All papers

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

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

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

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

The paper introduces a controlled benchmark evaluating how accurately LLMs determine whether software components match vulnerability advisory criteria.

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

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

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

The paper introduces an evaluation benchmark comprising four clinical datasets to assess large language model performance in the Swedish medical domain.

The paper introduces a zero-shot LLM pipeline to generate counterfactual examples for stress-testing NLP 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 analyzes 33,000 GitHub pull requests to examine why AI coding agent contributions succeed or fail.

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

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

The paper examines how along-path evaporation duct variations affect parabolic equation path-loss predictions using maritime radio measurements.

The paper provides a formal definition and an evaluation framework for concept map mining techniques.

The paper studies electrical impedance spectroscopy using an AC nanopore method to evaluate the dielectric properties of single nanoparticles.

The paper introduces an approximation method using Lagrange interpolation to derive closed-form solutions for continuous-time neural networks.

The paper presents a high-resolution analog-to-digital converter architecture using a sigma-delta modulator for ultrasound color Doppler imaging.

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

This paper presents a dataset, benchmark, and coarse-to-fine learning scheme to detect tiny oriented objects in imagery.

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

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

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

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

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

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

This paper evaluates an ensemble deep learning framework of two convolutional networks for robust and efficient deepfake video detection.

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

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.

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.

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.

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

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

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