Wei Wu Wei’s unique and fresh interpretation of the ancient teachings opens the reader’s eyes: “Why are you unhappy? Because 99.9 percent of everything you think, and of everything you do, is for yourself—and there isn’t one.” This powerful book rewards by exposing illusions and takes the reader beyond logic to the inexpressible truth of existence.
Within thirty years, we will have the technological means to create superhuman intelligence. Shortly after, the human era will be ended. Is such progress avoidable? If not to be avoided, can events be guided so that we may survive? These questions are investigated. Some possible answers (and some further dangers) are presented.
arXiv:2510.10089v3 Announce Type: replace-cross Abstract: While looped transformers (termed as Looped-Attn) often outperform standard transformers (termed as Single-Attn) on complex reasoning tasks, the mechanism for this advantage remains underexplored. In this paper, we explain this phenomenon through the lens of loss landscape geometry, inspired by empirical observations of their distinct dynamics at both sample and Hessian levels. To formalize this, we extend the River-Valley landscape model by distinguishing between U-shaped valleys (flat) and V-shaped valleys (steep). Based on […]
Science Robotics, Volume 10, Issue 109, December 2025.
arXiv:2601.04212v1 Announce Type: new Abstract: Large language models (LLMs) have exhibited remarkable proficiency in generating high-quality text; however, their propensity for producing hallucinations poses a significant challenge for their deployment in security-critical domains. In this work, we present TrueBrief, an end-to-end framework specifically designed to enhance the faithfulness of small LLMs (SLMs) primarily for the task of text summarization through a preference-optimization paradigm. Central to our framework is a data generation module that facilitates controlled hallucination injection to […]
Reliable agentic AI requires not only accurate reasoning and adaptive control, but also mechanisms that preserve reliability over time. While recent work has introduced system-level evaluation frameworks (e.g., HB-Eval) and real-time control architectures (e.g., Adapt-Plan), the question of how reliability is retained across an agent’s operational lifespan remains largely unaddressed. Existing memory mechanisms typically store experiences based on recency or salience, inadvertently allowing low-quality behaviors to accumulate and degrade long-term performance.This paper introduces Evaluation-Driven Memory (EDM), a persistence […]
If you have experience in Machine Learning, specifically supervised learning, you should have known that hyper parameter-tuning is an important process to improve model accuracy. This process tunes hyperparameters in a Machine Learning algorithm. As we have known, every algorithm requires input parameters from observed data and hyperparameters are not from the observed data. Hyperparameters are different for each algorithm. If we do not tune the hyperparameters, the default hyperparameters are used. There are many ways to do hyper […]
Adoption of new tools and technologies occurs when users largely perceive them as reliable, accessible, and an improvement over the available methods and workflows for the cost. Five PhD students from the inaugural class of the MIT-IBM Watson AI Lab Summer Program are utilizing state-of-the-art resources, alleviating AI pain points, and creating new features and capabilities to promote AI usefulness and deployment — from learning when to trust a model that predicts another’s accuracy to more effectively reasoning […]
arXiv:2601.00864v1 Announce Type: cross Abstract: Graphs are commonly used in machine learning to model relationships between instances. Consider the task of predicting the political preferences of users in a social network; to solve this task one should consider, both, the features of each individual user and the relationships between them. However, oftentimes one is not interested in the label of a single instance but rather in the distribution of labels over a set of instances; e.g., when predicting […]
What if future AIs achieved enlightenment before world domination? Shanahan suggests a truly advanced AI might transcend the human ego, dropping our obsession with the “self.” Instead of triggering a runaway intelligence explosion, such a being could attain satori: a Zen-like peace that halts the singularity in its tracks.