All papers

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.

Proposes Agentless, an agent-free approach to software development issues that compares favorably against autonomous software engineering agents.

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.

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

Maps Erlang and OTP fault-tolerance principles onto multi-agent LLM systems to evaluate supervisory reliability architectures.

Introduces AgentChaosBench, a benchmark for detecting and localizing injected runtime boundary faults in LLM-based multi-agent applications.

The paper shares eight lessons from building an agent pipeline that ports legacy C/C++ code to memory-safe CHERI hardware architectures.

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

Compares four multi-agent workflow architectures to determine how coordination structure affects filtering of static analysis alerts.

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

This paper introduces a multi-agent system that evaluates code extensibility and detects SOLID design violations through automated code extension.

The paper proposes a graph-based framework that integrates multi-agent decisions, rationales, and memory transitions to improve assistant explainability.

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

Proposes a conceptual framework to evaluate the resilience of LLM-based multi-agent systems in software engineering tasks.

The paper reviews architectures, reasoning methods, and multi-agent collaboration strategies for agentic language models.

Introduces READ-MAS, a multi-agent system using retrieval-augmented generation to decompose requirements engineering and software design.

Presents MOSAICO, an orchestration infrastructure for coordinating specialized collaborative LLM agents across software engineering tasks.

This paper presents a multi-agent framework where a central model coordinates specialized agents and tools for business workflow automation.

This paper presents a multi-agent architecture using structured states and human checkpoints to manage software requirements engineering workflows.

The paper evaluates five planning algorithms for breaking down complex goals into executable workflows in agentic AI systems.

The paper evaluates token usage, runtime, and energy consumption across four different memory setups for single-model multi-agent educational systems.

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 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.

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 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 proposes an adaptive strategy using software agents and competency-based ontologies to assist students in e-learning platforms.

The paper presents a spatio-temporal attention framework to predict trajectories of multiple mixed agents in unsignalized traffic roundabouts.

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

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

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 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.

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.

This paper examines challenges and recent machine learning approaches for automating FPGA high-level synthesis hardware design.

The paper compares traditional cloud encryption methods with artificial intelligence based encryption techniques across several performance metrics.

The paper proposes a persistent homology metric to evaluate the topological and structural stability of time-varying social networks.

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

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.

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

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