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LOGIGEN: Logic-Driven Generation of Verifiable Agentic Tasks

arXiv:2603.00540v1 Announce Type: new Abstract: The evolution of Large Language Models (LLMs) from static instruction-followers to autonomous agents necessitates operating within complex, stateful environments to achieve precise state-transition objectives. However, this paradigm is bottlenecked by data scarcity, as existing tool-centric reverse-synthesis pipelines fail to capture the rigorous logic of real-world applications. We introduce \textbf{LOGIGEN}, a logic-driven framework that synthesizes verifiable training data based on three core pillars: \textbf{Hard-Compiled Policy Grounding}, \textbf{Logic-Driven Forward Synthesis}, and \textbf{Deterministic State Verification}. Specifically, a Triple-Agent Orchestration is employed: the \textbf{Architect} compiles natural-language policy into database constraints to enforce hard rules; the \textbf{Set Designer} initializes boundary-adjacent states to trigger critical policy conflicts; and the \textbf{Explorer} searches this environmen

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Yucheng Zeng, Weipeng Lu, Linyun Liu, Shupeng Li, Zitian Qu, Chenghao Zhu, Shaofei Li, Zhengdong Tan, Mengyue Liu, Haotian Zhao, Zhe Zhou, Jianmin Wu · · 1 min read · 2 views
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arXiv:2603.00540v1 Announce Type: new Abstract: The evolution of Large Language Models (LLMs) from static instruction-followers to autonomous agents necessitates operating within complex, stateful environments to achieve precise state-transition objectives. However, this paradigm is bottlenecked by data scarcity, as existing tool-centric reverse-synthesis pipelines fail to capture the rigorous logic of real-world applications. We introduce \textbf{LOGIGEN}, a logic-driven framework that synthesizes verifiable training data based on three core pillars: \textbf{Hard-Compiled Policy Grounding}, \textbf{Logic-Driven Forward Synthesis}, and \textbf{Deterministic State Verification}. Specifically, a Triple-Agent Orchestration is employed: the \textbf{Architect} compiles natural-language policy into database constraints to enforce hard rules; the \textbf{Set Designer} initializes boundary-adjacent states to trigger critical policy conflicts; and the \textbf{Explorer} searches this environment to discover causal solution paths. This framework yields a dataset of 20,000 complex tasks across 8 domains, where validity is strictly guaranteed by checking exact state equivalence. Furthermore, we propose a verification-based training protocol where Supervised Fine-Tuning (SFT) on verifiable trajectories establishes compliance with hard-compiled policy, while Reinforcement Learning (RL) guided by dense state-rewards refines long-horizon goal achievement. On $\tau^2$-Bench, LOGIGEN-32B(RL) achieves a \textbf{79.5\% success rate}, substantially outperforming the base model (40.7\%). These results demonstrate that logic-driven synthesis combined with verification-based training effectively constructs the causally valid trajectories needed for next-generation agents.

Executive Summary

This article presents LOGIGEN, a novel logic-driven framework for generating verifiable agentic tasks. LOGIGEN employs a triple-agent orchestration to synthesize complex tasks across 8 domains, ensuring strict validity through state equivalence checks. The framework combines supervised fine-tuning and reinforcement learning to establish compliance with hard-compiled policy and refine long-horizon goal achievement. The authors demonstrate the effectiveness of LOGIGEN by achieving a 79.5% success rate on $ au^2$-Bench, outperforming the base model. This breakthrough has significant implications for the development of autonomous agents and highlights the critical need for logic-driven synthesis and verification-based training.

Key Points

  • ▸ LOGIGEN is a logic-driven framework for generating verifiable agentic tasks
  • ▸ The framework employs a triple-agent orchestration to synthesize complex tasks
  • ▸ LOGIGEN combines supervised fine-tuning and reinforcement learning for training
  • ▸ The authors achieve a 79.5% success rate on $ au^2$-Bench, outperforming the base model

Merits

Strength

The framework's ability to generate verifiable agentic tasks addresses a critical need in the development of autonomous agents.

Demerits

Limitation

The framework's complexity and reliance on a triple-agent orchestration may pose challenges for scalability and deployment.

Expert Commentary

The article presents a significant breakthrough in the development of autonomous agents. The LOGIGEN framework's ability to generate verifiable agentic tasks addresses a critical need in the field. However, the framework's complexity and reliance on a triple-agent orchestration may pose challenges for scalability and deployment. The use of logic-driven synthesis and verification-based training is a promising approach, and the authors' results demonstrate the effectiveness of this approach. Nevertheless, further research is needed to fully explore the potential of LOGIGEN and to address the challenges associated with its deployment.

Recommendations

  • ✓ Future research should focus on developing more efficient and scalable methods for the triple-agent orchestration.
  • ✓ The development of LOGIGEN has significant implications for policy decisions related to the development and deployment of autonomous agents, and policymakers should consider these implications when shaping regulations and guidelines.

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