AI companions in video games have evolved immensely from the scripted sidekicks of the past. Developers can now engineer entities as thinking systems capable of real-time adaptation, changing how players immerse themselves in digital worlds. This evolution steps away from pre-written decision trees and moves toward algorithmic responsiveness that changes gameplay loops. Integrating advanced machine learning into the depth of narrative play affects everything from core mechanics to accessibility. Five shifts stand out across modern AI-integrated games. 1. […]

Ver mais

Medical multimodal learning faces significant challenges with missing modalities prevalent in clinical practice. Existing approaches assume equal contribution of modality and random missing patterns, neglecting inherent uncertainty in medical data acquisition. In this regard, we propose the Aleatoric Uncertainty Modeling (AUM) that explicitly quantifies unimodal aleatoric uncertainty to address missing modalities. Specifically, AUM models each unimodal representation as a multivariate Gaussian distribution to capture aleatoric uncertainty and enable principled modality reliability quantification. To adaptively aggregate captured information, we […]

Ver mais

arXiv:2601.05411v1 Announce Type: new Abstract: This work investigates how measuring information entropy of text can be used to estimate its readability. We propose a visualization framework that can be used to approximate information entropy of text using multiple language models and visualize the result. The end goal is to use this method to estimate and improve readability and clarity of administrative or bureaucratic texts. Our toolset is available as a libre software on https://github.com/ufal/Glitter.

Ver mais

Teilhard reconciles evolutionary science with Catholic doctrine by reimagining original sin as a cosmic principle rather than a singular transgression in Eden. The Fall is ongoing; Creation, Fall, Incarnation, and Redemption are not sequential events, but simultaneous aspects of one continuous divine process as humanity struggles toward God through the long emergence of consciousness from matter.

Ver mais

arXiv:2603.02422v1 Announce Type: new Abstract: Exponential growth in the quantity of digital news, social media, and other textual sources makes it difficult for humans to keep up with rapidly evolving narratives about world events. Various visualisation techniques have been touted to help people to understand such discourse by exposing relationships between texts (such as news articles) as topics and themes evolve over time. Arguably, the understandability of such visualisations hinges on the assumption that people will be able […]

Ver mais

The topic of Multivariate Time Series Anomaly Detection (MTSAD) has grown rapidly over the past years, with a steady rise in publications and Deep Learning (DL) models becoming the dominant paradigm. To address the lack of systematization in the field, this study introduces a novel and unified taxonomy with eleven dimensions over three parts (Input, Output and Model) for the categorization of DL-based MTSAD methods. The dimensions were established in a two-fold approach. First, they derived from a […]

Ver mais

Hi Reddit, Really new to RL here, but super curious and excited to learn from you guys. I’m planning to work on a code-generation RL agent: The agent generates a program/configuration (Action), which is then compiled and run through a complex simulator (Environment) to calculate a performance metric (Reward). The Bottleneck: The simulation takes several minutes to run. I cannot assume instant feedback. The Question: Aside from massive parallelization, what algorithmic tricks exist for this ‘expensive reward’ regime? […]

Ver mais

arXiv:2602.20208v1 Announce Type: new Abstract: Model merging aims to integrate multiple task-specific fine-tuned models derived from a shared pre-trained checkpoint into a single multi-task model without additional training. Despite extensive research, task interference remains a major obstacle that often undermines the performance of merged models. In this paper, we propose ESM (Essential Subspace Merging) , a robust framework for effective model merging. We begin by performing Principal Component Analysis (PCA) on feature shifts induced by parameter updates. The […]

Ver mais

arXiv:2602.08142v1 Announce Type: cross Abstract: Machine learning applications require fast and reliable per-sample uncertainty estimation. A common approach is to use predictive distributions from Bayesian or approximation methods and additively decompose uncertainty into aleatoric (i.e., data-related) and epistemic (i.e., model-related) components. However, additive decomposition has recently been questioned, with evidence that it breaks down when using finite-ensemble sampling and/or mismatched predictive distributions. This paper introduces Variance-Gated Ensembles (VGE), an intuitive, differentiable framework that injects epistemic sensitivity via a […]

Ver mais