arXiv:2604.04948v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems depend critically on the quality of document preprocessing, yet no prior study has evaluated PDF processing frameworks by their impact on downstream question-answering accuracy. We address this gap through a systematic comparison of four open-source PDF-to-Markdown conversion frameworks, Docling, MinerU, Marker, and DeepSeek OCR, across 19 pipeline configurations for extracting text and other contents from PDFs, varying the conversion tool, cleaning transformations, splitting strategy, and metadata enrichment. Evaluation was […]

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TL;DR I tested 112 Product Hunt startups with 2,240 queries across ChatGPT and Perplexity. The results challenge conventional wisdom about “Generative Engine Optimization” (GEO): The Discovery Gap: ChatGPT recognizes 99% of products directly but recommends only 3% organically (30:1 ratio) GEO doesn’t work (yet): Zero correlation between GEO optimization and ChatGPT discovery Traditional SEO wins: Backlinks (r=+0.32) and Reddit presence (r=+0.40) are the strongest predictors Full paper: arXiv:2601.00912 n Code & Data:GitHub The Problem: ChatGPT Visibility for Startups […]

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Anthropic is redeploying Claude Fable 5, its most capable generally available model. On June 30, it announced that US export controls had lifted. The controls had covered Claude Fable 5 and Claude Mythos 5. Fable 5 returned to users globally on Wednesday, July 1. Mythos 5 access is restored to a set of US organizations. The models were pulled on June 12. A US government directive restricted them to non-foreign-nationals. Anthropic could not verify nationality in real time. […]

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How are you, hacker? 🪐 What’s happening in tech today, January 2, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, The Palmer Raids began in the US in 1920, NASA launched its farthest exploration in 2019, The Federal Rules of Evidence were enacted in the US in 1975, and we present you with these top quality stories. From The Brain at the Edge of Chaos. When Predictive Coding Fails and Randomness […]

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arXiv:2406.14059v3 Announce Type: replace-cross Abstract: Tracking the solution of time-varying variational inequalities is an important problem with applications in game theory, optimization, and machine learning. Existing work considers time-varying games or time-varying optimization problems. For strongly convex optimization problems or strongly monotone games, these results provide tracking guarantees under the assumption that the variation of the time-varying problem is restrained, that is, problems with a sublinear solution path. In this work we extend existing results in two ways: […]

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arXiv:2604.13132v1 Announce Type: new Abstract: Efficient spectrum management in massive-scale wireless networks is increasingly challenged by explosive action spaces and the computational intractability of traditional optimization. This study proposes a Large-Scale LLM-Driven Spectrum Access (LSA) framework rooted in Group Relative Policy Optimization (GRPO). To overcome the computational collapse caused by ultra-long prompts in large-scale scenarios, we develop a hierarchical state serialization mechanism that synthesizes global environment statistics with localized critical constraints, enabling the LLM to perform high-dimensional reasoning […]

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Financial markets are influenced by human behavior that deviates from rationality due to cognitive biases. Traditional reinforcement learning (RL) models for financial decision-making assume rational agents, potentially overlooking the impact of psychological factors. This study integrates cognitive biases into RL frameworks for financial trading, hypothesizing that such models can exhibit human-like trading behavior and achieve better risk-adjusted returns than standard RL agents. We introduce biases, such as overconfidence and loss aversion, into reward structures and decision-making processes and […]

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arXiv:2603.24806v1 Announce Type: new Abstract: Diffusion models are increasingly used for robot learning, but current designs face a clear trade-off. Action-chunking diffusion policies like ManiCM are fast to run, yet they only predict short segments of motion. This makes them reactive, but unable to capture time-dependent motion primitives, such as following a spring-damper-like behavior with built-in dynamic profiles of acceleration and deceleration. Recently, Movement Primitive Diffusion (MPD) partially addresses this limitation by parameterizing full trajectories using Probabilistic Dynamic […]

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