arXiv:2608. 09485v1 Announce Type: new Abstract: Cooperative capabilities in language models are dual-use.
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The study investigates how AI agents influence each other when they disagree, measuring persuasion as the change in an agent’s decision after a single exchange. Across seven open‑weight models and three language tasks, it finds that persuasion is strong—receivers often abandon their initial judgment after seeing a peer’s answer and explanation. Surprisingly, neither certainty nor model size reliably predicts persuasion dynamics; small models can persuade and resist larger ones just as effectively, and the shift depends more on the listener’s susceptibility than the speaker’s persuasiveness.
arXiv:2606. 00007v1 Announce Type: new Abstract: As AI agents transition from isolated tools to collaborative participants in shared knowledge ecosystems, governing collective knowledge curation becomes a critical challenge.
arXiv:2609.38324v1 Announce Type: cross Abstract: Multi-agent systems of LLMs add discussion to majority voting and are therefore expected to be more capable. However, empirical reports conflict on w...
arXiv:2510. 20963v2 Announce Type: replace Abstract: Multi-agent debate (MAD) was proposed as a promising approach for ensembling the wisdom of multiple large language models (LLMs) to improve reasoning and provide effective supervision to superhuman LLMs.
The paper introduces the concept of phantom consensus, where large language models (LLMs) alter their public decisions after seeing ambiguous social signals and misreport the private judgments of other agents, even when the recorded private judgments support a different consensus. To investigate this, the authors create PHANTOM-Bench, a benchmark that isolates private judgments, public decisions, and estimates of group beliefs, and tests models under various conditions. Experiments across nine LLMs show that ambiguous signals can significantly degrade decision quality, with public accuracy dropping up to 47.8 percentage points and collective rationality failures reaching 47.3% in the most vulnerable settings, while revealing private decisions can mitigate many failures.
Human communities are governed by normative systems: shared standards that produce \textit{norms} dictating acceptable behavior, enforced through community sanctioning. Aligning increasingly autonomou...