January 2026

Large reasoning models (LRMs) aim to solve diverse and complex problems through structured reasoning. Recent advances in group-based policy optimization methods have shown promise in enabling stable advantage estimation without reliance on process-level annotations. However, these methods rely on advantage gaps induced by high-quality samples within the same batch, which makes the training process fragile and inefficient when intra-group advantages collapse under challenging tasks. To address these problems, we propose a reinforcement learning mechanism named emph{textbf{R^3}} that along […]

Ver mais

While InfoNCE powers modern contrastive learning, its geometric mechanisms remain under-characterized beyond the canonical alignment–uniformity decomposition. We present a measure-theoretic framework that models learning as the evolution of representation measures on a fixed embedding manifold. By establishing value and gradient consistency in the large-batch limit, we bridge the stochastic objective to explicit deterministic energy landscapes, uncovering a fundamental geometric bifurcation between the unimodal and multimodal regimes. In the unimodal setting, the intrinsic landscape is strictly convex with a […]

Ver mais

We address the intrinsic dimensionality (ID) of high-dimensional trajectories, comprising $n_s = 4,000,000$ data points, of the Fermi-Pasta-Ulam-Tsingou (FPUT) $β$ model with $N = 32$ oscillators. To this end, a deep autoencoder (DAE) model is employed to infer the ID in the weakly nonlinear regime ($βlesssim 1$). We find that the trajectories lie on a nonlinear manifold of dimension $m^{ast} = 2$ embedded in a $64$-dimensional phase space. The DAE further reveals that this dimensionality increases to $m^{ast} […]

Ver mais

Decision-making in the business realm has become more critical and challenging due to the increasingly complex and fast-paced environments in which companies operate. Leaders are expected to act instantly on large data volumes, while considering market movements, customer actions, operational limits, and risks. Traditional analytics and AI systems are often found lacking because they analyze data in isolation, unaware of the surrounding context. The result is a decision that is technically sound but practically unsound. Here is where […]

Ver mais

The process of digital ad production has transformed into a fast-paced and data-heavy one that involves creative development, audience targeting, media buying, performance tracking, and optimization all the time. As competition for attention increases among different platforms, marketers have to deal with the problems of speed, scale, personalization, and efficiency more and more. It has become quite complicated to produce high-quality ads regularly while at the same time managing budgets and performance metrics. But here is where AI […]

Ver mais

The best financial APIs in 2026 empower developers, analysts, and fintech startups with real-time and historical data for stocks, forex, crypto, and commodities. From Marketstack’s global market coverage to Intrinio’s financial statements, these APIs streamline trading, portfolio tracking, and AI-powered analysis, making enterprise-grade market insights accessible to all.

Ver mais

The ad fraud issue has morphed into one of the most critical and expensive bugs in the digital advertising world. People who commit fraud keep on adopting new and more difficult practices, simply through the use of bots, click farms, and intricate automation, while the budgets for ads keep on getting bigger in search, display, and mobile channels. Such a scenario results in marketers and brands having to deal with the issues of budget wastage, incorrect performance metrics, […]

Ver mais