Search: “framework”

Competitive Market Behavior of LLMs

arXiv:2609.02580v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as economic agents, yet there is little evidence whether LLM agents are suited for participatin

safetyarxiv-cs-ai

Tutorials

Consistency as Regularization for Unsupervised Shadow Removal

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arXiv:2609.01806v1 Announce Type: new Abstract: Shadow removal is an important preprocessing step for many vision tasks, yet existing supervised methods require paired shadow and shadow-free images, w

tutorialsarxiv-cs-cv

3 Sept 2026

Contact-Constrained Lower-Limb Joint-Offset Calibration for Humanoid Robots

arXiv:2609.02306v1 Announce Type: new Abstract: Accurate joint encoder offsets are essential for kinematic consistency in humanoid lower limbs, yet existing calibration methods typically require exter

researcharxiv-cs-ro

CoViT: Instance-Correspondence Contrastive Learning for Vision Transformer

model-releasesarxiv-cs-cv

arXiv:2609.01787v1 Announce Type: new Abstract: Vision Transformers (ViT) excel in semantic understanding but fail to discriminate between object instances (e.g., identical embeddings for two dogs), l

CrashDiffuser: VLM-Guided Collision Intent Reasoning for Fine-Grained Safety-Critical Traffic Scenario Generation

arXiv:2609.02270v1 Announce Type: cross Abstract: Generating safety-critical scenarios is essential for evaluating autonomous driving systems. However, existing generators primarily focus on inducing

safetyarxiv-cs-ai

Data-Efficient Networks for Multi-Contrast MRI Reconstruction based on a Generalized Content/Style Prior

model-releasesarxiv-cs-cv

arXiv:2609.01959v1 Announce Type: cross Abstract: Multi-contrast MR scans contain redundant structural information that can be leveraged during reconstruction and potentially accelerate acquisition ti

Detecting Object Hallucinations in Large Vision-Language Models via Cross-Modal Attention Drifts and Mask-Based Verification

arXiv:2609.02028v1 Announce Type: new Abstract: Despite recent advances in large vision-language models (LVLMs), object hallucination remains a major barrier to their reliable deployment. Existing det

DMRL: Document-Mediated Reinforcement Learning for Skill Optimization in Advertising Recommendation

arXiv:2609.02170v1 Announce Type: new Abstract: Advertising recommendation requires continuously tuning complex system parameters while balancing commercial returns and user experience. Recent work ha

safetyarxiv-cs-lg

DPA: Decoupling Product-Agnostic Anomaly Representations for Zero-shot Anomaly Generation

model-releasesarxiv-cs-cv

arXiv:2609.02075v1 Announce Type: new Abstract: Industrial anomaly detection benefits from anomaly samples, yet newly deployed products typically provide only normal images, making anomaly samples dif

DWFF-Net: A Multi-Scale Farmland System Habitat Identification Method with Adaptive Dynamic Weight Feature Fusion

arXiv:2511.11659v3 Announce Type: replace Abstract: To address insufficient accuracy in multi-scale segmentation for agricultural habitat recognition, this study proposes a Dynamic Weighted Feature Fu

EarlyEval: Cheaper Agent Evaluation via Early Outcome Prediction

arXiv:2609.02783v1 Announce Type: new Abstract: Evaluating LLM agents is essential for guiding their development, yet it has grown prohibitively expensive: a single pass of a frontier model over an ag

Emotional regulation improves deep learning-based image classification

arXiv:2606.13081v2 Announce Type: replace-cross Abstract: Emotion significantly influences cognition, enhancing memory and learning under certain conditions. Drawing on this principle, emotion-augment

researcharxiv-cs-ai

Enhancing brain age estimation with structural MRI and synthesized cerebral blood volume maps

arXiv:2412.01865v5 Announce Type: replace-cross Abstract: BrainAGE is a promising imaging-derived biomarker of neurobiological ageing and disease risk, yet current approaches rely predominantly on T1-

researcharxiv-cs-lg

EvalDetectBench: A Benchmark for Measuring Evaluation Awareness in Frontier Language Models

arXiv:2609.01611v1 Announce Type: new Abstract: Frontier large language models can often recognize when they are being evaluated, a capability known as evaluation awareness. If models behave different

ExecRetrieval: Measuring the Functional-Correctness Gap in Code-Embedding Retrieval

arXiv:2609.01865v1 Announce Type: cross Abstract: Embedding-based code retrieval is a core component of coding agents and retrieval-augmented code generation, where retrieving correct code matters mor

FDARxBench: Benchmarking Regulatory and Clinical Reasoning on FDA Generic Drug Assessment

arXiv:2603.19539v2 Announce Type: replace-cross Abstract: We introduce an expert curated, real-world benchmark for evaluating document-grounded question-answering (QA) motivated by generic drug assess

From Detection to Characterization: A Large-Scale Study of Ragebait on Japanese X

arXiv:2609.02262v1 Announce Type: cross Abstract: Ragebait refers to online content intentionally designed to provoke anger or outrage and thereby increase attention and engagement. However, reliable

GDB-Reward: From Evaluation Metrics to Training Rewards for Graphic Design

arXiv:2609.02813v1 Announce Type: new Abstract: Text-to-image models excel at natural image synthesis but struggle with graphic design, where success depends on satisfying precise constraints on typog

Generalizable Brain Tumor Segmentation with Self-Training and Tumor-Aware Deformations

arXiv:2609.02600v1 Announce Type: new Abstract: This work presents an approach to the Generalizability Across Tumors (BraTS-GoAT) task of the BraTS 2026 Challenge, which focuses on robust segmentation

Applications

Generating Medical Image Counterfactuals using Causal Explanations

arXiv:2609.02697v1 Announce Type: new Abstract: Deep learning models have achieved impressive performance in medical image diagnosis, yet their deployment in clinical settings remains constrained by l

applicationsarxiv-cs-cv

Geometric Distillation from Rectified Stereo: Leveraging Epipolar Cues for Monocular Depth

arXiv:2607.15600v2 Announce Type: replace Abstract: Monocular depth foundation models have demonstrated remarkable generalization capabilities across diverse environments. However, they continue to st

safetyarxiv-cs-cv

Local Ai

GeoSPRINT: Geometric Redundancy-Aware Step Pruning for Inference in Diffusion Trajectories

arXiv:2609.02160v1 Announce Type: cross Abstract: Diffusion models achieve high sample quality but remain expensive at inference time because sampling requires many sequential neural function evaluati

local-aiarxiv-cs-ai

GMTRouter: Personalized LLM Router over Multi-turn User Interactions

arXiv:2511.08590v2 Announce Type: replace Abstract: Large Language Model (LLM) routing has demonstrated strong capability in balancing response quality with computational cost. As users exhibit divers

H3DNAS: Hardware-Aware ONNX-Native 3D Point Cloud Model Compression

model-releasesarxiv-cs-lg

arXiv:2609.02684v1 Announce Type: new Abstract: Deploying 3D point cloud models on edge hardware such as the NVIDIA Jetson Orin Nano is severely constrained by compute and memory budgets. Existing com

Handwriting Trajectory Recovery via Autoregressive Ordered Stroke Instance Prediction

arXiv:2609.02251v1 Announce Type: new Abstract: Handwriting trajectory recovery aims to infer the dynamic writing process hidden behind a static handwritten image. Since offline handwriting preserves

HarmReduction: Benchmarking LLMs in Harm Reduction Information Provision to Support People Who Use Drugs

arXiv:2507.21815v2 Announce Type: replace Abstract: Millions of individuals' well-being are challenged by the harms of substance use. Harm reduction as a public health strategy provides non-judgementa

Harness Engineering in LLM Tool Use via Agent-Native Reusable Tool Primitives

arXiv:2609.01736v1 Announce Type: cross Abstract: Large language models (LLMs) augmented with external tools have demonstrated remarkable capability in solving complex real-world tasks. However, exist

Local Ai

HeadWiseKV: Budgeted Per-Head Cache Residency for Hybrid Long-Context Language Models

arXiv:2609.02029v1 Announce Type: new Abstract: Long-context inference retains a growing key--value (KV) cache during decoding, which consumes substantial GPU memory and can reduce generation throughp

local-aiarxiv-cs-ai

Hearing the Whispers: Black-Box Membership Inference Attacks on Finetuned TTS Models

model-releasesarxiv-cs-lg

arXiv:2609.01723v1 Announce Type: cross Abstract: Text-to-Speech (TTS) foundation models are increasingly fine-tuned on private datasets to synthesize highly personalized voices, introducing severe pr

HINT: Human-Intent Inception for Long-Horizon Robot Manipulation

arXiv:2609.02653v1 Announce Type: new Abstract: Humans can perform complex manipulations given a simple intent through an overall instruction, while continuously adapting to evolving visual observatio

safetyarxiv-cs-ro

How Fast Do Agents Rot? An Empirical Study of Long-Horizon Degradation in LLM Agents for Production Decision-Making

arXiv:2609.01660v1 Announce Type: cross Abstract: Production deployments of large language model (LLM) agents remain unreliable on long, multi-step workflows even as benchmark success rates climb stea

Humanoid Safe Stop via Learned Stoppability Value

arXiv:2609.02358v1 Announce Type: cross Abstract: Humanoid robots responding to emergency stop commands typically execute a fixed maneuver, without reasoning about whether a safe stop is actually feas

safetyarxiv-cs-lg

Agents

HyGRAIL: Cost-Aware and Evidence-Grounded Scientific Hypothesis Discovery over Knowledge Graphs

arXiv:2609.02056v1 Announce Type: new Abstract: Scientific knowledge graphs organize entities and relations extracted from scientific literature, but they remain inherently incomplete. Missing typed l

agentsarxiv-cs-cl

HyperMC: Multi-Fidelity Hyperparameter Tuning for Stochastic Gradient MCMC

arXiv:2609.02138v1 Announce Type: cross Abstract: Stochastic gradient Markov chain Monte Carlo (SGMCMC) methods enable scalable Bayesian inference, but their performance depends strongly on hyperparam

researcharxiv-cs-lg

ICE: Intervention-Consistent Explanation Evaluation with Statistical Grounding for LLMs

arXiv:2603.18579v2 Announce Type: replace-cross Abstract: Evaluating whether explanations faithfully reflect a model's reasoning remains an open problem. Existing benchmarks use single interventions w

researcharxiv-cs-ai

IDEEA: training-free Input-Dependent stEEring via Activation cluster matching

arXiv:2609.02089v1 Announce Type: new Abstract: Steering aligns large language models (LLMs) by injecting a bias into selected activations at inference time, offering a far cheaper alternative to weig

safetyarxiv-cs-cl

Import What You Need: Learning When and How to Augment EHR Graphs with External Knowledge

arXiv:2609.01839v1 Announce Type: cross Abstract: Longitudinal prediction from electronic health records (EHRs) is limited by the sparsity and irregularity in patient trajectories, and knowledge augme

safetyarxiv-cs-ai

Improving Health Literacy through Lay Summarization of Radiological Reports: An Evaluation of BioNER and Retrieval-Augmented Generation

arXiv:2609.02396v1 Announce Type: new Abstract: Radiology reports are written primarily for clinicians, and their specialized terminology often makes them difficult for patients to interpret. As a res

Local Ai

InfraPatch: Cross-Task Targeted Grayscale Patch Attacks on Infrared-Adapted Vision-Language Models

arXiv:2609.02233v1 Announce Type: cross Abstract: Infrared vision-language models (IR-VLMs) have emerged as a promising paradigm for multimodal perception under low-visibility conditions, yet their ro

local-aiarxiv-cs-ai

Agents

InsightSeg: Reusing Correction Insights for Guideline-Consistent Segmentation

arXiv:2609.02002v1 Announce Type: cross Abstract: Guideline-consistent semantic segmentation requires more than category recognition, as real-world labeling policies demand fine-grained, task-specific

agentsarxiv-cs-ai

Koopman-Based Robust Model Predictive Control for Nonlinear Systems with Stochastic Intermittent Measurements

arXiv:2609.02079v1 Announce Type: new Abstract: Intermittent state measurements pose fundamental challenges to model predictive control of constrained nonlinear systems because prediction uncertainty

researcharxiv-cs-ro

Agents

KSG-Net: Key-Sparse and Global-Context Learning for Maritime 3D Ship Detection

arXiv:2609.02077v1 Announce Type: new Abstract: Accurate 3D ship detection in maritime environments is critical for autonomous navigation, yet remains challenging due to large-scale vessel variations,

agentsarxiv-cs-cv

Language Model Maps for Prompt-Response Distributions via Log-Likelihood Vectors

arXiv:2603.18593v2 Announce Type: replace Abstract: We propose a method that represents language models by log-likelihood vectors over prompt-response pairs and constructs model maps for comparing the

Latent Cluster Analysis for Vision-Language-Action Models

arXiv:2609.02634v1 Announce Type: new Abstract: Vision-Language-Action (VLA) Models are increasingly used in robotics for their ability to ground language and perception into action, yet the internal

researcharxiv-cs-ro

Tutorials

Latent unified smooth Hamiltonians for excited state chemistry

arXiv:2609.01871v1 Announce Type: cross Abstract: We describe a neural network architecture and training procedure designed to model electronic ground and excited states of arbitrary molecular systems

tutorialsarxiv-cs-lg

Learning-Based Reconstruction Attacks on Coordinate-Obfuscated Point Clouds

arXiv:2609.02568v1 Announce Type: cross Abstract: Volumetric video based on point cloud representations enables immersive virtual and augmented reality applications but introduces significant challeng

researcharxiv-cs-lg

Learning Evidence Sufficiency Boundaries for Selective Answering in Grounded Multi-Hop QA

arXiv:2609.01687v1 Announce Type: new Abstract: Grounded question answering systems should answer only when the supplied evidence supports the answer. In multi-hop QA, this requirement is difficult be

Learning to Fuse LLMs with Ontology Rankers for Rare-Disease Diagnosis

arXiv:2609.02473v1 Announce Type: new Abstract: Ontology rankers remain useful for rare-disease diagnosis because each candidate can be traced to matched patient phenotypes. Large language models (LLM