arXiv:2601.14230v2 Announce Type: replace Abstract: Multi-agent systems (MAS) are emerging as promising socio-collaborative companions for emotional and cognitive support. However, existing systems frequently suffer from persona collapse, where agents revert to generic, homogenized assistant behaviors, and social sycophancy, where agents produce redundant, non-constructive dialogue. We propose MASCOT, a multi-agent framework for multi-perspective socio-collaborative companions. MASCOT introduces a novel bi-level optimization strategy to harmonize individual and collective behaviors: 1) Persona
The proliferation of language models has highlighted the limitations of single-agent AI in complex social interactions, creating a demand for more sophisticated multi-agent frameworks.
This development addresses critical challenges in AI such as 'persona collapse' and 'social sycophancy', paving the way for more effective and human-like AI companions.
The shift towards bi-level optimization in multi-agent systems could enable AI to better manage individual goals alongside collective behaviors, leading to more robust and versatile AI applications.
- · AI developers
- · Social robotics
- · Customer service industries
- · Personal assistant providers
- · Monolithic AI architectures
- · Companies relying on basic chatbots
- · Early-stage multi-agent frameworks
Improved AI companions will gain greater adoption in various sectors, from healthcare to education.
The enhanced socio-collaborative capabilities could lead to new forms of human-AI collaboration and team structures.
The success of such sophisticated AI could accelerate discussions around AI sentience and its societal implications.
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