👩🏼‍❤️‍👨🏻 Dating as a timeless human topic Dating is one of the oldest human problems we continue to try to solve with technology, probably because it sits at the intersection of hope, vulnerability, ego, and timing, and rarely follows logic. Long before apps existed, people obsessed over mixed signals, situationships (before they had a name), and spiraling into why this person, why now, and why did it suddenly go cold? Technology didn’t invent this emotional chaos; it simply […]

Ver mais

ggml.ai joins Hugging Face to ensure the long-term progress of Local AI I don’t normally cover acquisition news like this, but I have some thoughts. It’s hard to overstate the impact Georgi Gerganov has had on the local model space. Back in March 2023 his release of llama.cpp made it possible to run a local LLM on consumer hardware. The original README said: The main goal is to run the model using 4-bit quantization on a MacBook. […] […]

Ver mais

arXiv:2601.02397v1 Announce Type: new Abstract: Dynamic nonzero sum games are widely used to model multi agent decision making in control, economics, and related fields. Classical methods for computing Nash equilibria, especially in linear quadratic settings, rely on strong structural assumptions and become impractical for nonlinear dynamics, many players, or long horizons, where multiple local equilibria may exist. We show through examples that such methods can fail to reach the true global Nash equilibrium even in relatively small games. […]

Ver mais

In this article, we are going to understand how self-attention works from scratch. This means we will code it ourselves one step at a time. Since its introduction via the original transformer paper, self-attention has become a cornerstone of many state-of-the-art deep learning models, particularly in the field of Natural Language Processing. Since self-attention is now everywhere, it’s important to understand how it works.

Ver mais

arXiv:2603.28809v1 Announce Type: new Abstract: Selecting appropriate values for the configurable parameters of Database Management Systems (DBMS) to improve performance is a significant challenge. Recent machine learning (ML)-based tuning systems have shown strong potential, but their practical adoption is often limited by the high tuning cost. This cost arises from two main factors: (1) the system needs to evaluate a large number of configurations to identify a satisfactory one, and (2) for each configuration, the system must execute […]

Ver mais

arXiv:2601.05461v1 Announce Type: new Abstract: Existing benchmarks treat multi-turn conversation and reasoning-intensive retrieval separately, yet real-world information seeking requires both. To bridge this gap, we present a benchmark for reasoning-based conversational information retrieval comprising 707 conversations (2,971 turns) across eleven domains. To ensure quality, our Decomposition-and-Verification framework transforms complex queries into fact-grounded multi-turn dialogues through multi-level validation, where atomic facts are verified against sources and explicit retrieval reasoning is generated for each turn. Comprehensive evaluation reveals that combining […]

Ver mais

I’m new to RL and was wondering what the current literature says on the accuracy of training an LLM (Qwen3 8B for example) using RL on the task of predicting the number of hours a software engineering PR took to make. Basically the inputs for the agent would be a pr with metadata and the full patch and the output would be a float value from 0.0 to theoretically infinity, although for the purpose of creating a good […]

Ver mais

I spent way too much time last week debugging a multi-agent workflow. Every time I fixed a bug, I had to re-run the entire pipeline – burning LLM credits and waiting for the same API calls to execute again. So I built Flight Recorder – a replay debugger for AI agents. **What it does:** – Records every agent call (inputs, outputs, errors) – Shows you the root cause when something fails – Lets you replay from the exact […]

Ver mais

Accurate prediction of main engine power is essential for vessel performance optimization, fuel efficiency, and compliance with emission regulations. Conventional machine learning approaches, such as Support Vector Machines, variants of Artificial Neural Networks (ANNs), and tree-based methods like Random Forests, Extra Tree Regressors, and XGBoost, can capture nonlinearities but often struggle to respect the fundamental propeller law relationship between power and speed, resulting in poor extrapolation outside the training envelope. This study introduces a hybrid modeling framework that […]

Ver mais

I’m a PhD student and already published papers in A/B ranked paper (10+). My field of work never allowed me to work on something really exciting and a core A* conference. But finally after years I think I have work worthy of some discussion at the top venue. I’m referring to papers (my field and top papers) from previous editions and I notice that there’s a big difference on how people write, how they put their message on […]

Ver mais