A survey that unifies prior work on multi-agent LLM systems via the LIFE framework, mapping dependencies across collaboration, failure attribution, and autonomous self-evolution while identifying cross-stage challenges.
arXiv preprint arXiv:2405.03256 , year =
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 3
dataset 1
citation-polarity summary
verdicts
UNVERDICTED 6representative citing papers
ArgRE embeds abstract argumentation into multi-agent requirements negotiation to deliver argument-level traceability, higher evaluator-rated justifications, and improved compliance coverage over heuristic baselines.
REAgent improves LLM patch generation for software issues by 17.4% on average through automated construction, quality checking, and iterative refinement of structured issue-oriented requirements.
A clustering-based pipeline generates individual and integration-level test specifications from thousands of automotive requirements by grouping embeddings, summarizing clusters, and applying LLM calls with bounded context and standards grounding.
OpsAgent presents a training-free multi-agent framework with dual self-evolution for automated incident management in microservices, claiming SOTA results on OPENRCA benchmark and successful production deployment at Lenovo.
A literature survey that collects and categorizes 124 papers on LLM-based agents for software engineering from SE and agent perspectives.
citing papers explorer
-
Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems
A survey that unifies prior work on multi-agent LLM systems via the LIFE framework, mapping dependencies across collaboration, failure attribution, and autonomous self-evolution while identifying cross-stage challenges.
-
ArgRE: Formal Argumentation for Conflict Resolution in Multi-Agent Requirements Negotiation
ArgRE embeds abstract argumentation into multi-agent requirements negotiation to deliver argument-level traceability, higher evaluator-rated justifications, and improved compliance coverage over heuristic baselines.
-
REAgent: Requirement-Driven LLM Agents for Software Issue Resolution
REAgent improves LLM patch generation for software issues by 17.4% on average through automated construction, quality checking, and iterative refinement of structured issue-oriented requirements.
-
Cluster-Aware Dual-Level Test Specification Generation for Large-Scale Automotive Software Requirements
A clustering-based pipeline generates individual and integration-level test specifications from thousands of automotive requirements by grouping embeddings, summarizing clusters, and applying LLM calls with bounded context and standards grounding.
-
OpsAgent: An Evolving Multi-agent System for Incident Management in Microservices
OpsAgent presents a training-free multi-agent framework with dual self-evolution for automated incident management in microservices, claiming SOTA results on OPENRCA benchmark and successful production deployment at Lenovo.
-
Large Language Model-Based Agents for Software Engineering: A Survey
A literature survey that collects and categorizes 124 papers on LLM-based agents for software engineering from SE and agent perspectives.