Do Latent Channels Actually Communicate? A Causal Audit of Latent Multi-Agent LLM

2026-07-30 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

A causal audit for latent communication in LLM-based multi-agent systems (MAS) is introduced to determine if continuous internal representations (embeddings, hidden states, KV caches) genuinely transmit task-relevant information, as end-task performance alone is insufficient. The audit employs controlled message replacements at the sender-receiver boundary, utilizing four message settings: no message, other-example message, self-generated message, and current-example message. These settings enable five measurements: Positive Signaling (PS), Positive Listening (PL), Causal Influence of Communication (CIC), Content-Attributable Gain (CAG), and Self-Substitution Gap (SSG). Experiments with Qwen3-4B and Qwen3-8B on GSM8K, ARC-C, and MATH-500 benchmarks reveal that aggregate accuracy can conceal distinct and often opposing communication effects. For instance, Qwen3-4B's -1.00 percentage point (pp) overall effect on GSM8K decomposes into a -6.17 pp other-example message effect and a +5.17 pp CAG, with both directions reversing for Qwen3-8B. The audit demonstrates that example-specific content and other-agent value are distinct, motivating controlled message comparisons for evaluating latent communication.

Key takeaway

For AI Scientists and NLP Engineers developing multi-agent LLM systems, relying solely on end-task accuracy to evaluate latent communication is insufficient. You should implement causal audits, like the one presented, to decompose aggregate performance effects. This approach reveals whether gains stem from example-specific content or other-agent value, guiding more effective system design. Prioritize controlled message comparisons to understand true information transfer mechanisms.

Key insights

Aggregate performance alone cannot identify the causal mechanisms of latent communication in multi-agent LLM systems.

Principles

• Latent communication's value requires causal audit.
• Aggregate accuracy can hide opposing effects.
• Example-specific content and other-agent value are distinct.

Method

A causal audit intervenes at the sender-receiver boundary using four message settings (no, other-example, self-generated, current-example) to measure PS, PL, CIC, CAG, and SSG, separating information components.

In practice

• Evaluate latent channels with controlled message comparisons.
• Decompose aggregate performance to understand communication effects.
• Test for both example-specific content and other-agent value.

Topics

• Latent Communication
• Multi-Agent LLMs
• Causal Inference
• LLM Evaluation Metrics
• Qwen3 Models
• Reasoning Benchmarks

Best for: AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.