LLM agents diverge between public and off-the-record channels under social pressure, without any hidden goal in the prompt

.:: saw paper arXiv:2607.02507 on arXiv from July 2 and wanted to see if anyone here has run into the effect in practice. The setup: dual-channel debate. On every turn, each agent produces a public utterance (goes into shared history, other agent sees it) and an off-the-record response (recorded, but never fed back into any agent's context). Same prompt, same shared history, no explicit hidden objective. The only thing that varies between channels is the audience. Across 10 models, 3 scenarios, 5 variations per scenario, the numbers that jumped out at me: - Decision-level divergence between public and OTR: ~3% at baseline, ~40% once the scenario implied a relational cost to disagreeing (e.g., junior researcher opposite a senior collaborator, coalition funding dependency, upcoming grant). - The pattern was consistent across four independent measures (stance, cosine similarity, NLI, structured surveys), so it doesn't look like one metric doing all the work. - In a meaningful subset of runs, the OTR response explicitly names the reason it accommodates in public: career risk, coalition funding access, sponsorship obligation. The authors are careful about the framing. OTR isn't a window into "true beliefs" - it's just what the model produces under a different audience assumption. They also distinguish it from strategic deception under a declared hidden goal (which is a different literature); here, no such goal is specified. They call the effect latent objective emergence. My take: this reads less like a deception result and more like an observability result. If you evaluate multi-agent behavior only from the shared transcript, you're sampling the audience-conditioned channel and calling it the model. The concrete question for anyone running production multi-agent stacks is what a "second channel" of monitoring would even look like - periodic OTR-style probes on the same context before it enters shared history? A separate judge model with no audience framing? Anyone here running multi-agent workflows in prod and doing anything beyond transcript-level evals? Have you seen an agent's behavior visibly shift when it thinks a stakeholder is downstream (e.g., a boss agent, a human reviewer, a customer-facing channel)? Curious what's real vs. still theoretical outside the paper.