All papers

Develops a recursive self-improvement framework where an evolving meta-agent adapts agent workflows based on execution trajectories and feedback.

Develops a domain harness and policy learning pipeline to improve language model agent execution in computer-aided design environments.

Improves deep search agents by refining search queries and filtering evidence to align retrieval with multi-step reasoning goals.

Combines language model dialogue with workflow automation to process satellite imagery for internal solitary wave detection and forecasting.

Uses tool-augmented language models to extract clinical variables from patient notes and reliably execute deterministic medical calculation formulas.

Proposes a training framework enabling aerial vision-language agents to navigate in full action chunks and stop reliably at destinations.

Integrates reinforcement learning and tabular model guidance to help language models select features and predict emergency department clinical risk.

The paper presents an agent system that generates, edits, and renders interactive virtual worlds from text using executable code rules.

Proposes a training-free structural lexical retriever to select relevant agent cards without overloading model context windows.

Uses reinforcement learning to train an agent module that selects, sizes, and orders retrieved memories for downstream tasks.

Presents a multi-agent system for paper screening and data extraction in systematic literature reviews following standard guidelines.

Introduces a multimodal harness and benchmark pairing continuous video with clinical records to assist patient monitoring in hospitals.

The paper proposes an adaptive skill framework that extracts patterns from trajectories to enable multi-modal agents to self-evolve.

The paper introduces an agentic framework combining language model reasoning with symbolic engines for formal inequality proving in Lean4.

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.

Presents the proceedings and overview of the 2nd International Workshop on Multi-Agent Systems for Automated Software Engineering.

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.

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

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This paper presents a control and communication framework using reachable sets and barrier functions to guarantee multi-agent safety during intermittent communication.

This paper introduces a posetal game framework for auditable multi-agent motion planning under partially ordered legal constraints.

This paper establishes analytical geometric bounds on coverage probability and timing uncertainty for multi-agent drone meshes intercepting approaching objects.

Analyzes rational strategy synthesis and equilibrium guarantees for stochastic multi-agent systems with probabilistic temporal logic objectives.

Introduces an auditing framework that checks counterfactual execution traces to ensure language model routing uses only legitimate information.

Proposes a hybrid architecture combining global state-space modeling with local transformer attention and lexical memory for efficient inference.

Presents a benchmark with open-ended tasks and rubrics to assess social awareness and response generation in Chinese.

Introduces an off-policy reinforcement learning algorithm for language models using verifiable rewards without importance sampling weights.

Introduces a benchmark combining natural, scripted, and synthetic audio to evaluate phonetic error detection in speech models.

Tests whether linguistically accurate morpheme boundaries in tokenizers improve translation performance compared to scrambled control segmentations.

Proposes a hierarchical multimodal transformer to model long-term behavioral changes across multi-session interactions for cognitive impairment detection.

Evaluates a multimodal screening method using voice, video, and text from voice-assistant interactions to detect cognitive impairment.