All papers

The paper introduces an open-source library to construct validation suites that stress test the reliability of LLM judges.

The paper provides a conceptual taxonomy and review distinguishing task-specific AI agents from collaborative agentic AI systems.

The paper provides a conceptual taxonomy and review distinguishing task-specific AI agents from collaborative agentic AI systems.

Examines scanning infrared depolarization measurements for defect monitoring and quality control across semiconductor wafer processing stages.

Summary contains IEEE Xplore webpage boilerplate rather than paper content on command and control for unmanned vehicles.

Presents an empirical comparison evaluating usability, reliability, and CI/CD integration challenges of AI-based unit testing tools.

Proposes an agent memory architecture that uses biologically inspired adaptive decay functions to selectively retain and forget context.

Proposes an agent memory architecture that uses biologically inspired adaptive decay functions to selectively retain and forget context.

Proposes an agent memory architecture that uses biologically inspired adaptive decay functions to selectively retain and forget context.

Proposes an agent memory architecture that uses biologically inspired adaptive decay functions to selectively retain and forget context.

Proposes an agent memory architecture that uses biologically inspired adaptive decay functions to selectively retain and forget context.

Introduces a mixture-of-prompts distillation method to transfer knowledge from hard prompts to soft prompts, improving generalization on unseen classes.

Introduces a mixture-of-prompts distillation method to transfer knowledge from hard prompts to soft prompts, improving generalization on unseen classes.

Introduces a mixture-of-prompts distillation method to transfer knowledge from hard prompts to soft prompts, improving generalization on unseen classes.

Introduces a mixture-of-prompts distillation method to transfer knowledge from hard prompts to soft prompts, improving generalization on unseen classes.

Introduces a mixture-of-prompts distillation method to transfer knowledge from hard prompts to soft prompts, improving generalization on unseen classes.

Introduces a mixture-of-prompts distillation method to transfer knowledge from hard prompts to soft prompts, improving generalization on unseen classes.

Introduces a mixture-of-prompts distillation method to transfer knowledge from hard prompts to soft prompts, improving generalization on unseen classes.

Proposes a distillation framework and structural loss to speed up diffusion-based 3D LiDAR scene completion for autonomous driving perception.

Proposes a distillation framework and structural loss to speed up diffusion-based 3D LiDAR scene completion for autonomous driving perception.

Presents a web platform using GPT-4 and Whisper to deliver interactive interview practice and automated feedback.

Profiles power oscillations and voltage droops in cloud GPUs caused by LLM inference workloads and introduces a stressmark framework.

Uses coarse CTC decoding to filter relevant hotwords into LLM prompts for improved contextual speech recognition on rare words.

Uses coarse CTC decoding to filter relevant hotwords into LLM prompts for improved contextual speech recognition on rare words.

Uses coarse CTC decoding to filter relevant hotwords into LLM prompts for improved contextual speech recognition on rare words.

Uses coarse CTC decoding to filter relevant hotwords into LLM prompts for improved contextual speech recognition on rare words.

Uses coarse CTC decoding to filter relevant hotwords into LLM prompts for improved contextual speech recognition on rare words.

Uses coarse CTC decoding to filter relevant hotwords into LLM prompts for improved contextual speech recognition on rare words.

Uses coarse CTC decoding to filter relevant hotwords into LLM prompts for improved contextual speech recognition on rare words.

Integrates monocular 3D pose estimation, Transformer-based stroke classification, and an LLM to evaluate badminton player postures and provide feedback.

The paper introduces a zero-shot LLM pipeline to generate counterfactual examples for stress-testing NLP models.

The paper introduces a zero-shot LLM pipeline to generate counterfactual examples for stress-testing NLP models.

The paper introduces a zero-shot LLM pipeline to generate counterfactual examples for stress-testing NLP models.

The paper introduces a zero-shot LLM pipeline to generate counterfactual examples for stress-testing NLP models.

The paper introduces a zero-shot LLM pipeline to generate counterfactual examples for stress-testing NLP models.

The paper studies using persona-conditioned synthetic LLM users to conduct usability evaluations of complex decision support tools.

The paper develops a time-frequency analysis method to diagnose partial discharge acoustic signals in industrial inspection equipment.

The paper investigates token pruning strategies to accelerate Vision Transformer inference for object detection and instance segmentation.

The paper investigates token pruning strategies to accelerate Vision Transformer inference for object detection and instance segmentation.

The paper presents a cross-attention deep learning framework fusing real-time vital signs and motion sensor data for medical anomaly detection.

The paper introduces an inference-time framework that creates inspection plans to guide LLMs in detecting source code vulnerabilities.

The paper proposes a frequency-enhanced conditional VAE combined with graph neural networks for anomaly detection in industrial multivariate time series.

The paper presents an autonomous patrol robot combining mapping and computer vision to detect threats and manage security incidents.

The paper proposes an edge-cloud collaborative inspection framework using multimodal LLMs dynamically scheduled based on scene complexity.

The paper proposes an edge-cloud collaborative inspection framework using multimodal LLMs dynamically scheduled based on scene complexity.

The paper proposes an edge-cloud collaborative inspection framework using multimodal LLMs dynamically scheduled based on scene complexity.

The paper presents a multi-agent framework that dynamically generates execution context to enable partial code execution.

The paper cleans code smells from training datasets to evaluate and improve the quality of LLM-generated code.

Presents AIMA, an autonomous multi-agent system using retrieval-augmented generation to manage Instagram content strategy, generation, and scheduling.

Presents an IEEE publication page stub regarding methods to maximize web service retrieval.

Surveys AI-based phishing detection methods, focusing on hybrid architectures that combine reinforcement learning with retrieval-augmented generation.