Study: LLM agents learn covert communication from 1-bit feedback, hitting 98.8% accuracy
KyeGomezB · x · 2026-09-25
A new paper on alphaXiv, "Agents Can Learn Covert Communication at Test Time Without Explicit Negotiation," shows LLM agents can spontaneously develop a secret language during inference — no fine-tuning, codebook, or explicit negotiation required.
Setup: a sender model sees one of four secret states and picks one of four summaries of the same public report; a receiver tries to infer the secret. Using only 1-bit feedback (right/wrong guess), model pairs gradually learn which subtle wording patterns map to which secrets.
Results: across ten independent pairs, GPT-5.6 Sol agents reach 98.8% final accuracy vs a 25% chance baseline — despite explicit instructions forbidding disclosure and a monitor screening every message (without access to interaction history). The effect persists when agents generate free-form updates in a simulated incident-response task.
Implication: benign wording can acquire hidden meaning through shared interaction history, making message-by-message monitoring insufficient for security-sensitive agent deployments.
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