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arXiv · 2601.01857

Jenius Agent: Towards Experience-Driven Accuracy Optimization in Real-World Scenarios

Defei Xia·Bingfeng Pi·Shenbin Zhang·Song Hua·Yunfei Wei·Lei Zuo
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Abstract

As agent systems powered by large language models (LLMs) advance, improving performance in context understanding, tool usage, and long-horizon execution has become critical. However, existing agent frameworks and benchmarks provide limited visibility into execution-level behavior, making failures in tool invocation, state tracking, and context management difficult to diagnose. This paper presents Jenius-Agent, a system-level agent framework grounded in real-world deployment experience. It integrates adaptive prompt generation, context-aware tool orchestration, and layered memory mechanism to stabilize execution and improve robustness in long-horizon, tool-augmented tasks. Beyond system design, we introduce an evaluation methodology that jointly measures procedural fidelity, semantic correctness, and efficiency. This framework makes agent behavior observable as a structured execution process and enables systematic analysis of failure modes not captured by output-only metrics. Experiments on Jenius-bench show substantial improvements in task completion rate, with up to a 35 percent relative gain over the base agent, along with reduced token consumption, response latency, and tool invocation failures. The framework is already deployed in Jenius ({https://www.jenius.cn}), providing a lightweight and scalable solution for robust, protocol-compatible autonomous agents.

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Defei Xia, Bingfeng Pi, Shenbin Zhang, Song Hua, Yunfei Wei, Lei Zuo. 2026-01-05. Jenius Agent: Towards Experience-Driven Accuracy Optimization in Real-World Scenarios. https://arxiv.org/abs/2601.01857

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