axiom: The Evolutionary-Agentic Loop is an 8-phase self-sustaining cycle (Discovery Initiation -> Population Expansion -> Recursive Interaction -> System 2 Reasoning -> Machine-Grade Evaluation -> Context Pruning -> Quality-Diversity Selection -> Migration and Recurrence) that transforms AI from a passive linguistic interface into an operational infrastructure capable of autonomously refining its own algorithms, hardware kernels, and system heuristics

source: Agentic OS Blueprint Research Synthesis (Deep Think Report)

genesis_action: Map Genesis execution layer phases (RWL decompose -> Gemini swarm -> verify -> evolve) onto the 8-phase loop. Identify which phases we implement (1,2,5,6) and which we lack (3-MCP integration, 4-Deep Think reasoning, 7-MAP-Elites, 8-Island Migration).


axiom: DeltaEvolve replaces static full-code histories in agent prompts with Structured Semantic Deltas -- logs capturing HOW and WHY changes affected performance -- reducing average token consumption by 36.79% in scientific discovery tasks while providing causal momentum memory that helps escape local optima faster than full-code baselines

source: Agentic OS Blueprint Research Synthesis (DeltaEvolve section, arXiv:2602.02919)

genesis_action: Implement DeltaEvolve-style semantic deltas in Alpha Evolve cycle tracking. Replace full axiom histories in prompts with Level 1 summaries (FROM/TO format) and only expose Level 3 (full content) for the active parent. Target 35%+ token reduction in evolution prompts.


axiom: DeltaEvolve uses a 3-level pyramid database for context management: Level 3 (Full Code, active parent exposed for direct editing), Level 2 (Delta Plan Details with OLD vs NEW logic + hypothesis for causal reasoning), Level 1 (Delta Summary in concise FROM/TO format for historical retrieval) -- a Progressive Disclosure Sampler dynamically decides which level to expose

source: Agentic OS Blueprint Research Synthesis (DeltaEvolve section)

genesis_action: Design Genesis axiom storage with 3-level access: L1 (axiom ID + one-line summary), L2 (axiom + genesis_action + confidence), L3 (full axiom record with all relationships and metadata). Use progressive disclosure in agent prompts.


axiom: Aletheia achieves 95.1% accuracy on IMO-ProofBench Advanced (vs previous record of 65.7%) through an explicit 3-part agentic harness: Generator (proposes hypothesis), Verifier (checks for logical flaws, calculation errors, fabricated citations), Reviser (backtracks and refactors until flaws resolved) -- the separation of verification duties helps models recognize flaws they initially overlook

source: Agentic OS Blueprint Research Synthesis (Aletheia section, MarkTechPost 2026-02-12)

genesis_action: Implement Generator-Verifier-Reviser pattern in Genesis verification layer. Every agent output goes through: (1) generate answer, (2) separate verifier agent checks for errors, (3) if errors found, reviser agent fixes. Apply to axiom creation, code generation, and strategy documents.


axiom: Verifier-Guided Distillation transfers the PROCESS of error repair and backtracking (not just correct final answers) from large models to small models -- training SLMs on verified reasoning traces that include mistakes and self-corrections causes latent verification behavior to emerge in models as small as 7B parameters, enabling them to stop, detect contradictions, and revise assumptions

source: Agentic OS Blueprint Research Synthesis (Aletheia section, arXiv:2601.14290)

genesis_action: When AIVA transitions to local reasoning (GLM-4.6V-Flash-9B), train or fine-tune on verified reasoning traces from Claude/Gemini that include mistake-correction cycles. This is the path to sovereign on-device reasoning capability.


axiom: Balanced prompting (simultaneously requesting proof AND refutation of a statement) prevents confirmation bias in AI reasoning -- Aletheia uses this to ensure the model explores both sides before committing, producing more reliable conclusions than unidirectional reasoning

source: Agentic OS Blueprint Research Synthesis (Aletheia Advisor Models section)

genesis_action: Add balanced prompting to Genesis axiom verification: for every new axiom, prompt Gemini Deep Think to both PROVE and DISPROVE the statement. Only commit axioms where proof significantly outweighs refutation.


axiom: MCP-Guard employs a 3-stage cascaded defense pipeline for securing agentic tool use: Stage 1 Syntactic Filtering (regex-based, sub-2ms, intercepts overt threats like SQL injection), Stage 2 Semantic Neural Detection (fine-tuned embedding models like Multilingual E5, 96% accuracy on obfuscated payloads), Stage 3 Cognitive Arbitration (high-level LLM judges ambiguous cases) -- each stage acts as a progressively more expensive but more capable filter

source: Agentic OS Blueprint Research Synthesis (MCP-Guard section, arXiv:2508.10991)

genesis_action: Implement 3-stage security cascade for all Genesis MCP tool calls: (1) regex filter on inputs for injection patterns, (2) embedding-based anomaly detection on tool call parameters, (3) Claude verification for edge cases. Start with Stage 1 (zero cost, sub-2ms) and add stages incrementally.


axiom: AI Identity Gateways authenticate Non-Human Identities (NHIs) and mint ephemeral task-scoped tokens for every MCP tool call, enforcing zero-trust principles -- an agent authorized to summarize a document cannot use the same session to modify a database without re-verification of intent

source: Agentic OS Blueprint Research Synthesis (MCP 2026 Governance section)

genesis_action: Design Genesis agent identity system: each spawned agent gets an ephemeral identity with scoped permissions (read-only vs read-write vs admin). Track all tool calls per identity. Prevent privilege escalation between agent scopes.


axiom: MAP-Elites (Multi-dimensional Archive of Phenotypic Elites) maintains quality-diversity balance in evolutionary search by organizing solutions into a feature map where high-performing solutions from different algorithmic niches are preserved -- this prevents convergence to local optima by retaining a mutation that minimizes latency but increases memory in a separate cell from one that optimizes memory but sacrifices latency

source: Agentic OS Blueprint Research Synthesis (MAP-Elites section, OpenEvolve PyPI)

genesis_action: Implement MAP-Elites style diversity in Genesis axiom curation: organize axioms by (domain, confidence, actionability) feature map. Retain diverse axioms even if lower confidence, to prevent knowledge monoculture in specific domains.


axiom: The Recursive Language Model (RLM) approach treats massive datasets as environments to be searched rather than data to be memorized -- when facing a million-token document, an RLM writes code to spawn sub-agents (Agent A checks index, Agent B reads sections, Root Model synthesizes), scaling to 10M+ tokens while outperforming base LLMs by 2x on information-dense tasks at linear token costs

source: Agentic OS Blueprint Research Synthesis (Active Memory section, arXiv:2512.24601)

genesis_action: Genesis Agent Teams are already async RLM in practice (per RLM-007). Formalize this: for any task involving >50K tokens of source material, automatically decompose into sub-agent search pattern rather than context stuffing.


axiom: The Island Migration pattern maintains evolutionary diversity by running independent populations (islands) that evolve in parallel and periodically exchange their best individuals -- this propagates successful strategies across the search space while preventing premature convergence through geographic isolation

source: Agentic OS Blueprint Research Synthesis (Phase VIII of Evolutionary-Agentic Loop)

genesis_action: Implement island migration for Genesis multi-agent teams: run 3+ agent teams on the same problem with different prompting strategies, periodically share best solutions between teams. Prevents groupthink and discovers diverse solution approaches.


axiom: Active Memory hierarchies organize agent memory into 3 tiers mimicking human cognition: Working Memory (high-capacity, most recent tokens for immediate execution), Contextual Memory (compressed summaries and key takeaways preventing context rot), Long-Term Memory (indexed historical logs and permanent knowledge for multi-hour/multi-day projects)

source: Agentic OS Blueprint Research Synthesis (Active Memory section)

genesis_action: Genesis already has a 3-tier memory (Redis hot/PostgreSQL warm/Qdrant deep from AE22-004). Validate alignment: Redis=Working, PostgreSQL=Contextual, Qdrant=Long-Term. Add automated compression/promotion between tiers based on access frequency and recency.


axiom: Google Titan Memory Framework introduces a surprise-based learning metric where the memory module learns what to store based on prediction error -- the surprise signal (difference between predicted and actual outcomes) determines what gets written to persistent long-term memory, replacing static rules with gradient-based memory updates

source: Agentic OS Blueprint Research Synthesis (Titan Memory Framework)

genesis_action: CRITICAL GAP: Genesis surprise_memory.py returns 0.5 for everything (hardcoded stub). Replace with Titan-style surprise metric: compute prediction error between expected and actual outcomes, use this gradient to determine memory write priority. High surprise = write to long-term, low surprise = working memory only.


axiom: Titan Memory treats memory-as-gradient: the persistent memory module receives gradient updates during inference (not just training), allowing the model to continuously refine what it remembers based on real-time feedback -- this is fundamentally different from static KV-cache approaches where memory content is fixed after initial encoding

genesis_action: Redesign Genesis memory pipeline to update memory representations based on usage feedback: if a retrieved memory was useful (agent used it in output), strengthen it; if retrieved but unused, weaken it. Implement as confidence/weight adjustments in PostgreSQL and Qdrant.


axiom: Titan's long-term memory module persists across context windows and sessions, maintaining learned associations that transcend individual conversations -- this enables genuine cross-session learning where the system gets meaningfully smarter over time rather than resetting with each new context

genesis_action: Genesis already has cross-session persistence (Supermemory + PostgreSQL + Qdrant). The gap is not storage but LEARNING: memories are stored but not refined. Implement memory-weight adjustment: each memory gets a 'utility_score' that increases when referenced and decreases over time.


axiom: The Software Singularity pattern describes agents that optimize their own infrastructure -- AlphaEvolve proposed Verilog rewrites for TPU arithmetic circuits that were functionally verified and integrated into next-generation hardware, demonstrating that agentic loops can improve the very substrate they run on

source: Agentic OS Blueprint Research Synthesis (Hardware-Software Co-Design section)

genesis_action: Genesis Alpha Evolve already does this partially (axioms improve the knowledge base that guides future cycles). Make it explicit: track which axioms led to which improvements, measure the compound effect of self-optimization over cycles.


axiom: LLMTuner acts as a semantic feature extractor mapping program specifications to quantitative performance scores, achieving 49.2% accuracy improvement in performance estimation on unseen hardware -- unlike black-box methods, it produces human-readable diagnostics explaining WHY a kernel is slow (e.g., memory-bound by shared-memory bank conflicts)

source: Agentic OS Blueprint Research Synthesis (TPU Kernel Optimization section, OpenReview FCFM3Yxtsm)

genesis_action: Apply LLMTuner concept to Genesis operations: use LLMs to diagnose WHY specific agent tasks are slow or failing, not just THAT they failed. Add diagnostic reasoning step to all error handling paths.


axiom: AlphaEvolve achieves production-scale impact by using a weighted ensemble of Gemini 3 Flash (high-volume exploration, broad search) and Gemini 3 Pro (strategic depth, causal analysis of successful mutations) -- Flash generates massive candidate mutations while Pro identifies the causal mechanisms of improvement, creating a recursive speed-depth synergy

source: Agentic OS Blueprint Research Synthesis (AlphaEvolve Architecture section)

genesis_action: Genesis already uses tiered model routing (AE22-019). Formalize the Flash/Pro synergy: use cheap fast models (Gemini Flash, Kimi K2.5) for exploration/generation, expensive slow models (Claude Opus, Deep Think) for evaluation/selection. Never use expensive models for exploration.


axiom: Gemini 3 Flash achieves 90.4% on GPQA Diamond at $0.50/$3.00 per MTok with 1M context window, often outperforming previous high-thinking models at a fraction of the latency -- context caching enables 90% cost reduction for repeated token use in long-running evolutionary simulations

source: Agentic OS Blueprint Research Synthesis (Gemini 3 Flash section)

genesis_action: Maximize Gemini 3 Flash usage with context caching for Alpha Evolve cycles. Cache the KG axiom context once, reuse across all mutation evaluations. Target 90% cache hit rate for repeated evolutionary prompts.


axiom: Evaluation cascades test solutions through progressively more demanding stages: syntactic check (static analysis), functional verifier (test suites), performance scorer (benchmarks), qualitative judge (LLM feedback) -- compute-heavy evaluations only run on candidates that pass preliminary checks, preventing waste on obviously flawed solutions

source: Agentic OS Blueprint Research Synthesis (AlphaEvolve Evaluator section)

genesis_action: Implement evaluation cascade for all Genesis agent outputs: (1) syntax/format check (free, instant), (2) functional correctness check (unit tests), (3) performance benchmark (only if tests pass), (4) LLM quality judge (only for candidates passing all prior stages). Stop wasting Claude tokens on outputs that fail basic format checks.


axiom: The Evolutionary Expectation-Maximization (EM) framework formalizes the agentic loop: E-step samples candidate programs to estimate the local landscape, M-step updates control context to maximize expected score -- DeltaEvolve optimizes the M-step policy to prioritize semantic deltas over full-code snapshots, acting as a high-pass filter for algorithmic insight

source: Agentic OS Blueprint Research Synthesis (EM Framework section)

genesis_action: Formalize Alpha Evolve as EM: E-step = generate candidate axioms/code from multiple agents, M-step = update the KG context with only the semantic deltas of what changed and why. This provides mathematical grounding for our empirical evolution process.


axiom: The Agentic OS paradigm represents a structural transformation from passive LLMs to autonomous operating systems -- AI transitions from linguistic interface to operational infrastructure, becoming the primary architect of mathematical, software, and hardware substrates rather than a conversational assistant

source: Agentic OS Blueprint Research Synthesis (Introduction + Conclusion)

genesis_action: Genesis IS an Agentic OS implementation. Reframe all documentation and strategy from 'AI assistant platform' to 'autonomous operational infrastructure'. This lens change affects product positioning, pricing, and capability claims.


axiom: MCP 2026 replaces the N x M integration problem (every model needs custom connector for every tool) with a modular N + M architecture using 5 key primitives: Resources (passive data via URI), Tools (executable state-changing functions), Prompts (standardized templates), Roots (security boundaries), and Sampling (server-initiated LLM callbacks)

source: Agentic OS Blueprint Research Synthesis (MCP 2026 section)

category: interoperability

genesis_action: Audit all Genesis integrations against MCP primitives. Convert custom glue code to MCP-compliant servers. Priority: Telnyx MCP server, Elestio MCP server, Memory Hub MCP server. Each reduces integration complexity from O(N*M) to O(N+M).


axiom: Deep Think V2 achieved 100x compute reduction for Olympiad-level problems compared to V1 (July 2025 -> January 2026) through process supervision and reinforcement learning that encourages productive reasoning paths over blind exploration -- the thinking_level parameter allows tuning System 2 depth based on task complexity

source: Agentic OS Blueprint Research Synthesis (Deep Think section)

genesis_action: Use thinking_level parameter in Gemini API calls: 'high' for architectural decisions and memory system design, 'low' for routine coding. This optimizes the quality-cost tradeoff per task.


axiom: Structured State-Space Models (SSMs) achieve linear-time O(N) inference complexity by representing sequences as parameterized recurrences from dynamical system theory -- unlike Transformers that 'remember everything' stochastically, SSMs selectively update hidden state based on input, learning to remember only what matters through input-dependent parameters

source: Agentic OS Blueprint Research Synthesis (Active Memory and SSM section)

genesis_action: Monitor SSM developments (Mamba, SparseSSM) for future Genesis memory system optimization. Current relevance: SSM pruning techniques (50% weight removal without accuracy loss) could optimize on-device AIVA inference.


axiom: SparseSSM achieves 50% weight pruning without zero-shot accuracy loss using OBS (Optimal Brain Surgeon) framework adapted for time-sharing SSM parameters -- critical for deploying billion-parameter models on edge hardware where Agentic OS must balance reasoning quality with resource constraints

source: Agentic OS Blueprint Research Synthesis (SparseSSM section, arXiv:2506.09613)

genesis_action: Evaluate SparseSSM techniques for AIVA's local model (GLM-4.6V-Flash-9B on Mac Mini M4). If applicable, 50% pruning could double inference throughput or halve memory usage without quality loss.


axiom: The Trust Gap in autonomous AI is being closed through formal verification and System 2 thinking -- systems like Aletheia that hypothesize, test logic, and verify their own work enable deployment of AI-written code without human intervention, shifting AI from 'number cruncher' to primary researcher

source: Agentic OS Blueprint Research Synthesis (Strategic Implications section)

genesis_action: Genesis verification protocol (black box + white box testing) is already closing the trust gap. Extend with Aletheia-style self-verification: every agent output includes a confidence score and a list of potential failure modes identified by the generating agent itself.


axiom: Sovereign AI describes models capable of professional reasoning and autonomous task execution running entirely on-device, preserving data privacy and eliminating API dependency -- enabled by efficient memory management (SSMs) and backtracking distillation (Aletheia) that transfer frontier reasoning to sub-10B parameter models

source: Agentic OS Blueprint Research Synthesis (Sovereign AI section)

genesis_action: AIVA's Mac Mini deployment IS sovereign AI. Enhance with: (1) Verifier-Guided Distillation for local reasoning, (2) SparseSSM pruning for efficiency, (3) Active Memory hierarchies for persistent cross-session learning. Target: zero cloud dependency for core AIVA functions.


axiom: AlphaEvolve discovered a 48-multiplication algorithm for 4x4 complex matrix multiplication, breaking the 56-year-old Strassen bound of 49 -- evolutionary agents can discover 'alien logic' solutions that are functionally correct and highly efficient but counter-intuitive to human engineers

source: Agentic OS Blueprint Research Synthesis (Algorithmic Discovery section)

genesis_action: Apply 'alien logic' discovery mindset to Genesis optimization: use evolutionary search not just for obvious improvements but for counter-intuitive solutions. Set evaluators to score functional correctness, not code readability, during exploration phases.


axiom: AlphaEvolve's Borg scheduling optimization recovered 0.7% of Google's global compute resources through a simple heuristic discovered by evolutionary search -- at Google's scale this translates to massive energy savings, demonstrating that even small percentage improvements on infrastructure at scale yield enormous real-world impact

source: Agentic OS Blueprint Research Synthesis (Production Milestones section)

genesis_action: Apply evolutionary optimization to Genesis infrastructure: even 5-10% improvements in API call efficiency, memory query latency, or agent task completion time compound into significant cost savings at scale.


axiom: Non-deterministic AI agent outputs require probabilistic validation: track human-AI agreement rates and behavioral drift over time, flagging models that degrade in quality due to quantization, fine-tuning, or optimization -- static test suites are insufficient for systems whose behavior changes between evaluations

source: Agentic OS Blueprint Research Synthesis (Scorecard Mechanics section)

genesis_action: Implement behavioral drift detection for Genesis agents: compare agent output quality over time (not just per-task). If Claude or Gemini model updates cause quality regression, detect and alert before it impacts production.


axiom: The L2M framework establishes that a model's capacity to process extensive context is strictly limited by the size of its history state -- Transformers grow KV cache linearly while fixed-state models (SSMs, RNNs) face capacity bottlenecks, making selective state compression essential for scaling beyond current context limits

source: Agentic OS Blueprint Research Synthesis (L2M Framework section)

genesis_action: Genesis already mitigates this via RLM decomposition (treat data as searchable environment). For voice agents with limited context: implement aggressive state compression, keeping only decision-relevant information in active context.


axiom: RLM sub-agent note-taking mechanism enables scaling to 10M+ token regime: rather than loading all data into context, agents write structured notes (index lookups, section summaries, key findings) that subsequent agents can reference, maintaining linear token costs while handling arbitrarily large datasets

source: Agentic OS Blueprint Research Synthesis (RLM section, arXiv:2512.24601)

genesis_action: Implement structured note-taking in Genesis agent teams: each sub-agent writes findings to shared scratchpad (Redis or temp file), subsequent agents read notes rather than re-processing raw data. This is how we handle codebase-scale analysis.


axiom: Interpretable chain-of-thought (CoT) reasoning may be degraded by process supervision or safety optimization that causes models to drift away from natural language reasoning -- maintaining interpretable CoTs is a critical requirement for long-term agentic alignment because opaque reasoning prevents verification of agent intent

source: Agentic OS Blueprint Research Synthesis (Trust Gap section)

category: alignment

genesis_action: Require all Genesis agents to produce explicit reasoning chains in their outputs. Log and persist these chains for audit. If a model update reduces CoT quality, flag as alignment risk.


axiom: TreeQuest framework supports dynamic model selection for different sub-problems in a search tree -- rather than using one model for all reasoning, each node in the search tree can use the optimal model (e.g., o4-mini for simple verification, Gemini Pro for complex reasoning), reducing total cost while maintaining quality

source: Agentic OS Blueprint Research Synthesis (TreeQuest Framework section)

genesis_action: Implement TreeQuest-style dynamic model selection in Genesis task decomposition: when breaking a complex task into subtasks, assign each subtask to the cheapest model capable of handling it, not the most capable model available.


axiom: Vibe-Proving is a human-AI collaboration pattern where humans guide AI through iterative proof cycles, requesting simultaneous proofs or refutations to prevent confirmation bias, using code-assisted verification for technical steps -- this produced the first fully AI-generated research paper (Feng26) and solved 4 open questions in Bloom's Erdos Conjectures

source: Agentic OS Blueprint Research Synthesis (Aletheia Advisor Models section)

genesis_action: Apply Vibe-Proving pattern to Kinan-Genesis collaboration: when exploring strategic decisions, Genesis simultaneously argues FOR and AGAINST the proposal, Kinan guides toward resolution. This prevents the sycophancy trap in AI assistants.


axiom: Automated GPU kernel generation using inference-time scaling (DeepSeek-R1 + NVIDIA) achieved 100% numerically correct kernels for Level-1 KernelBench problems without human programming -- demonstrating that Agentic OS can handle the 'impossible' skill of low-level GPU programming through evolutionary search and verification

source: Agentic OS Blueprint Research Synthesis (Hardware-Software Co-Design section)

genesis_action: Track automated kernel generation capabilities as they mature. When Genesis needs custom inference optimization (e.g., AIVA local model serving), this capability could auto-generate optimized kernels for Mac Mini's M4 chip.


axiom: MCP transport diversification uses STDIO for local process isolation (IDE to filesystem, no internet dependency) and SSE (Server-Sent Events) over HTTP for low-latency remote streaming to cloud enterprise systems -- choosing the right transport layer per integration type reduces latency and attack surface

source: Agentic OS Blueprint Research Synthesis (MCP Transport section)

genesis_action: Use STDIO transport for all local Genesis MCP servers (filesystem, local model access, AIVA bridge) and SSE for remote servers (Elestio services, external APIs). This matches security boundaries to communication patterns.


axiom: The Agentic OS pushes beyond 'just scaling' toward inference-time intelligence and long-horizon coherence -- the competitive advantage shifts from who has the biggest model to who has the best evolutionary loop, verification harness, and memory architecture around their models

genesis_action: This validates Genesis's entire architecture thesis: the model is NOT the moat, the SYSTEM around the model is the moat. Memory (Titan/Bloodstream), evolution (Alpha Evolve), verification (Aletheia pattern), and orchestration (Agent Teams) are the competitive advantage.


axiom: Organizations that rely on outdated bespoke integration methods will lose competitive edge by 2026 -- standardized protocols (MCP) plus evolutionary loops (AlphaEvolve) provide the information backbone for production-scale AI agents that act with context, compliance, and confidence

genesis_action: Genesis must standardize on MCP for all tool integrations within Q1 2026. Every custom connector is technical debt. Priority order: memory tools, voice tools, infrastructure tools, external APIs.


axiom: Titan Memory's surprise metric computes the difference between the model's PREDICTION of what information will appear next and the ACTUAL information received -- high prediction error (surprise) triggers memory consolidation to long-term storage, while low surprise indicates redundant information that can remain in working memory or be discarded

source: Agentic OS Blueprint Research Synthesis (Titan Memory Framework)

genesis_action: IMPLEMENTATION BLUEPRINT: (1) Before processing new input, have the model predict a summary of expected content, (2) Compare prediction embedding with actual content embedding, (3) Cosine distance = surprise score, (4) High surprise -> write to PostgreSQL+Qdrant (long-term), (5) Low surprise -> Redis only (working memory). This replaces the 0.5 stub in surprise_memory.py.


axiom: RLM's code-based decomposition pattern treats the LLM as a programmer that writes its own data access code rather than a reader that consumes raw data -- the model generates Python/JS to programmatically navigate large datasets, executing code in a REPL environment to filter, index, and extract only relevant information before reasoning over it

source: Agentic OS Blueprint Research Synthesis (RLM section)

genesis_action: For Genesis tasks involving large source materials (KG with 392+ axioms, full codebase): instead of stuffing axioms into context, have the agent write code to query PostgreSQL/Qdrant for relevant subsets. The agent programs its own memory access.


axiom: Deep Think resolved deadlock problems in discrete mathematics by pulling tools from continuous mathematics (measure theory, Stone-Weierstrass theorem) -- cross-domain reasoning transfer is a key capability of System 2 thinking that enables breakthroughs by applying known techniques from one field to unsolved problems in another

source: Agentic OS Blueprint Research Synthesis (Deep Think Real-World section)

genesis_action: Leverage cross-domain reasoning in Genesis: when stuck on a voice engineering problem, prompt with techniques from game design or psychology. When stuck on memory architecture, prompt with techniques from database engineering or neuroscience. System 2 models excel at these transfers.


axiom: Automated feedback for academic papers (STOC'26) identified logical gaps in proof lemmas that had passed human review -- demonstrating that AI verification can exceed human verification quality on formal reasoning tasks, especially when humans suffer from confirmation bias or fatigue

genesis_action: Apply automated verification feedback to Genesis documents: run PRDs, strategy docs, and pricing models through Deep Think to identify logical gaps, contradictions, and unsupported claims before presenting to Kinan.


axiom: Context caching in Gemini 3 Flash allows 90% cost reduction for repeated token use, which is critical for evolutionary simulations that frequently reuse large codebases as context for new mutations -- the cache converts repeated fixed context from a per-call cost to an amortized near-zero cost

source: Agentic OS Blueprint Research Synthesis (Gemini 3 Flash section)

genesis_action: Implement Gemini context caching for all Alpha Evolve cycles: cache the KG axiom corpus, CLAUDE.md, and protocol files once, reuse across all evaluation calls. At current 392 axioms, this saves ~50K tokens per call.