aiqbench analysis
The paper argues that open-world settings, where tasks are unknown in advance and data arrives grouped rather than i.i.d., demand three properties: inference-time learning, a rich representation, and predictive disentanglement. Its central empirical claim is qualitative: models that have these properties keep improving as related-task data accumulates, while those that lack them degrade regardless of scale or layer design. For readers, the practical takeaway is that scaling alone may not be enough for open-ended adaptation, though the evidence rests on language models up to 8B and still needs broader validation.
Open-world AI: a Tale of Three Requirements
arXiv:2610.20782v1 Announce Type: new Abstract: The closed world asks AI for specialized competence and lets it manufacture the iid data its theory assumes: a fixed criterion licenses collecting examples until the task is covered. The open world asks for versatility --- taking up tasks a machine was not built for, from few examples and with little prior from a human designer --- and offers data that is grouped, not shuffled. Its tasks are not known in advance, so a machine must do inference-time learning (1st requirement). Modeling tasks as sharing latent factors and decomposing the training objective, we derive two more: a *rich representation*, retaining more than iid generalization requires, and a predictive disentanglement that aligns inputs and outputs into matched low-dimensional groups. The analysis further predicts the need for a consistent inner learner, and the benefit of long contexts spanning related tasks. We test these claims on language models up to 8B, asking whether a model has a property (qualitative) rather than how much it wins (quantitative). Models that meet the requirements improve as data from related tasks accumulates; the rest degrade, and neither scale nor layer-type interleaving repairs this.
Abstract:The closed world asks AI for specialized competence and lets it manufacture the iid data its theory assumes: a fixed criterion licenses collecting examples until the task is covered. The open world asks for versatility --- taking up tasks a machine was not built for, from few examples and with little prior from a human designer --- and offers data that is grouped, not shuffled. Its tasks are not known in advance, so a machine must do inference-time learning (1st requirement). Modeling tasks as sharing latent factors and decomposing the training objective, we derive two more: a *rich representation*, retaining more than iid generalization requires, and a predictive disentanglement that aligns inputs and outputs into matched low-dimensional groups. The analysis further predicts the need for a consistent inner learner, and the benefit of long contexts spanning related tasks. We test these claims on language models up to 8B, asking whether a model has a property (qualitative) rather than how much it wins (quantitative). Models that meet the requirements improve as data from related tasks accumulates; the rest degrade, and neither scale nor layer-type interleaving repairs this.
Artificial Intelligence (cs.AI)
arXiv:2610.20782 [cs.AI]
(or arXiv:2610.20782v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.20782 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 280 words
