aiqbench analysis
This arXiv preprint tests whether curiosity-style signals (novelty-seeking, epistemic, and social) improve interactions among multiple LLM agents, reporting gains on seven of eight tasks. Its most useful findings are the trade-offs: country-conditioned cultural grounding helps creative and bias-related tasks but can hurt precision-oriented reasoning, and the inference-time cost is compared against single-agent baselines. Readers should treat the headline figures as unreplicated preprint results until the benchmarks, evaluation setup, and token accounting are checked.
CuriousTogether: Implications of Curiosity and Cultural Grounding in Multi-Agent LLM Interactions
arXiv:2611.01301v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed in multi-agent settings, exhibiting emergent collective behaviors, yet the role of curiosity as a driver of such dynamics remains understudied. We introduce CuriousTogether, a framework evaluating curiosity-driven multi-agent LLM interactions across three complementary dimensions: novelty-seeking, epistemic and social curiosity. Across eight tasks spanning reasoning, creative generation, and social bias mitigation, curiosity-guided multi-agent settings yield improvements over non-curious ones on seven out of eight tasks, with gains of up to 20.6% in story generation (semantic diversity) and a 14.9 pp reduction in stereotype rates. Furthermore, country-conditioned cultural grounding show benefits for story generation and social tasks, but can hurt precision-oriented reasoning. Finally, we show that inference-time multi-agent curiosity-based approach offers favorable quality-token trade-offs, compared with single-agent baselines. Together, these findings support curiosity-guided interaction as a practical mechanism for improving multi-agent collaboration, with downstream benefits for reasoning, creative, and societal tasks.
Abstract:Large Language Models (LLMs) are increasingly deployed in multi-agent settings, exhibiting emergent collective behaviors, yet the role of curiosity as a driver of such dynamics remains understudied. We introduce CuriousTogether, a framework evaluating curiosity-driven multi-agent LLM interactions across three complementary dimensions: novelty-seeking, epistemic and social curiosity. Across eight tasks spanning reasoning, creative generation, and social bias mitigation, curiosity-guided multi-agent settings yield improvements over non-curious ones on seven out of eight tasks, with gains of up to 20.6% in story generation (semantic diversity) and a 14.9 pp reduction in stereotype rates. Furthermore, country-conditioned cultural grounding show benefits for story generation and social tasks, but can hurt precision-oriented reasoning. Finally, we show that inference-time multi-agent curiosity-based approach offers favorable quality-token trade-offs, compared with single-agent baselines. Together, these findings support curiosity-guided interaction as a practical mechanism for improving multi-agent collaboration, with downstream benefits for reasoning, creative, and societal tasks.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2611.01301 [cs.CL]
(or arXiv:2611.01301v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2611.01301 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 244 words