Decoupling from China is impossible, and the US cannot export control its way out of competition, notes #CondoleezzaRice#, former US Secretary of State and now Co-Chair of the #AspenStrategyGroup#.

LATEST: 📈 BlackRock's Robert Mitchnick says Bitcoin decoupling from equities is "healthy," noting its outperformance during July's AI pullback validates its role as a "diversifier and potentially a hedge."

Satoshi’s creation will forever remain a watershed moment in history. Yet, it’s time for crypto assets to start decoupling from Bitcoin. They share little in common, with major developments and progress while Bitcoin remains ossified from its early days.

Decouple "search" from "reasoning" in LLM agents and you can cut search costs by up to 98% while keeping accuracy nearly intact 🔌 Title: Decoupling Search from Reasoning: A Vendor-Agnostic Grounding Architecture for LLM Agents URL: 🔌 Overview DSG separates search-based grounding from the language model's reasoning. It runs as an independent gateway compatible with the Model Context Protocol (MCP), acting as a vendor-agnostic intermediary layer. ❓ Challenges Solved In production LLM agents, real-time search grounding is tightly coupled to the model provider. ・This makes systems hard to inspect, reconfigure, repurpose, or migrate ・Search can cause "Search-Induced Verbosity" that violates strict output requirements Bundling search with reasoning was a bottleneck for both flexibility and cost. 💡 Methodology & Proposed Approach It places grounding at the interface between search and generation, not inside the model, exposing previously model-embedded elements as controllable first-class features. ・Provider routing (choose and switch search providers) ・Source-aware context rendering ・Configurable fallback mechanisms ・Retrieval-depth management ・Both exact and semantic caching 📊 Experimental Results ・SimpleQA: 86.1% accuracy (vs 87.7% native search) while cutting search costs by 91% ・99.4% warm-cache hit rate with 68% latency reduction ・Production e-commerce: matched native-search accuracy while cutting search costs by over 98% ・But native search kept an edge on recency-sensitive FreshQA queries #LLMAgents# #Search#

AI can now remember, update, and forget facts without any external retrieval. A new paradigm for native memory in foundation models has just been published. Title: Metis: Memory Foundation Model 🔍 Overview Current external memory systems like RAG face three fundamental limitations: decoupling from the model backbone, inability to propagate gradients through discrete memory operations, and added inference latency. Metis integrates memory natively into Transformer blocks — analogous to how Chain-of-Thought became intrinsic to LLMs — eliminating external module dependency while enabling learned, end-to-end memory behavior. 🛠 Problem and Approach Two core components are introduced. The Local Memory Block maintains a dense memory network updated across inference steps via exponential moving average, with learned importance scoring and Top-ρ token selection. The Hyper Memory Block uses static learned parameters to enable memory transformation through the forward pass. Four memory operations — Remember, Update, Forget, and Reflect — are executed purely through forward computation without any background gradient updates. 📊 Experimental Results On the MemOps benchmark (no-context setting), Metis-27B achieves 24.76% average performance: · Baseline Qwen3.5-27B (no context): 1.69% · Test-time training Temp-LoRA-27B: 9.70% · Parametric memory δ-Mem: 4.38% On the Metis internal test set, the model reaches 73.77% average with Reflect (multi-hop reasoning) at 93.44%. It outperforms all no-context baselines across model sizes and tasks. 💡 Practical Significance Native memory avoids the retrieval, ranking, and prefilling overhead of RAG through parallelizable computation, keeping inference latency low. Domain adaptation via post-training is supported. Model checkpoints and code are publicly available on GitHub (MemTensor/Metis) and HuggingFace. #LLM# #AIAgent#

Bitcoin has OFFICIALLY beat shitcoins. Bitcoin: $1.28T. Top 125 other cryptos combined, excluding stablecoins: $604B. BTC is now worth 2.12× the rest. In Nov. 2021, the rest led by $239B. Today, BTC leads by $678B. That’s a $916B gap reversal. The decoupling isn’t coming. It already happened.

Chart of the Week: weekly token launches climbed from ~80K last summer to a peak of ~254K the week of 26 Jan, and have held ~190–220K/week since. PUMP's weekly-average price went the other way - down ~80% from a ~$0.008 mid-September peak to ~$0.0015 — fully decoupling from launch activity.