All papers

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

This paper presents a benchmark to evaluate how adversarial attacks affect reasoning security in multi-agent debate systems.

The paper analyzes 33,000 GitHub pull requests to examine why AI coding agent contributions succeed or fail.

The paper introduces a system layer that separates control flow from untrusted data to protect language model agents against prompt injection attacks.

This paper introduces specification templates to define operational boundaries, authority, and oversight for autonomous software agents.

This survey paper reviews attacks, defenses, and evaluation methods across 135 studies on secure retrieval-augmented generation systems.

Studies how chain-of-thought reasoning affects social bias and code quality during LLM code generation.

Proposes DataFilter, a model-agnostic test-time defense that strips prompt injection instructions from data before reaching an LLM.

The paper proposes a metamorphic testing method with mutation operators to evaluate how retrieval-augmented generation systems handle changing document corpora.

This paper proposes a non-interactive secure inference framework for Transformer models using fully homomorphic encryption to reduce user overhead.

This paper presents a hierarchical classification method using layer hidden states to detect prompt injection attacks with lower latency.

The paper presents an autonomous framework using multiagent reinforcement learning to monitor, diagnose, and recover from failures in production AI systems.

Discusses artificial intelligence governance frameworks and risk assessment methods for cybersecurity and IT environments.

Presents a taxonomy and lifecycle analysis of hallucination causes in large language models and multimodal systems.

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

Surveys hallucination in language models through a mechanistic framework that maps internal causes to observable errors.

This survey provides an algorithmic overview and failure mode analysis of reinforcement learning techniques applied to language models.

The authors introduce a training loop where multimodal models use text and visual abilities alongside program execution to generate their own training data.

The paper introduces an interactive web-based tool for analyzing and visualizing execution trajectories of software engineering agents.

The paper introduces a method to reduce redundant external knowledge retrieval by estimating what a language model already knows.

The paper applies the sequential minimal optimization algorithm to detect SQL injection attacks using network traffic data.

The paper evaluates ten models for detecting Indonesian AI-generated text and examines how paraphrasing affects detection accuracy.

This paper introduces an adaptive framework to detect radio jamming attacks within machine learning integrated 5G Open RAN architectures.

This position paper proposes multi-agent reinforcement learning and mechanism design to reform academic peer review processes.

This paper presents an agentic framework to compile large multi-modal requirement documents into runnable software systems using test-driven workflows.

This paper surveys foundation models, agent systems, datasets, and computational tools applied to materials science discovery.

The paper evaluates twenty-one language models used as judges across three benchmarks, finding widespread discrepancies between agreement metrics and underlying validity.

The paper analyzes extreme faults in modular multilevel converters and proposes a protection scheme using integrated gate-commutated thyristors.

The paper presents a network utilizing self-attention and spatio-temporal convolutions for 3D skeleton-based human action recognition.

The paper proposes an uncertainty-aware multimodal descriptor fusing sequential camera and LiDAR data for robotic place recognition.

The paper develops a microprocessor-based smart wearable glass system with GPS monitoring to assist visually impaired individuals.

The authors introduce CharXiv, a benchmark of realistic scientific charts designed to test descriptive and reasoning capabilities in multimodal language models.

This paper reports the fabrication and continuous-wave optical performance of laterally coupled distributed feedback semiconductor lasers.

This paper evaluates a buffer-free transistor structure designed to suppress current drift and lag in high-power electronics.

This paper surveys capacitance-voltage measurement techniques to evaluate defect states and reliability in gallium nitride transistor devices.

This paper presents an empirical study evaluating open-source language models for automated unit test generation in software development.

This paper evaluates the ability of language models to generate non-functional requirements from functional software specifications.

This paper introduces an evaluation benchmark using multimodal models to assess generated videos across human preference dimensions.

This paper combines evolutionary computation with language models to optimize register-transfer level hardware code for correctness and efficiency.

The paper presents circuit and electromagnetic models to simulate bulk current injection testing across complex wiring harnesses.

The paper implements high-frequency injection sensorless control on an FPGA-driven silicon carbide inverter for permanent magnet synchronous machines.

The paper examines security risks and protocol vulnerabilities when deploying commercial IT networking technologies in industrial communication systems.

The paper demonstrates reducing laser amplitude and phase noise by injecting femtosecond squeezed vacuum into ultrafast light sources.

The paper evaluates the storage reliability of an engine control circuit module.

The paper presents a zero-failure reliability demonstration method using degradation data for systems.

This paper presents AVNet, an optimized convolutional network designed for efficient FPGA deployment to classify circuit board defects.

This paper presents a mid-training method, corpus, and benchmark to help language models understand and reason about time series data.

This paper compares the quality and coverage of unit test cases generated by ChatGPT against an automated generation tool.

This paper introduces an open-source infrastructure and benchmark for multi-fidelity AI-assisted photonic simulation and inverse design.

The paper reviews retrieval-augmented generation paradigms and examines frameworks across retrieval, generation, and modular components.

The paper presents an 8-bit weight and activation post-training quantization technique that migrates activation outliers to weights.

The paper introduces a post-training mixed-precision quantization method using GPU-compatible 2D weight tiles for efficient inference.

The paper presents an open-source multimodal language model combining a small language backbone with vision encoders for efficient inference.

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.

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

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