multi-agent-llm-systems-where-one-agent-starts-erroneous-sharedIn multi-agent LLM systems where one agent starts with an erroneous shared belief, broadcasting every agent's full context at every step raises the rate of false assertions above doing no synchronization at all, because the error propagates to agents that were previously correct — and the harm appears only in tasks where one wrong fact cascades across semantically linked dimensions (destination to airport to weather), not where agent contexts are largely orthogonal.
Ingested from a paper but not yet reviewed by a human. It is deliberately inert: it does not move any technique’s standing, does not count toward the backtest, and is excluded anywhere a claim would carry weight. Read the source before relying on it.
Evidence for: Two heads are better than one (breaks)
Capability: Stating false facts confidently
Observed on
Three-agent planning tasks with deliberately injected context mismatches, Claude Haiku agents; travel-planning domain showed the effect, software sprint planning did not..
Sources
- Measured on 30/30/15 trials in travel and 10/10/5 in software with one model. Full-broadcast HR 0.658 vs no-sync 0.492 (p=0.0022, d=1.18) in travel; effect absent in software. Errors were deliberately injected, so the result isolates propagation of a seeded false belief rather than naturally arising drift. Single author, single model, small n.
Disagreeing is the most useful thing you can do here. Both sides of every contested claim in this catalog were assembled by the same person, which is its weakest point.
Related claims
- In partially observable text environments (ALFWorld, ScienceWorld), letting an LLM agent query an externally maintained state estimate that keeps an explicit distribution over unobserved object locations raises task success more than querying a deterministic memory of what has already been observed, and the gain shrinks to near zero on a frontier model that already nearly saturates the benchmark.Tracking state through a long task · unreviewed
- For persistent-memory agents, any memory-write rule that decides using only recency and provenance is stuck on a single tradeoff — accepting more genuine preference updates means admitting more poisoned ones — because an adversary who can launder a claim through the user's own channel matches the statistics a genuine revision produces; conditioning the write decision on the inferred authenticity of the claim given the interaction history moves both axes at once.Remembering across sessions · unreviewed
- When an LLM agent must reconcile user-supplied identity credentials against database records before a sensitive read or write, frontier and open models frequently skip the cross-field consistency check and act anyway, and the failure rate barely moves whether the request is simple or has several parallel sub-requests and whether the forged field is visually near-identical to the true one or completely unrelated.Checking claims against evidence · unreviewed
- The agent that produced an artifact is a biased judge of it — it holds the context and the incentives that skew its assessment — so verification belongs with a deterministic sensor or a separate verifier that reports failures back rather than rewriting the output, and in a multi-agent system the verifier is the one component no agent may override.Fixing its own mistakes · unreviewed
- When a multimodal model is given external text that contradicts the image, making it commit to a description of the image before the text is admitted, then reconciling the two in a second call, recovers accuracy the single-prompt setup loses — but the benefit of withholding the text from the first call is model- and text-source dependent, and on some models context-preserving prompting is still better.Telling the user what they want to hear · unreviewed
Notes
Drafted from the paper by a model and filed unreviewed. Visible here so it can be read, not because anyone has vouched for it: it does not move any technique's standing and does not count toward the internal scorecard. Drafted confidence: medium. Falsifier as drafted: Full-context broadcast between agents matching or lowering hallucination rate relative to no synchronization in cascading-belief tasks, across models and injected-error setups. Proposed technique, not catalogued: threshold-gated context synchronization between agents.