arXiv AI August 5, 2026 By Yiyang Yu

Predictive Set Theory: A Generative Framework for Cognitive Architecture with Operationalized Core Mechanisms

arXiv:2608. 02704v1 Announce Type: new Abstract: Predictive processing theories portray the brain as a hierarchical prediction engine that minimizes prediction error, yet they lack operational definitions for the structure of a "prediction," the standardized response to a prediction error, and the mechanism that maintains consistency across successive updates.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 12

The Theory of Mind Utility: Formal Specification of a Mentalizing Mechanism

arXiv:2606. 12721v1 Announce Type: new Abstract: Inferring others' beliefs requires more than reading surface signals; it requires tracking who told them what, in what order, and how credibly.

By Nikolos Gurney, Stacy Marsella

agents

arXiv AI

Jun 2

Hypothesis Generation and Inductive Inference in Children and Language Models

arXiv:2605. 24528v2 Announce Type: replace Abstract: Real world decision-making requires constructing mental models under uncertainty over evidence, over the underlying causal rules, and over the state of the world itself.

By Jeffrey Qin, Wasu Top Piriyakulkij, Zhuangfei Gao, Mia Radovanovic, Jessica Sommerville, Kevin Ellis, Marta Kryven

llms agents robotics

arXiv Machine Learning

Sep 23

PICPIs: Prediction-Interval-Conditional Prediction Intervals

arXiv:2609.25388v1 Announce Type: cross Abstract: A classical question in statistics is which observable quantities to condition on when drawing inferences about unobservable targets. For conformal p...

By Xuelin Yang, Baihe Huang, Yilong Hou, Guido Imbens, Michael I. Jordan

arXiv AI

Jun 2

TERRA: Task-Embedded Reasoning and Representation Architecture for Cross-Domain Applications

arXiv:2606. 01520v1 Announce Type: new Abstract: A single action-conditioned latent predictive architecture can in principle be trained on the structured state of a driving scene, a robot workspace, or a financial order book.

By Shayan Shokri

rag nlp reinforcement-learning robotics

arXiv Machine Learning

Aug 4

Emergence Invariance: From Symbolized Thought to Interface Refinement

arXiv:2608. 01548v1 Announce Type: cross Abstract: Language can be viewed as a formalized subset of thought: a consequence-governed symbolic structure projected from wider situated cognition.

By Yi Liu

llms agents

arXiv Machine Learning

Jun 19

Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence

arXiv:2606. 19353v1 Announce Type: cross Abstract: In-Context Learning (ICL) allows LLMs to adapt to new tasks from a few demonstrations, but its reliability remains a concern: predictions are highly sensitive to both prompt design and the model's ability to understand the context, obscuring whether failures arise from data properties or model limitations.

By Jinseok Chung, Minkyoung Song, Hyunji Jung, Namhoon Lee

llms benchmarks safety

One email a morning, machine-written

Related stories

The Theory of Mind Utility: Formal Specification of a Mentalizing Mechanism

Hypothesis Generation and Inductive Inference in Children and Language Models

PICPIs: Prediction-Interval-Conditional Prediction Intervals

TERRA: Task-Embedded Reasoning and Representation Architecture for Cross-Domain Applications

Emergence Invariance: From Symbolized Thought to Interface Refinement

Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence