Prosociality by Coupling, Not Mere Observation: Homeostatic Sharing in an Inspectable Recurrent Artificial Life Agent
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866913025198391296 |
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| author | Sanyal, Aishik |
| author_facet | Sanyal, Aishik |
| contents | Artificial agents can be made to "help" for many reasons, including explicit social reward, hard-coded prosocial bonuses, or direct access to another agent's internal state. Those possibilities make minimal prosocial behavior hard to interpret. Building on ReCoN-Ipsundrum, an inspectable recurrent controller with affect-coupled regulation, I add an explicit homeostat and a social coupling channel while keeping planning strictly self-directed: the agent scores only its own predicted internal state, and no partner-welfare reward term is introduced. I compare four matched conditions in two toy worlds. In a one-step FoodShareToy, an exact solver finds a sharp switch from EAT to PASS at $λ* \approx 0.91$ for the default state. In the experimental runs, the self-only and partner-observing conditions never help, whereas the affectively coupled conditions always do. In a multi-step SocialCorridorWorld, the same dissociation reappears: coupling flips help rate and partner recovery from 0 to 1 and cuts rescue latency from 18 to 9 steps, while raising mutual viability from 0.15 to 0.33. Sham lesions preserve helping, but coupling-off and shuffled-partner lesions abolish it in both tasks. A coupling sweep shows a load-dependent feasibility boundary: under low load, helping appears for $λ \geq 0.25$, whereas under medium and high loads no tested value rescues the partner within horizon. The result is a narrow claim for artificial life: in this minimal architecture, helping appears when another's need is routed into self-regulation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_10760 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Prosociality by Coupling, Not Mere Observation: Homeostatic Sharing in an Inspectable Recurrent Artificial Life Agent Sanyal, Aishik Multiagent Systems Artificial Intelligence Artificial agents can be made to "help" for many reasons, including explicit social reward, hard-coded prosocial bonuses, or direct access to another agent's internal state. Those possibilities make minimal prosocial behavior hard to interpret. Building on ReCoN-Ipsundrum, an inspectable recurrent controller with affect-coupled regulation, I add an explicit homeostat and a social coupling channel while keeping planning strictly self-directed: the agent scores only its own predicted internal state, and no partner-welfare reward term is introduced. I compare four matched conditions in two toy worlds. In a one-step FoodShareToy, an exact solver finds a sharp switch from EAT to PASS at $λ* \approx 0.91$ for the default state. In the experimental runs, the self-only and partner-observing conditions never help, whereas the affectively coupled conditions always do. In a multi-step SocialCorridorWorld, the same dissociation reappears: coupling flips help rate and partner recovery from 0 to 1 and cuts rescue latency from 18 to 9 steps, while raising mutual viability from 0.15 to 0.33. Sham lesions preserve helping, but coupling-off and shuffled-partner lesions abolish it in both tasks. A coupling sweep shows a load-dependent feasibility boundary: under low load, helping appears for $λ \geq 0.25$, whereas under medium and high loads no tested value rescues the partner within horizon. The result is a narrow claim for artificial life: in this minimal architecture, helping appears when another's need is routed into self-regulation. |
| title | Prosociality by Coupling, Not Mere Observation: Homeostatic Sharing in an Inspectable Recurrent Artificial Life Agent |
| topic | Multiagent Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2604.10760 |