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

📝 Original Info

• Title: Jenius Agent: Towards Experience-Driven Accuracy Optimization in Real-World Scenarios
• ArXiv ID: 2601.01857
• Date: 2026-01-05
• Authors: Defei Xia, Bingfeng Pi, Shenbin Zhang, Song Hua, Yunfei Wei, Lei Zuo

📝 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% relative gain over the base agent, along with reduced token consumption, response latency, and tool invocation failures. The fra...

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