Introduces a self-driven collaboration paradigm where an agent uses its own 'reflection' signals to escalate difficult tasks to a stronger model tier.
March 30, 2026
Original Paper
AgentCollab: A Self-Evaluation-Driven Collaboration Paradigm for Efficient LLM Agents
arXiv · 2603.26034
The Takeaway
Instead of static routing or expensive fixed ensembles, this framework allows small/cheap models to handle 'easy' reasoning steps and only triggers larger/costly models when the internal failure signal spikes. This significantly improves the cost-accuracy Pareto frontier for complex, multi-step agent tasks.
From the abstract
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 se