Latent Cache Flow: AI Models Communicate Without TextTensor Cache: Retains Evicted Tokens for Longer Transformer MemoryWhen LLMs Need CoT Reasoning: Entropy Phase Transitions Provide AnswersInductive Deductive Synthesis: AI Generates Formally Verified CodeTranscoders Trace Visual Grounding and Hallucinations in VLMsMedExpMem: Medical VLMs Learn to Differentiate Confusable DiseasesGitHub Guide: Manage Projects with Git in VS CodeOpenClaw Releases v2026.5.25-beta.1Latent Cache Flow: AI Models Communicate Without Text

Transformer KV caches grow linearly with context length, and sliding-window caching discards evicted tokens, losing early evidence. Tensor Cache introduces a two-level caching mechanism that stores evicted tokens in compressed associative memory instead of deleting them. It maintains memory efficiency while preserving retrieval of key evidence in long contexts, benefiting RAG systems and long-conversation applications.

Chain-of-thought (CoT) reasoning is a default LLM strategy, but empirical evidence shows a paradox: CoT sometimes greatly improves accuracy and sometimes adds unnecessary overhead. This study analyzes LLM internal states through entropy phase transitions from a dynamical systems perspective, finding that CoT benefits emerge clearly only when task complexity exceeds a specific threshold. Developers can use this to balance inference cost and accuracy.

AI excels at generating and testing code but struggles with formal guarantees that testing alone cannot provide, such as distributed system consistency and fault tolerance. Inductive Deductive Synthesis combines inductive and deductive reasoning to generate formally verified system code. It helps engineers reduce manual verification effort and improve reliability of critical infrastructure code.

Vision-Language Models (VLMs) perform well on multimodal reasoning, but how visual inputs become text remains unclear. Existing SAEs analyze static residual streams and miss dynamic computation. Transcoders trace causal paths during VLM generation, pinpointing feature sources for visual grounding and hallucinations. Researchers can use this to identify exactly where models produce incorrect descriptions, providing interpretability support for VLMs.

Experienced physicians acquire the ability to differentiate confusable conditions through clinical practice. Current medical VLMs lack this capability and often misjudge similar symptoms. MedExpMem introduces experience memory into medical VLMs, letting models accumulate differential diagnosis experience by simulating clinical practice. This improves accuracy in distinguishing similar conditions, with practical value for clinical diagnostic assistance systems.

GitHub released a beginner tutorial on managing repositories directly in VS Code. It covers cloning repos, committing code, branching, and creating pull requests using built-in version control features. Developers can handle daily Git workflows without leaving the editor or installing extra CLI tools.

OpenClaw released v2026.5.25-beta.1. Recent updates include v2026.5.25-beta.1, v2026.5.25-alpha.2, OpenClaw 2026.5.24-beta.2, and v2026.5.25-alpha.1. Specific changelog details are not yet available.