In any analysis project, raw tables of numbers often don’t tell the full story. Visualisations simplify complexity by transforming data into shapes that our brains can quickly understand, emphasising trends, outliers, and regime shifts that might be overlooked in raw data. This is especially vital in finance and trading, where clear visuals can uncover risks, opportunities, and patterns, directly affecting decisions on position sizing, timing, and confidence. Today, we will use FMP APIs to interpret earnings data: extracting […]

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:::info Astounding Stories of Super-Science April 2004, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. THE COUNTRY OF THE BLIND – II. — THE CONE. Astounding Stories of Super-Science April 2004: THE COUNTRY OF THE BLIND – II. — THE CONE. By H. G. Wells ::: The night was hot and overcast, the sky red-rimmed with the lingering sunset of midsummer. They sat at the open […]

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Reward Machines (RMs) are an established mechanism in Reinforcement Learning (RL) to represent and learn sparse, temporally extended tasks with non-Markovian rewards. RMs rely on high-level information in the form of labels that are emitted by the environment alongside the observation. However, this concept requires manual user input for each environment and task. The user has to create a suitable labeling function that computes the labels. These limitations lead to poor applicability in widely adopted RL frameworks. We […]

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arXiv:2602.00977v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in domains where errors carry high social, scientific, or safety costs. Yet standard confidence estimators, such as token likelihood, semantic similarity and multi-sample consistency, remain brittle under distribution shift, domain-specialised text, and compute limits. In this work, we present Structural Confidence, a single-pass, model-agnostic framework that enhances output correctness prediction based on multi-scale structural signals derived from a model’s final-layer hidden-state trajectory. By combining spectral, local-variation, […]

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arXiv:2602.05051v1 Announce Type: new Abstract: Offline reinforcement learning (RL) aims to learn the optimal policy from a fixed dataset generated by behavior policies without additional environment interactions. One common challenge that arises in this setting is the out-of-distribution (OOD) error, which occurs when the policy leaves the training distribution. Prior methods penalize a statistical distance term to keep the policy close to the behavior policy, but this constrains policy improvement and may not completely prevent OOD actions. Another […]

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arXiv:2602.12342v1 Announce Type: new Abstract: How can we train agents to navigate uncertainty over long horizons? In this work, we propose {Delta}Belief-RL, which leverages a language model’s own intrinsic beliefs to reward intermediate progress. Our method utilizes the change in the probability an agent assigns to the target solution for credit assignment. By training on synthetic interaction data, {Delta}Belief-RL teaches information-seeking capabilities that consistently outperform purely outcome-based rewards for Reinforcement Learning, with improvements generalizing to out-of-distribution applications ranging […]

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arXiv:2604.02396v1 Announce Type: new Abstract: The deep integration of communication with intelligence and sensing, as a defining vision of 6G, renders environment-aware channel prediction a key enabling technology. As a representative 6G application, vehicular communications require accurate and forward-looking channel prediction under stringent reliability, latency, and adaptability demands. Traditional empirical and deterministic models remain limited in balancing accuracy, generalization, and deployability, while the growing availability of onboard and roadside sensing devices offers a promising source of environmental priors. […]

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Try signing up for any SaaS product right now. You’ll click buttons, fill forms, verify your email, maybe solve a CAPTCHA. Takes 30 seconds, no big deal. Now imagine you’re an AI agent. You don’t have a browser. You don’t have fingers. You don’t have an email inbox. That 30-second flow? It’s a wall. This is the state of the agentic internet in 2026. Agents can discover tools via MCP. They can pay for things via x402. They […]

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