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arXiv:2609.18228v1 Announce Type: new Abstract: Journal Entry Tests (JETs) are a mandatory part of annual audits to evaluate and assess both highrisk audit areas and potential material misstatements.

tutorialsarxiv-cs-lg

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APGEM: Adaptive Policy-Guided Error Mitigation for Quantum Reinforcement Learning on a Real-World CVRP Case Study

DGX agent

arXiv:2609.18219v1 Announce Type: cross Abstract: Quantum Reinforcement Learning (QRL) represents policies as variational quantum circuits (VQCs), making it attractive for combinatorial optimization s

safetyarxiv-cs-ai

17 Sept 2026

Approximating High Dimensional Self-Motion Manifolds via Deep Generative Models

arXiv:2609.18169v1 Announce Type: new Abstract: Self-motion manifold (SMM) characterizes the geometric structure of the infinite inverse kinematic solutions set of a redundant manipulator at a fixed e

researcharxiv-cs-ro

Approximation of the Basset force in the Maxey-Riley-Gatignol equations via universal differential equations

arXiv:2604.08194v2 Announce Type: replace Abstract: The Maxey-Riley-Gatignol equations (MaRGE) model the motion of spherical inertial particles in a fluid. They contain the Basset force, an integral t

Ask the Tool, Don't Guess: Agent Tool Calls Hold Their Progress, and the Serving System Should Read It

arXiv:2609.18849v1 Announce Type: cross Abstract: An agentic request spends substantial wall-clock time waiting for tools, and its KV cache holds GPU memory the whole time. Serving systems decide whet

ASLEval: Measuring Privacy Exposure Displacement in LLM Agent Sessions

arXiv:2609.18864v1 Announce Type: cross Abstract: Privacy evaluations of tool-using LLM agents often inspect a designated action, final response, or attacker report. These local proxies can miss unaut

local-aiarxiv-cs-ai

ASPIRE: Asynchronous Batched Self-Speculative Decoding for Long-Context LLM Inference

arXiv:2609.17943v1 Announce Type: cross Abstract: Long-context LLM inference is bottlenecked by attention, whose repeated KV-cache reads make decoding memory-bound. Self-speculative decoding alleviate

researcharxiv-cs-cl

Asymptotically Optimal Multi-Robot Task and Motion Planning

arXiv:2609.18813v1 Announce Type: new Abstract: Multi-robot task and motion planning (MR-TAMP) requires jointly reasoning about discrete task decisions and continuous collision-free motions of multipl

researcharxiv-cs-ro

Attention Dispersion as a Diagnostic Signal for Hallucination in Large Language Models

arXiv:2609.18320v1 Announce Type: new Abstract: Large Language Models (LLMs) frequently exhibit hallucinations, presenting a major barrier to reliability in complex reasoning tasks. While traditional

safetyarxiv-cs-cl

Audio for Sports Highlight Detection: A Comparative Empirical Study

model-releasesarxiv-cs-cv

arXiv:2609.17923v1 Announce Type: new Abstract: Sports highlight detection aims to identify the most exciting and meaningful moments from long sports videos. While existing methods often emphasize vis

Automated Dental Caries Segmentation in Panoramic Radiographs Using Dual-Stage Deep Learning

arXiv:2609.18952v1 Announce Type: cross Abstract: Early detection of dental caries remains challenging due to limitations in traditional diagnostic methods, particularly for proximal lesions in poster

local-aiarxiv-cs-ai

Autonomy in Check: Governor-Mediated Adaptive Security at the Edge

arXiv:2609.18338v1 Announce Type: cross Abstract: Adaptive security at the network edge increasingly relies on automated planners, including rule-based controllers, learned policies, and LLM-assisted

safetyarxiv-cs-ai

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

arXiv:2609.18123v1 Announce Type: new Abstract: Large language model agents tune GPU kernels and serving engines through a closed loop of propose, measure, and keep, but the measurements behind this l

Bad Genius: Counterfactual-Guided Harness Evolution Beyond Task-Specific Shortcuts

arXiv:2609.18366v1 Announce Type: new Abstract: Reliable agent evaluation is complicated by automatic harness optimization, which repeatedly uses a released benchmark B_{rel} to guide a Proposer that

BadQubits: An LLM-Based Framework for Static Pre-Execution Detection of Structurally Harmful Quantum Circuits

arXiv:2609.18965v1 Announce Type: cross Abstract: This paper presents BadQubits, an LLM-based framework for static pre-execution detection of structurally harmful OpenQASM 2.0 circuits. The framework

Bayesian Quadrature

arXiv:2602.16218v2 Announce Type: replace Abstract: Bayesian quadrature is a probabilistic, model-based approach to numerical integration, the estimation of intractable integrals, or expectations. Alt

Behavior2Value: Benchmarking and Empowering LLMs for Consumer Value Measurement from E-commerce Behaviors

model-releasesarxiv-cs-cl

arXiv:2609.18203v1 Announce Type: new Abstract: Human values are deep motivational orientations that shape human behaviors. In e-commerce, they reveal the stable drivers behind users' purchase decisio

Behavioral Fingerprinting and Navigation Prediction in Web Browsing

arXiv:2609.18273v1 Announce Type: new Abstract: Web browsing often appears ephemeral: users visit a few websites, complete a task, and move on. However, even short fragments of browsing activity can c

BENCHCOMPASS: From Scores to Signals for Training and Harness Decisions in Payment-Domain LLMs

arXiv:2609.18270v1 Announce Type: new Abstract: Payment operations are a critical financial infrastructure, but the value of large language models in this domain remains unclear because payment rules

Benchmarking Large Language Models for Biomedical Relation Extraction

model-releasesarxiv-cs-cl

arXiv:2609.19071v1 Announce Type: new Abstract: Extracting SNP-phenotype associations from biomedical literature is vital but challenging. We benchmarked diverse NLP models, including MLMs, hybrid arc

Benchmarking Tabular Foundation Models as Surrogates in Expensive Evolutionary Optimization

arXiv:2609.18130v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) are effective methods for solving expensive optimization problems (EOPs), where surrogate models re

Benchmarking Visual-Inertial Odometry in Subterranean Environments Under Sensor Degradation, Miscalibration, and Dynamic Occlusion

model-releasesarxiv-cs-ro

arXiv:2609.18628v1 Announce Type: new Abstract: Visual-inertial odometry (VIO) is a core capability for autonomous operation in GPS-denied subterranean environments, yet its reliability can degrade sh

Beyond Accuracy: How Procedural Traces Shift the Decision Criterion of LLM Overseers

arXiv:2609.18204v1 Announce Type: cross Abstract: Organizations increasingly use oversight loops where one large language model (LLM) audits another's outputs alongside procedural traces of claimed st

researcharxiv-cs-ai

Beyond Direct Sensing: Harnessing Indirect Observations from Third-Party Sensors in Vehicle Tracking

arXiv:2609.18173v1 Announce Type: cross Abstract: Vehicle tracking is fundamental to applications ranging from urban mobility and public safety to security and defense. Conventional tracking relies on

safetyarxiv-cs-lg

Beyond EER: Multi-Dimensional Evaluation of Information Leakage in Speaker De-Identification

arXiv:2609.18673v1 Announce Type: cross Abstract: Speaker de-identification (SDID) aims to preserve privacy by concealing speaker identity while maintaining speech utility. However, current evaluation

safetyarxiv-cs-ai

Beyond Embedding Transfer: Component Roles in Grokking Transfer and Stability

model-releasesarxiv-cs-lg

arXiv:2609.18078v1 Announce Type: new Abstract: Warm-start transfer can make algorithmic tasks generalize rapidly, yet it is unclear which model components provide the gain and whether that gain remai

Beyond frequency measures: Can contextual embeddings capture meaning change in scientific texts?

arXiv:2609.18804v1 Announce Type: new Abstract: Identifying technological trends is a core scientometric task, yet traditional frequency-based approaches struggle to capture substantial meaning shifts

researcharxiv-cs-cl

Beyond Outcomes: Dual-View Relational Learning for Efficient Agent Benchmarking

arXiv:2609.18909v1 Announce Type: cross Abstract: Agent benchmarks are substantially more costly to evaluate than conventional LLM benchmarks. Benchmark compression is therefore a natural solution, ye

Beyond Performance Metrics: Uncertainty Mapping of Label Ambiguity in Fazekas Score Prediction

arXiv:2609.17753v1 Announce Type: new Abstract: Reference labels used to train medical image classification models are not always as certain as they may appear, and this uncertainty has implications o

local-aiarxiv-cs-cv

Beyond Pixel Similarity: Task-Aware Evaluation of GAN-Based Synthetic Sonar Data for Robotic Perception

arXiv:2609.18100v1 Announce Type: cross Abstract: Synthetic data can reduce the cost of collecting and annotating training data for robotic perception, but generating sensor observations that preserve

Beyond Quadratic Loss: The Stability Phase Diagram of Adam

arXiv:2609.18314v1 Announce Type: new Abstract: Loss spikes are recurrent instabilities in neural-network training and can arise from multiple mechanisms. For Adam in particular, macroscopic loss spik

Beyond Random Couplings: Contrastive Noise Alignment in Generative Flows

arXiv:2609.18488v1 Announce Type: cross Abstract: Diffusion and flow-matching models are typically trained by corrupting data through independently sampled Gaussian noise. While simple and scalable, t

safetyarxiv-cs-lg

Beyond Routine Compliance: Cunning Data Cultivates Safety Vigilance in Large Language Models

arXiv:2609.18515v1 Announce Type: new Abstract: Safety alignment teaches large language models (LLMs) to recognize harmful requests and reject risky instructions. Yet aligned models can fail when harm

Beyond Static RAG: An Adaptive, Tri-Metric Routing Framework for Efficient Long-Context Inference on Commodity GPUs

arXiv:2609.17564v1 Announce Type: new Abstract: Deploying retrieval-augmented generation (RAG) on commodity GPUs such as the NVIDIA T4 (16 GB VRAM) exposes a practical failure mode we call the Compres

local-aiarxiv-cs-lg

Beyond the Previous Layer: Residual Predictive Structure in Sparse MoE Routing

model-releasesarxiv-cs-lg

arXiv:2609.17940v1 Announce Type: new Abstract: Sparse mixture-of-experts models route each token through a sequence of expert selections. We ask whether the immediately preceding selection adequately

Beyond Truncation: Rethinking LLM Decoding as Ensemble Pruning

arXiv:2609.18723v1 Announce Type: new Abstract: We introduce Mahalanobis-Ensemble Decoding (ME-Decoding), a novel Large Language Model (LLM) decoding framework that frames candidate token selection as

researcharxiv-cs-ai

Bias Amplification in Multi-Agent Network: How Biased Agents Shape Opinions and Rhetoric

model-releasesarxiv-cs-lg

arXiv:2609.18306v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in applications involving interaction between agents, where their output plays a role in collecti

BLADE: ReliaBle Dynamic Hardware-Aware SNN-ANN Boundary SeLection for Event-BAseD Object DEtection

arXiv:2609.17562v1 Announce Type: cross Abstract: Hybrid Spiking Neural Network (SNN)-Artificial Neural Network (ANN) architectures combine the energy efficiency of SNNs with the superior detection ac

local-aiarxiv-cs-ai

Agents

Body-Motion Control of a Simulated Aerial Swarm from a First-Person View

arXiv:2609.18881v1 Announce Type: new Abstract: First-person-view (FPV) teleoperation of aerial swarms requires an operator to coordinate collective translation, viewing direction, and formation spaci

agentsarxiv-cs-ro

BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models

arXiv:2505.01912v3 Announce Type: replace-cross Abstract: Data-driven molecular discovery leverages artificial intelligence/machine learning (AI/ML) and generative modeling to filter and design novel

Bracketing Uncertainty in Clustering Under the Manifold Hypothesis

arXiv:2609.17892v1 Announce Type: cross Abstract: The manifold hypothesis suggests a natural criterion for clustering: partition data according to the manifold component from which each point is drawn

Breaking the T^{2/3} Barrier for Sequential Calibration

arXiv:2406.13668v4 Announce Type: replace Abstract: A set of probabilistic forecasts is calibrated if each prediction of the forecaster closely approximates the empirical distribution of outcomes on t

Building Trust in Artificial Intelligence: A Necessity for Railway Applications

arXiv:2609.18278v1 Announce Type: new Abstract: Artificial Intelligence (AI) is currently only applied to non-safety critical applications due to the strict standards and regulations for railway indus

safetyarxiv-cs-ai

Butterfly Effect and the Kinetic Energy Cascade in Probabilistic Machine Learning Weather Prediction Models

arXiv:2609.18489v1 Announce Type: cross Abstract: This study analyses kinetic energy (KE) spectra, difference kinetic energy (DKE) spectra, and signatures of KE transfer across spatial scales in four

Bypassing the Rationale: Causal Auditing of Implicit Reasoning in Language Models

arXiv:2602.03994v3 Announce Type: replace-cross Abstract: Chain-of-thought (CoT) prompting is widely used as a reasoning aid and is often treated as a transparency mechanism. Yet behavioral gains unde

Applications

CADSplat: Sparse-View 3D Gaussian Splatting Aided by CAD Models for Robust, Photorealistic Digital-Twin Reconstruction

arXiv:2609.18473v1 Announce Type: new Abstract: We present CADSplat, a framework that reconstructs photorealistic, geometrically accurate digital twins from sparse (<15 views), wide-baseline posed ima

applicationsarxiv-cs-cv

Calibrated Probabilistic Obstruction Reasoning with Vision-Language Models for Grasping in Clutter

model-releasesarxiv-cs-ro

arXiv:2609.18718v1 Announce Type: new Abstract: Retrieving a target from clutter requires deciding whether to grasp the target, remove a blocker, or defer. Existing methods typically commit to a singl

CALIPER: Metric-Grounded Model-Free Recognition of Visually Similar Industrial Parts

arXiv:2609.17820v1 Announce Type: new Abstract: Fine-grained recognition of visually similar industrial parts is challenging when classes differ primarily in physical dimensions. Normalizing detected

researcharxiv-cs-cv