It is demonstrated that communication format and implementation details determine outcomes more than architectural complexity, and that simpler approaches match or outperform multi-agent pipelines for local 7B model deployment.
Abstract
Multi-agent LLM pipeline systems break down the task among multiple roles for better reasoning, but are benchmarked mainly with large-scale commercial models. In this study, we investigate Parishad, a structured multi-agent system involving five roles, by deploying it on Qwen2.5-7B-Instruct, a local model, on two datasets: GSM8K (500 questions) and HumanEval (164 questions), compared with prompting directly and two-call self-refinement. The multi-agent system drops GSM8K accuracy from 75.0\% to 45.0\% with JSON data format due to the error accumulation problem. With plaintext format, the accuracy is restored to 82.0\%. A two-call self-refinement strategy (V1) can achieve 86.2\% accuracy on GSM8K, with 7.4$\times$ lower token usage. However, the same V1 implementation on HumanEval---where direct accuracy is already 96.3\%---actively destroys performance (66.5\%). A task-aware gated redesign (V2) applied to HumanEval preserves accuracy at 95.1\%. Our results demonstrate that communication format and implementation details determine outcomes more than architectural complexity, and that simpler approaches match or outperform multi-agent pipelines for local 7B model deployment. All code and data are released.
To test whether the taxonomy supports mitigation, TART, Taxonomy-Guided Actionable Representation, is introduced that makes the taxonomy's key aspects explicit to the planner and downstream sub-agents and consistently improves performance.
Vikas Pahuja, J. Brokman, O. Hofman et al.· 0 citations
ExRole is introduced, a trajectory-to-role framework that learns future-aware role prototypes from prefix-local team traces, resolves them into readable instructions and token-aligned role markers, and optionally routes shared LoRA rank slots with turn-aligned credit.
Zhou Liu, Chaoyang Han, Zewei Pan et al.· 0 citations
A multi-agent pipeline-based approach for solving competitive programming problems via Large Language Models (LLMs) is developed and the Writer-Reviewer pipeline where the Writer Agent produces Python solutions and the Reviewer Agent gives static natural language feedback is analyzed.
Temel Kaan Ekiz, M. Z. Konyar· ADBA Computer Science· 0 citations
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 taxonomy of 8 categories covering ambiguous and unanswerable questions is addressed, a multi-agent generation pipeline with a two-stage process (NLQ generation followed by SQL grounding) and an explicit Category Conformance validation stage are addressed.
Giovanni Sullutrone, Luca Sala, Sania Aftar et al.· arXiv.org· 0 citations
AgentWeave is introduced, a deterministic pre-inference routing layer that constructs a bounded model-visible action space using eligibility, requirement, capability, and routing signals and shows lower mean local-model latency.
Saurav Singla, A. Singla, Advik Gupta et al.· 0 citations
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