Adaptive Latent Agentic Reasoning
Dongwon Jung, Peng Shi, Yi Zhang, Junshan Zhang, Muhao Chen
Why It Matters
What makes this one worth your time
This work is relevant for AI researchers and engineers aiming to optimize the performance of LLM agents by reducing unnecessary computational overhead while maintaining decision-making accuracy.
ALAR enhances LLM agent efficiency by balancing latent and explicit reasoning.
Summary
The paper introduces Adaptive Latent Agentic Reasoning (ALAR), a framework designed to improve the efficiency of large language model (LLM) agents by using compact latent reasoning for routine tasks and explicit chain-of-thought reasoning for complex decisions. The approach is validated through experiments on agentic search and tool-use benchmarks, showing significant reductions in generated tokens while maintaining or improving task accuracy.
Key contributions
- Introduction of the ALAR framework for adaptive reasoning in LLM agents.
- Demonstration of significant token reduction in agentic tasks while maintaining accuracy.
Notable insights
- The dual-mode framework of ALAR selectively uses latent reasoning for routine tasks, reserving explicit reasoning for complex decisions.
- ALAR uses the agent's actions as supervision anchors to learn when latent reasoning suffices.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2606.02871v1 Announce Type: cross Abstract: Large reasoning models improve performance by generating extended chain-of-thought (CoT) reasoning, but this behavior becomes inefficient when applied to LLM agents. Current LLM agents often generate verbose textual reasoning at every decision step and allocate reasoning effort nearly uniformly across turns, leading to substantial inefficiency in multi-turn agentic trajectories. We propose Adaptive Latent Agentic Reasoning (ALAR), a dual-mode framework that uses compact latent reasoning for routine turns and selectively escalates to explicit chain-of-thought when deeper deliberation is needed. ALAR learns latent reasoning by using the agent's actions as supervision anchors and is further optimized to use latent reasoning when it is sufficient for task success and reserve explicit CoT for harder decisions. Experiments on agentic search and tool-use benchmarks show that ALAR maintains comparable or better task accuracy while substantially reducing generated tokens by up to 43.6% in search and 84.6% in tool use. These results demonstrate that ALAR improves the accuracy-efficiency trade-off of LLM agents by reducing unnecessary textual reasoning while preserving explicit deliberation for harder decision steps.