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Coadaptive Value Alignment.
Nathan Tsoi, Eric Hsiung, Masayuki Yamazaki, Swarat Chaudhuri, Peter
Stone, and and Joydeep Biswas.
In Proceedings of the 25th International
Conference on Autonomous Agents and Multiagent Systems, January 2026.
The integration of autonomous agents into human society is a grand challenge for AI. In order to achieve widespread acceptance, agents must conform to the values of people with whom they interact. Current approaches treat the value alignment problem as a unidirectional interaction where the aim is to imbue an agent's actions with human values. Our Coadaptive Value Alignment paradigm acknowledges that human perceptions, expectations, and values continuously evolve in response to agent actions. We conceptualize human-agent interaction as an adaptive loop where the agent actively models and intentionally influences the human's perception, rather than just acting according to static human values. For instance, unlike a traditional agent that simply maximizes speed, an adaptive agent could detect a drop in user trust and strategically sacrifice short-term efficiency to repair the relationship. This perspective transforms value alignment into a multi-agent challenge where all actors must identify and adhere to a shared, implicit social contract. The opportunity to create a virtuous cycle of self-improvement is accompanied by the risk of negative reinforcement, which could result in undesired behaviors. We outline the core framework components, present a research road map for the MAS community, and propose that this dynamic perspective is critical for creating truly collaborative social partners.
@InProceedings{tsoi2026coadaptive,
author = {Nathan Tsoi and Eric Hsiung and Masayuki Yamazaki and Swarat Chaudhuri and Peter Stone and and Joydeep Biswas},
title = {Coadaptive Value Alignment},
booktitle = {Proceedings of the 25th International Conference on Autonomous Agents and Multiagent Systems},
year = {2026},
month = {January},
location = {Paphos, Cyprus},
abstract = {The integration of autonomous agents into human society is a grand challenge for AI. In order to achieve widespread acceptance, agents must conform to the values of people with whom they interact. Current approaches treat the value alignment problem as a unidirectional interaction where the aim is to imbue an agent's actions with human values. Our Coadaptive Value Alignment paradigm acknowledges that human perceptions, expectations, and values continuously evolve in response to agent actions. We conceptualize human-agent interaction as an adaptive loop where the agent actively models and intentionally influences the human's perception, rather than just acting according to static human values. For instance, unlike a traditional agent that simply maximizes speed, an adaptive agent could detect a drop in user trust and strategically sacrifice short-term efficiency to repair the relationship. This perspective transforms value alignment into a multi-agent challenge where all actors must identify and adhere to a shared, implicit social contract. The opportunity to create a virtuous cycle of self-improvement is accompanied by the risk of negative reinforcement, which could result in undesired behaviors. We outline the core framework components, present a research road map for the MAS community, and propose that this dynamic perspective is critical for creating truly collaborative social partners.},
}
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