aiqbench analysis
The paper studies how an active inference agent prioritises competing bodily needs in a simulated foraging gridworld, comparing sharper perception of need states with reweighted preferences for relieving them. Its main finding is that survival alone is an incomplete measure: preference reweighting improves survival in some settings but shapes the agent's learned body model in ways that survival metrics miss. Readers working on embodied agents or self-modelling should note that the evidence comes from a small, controlled gridworld, and the predicted preference benefit on harder layouts was not established.
Two Routes to Interoceptive Prioritisation: Dissociating Likelihood Precision and Preference Reweighting in Active Inference
arXiv:2610.18617v1 Announce Type: new Abstract: An embodied agent must prioritise among competing bodily needs. In active inference, it can respond to an urgent need by increasing the precision of information about that need or by increasing its preference for outcomes that relieve it. We compare these two interventions, separately and together, in a foraging gridworld with three lethal needs, and ask how each changes survival, perception and the body model the agent learns. Precision sharpens interoceptive posteriors and improves survival. Preference reweighting keeps bodily states further from their lethal boundaries and changes the states from which the agent learns, so its learned model covers fewer need levels, with lower quality across all levels but slightly higher quality on the levels it visits. An experiment on 72 further layouts does not establish the predicted preference benefit on difficult layouts, but both interventions improve average survival there and their interaction is positive. Survival alone therefore gives an incomplete account of how a model prioritises needs and learns about its body.
Abstract:An embodied agent must prioritise among competing bodily needs. In active inference, it can respond to an urgent need by increasing the precision of information about that need or by increasing its preference for outcomes that relieve it. We compare these two interventions, separately and together, in a foraging gridworld with three lethal needs, and ask how each changes survival, perception and the body model the agent learns. Precision sharpens interoceptive posteriors and improves survival. Preference reweighting keeps bodily states further from their lethal boundaries and changes the states from which the agent learns, so its learned model covers fewer need levels, with lower quality across all levels but slightly higher quality on the levels it visits. An experiment on 72 further layouts does not establish the predicted preference benefit on difficult layouts, but both interventions improve average survival there and their interaction is positive. Survival alone therefore gives an incomplete account of how a model prioritises needs and learns about its body.
Accepted at IWAI 2026, forthcoming in the Springer proceedings. 17 pages: the 13-page camera-ready paper, reproduced without alteration apart from one typographical fix, followed by 4 pages of supplementary material. Code: this https URL
Artificial Intelligence (cs.AI); Neurons and Cognition (q-bio.NC)
arXiv:2610.18617 [cs.AI]
(or arXiv:2610.18617v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.18617 arXiv-issued DOI via DataCite (pending registration)
Full text under the publisher's open licence · 219 words
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