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The Conductor LLM trains agents for optimal communication topology

By PulseAugur Editorial · [1 sources] ·

Researchers have developed "The Conductor," a 7B parameter model designed to optimize communication topologies and instructions for multi-agent LLM systems. This model, detailed in a forthcoming ICLR 2026 paper, utilizes recursive self-application to handle complex queries. The Conductor has demonstrated superior performance against existing multi-agent baselines, achieving comparable results with fewer model calls, and is now integrated into Sakana's Fugu-Ultra product. AI

IMPACT This model could improve the efficiency and effectiveness of multi-agent LLM systems by optimizing communication and task delegation.

RANK_REASON The cluster describes a new model and its capabilities presented in a research paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Mastodon — sigmoid.social →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

The Conductor LLM trains agents for optimal communication topology

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The cluster describes a new model and its capabilities presented in a research paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Mastodon — sigmoid.social TIER_1 English(EN) · BenjaminHan ·

    When does RL actually work for training an LLM coordinator? The Conductor (ICLR 2026) trains a 7B model to write the communication topology and per-agent instru

    When does RL actually work for training an LLM coordinator? The Conductor (ICLR 2026) trains a 7B model to write the communication topology and per-agent instructions for a pool of LLMs, calling itself recursively on hard queries. It beats multi-agent baselines at roughly three m…

    LINKS benjaminhan.net/…/20260629-conductor-llm-…

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