Aug 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
This work introduces TriArchitect, a novel shared-state multi-agent framework designed to bridge the gap between probabilistic reasoning and deterministic structural integrity, and introduces a rigorous Validator-Veto Protocol, which enforces a "compilation-as-consensus" rule to reject hallucinated proposals before they corrupt the codebase.
Abstract
As the global software infrastructure ages, the modernization of legacy systems—particularly the migration of monolithic Java 8 applications to contemporary Long-Term Support (LTS) versions like Java 17 or 21—has become a critical imperative for security, performance, and maintainability. While Large Language Models (LLMs) have demonstrated exceptional proficiency in isolated code generation tasks, their application to repository-scale migration is severely constrained by "contextual hallucinations"—a phenomenon where models generate syntactically correct but structurally invalid code due to a lack of global state awareness. Recent empirical studies (e.g., Collu-Bench [1]) indicate that determining the hallucination status of generated code remains an unsolved problem, with prediction accuracy often below 33%. In this work, we introduce TriArchitect, a novel shared-state multi-agent framework designed to bridge the gap between probabilistic reasoning and deterministic structural integrity. TriArchitect introduces three key innovations: (1) the Typed Migration Graph (TMG), a formal directed acyclic graph that serves as a persistent semantic memory of the migration state; (2) a specialized role-based multi-agent architecture comprising an Archeologist (analysis), Architect (synthesis), and Validator (verification); and (3) a rigorous Validator-Veto Protocol, which enforces a "compilation-as-consensus" rule to reject hallucinated proposals before they corrupt the codebase. We evaluate TriArchitect on J8-to-J17-Bench, a comprehensive benchmark of 1,000 method-level and class-level migration tasks. Results demonstrate a System Success Rate (SSR) of 68.4%, statistically outperforming significant baselines including OpenRewrite (62.0%), SWE-Agent (63.5%), AutoCodeRover (64.8%), and raw GPT-5.1 (65.2%), while reducing token consumption by 40% through state-aware prompting.
Multi-agent systems built on large language models (LLMs) are increasingly deployed for complex tasks requiring autonomous planning, tool use, and inter-agent coordination. However, the non-deterministic nature of LLM outputs and the emergent behavior arising from agent interactions render traditional test oracles ineffective, creating a critical gap in quality assurance for agentic AI. This work introduces MORPHAGENT, a framework designed to address the oracle problem in multi-agent LLM systems through trace-based behavioral analysis. Our contributions are threefold: (1) goal-preservation relations that verify consistent goal achievement under input perturbations, (2) coordination-consistency relations that validate inter-agent delegation and communication patterns under agent substitution and reordering, and (3) tool-use integrity relations that ensure semantic equivalence of tool invocation sequences under prompt paraphrasing. MorphAgent instruments agent execution to capture structured traces comprising planning steps, tool calls, message exchanges, and final outputs, then systematically applies metamorphic transformations and checks behavioral invariants without requiring ground-truth oracles. We evaluate the framework on four multi-agent benchmarks spanning code generation, research synthesis, customer service, and data analysis tasks, encompassing 2,840 source-followup execution pairs across three LLM backends. Results show that MORPHAGENT detects 82.0% of seeded behavioral faults, including 90.3% of coordination failures and 81.7% of goal-deviation faults, while maintaining a false positive rate of 6.1%. The framework uncovers 14 previously unreported behavioral anomalies in established multi-agent frameworks, demonstrating its practical utility for assuring agentic AI reliability. These results suggest that trace-based metamorphic testing can serve as a practical foundation for reliable validation of emerging agentic AI systems.
Gopalakrishnan Marimuthu· International Conference on...· 0 citations
A unified systems foundation and reference architecture for the agentic skills ecosystem is established, formalize skills as externalized procedural knowledge bridging high-level cognitive planning with deterministic execution environments, and systematically delineate the architecture across a nine-stage lifecycle.
Sanket Badhe, D. Shah, Priyanka Tiwari et al.· 0 citations
LLM-based code generation fails when correctness depends on execution-dependent coupling: the meaning of one routine is defined by the runtime behavior of another, a relationship that cannot be resolved from textual descriptions alone. This limitation, which we call static binding, is not confined to explicitly coupled problems; it appears to varying degrees whenever correctness depends on joint execution behavior across components, from explicit cross-coupled optimizers to subtler joint constraints in packing, routing, and symbolic search. This paper proposes dynamic context adaptation, a sample-efficient validation-generation loop designed for this setting. A validation agent extracts structured diagnostic information from execution traces, providing gradient-like guidance to a generation agent that proposes multiple candidates per iteration. A knowledge graph derived from the problem description supplies semantic constraints to the generation agent. Simulated annealing selects among candidates to avoid greedy collapse. Our method outperforms zero-shot, Reflexion, and OpenEvolve on seven of eight problems at both 300 and 600 evaluations (p<0.01), a regime where population-based search has not yet accumulated sufficient diversity to compete. Notably, on the primary motivating problem (cross-coupled optimization), our method also achieves the best score at 1000 evaluations, consistent with the hypothesis that structured execution feedback is most beneficial when correctness depends on runtime coupling. Ablation results confirm that structured execution feedback is the primary driver.
Gnaneswar Villuri, Hashmath Shaik, Alex Doboli· 0 citations
DDBench is introduced, a code-repair benchmark of 60 historical bugs mined from 13 open-source distributed systems, partitioned into three difficulty tiers, isolating the effect of debugging context from model capability.
Yibo Yan, Huijuan Wang, Junzhou He et al.· 0 citations
Repo0 is presented, a continuous structural evolution framework for zero-to-all code generation that maintains an explicit architectural state instantiated as a Dual-Directed-Acyclic-Graph (Dual-DAG), consisting of a requirement-level DAG, a component-level DAG, and their alignment relation.
Si-Lin Chen, Haoyi Teng, Xiao-Dong Gu et al.· 0 citations
A configurable multi-agent framework for benchmarking LLMs in Prolog code generation that combines a Code Generator Agent, a deterministic execution layer using SWI-Prolog, and an evaluator based on the LLM-as-a-Judge paradigm that supports model-agnostic experimentation and evaluates outputs across functional correctness, efficiency, structural properties and evaluator reliability.
Nikolaos Karamousalidis, P. Kefalas· SN Computer Science· 0 citations
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