Yunus Güngör @codevoygee

Chain-of-Agents Interesting idea to train a single model with the capabilities of a multi-agent system. 84.6% reduction in inference cost! Distillation and Agentic RL are no joke! Here are my notes:

Problem-solving is at least 50% of every job in tech and science. Mastering problem-solving will make your technical skill level shoot up like a hockey stick. Yet, we are rarely taught how to do so. Here are my favorite techniques that'll loosen even the most complex knots:

Absolutely Golden resource: A Comprehensive Survey of Self-Evolving AI Agents Self‑evolving agents are built to adapt themselves safely, not just run fixed scripts, guided by 3 laws, endure, excel, evolve. The survey maps a 4‑stage shift, MOP (Model Offline Pretraining) to…

Wow, this is really cool! This reserach answers this question: what if your computer-use AI was not a black box? OpenCUA: Open Foundations for Computer-Use Agents Researchers from HKU, Moonshot AI, and others present OpenCUA—a fully open-source framework for building and…

introducing qqWen: our fully open-sourced project (code+weights+data+detailed technical report) for full-stack finetuning (pretrain+SFT+RL) a series of models (1.5b, 3b, 7b, 14b & 32b) for a niche financial programming language called Q All details below!

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The best Chinese open agentic/reasoning models. When to use each? • Kimi K2 – if you need a well-rounded, strong open base with agentic plus long-context strength. • GLM-4.5 – the most tool-savvy, agent-native model today. • Qwen3 – the best one if you need control,…

Graph theory will seriously enhance your engineering skills. Here's why you must be familiar with graphs:

"Attention is all you need" implemented from scratch using PyTorch:

Context engineering is the new prompt engineering. And it’s becoming the most critical AI skill. Together with @MiqJ (Product Lead at @OpenAI) we created a comprehensive guide. Key insights: 🧵👇

Context Engineering @dbreunig and I did a meetup on context engineering last night. Wanted to share slides (below) + a recap of some themes / discussion points. 1/ Context grows w/ agents. @ManusAI_HQ mentions typical task requires ~50 tool calls. manus.im/blog/Context-E… 2/…

This paper makes a bold claim! AlphaGo Moment for Model Architecture Discovery The researchers introduce ASI-Arch, the first Artificial Superintelligence for AI Research (ASI4AI), enabling fully automated neural architecture innovation. No human-designed search space. No human…

Beautiful @GoogleResearch paper. LLMs can learn in context from examples in the prompt, can pick up new patterns while answering, yet their stored weights never change. That behavior looks impossible if learning always means gradient descent. The mechanisms through which this…

Brilliant paper from Google. Test-Time Diffusion Deep Researcher (TTD-DR) - Conceptualizes research report generation as a diffusion process. The agent rewrites itself every step, so errors fade instead of pile up. TTD-DR builds a living draft, keeps feeding it fresh search…

「 Deep Research, WebDancer, WebSailor 」 - 问：“一部知名电视剧：女二 1993 年入行；女一现任丈夫是浙江湖州人；男一六年后登上春晚。剧名是什么？” - 答案：父母爱情 这种题目来自 BrowseComp-ZH，是典型的检测模型 “超级深度” (Level-3) 的 Deep Research 能力的benchmark。 分享两篇文章…

Good answers follow good reasoning VeriFree is a new method that keeps the benefits of reinforcement learning (RL) but gets rid of a verifier model and rule-based checking. It trains the model to get closer to a known good answer, called a reference answer. Benefits: • It's…

🧐How can we teach Multimodal models or Agents “When to Think” like humans? 👉Check Out: Think-or-Not (TON) 🔥Selective Reasoning via Reinforcement Learning for Vision-Language Models arXiv: arxiv.org/pdf/2505.16854 code: github.com/kokolerk/TON We introduce “thought dropout”…

Learn to Reason via Mixture-of-Thought Interesting paper to improve LLM reasoning utilizing multiple reasoning modalities: - code - natural language - symbolic (truth-table) representations Cool idea and nice results. My notes below:

Trust your AI, but can it trust itself? 🤔 Introducing an online reinforcement learning framework, RISE (Reinforcing Reasoning with Self-Verification), enabling LLMs to simultaneously level-up BOTH their problem-solving AND self-checking skills! 🧐 Problems tackled: ✅…

Elegant theoretical derivations are exclusive to physics. Right?? Wrong! In a new preprint, we: ✅"Derive" a spiking recurrent network from variational principles ✅Show it does amazing things like out-of-distribution generalization 👉[1/n]🧵 w/ co-lead @dekelgalor & Jake Yates

What would truly open-source AI look like? Not just open weights, open code/data, but *open development*, where the entire research and development process is public *and* anyone can contribute. We built Marin, an open lab, to fulfill this vision: