-Artificial Agents Interactions

arXiv:2609.05037v1 Announce Type: cross Abstract: As LLMs take on roles requiring moral advice, understanding how they attribute moral agency becomes critical. Humans possess moral agency, the capacit

How Does mHC Use Its Residual Streams? Selective Routing and Near-Identity Mixing

arXiv:2609.05309v1 Announce Type: cross Abstract: Hyper-Connections and their manifold-constrained variant mHC widen a residual pathway from one stream to n, yet how trained models use this capacity r

How Faithful Is Attribution for Sales Forecasting? A Counterfactual Study

arXiv:2609.04797v1 Announce Type: new Abstract: Deep models for sales forecasting, such as WaveNet-style dilated convolutional networks, are accurate but opaque: when a single model predicts sales for

researcharxiv-cs-lg

Hardware

How far can we go with ImageNet for Text-to-Image generation?

arXiv:2502.21318v4 Announce Type: replace Abstract: Recent text-to-image (T2I) generation models have achieved remarkable sucess by training on billion-scale datasets, following a `bigger is better' p

hardwarearxiv-cs-cv

How Much Does Corpus Choice Change Dependency-Distance Estimates?

model-releasesarxiv-cs-cl

arXiv:2609.04223v1 Announce Type: new Abstract: Dependency-distance estimates derived from a single corpus are routinely treated as properties of a language, yet this assumption has not been tested ac

How to Speculate about Uncertainty in Agentic Coding? A Draft-Model Gate Method

arXiv:2609.05274v1 Announce Type: new Abstract: LLM agents deployed for software engineering fail expensively: they act confidently wrong, and bad actions are recognized only after costly execution an

Human-Human & Human-Robot Interaction Transformer (H2INT) for Robot Navigation in Dense and Uncertain Crowds

arXiv:2609.05300v1 Announce Type: new Abstract: Safe robot navigation in dense crowds requires reasoning about pedestrian motion and how it may change in response to a robot. However, many learning-ba

safetyarxiv-cs-ro

Hyperedge Anomaly Detection with Hypergraph Neural Network

arXiv:2412.05641v2 Announce Type: replace-cross Abstract: Hypergraph is a data structure that enables us to model higher-order associations among data entities. Conventional graph-structured data can

ICM-Bench: Person-Level Identity Reasoning in Multimodal Agents with Long-Term Memory

model-releasesarxiv-cs-cv

arXiv:2609.04438v1 Announce Type: new Abstract: Long-horizon multimodal agents should remember not only what happened but also who participated. This capability depends on linking recurring faces, voi

Impact of Data Loss in Postprocessing on Training and Inference of Quantum Neural Networks

arXiv:2609.05060v1 Announce Type: cross Abstract: As quantum hardware scales to larger devices, the classical software layers that interface with it must evolve in step. Postprocessing routines develo

Importance-Aware Low-Rank Distillation of Diffusion Transformers

model-releasesarxiv-cs-cv

arXiv:2609.04646v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have emerged as a dominant architecture for high-quality text-to-image generation, yet their scale poses challenges for ef

Improving Language Identification for Code-Switched Utterances with Integer Linear Programming

arXiv:2609.05099v1 Announce Type: new Abstract: Automatic identification of code-switched (CS) utterances remains a challenge for language identification (LID) systems, causing such texts to be underr

Inductive Venn-Abers and related regressors

arXiv:2605.06646v2 Announce Type: replace Abstract: Venn-Abers predictors are probabilistic predictors that enjoy appealing properties of validity, but their major limitation is that they have been ap

researcharxiv-cs-lg