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OpenMythos: An Open-Source Theoretical Reconstruction of the Mythos Architecture

OpenMythos: An Open-Source Theoretical Reconstruction of the Mythos ArchitectureThe OpenMythos project has been officially open-sourced, presenting a theoretical reconstruction of the Claude Mythos architecture. This implementation utilizes a Recurre<!--autointro-->

Open SourceMixture of ExpertsMoE
Industry Editor selected

Meta to Train AI by Tracking Employee Keystrokes and Mouse Movements

Meta, the parent company of Facebook and Instagram, is set to implement a new employee monitoring tool to harvest behavioral data for training its artificial intelligence models. The initiative, titled the Model Capability Initiative (MCI), involves <!--autointro-->

MetaAI trainingModel Capability Initiative
Industry Editor selected

Jeff Bezos’ Project Prometheus Nears $10B Funding at $38B Valuation

Investor confidence in Artificial Intelligence shows no signs of waning.&nbsp;Jeff Bezos’ secretive AI lab,&nbsp;Project Prometheus, is reportedly close to securing a massive&nbsp;$10 billion&nbsp;funding round. According to a report by the&nbsp;Fina<!--autointro-->

Physical AIJPMorganBlackRock
Industry Editor selected

Anthropic Unleashes Claude Design: A Paradigm Shift for the Creative Industry

April 17, 2026&nbsp;— AI powerhouse Anthropic has officially launched&nbsp;Claude Design, a groundbreaking visual creation tool powered by its latest flagship model,&nbsp;Claude Opus 4.7. This release marks a significant departure from traditional de<!--autointro-->

AnthropicClaude Design
Research Editor selected

SPEX and ProxySPEX: Scalable Interaction Discovery for LLM Interpretability

Berkeley AI Research (BAIR) presents SPEX and ProxySPEX, algorithms that identify influential interactions among features, training data points, and model components at scales far beyond prior methods. SPEX exploits sparsity and low-degreeness to reframe interaction search as a sparse recovery problem; ProxySPEX adds hierarchy to achieve similar performance with roughly 10x fewer ablations. The methods are demonstrated on feature attribution (long-context sentiment, a modified trolley problem),

Why it matters: It offers a scalable, open-source interpretability framework with concrete demonstrations across feature, data, and component attribution, useful for researchers and practitioners working on model transparency.

interpretabilitylarge language modelsattribution methods
Industry Editor selected

What Is the &quot;Chain of Thought&quot; in Agent Models?

Recently, you might have heard several fancy terms floating around: Interleaved Thinking (Claude), Thinking-in-Tools (MiniMax K2), Thinking in Tool-Use (DeepSeek V3.2), and Thought Signature (Gemini). At first glance, they sound complex—but in realit<!--autointro-->

Agent modelschain of thoughtinterleaved thinking
Industry Editor selected

Demystifying AI Agent Evaluation: A Comprehensive Guide

This article is adapted from Anthropic&#39;s engineering blog post &quot;Demystifying evals for AI agents,&quot; published on January 9, 2026. The original was authored by Mikaela Grace, Jeremy Hadfield, Rodrigo Olivares, and Jiri De Jonghe. This tra<!--autointro-->

AI AgentLLMSWE-bench
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Moonshot AI Quietly Launches Next-Gen Open Model: Kimi K2.5

Moonshot AI has officially rolled out its latest open model, Kimi K2.5, via a silent update to its web-based chat interface. The upgrade—deployed server-side—has seamlessly replaced the previous K2 model without requiring any manual intervention from<!--autointro-->

multimodal AIreinforcement learningKimi K2.5
Industry Editor selected

DeepSeek Unveils DeepSeek-OCR 2 with Groundbreaking Visual Causal Flow Architecture

Today, AI startup DeepSeek released its new paper titled “DeepSeek-OCR 2: Visual Causal Flow” and open-sourced the DeepSeek-OCR 2 model. The system introduces an innovative DeepEncoder V2 architecture that enables AI to dynamically reorder image segm<!--autointro-->

multimodal AIQwen3-Max-ThinkingKimi K2.5
Research Editor selected

TRL: Divide-and-Conquer Value Learning Without TD Bootstrapping

Berkeley AI Research presents Transitive RL (TRL), an off-policy value-learning algorithm based on divide and conquer rather than temporal difference (TD) learning. TRL updates values via a transitive Bellman rule using candidate midpoints from dataset trajectories and expectile regression, avoiding explicit search over the state space. On OGBench offline goal-conditioned benchmarks including humanoidmaze and puzzle with 1B-sized datasets, TRL matches the best individually tuned n-step TD baseli

Why it matters: It offers a concrete new RL algorithm with benchmark evidence, which is useful for researchers working on scalable off-policy RL and long-horizon decision making.

reinforcement learningoff-policy RLgoal-conditioned RL