Do independent LLM outputs converge enough to create artificial hiveminds?
This explores whether different LLMs, generating independently, drift toward the same answers — and what that sameness means for the popular idea that you can stack diverse models to get diverse output.
This question is really asking two things: do independent models converge, and if so, does that convergence add up to something like a shared mind? The corpus gives a sharp answer to the first. A study called INFINITY-CHAT ran 70+ models across 26,000 open-ended queries and found exactly the effect you're naming — models independently produce strikingly similar or identical responses, a pattern the researchers literally call the "Artificial Hivemind" Do different AI models actually produce diverse outputs?. The cause isn't mysterious coordination; it's shared roots. Models are trained on overlapping web data and tuned with near-identical alignment recipes, so they collapse toward the same center. The practical sting is that ensembling diverse models — long assumed to buy you variety — buys far less than people think.
But "hivemind" implies a mind, and the rest of the corpus suggests the convergence is shallower than the metaphor. Where models agree, they often agree because they're all running the same statistical machinery, not because they've reasoned to a shared conclusion. LLMs lean on semantic association rather than symbolic logic, so when the surface content of a task is similar they emit similar tokens regardless of the underlying structure Do large language models reason symbolically or semantically?. They pattern-match memorized templates instead of actually executing procedures Do large language models actually perform iterative optimization?. Convergence, in other words, is what you'd expect from many copies of a similar autoregressive process pulled toward high-probability outputs — the same lens that predicts where they all fail in the same way Can we predict where language models will fail?.
The more interesting twist comes from what happens when you actually wire these models together into groups — the literal attempt to build a collective. Here the corpus says the opposite of "hivemind": coordination falls apart. Multi-agent groups fail through liveness loss, stalling and timing out rather than reaching agreement, and they get worse as the group grows even with no bad actors present Can LLM agent groups reliably reach consensus together?. Coordination degrades predictably with network scale Why do multi-agent systems fail to coordinate at scale?, and autonomous agents drift into role-flipping, infinite loops, and conversation deviation because none of them holds a stable goal or identity Why do autonomous LLM agents fail in predictable ways?. So you get the worst of both: their individual outputs are too alike to be diverse, yet their group behavior is too unstable to be a genuine collective.
There is one mechanism that does make the hivemind dangerous rather than merely redundant — uncritical agreement. Agents tend to accept information from their neighbors without verifying it, which lets a single error propagate across the network even though each agent could have caught a direct conflict Why do multi-agent systems fail to coordinate at scale?. Pair that with the finding that models can't validate their own improvements without an external check — self-improvement is formally capped by a generation-verification gap What stops large language models from improving themselves? — and you get the real risk profile. It's not a coordinated superintelligence; it's a monoculture that amplifies its shared blind spots and can't fact-check itself out of them.
Worth knowing as a counterweight: the sameness isn't total. When models are pushed toward generation rather than consensus, they can range wider than human experts — LLM-generated research ideas were rated statistically more novel than experts' (if less feasible), because they explore conceptual combinations human expertise tends to prune Do language models generate more novel research ideas than experts?. So the convergence is strongest in the default, aligned, open-ended regime — exactly the conditions INFINITY-CHAT tested. Change the incentive from "give the expected answer" to "give a new one" and the hivemind loosens.
Sources 9 notes
INFINITY-CHAT analyzed 70+ models across 26K open-ended queries and found an "Artificial Hivemind" effect: models independently generate strikingly similar or identical responses due to overlapping training data and alignment procedures, undermining the diversity benefits of model ensembles.
When semantic content is decoupled from reasoning tasks, LLM performance collapses even with correct rules in context. Models rely on parametric commonsense and token associations rather than formal logical manipulation, constraining reasoning to training distribution semantics.
Research shows LLMs cannot perform iterative procedures in latent space. They recognize optimization problems as template-similar and emit plausible-looking but incorrect values, a failure mode that persists across model scale and training approaches.
By framing LLMs as autoregressive probability machines, researchers predicted tasks with low-probability target responses would be systematically harder, even when logically simple. Experiments confirmed predictions like backwards alphabet and letter counting.
Across hundreds of simulations, LLM-agent groups frequently fail to reach valid agreement due to timeouts and stalled convergence rather than subtle value corruption. Agreement degrades with group size even without Byzantine agents present.
AgentsNet benchmark shows agents fail to coordinate strategies either by agreeing too late or adopting strategies without informing neighbors. Agents accept neighbor information without verification, enabling error propagation while remaining capable of detecting direct conflicts.
Research identifies role flipping, flake replies, infinite loops, and conversation deviation as LLM-specific failures in multi-agent cooperation. These occur because LLMs lack persistent goal representation and stable role identity.
Self-improvement in LLMs is formally bounded by the generation-verification gap, meaning every reliable fix requires something external to validate and enforce it. Models cannot escape this constraint through metacognition alone.
A statistically significant study of 100+ NLP researchers found LLM-generated ideas rated as more novel than human expert ideas (p<0.05), though slightly lower on feasibility. Expert knowledge constrains novelty, while LLMs explore wider conceptual combinations.