arXiv:2608. 04616v1 Announce Type: new Abstract: An information theory-based framework is proposed in attempt to explain insistence on sameness in autism as an instance of a general behavior pattern in which an individual tries to reduce surprise and uncertainty.
An information theory-based framework is proposed in attempt to explain insistence on sameness in autism as an instance of a general behavior pattern in which an individual tries to reduce surprise and uncertainty. It offers a new definition of autism as an impairment in which cognitive functions are restricted to discrimination, memorization and prediction of tangible properties of the environment.
The paper derives an information‑theoretic bound for a shared‑state cognitive architecture that uses an auxiliary variable to mediate context. It shows that the residual dependence of observable behavior on context, given the shared state, is bounded by the information carried by the auxiliary variable and its conditional entropy. A recognition‑memory example illustrates how to compute and compare this bound across different representational choices, providing a framework for analyzing context‑memory‑control trade‑offs in cognitive models and artificial agents.
arXiv:2606. 26502v1 Announce Type: new Abstract: Large reasoning models (LRMs) take longer on harder problems, just as humans do.
Large reasoning models (LRMs) take longer on harder problems, just as humans do. This surface similarity hides an opposite pattern within items.
arXiv:2608. 11225v1 Announce Type: new Abstract: AI "personality clones" force a re-examination of personal identity in operational terms.
arXiv:2606. 15779v1 Announce Type: cross Abstract: Multimodal models can name the action units (AUs) behind a facial emotion, but their AU->emotion rationales are typically plausible rather than faithful: nothing forces the AUs a model invokes to be the AUs that actually drive its prediction.
The study investigates how humans and AI collaborate on a puzzle task, focusing on referential uncertainty—when a description could refer to multiple objects. It finds that eliciting a belief distribution over candidate pieces yields better calibration and discrimination than raw action probabilities, and that precise descriptions or well‑targeted hedges significantly reduce the acceptance of wrong placements. However, the AI rarely externalizes uncertainty, and poorly targeted hedges can be counterproductive.
arXiv:2608. 11243v1 Announce Type: new Abstract: We argue that a single structural fact organizes a wide range of phenomena in contemporary AI safety: a semantic safety constraint (e.
arXiv:2607. 11983v1 Announce Type: cross Abstract: A specialist tolerates blind spots that a generalist does not.
arXiv:2604. 09229v2 Announce Type: replace-cross Abstract: von Economo neurons (VENs) are large bipolar projection neurons found exclusively in the anterior cingulate cortex (ACC) and frontal insula of species with complex social cognition, including humans, great apes, cetaceans, and elephants.
The paper demonstrates that a preference‑optimization objective can learn to distinguish reliable from unreliable sources by installing a prior‑dependent reliability switch. By training on data where a source’s stated reliability is paired with its answer, the model learns to flip its response only when the stated reliability exceeds a threshold that grows with the model’s prior. Experiments on Qwen2.5‑7B‑Instruct and Llama‑3.1‑8B show that this switch generalizes to unseen reliability values and follows stated reliability over role prestige, whereas supervised imitation fails to learn it.
The paper introduces Causal Memory Policy (CMP), a framework that identifies the utility of memories in memory‑augmented language models by intervening on retrieval rather than on storage. CMP reserves fixed context slots for memories sampled with known propensities and estimates utility using self‑normalized inverse propensity weighting, providing unbiased estimates and exact variance. Experiments show that CMP improves discrimination between required and non‑required memories and reveals that identified utility alone is insufficient for retention decisions across unseen queries.