arXiv:2606.01667v1 Announce Type: new Abstract: Test-time scaling has become a major way to improve large language model reasoning, but its orchestration has remained designer-engineered: a fixed sample budget, a fixed refinement loop, a fixed scoring rule, or a fixed search policy decides how compute is spent, leaving the model in charge of solving but not of orchestration. We introduce ATLAS, an agentic test-time scaling framework in which an LLM orchestrator owns the control loop end-to-end. Through a single action, explore, which dispatches a fresh independent solver on the original proble
The paper introduces a novel framework for LLM orchestration, addressing limitations in current test-time scaling methods that rely on fixed, human-engineered policies.
This development allows LLMs to autonomously manage their own computational resources and refinement processes, leading to more efficient and powerful AI reasoning capabilities.
LLMs can now 'own' their control loop end-to-end, moving beyond mere problem-solving to self-orchestration of compute and refinement strategies.
- · AI developers
- · Cloud computing providers
- · Large Language Model ecosystems
- · Teams focused on fixed, manual LLM orchestration
- · Current generation of 'designer-engineered' AI systems
Increased efficiency and performance of LLMs in complex reasoning tasks through autonomous orchestration.
Accelerated development of more sophisticated AI agents capable of self-optimization and resource management.
Potential for entirely new classes of AI applications where real-time, adaptive compute allocation is critical, impacting various industries.
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Read at arXiv cs.LG