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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2603.26034 |
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| _version_ | 1866912984873304064 |
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| author | Gao, Wenbo Liu, Renxi Wang, Xian Guo, Fang Yang, Shuai Chen, Xi Zhen, Hui-Ling Chen, Hanting Lin, Weizhe Li, Xiaosong Wang, Yaoyuan |
| author_facet | Gao, Wenbo Liu, Renxi Wang, Xian Guo, Fang Yang, Shuai Chen, Xi Zhen, Hui-Ling Chen, Hanting Lin, Weizhe Li, Xiaosong Wang, Yaoyuan |
| contents | Autonomous agents powered by large language models (LLMs) perform complex tasks through long-horizon reasoning and tool interaction, where a fundamental trade-off arises between execution efficiency and reasoning robustness. Models at different capability-cost levels offer complementary advantages: lower-cost models enable fast execution but may struggle on difficult reasoning segments, while stronger models provide more robust reasoning at higher computational cost. We present AgentCollab, a self-driven collaborative inference framework that dynamically coordinates models with different reasoning capacities during agent execution. Instead of relying on external routing modules, the framework uses the agent's own self-reflection signal to determine whether the current reasoning trajectory is making meaningful progress, and escalates control to a stronger reasoning tier only when necessary. To further stabilize long-horizon execution, we introduce a difficulty-aware cumulative escalation strategy that allocates additional reasoning budget based on recent failure signals. In our experiments, we instantiate this framework using a two-level small-large model setting. Experiments on diverse multi-step agent benchmarks show that AgentCollab consistently improves the accuracy-efficiency Pareto frontier of LLM agents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_26034 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AgentCollab: A Self-Evaluation-Driven Collaboration Paradigm for Efficient LLM Agents Gao, Wenbo Liu, Renxi Wang, Xian Guo, Fang Yang, Shuai Chen, Xi Zhen, Hui-Ling Chen, Hanting Lin, Weizhe Li, Xiaosong Wang, Yaoyuan Computation and Language Autonomous agents powered by large language models (LLMs) perform complex tasks through long-horizon reasoning and tool interaction, where a fundamental trade-off arises between execution efficiency and reasoning robustness. Models at different capability-cost levels offer complementary advantages: lower-cost models enable fast execution but may struggle on difficult reasoning segments, while stronger models provide more robust reasoning at higher computational cost. We present AgentCollab, a self-driven collaborative inference framework that dynamically coordinates models with different reasoning capacities during agent execution. Instead of relying on external routing modules, the framework uses the agent's own self-reflection signal to determine whether the current reasoning trajectory is making meaningful progress, and escalates control to a stronger reasoning tier only when necessary. To further stabilize long-horizon execution, we introduce a difficulty-aware cumulative escalation strategy that allocates additional reasoning budget based on recent failure signals. In our experiments, we instantiate this framework using a two-level small-large model setting. Experiments on diverse multi-step agent benchmarks show that AgentCollab consistently improves the accuracy-efficiency Pareto frontier of LLM agents. |
| title | AgentCollab: A Self-Evaluation-Driven Collaboration Paradigm for Efficient LLM Agents |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2603.26034 |