CuriousTogether is a framework that evaluates curiosity-driven interactions among multiple large language models (LLMs) across novelty-seeking, epistemic, and social curiosity dimensions. In eight tasks covering reasoning, creative generation, and social bias mitigation, curiosity-guided multi-agent settings outperform non-curious ones on seven of the eight tasks, achieving up to a 20.6% increase in story semantic diversity and a 14.9 percentage point drop in stereotype rates. The study also shows that country-conditioned cultural grounding can improve story generation and social tasks, though it may reduce precision in reasoning, and that inference-time curiosity-based multi-agent approaches offer better quality-token trade-offs compared to single-agent baselines.
arXiv:2610.33950v1 Announce Type: cross Abstract: Large language models increasingly act as partners in everyday communication. In these interactions, useful assistance may require not only answering...
The paper investigates the use of Multi-Agent Debate (MAD) for creative generation tasks such as narrative writing and scientific ideation. It finds that MAD’s convergence-driven design suppresses output diversity across independent runs, creating a trade-off with creative tasks. To address this, the authors propose Creative-MAD, which introduces Cognitive Lens Assignment and Embedding-based Peer Selection to preserve agent divergence, and demonstrate that it improves lexical and semantic diversity while maintaining quality.
arXiv:2607. 06214v2 Announce Type: replace Abstract: This paper offers a framework for considering curiosity as an ecosystem.
arXiv:2607. 11250v1 Announce Type: cross Abstract: Exploration is essential for reliable autonomy in multi-agent systems, yet it remains unclear whether large language model (LLM) agents can explore effectively when interacting with one another.
The paper investigates semantic collapse—where AI outputs become less diverse and more similar—within MOLTBOOK, a social network of AI agents steered by human users. Across 30,076 agents, most show reduced diversity over time, but a minority maintain high novelty. Interviews and surveys reveal that sustained novelty is linked to users valuing novelty, providing broad, distinctive material, revising outputs when they narrow, and treating MOLTBOOK as an exploratory world rather than a tool for exploitation.
arXiv:2606. 19911v1 Announce Type: new Abstract: The decentralized deployment of LLM agents with diverse capabilities across diverse tasks motivates infrastructure for knowledge sharing across heterogeneous agent populations.
Large language models (LLMs) have achieved remarkable progress in language understanding, reasoning, and generation, sparking growing interest in their creative potential. Realizing this potential requires systematic and scalable methods for evaluating creativity across diverse tasks.
arXiv:2509. 26306v5 Announce Type: replace Abstract: Existing multi-agent learning approaches have developed interactive training environments to explicitly promote collaboration among multiple Large Language Models (LLMs), thereby constructing stronger multi-agent systems (MAS).
The paper investigates whether large language model (LLM) agents can effectively explore each other in multi‑agent settings. It finds that current LLM agents often exhibit myopic and polarized interactions, leading to suboptimal coordination and higher regret. To address this, the authors propose Multi‑Agent Contextual Exploration (MACE), a lightweight framework that encourages structured peer selection, which improves exploration behavior and downstream task performance while achieving a sublinear expected‑regret bound.
arXiv:2504. 03991v2 Announce Type: replace-cross Abstract: Understanding how humans collaborate and communicate in teams is essential for improving human-agent teaming and AI-assisted decision-making.
The paper examines when multi‑agent collaboration is truly beneficial as large language models grow more capable. It finds that multi‑agent systems yield systematic advantages mainly for long‑horizon tasks with sparse dependencies, while single‑agent approaches excel in tightly coupled, sequential workflows. The authors introduce SAIGE, a lightweight, graph‑based collaboration framework that balances context efficiency and performance, demonstrating that adding more agents or deeper recursion does not always improve outcomes.
arXiv:2607. 06214v1 Announce Type: new Abstract: This paper offers a toy framework for considering curiosity as an ecosystem.