LENS: Learning Architecture Navigator for LLM Agentic Systems

AAAI 2026 Workshop TrustAgent Submission40 Authors

Keywords: LLM, Agent, Multi-Agent System, Agentic System

Abstract: Large Language Model (LLM)-empowered multi-agent systems extend the cognitive boundaries of individual agents through disciplined collaboration, while constructing these systems often requires labor-intensive manual designs. A frontier effort to automate this process is to optimize an Agentic Supernet, a probabilistic distribution of architectures from which query-dependent workflows can be dynamically sampled. However, while this paradigm allows for dynamic resource allocation, its underlying optimization process presents a critical performance bottleneck: inconsistent architectural feedback suppresses reliable credit assignment and prematurely narrows exploration, missing innovative and efficient designs. To address this, we introduce **LENS** (**L**earning-**E**nhanced **N**eural **S**earch for Agentic Workflows), a dual-module framework that systematically resolves both challenges. The *Adaptive Diversity Module (ADM)* maintains comprehensive exploration across the architectural space, while the *Retrospective Guidance Module (RGM)* learns from historical evaluations to provide stable search direction. By decoupling diversity maintenance from directional guidance, LENS achieves robust search that discovers higher-utility, lower-cost configurations. Comprehensive evaluations across diverse benchmarks demonstrate that LENS is: **(I) higher-performing**, achieving up to 13.63% accuracy improvement on challenging benchmarks with the same search budget; **(II) more sample-efficient**, requiring only 30 training samples to outperform baselines trained on much larger datasets; and **(III) more cost-effective**, reducing inference token consumption by 7.8% while significantly improving performance.

Submission Number: 40