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kanaria007
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posted an update about 11 hours ago
✅ New Article: *Post-Transformer Decision Cores* (v0.1) Title: 🚀 Post-Transformer Decision Cores: Goal-Native Engines Beyond LLMs 🔗 https://huggingface.co/blog/kanaria007/post-tranformer-decision-cores --- Summary: Transformers are powerful—but in SI-Core they’re *not the essence of intelligence*. A *Decision Core* is anything that satisfies the *Jump contracts* (OBS/ETH/MEM/ID/EVAL + RML), and those contracts don’t require next-token prediction. This article sketches what “post-Transformer” looks like in practice: *goal-native, structure-aware controllers* that may use LLMs as tools—but don’t depend on them as the runtime brain. > Don’t relax the contracts. > Replace the engine behind them. --- Why It Matters: • Makes LLMs *optional*: shift them to “genesis / exploration / explanation,” while routine high-stakes Jumps run on structured cores • Improves boring-but-critical properties: *determinism (CAS), fewer inconsistencies (SCI), fewer ETH violations (EAI), better rollback (RBL/RIR)* • Enables gradual adoption via *pluggable Jump engines* and domain-by-domain “primary vs fallback” switching --- What’s Inside: • The architectural inversion: *World → OBS → SIM/SIS → Jump (Decision Core) → RML → Effects* (LLM is just one engine) • Three compatible post-Transformer directions: 1. *World-model + search controllers* (MPC/MCTS/anytime search with explicit GCS + ETH constraints) 2. *Genius-distilled specialized controllers* (distill structure from GeniusTraces; LLM becomes a “genesis tool”) 3. *SIL-compiled Decision Programs* (typed Jump entrypoints, compiler-checked invariants, DPIR/GSPU targeting) • A realistic migration path: LLM-wrapped → Genius library → shadow dual-run → flip primary by domain → SIL-compiled cores • How this connects to “reproducing genius”: GRP provides trace selection/format; this article provides the engine architectures --- 📖 Structured Intelligence Engineering Series
updated a dataset 1 day ago
kanaria007/agi-structural-intelligence-protocols
posted an update 3 days ago
✅ New Article: *Genius Replay Protocol* (v0.1) Title: 🧠 Genius Replay Protocol: Capturing and Replaying High-Value Jumps 🔗 https://huggingface.co/blog/kanaria007/genius-replay-protocol --- Summary: “Genius” isn’t magic—it’s a **high-value Jump (or short Jump sequence)** that reliably produces outsized GCS gains with strong robustness and reuse potential. This article defines the **Genius Replay Protocol (GRP)**: a safe way to **capture, validate, store, and replay** those exceptional moves as reusable macro-intelligence—without turning them into copy-pasted folklore. At the core is a **GeniusTrace** bundle: *ContextSignature* (where it applies) + *JumpSequence* (what was done) + *EvalSummary* (why it worked) + *EthicsTrace* (why it’s allowed). > Don’t replay outcomes. > Replay **structure**, under today’s constraints. --- Why It Matters: • Turns “it worked once” into a **reproducible asset** • Prevents unsafe cargo-culting by replaying **structure**, not brittle outputs • Ensures every replay is re-checked against **current ETH / goal surfaces / OBS requirements** • Enables a pipeline: **automatic discovery → robustness checks → promotion**, not anecdote collection --- What’s Inside: • A concrete definition of “Genius” (GCS outliers + robustness + generalizability) • The GeniusTrace schema: ContextSignature / JumpRecord / EvalSummary / EthicsTrace • Three replay modes: **Exact / Structural / Suggestion** (human-facing) • A **Replay Safety Checker**: context distance, under-observation, ETH compatibility, drift risk • Operational patterns: policy bootstraps, incident recovery, cross-domain structural motifs • Governance: re-validation cadence, fairness/privacy constraints, sharing + semantic redaction --- 📖 Structured Intelligence Engineering Series
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