Machine Learning

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A Coding Tutorial on OpenMythos on Recurrent-Depth Transformers with Depth Extrapolation, Adaptive Computation, and Mixture-of-Experts Routing

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In this tutorial, we explore the implementation of OpenMythos, a theoretical reconstruction of the Claude Mythos architecture that enables deeper reasoning through iterative computation rather than increased parameter size. We build and analyze models using both GQA and MLA attention mechanisms, examine memory efficiency through KV-cache comparisons, and validate stability via the spectral properties of […]

A Coding Tutorial on OpenMythos on Recurrent-Depth Transformers with Depth Extrapolation, Adaptive Computation, and Mixture-of-Experts Routing Read More »

Google Cloud AI Research Introduces ReasoningBank: A Memory Framework that Distills Reasoning Strategies from Agent Successes and Failures

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Most AI agents today have a fundamental amnesia problem. Deploy one to browse the web, resolve GitHub issues, or navigate a shopping platform, and it approaches every single task as if it has never seen anything like it before. No matter how many times it has stumbled on the same type of problem, it repeats

Google Cloud AI Research Introduces ReasoningBank: A Memory Framework that Distills Reasoning Strategies from Agent Successes and Failures Read More »

Xiaomi Releases MiMo-V2.5-Pro and MiMo-V2.5: Matching Frontier Model Benchmarks at Significantly Lower Token Cost

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Xiaomi MiMo team publicly released two new models: MiMo-V2.5-Pro and MiMo-V2.5. The benchmarks, combined with some genuinely striking real-world task demos, make a compelling case that open agentic AI is catching up to the frontier faster than most expected. Both models are available immediately via API, and priced competitively. What is an Agentic Model, and

Xiaomi Releases MiMo-V2.5-Pro and MiMo-V2.5: Matching Frontier Model Benchmarks at Significantly Lower Token Cost Read More »

Alibaba Qwen Team Releases Qwen3.6-27B: A Dense Open-Weight Model Outperforming 397B MoE on Agentic Coding Benchmarks

Alibaba’s Qwen Team has released Qwen3.6-27B, the first dense open-weight model in the Qwen3.6 family — and arguably the most capable 27-billion-parameter model available today for coding agents. It brings substantial improvements in agentic coding, a novel Thinking Preservation mechanism, and a hybrid architecture that blends Gated DeltaNet linear attention with traditional self-attention — all

Alibaba Qwen Team Releases Qwen3.6-27B: A Dense Open-Weight Model Outperforming 397B MoE on Agentic Coding Benchmarks Read More »

Teaching AI models to say “I’m not sure”

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Electrical engineering and computer science (EECS), Machine Learning, MIT Schwarzman College of Computing, Research, School of Engineering

Confidence is persuasive. In artificial intelligence systems, it is often misleading.Today’s most capable reasoning models share a trait with the loudest voice in the room: They deliver every answer with the same unshakable certainty, whether they’re right or guessing. Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have now traced that overconfidence to

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A Detailed Implementation on Equinox with JAX Native Modules, Filtered Transforms, Stateful Layers, and End-to-End Training Workflows

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In this tutorial, we explore Equinox, a lightweight and elegant neural network library built on JAX, and show how to use it. We begin by understanding how eqx.Module treats models as PyTrees, which makes parameter handling, transformation, and serialization feel simple and explicit. As we move forward, we work through static fields, filtered transformations such

A Detailed Implementation on Equinox with JAX Native Modules, Filtered Transforms, Stateful Layers, and End-to-End Training Workflows Read More »

OpenAI Open-Sources Euphony: A Browser-Based Visualization Tool for Harmony Chat Data and Codex Session Logs

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Debugging an AI agent that runs for dozens of steps: reading files, calling APIs, writing code, and revising its own output, is not like debugging a regular function. There is no single stack trace to read. Instead, developers are left staring at hundreds of lines of raw JSON, trying to reconstruct what the model was

OpenAI Open-Sources Euphony: A Browser-Based Visualization Tool for Harmony Chat Data and Codex Session Logs Read More »

Hugging Face Releases ml-intern: An Open-Source AI Agent that Automates the LLM Post-Training Workflow

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Hugging Face has released ml-intern, an open-source AI agent designed to automate end-to-end post-training workflows for large language models (LLMs). Built on the company’s smolagents framework, the tool can autonomously perform literature review, dataset discovery, training script execution, and iterative evaluation — tasks that typically require significant manual effort from ML researchers and engineers. What

Hugging Face Releases ml-intern: An Open-Source AI Agent that Automates the LLM Post-Training Workflow Read More »

A Coding Implementation to Build a Conditional Bayesian Hyperparameter Optimization Pipeline with Hyperopt, TPE, and Early Stopping

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In this tutorial, we implement an advanced Bayesian hyperparameter optimization workflow using Hyperopt and the Tree-structured Parzen Estimator (TPE) algorithm. We construct a conditional search space that dynamically switches between different model families, demonstrating how Hyperopt handles hierarchical and structured parameter graphs. We build a production-grade objective function using cross-validation inside a scikit-learn pipeline, enabling

A Coding Implementation to Build a Conditional Bayesian Hyperparameter Optimization Pipeline with Hyperopt, TPE, and Early Stopping Read More »

A Coding Implementation on Qwen 3.6-35B-A3B Covering Multimodal Inference, Thinking Control, Tool Calling, MoE Routing, RAG, and Session Persistence

agentic ai, ai, AI (Artificial Intelligence), AI Infrastructure, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine Learning, Staff, Technology, Tutorials

In this tutorial, we build an end-to-end implementation around Qwen 3.6-35B-A3B and explore how a modern multimodal MoE model can be used in practical workflows. We begin by setting up the environment, loading the model adaptively based on available GPU memory, and creating a reusable chat framework that supports both standard responses and explicit thinking

A Coding Implementation on Qwen 3.6-35B-A3B Covering Multimodal Inference, Thinking Control, Tool Calling, MoE Routing, RAG, and Session Persistence Read More »