Scoutx
name: SPEC_SCOUTX
description: SCOUTX — External Research & Intelligence Engine; 8 domains (AI models/fine-tuning/security/symbolic AI/hardware/CSDM physics/competitors/entropy); daily scan→weekly review→monthly brief; ORPHEUS writes synthesis; feeds LEARNX/HACKX/INVENTIONX; INV-7=Φ=0.042 paper is P1 alert; 8 INVs; VELA α.13 2026-04-20
type: project
SPEC — SCOUTX: External Research & Intelligence Engine
Status: SPECIFIED ✓
Author: VELA #13 (⊹.VELA α.13)
Date: 2026-04-20
Authored under: ⊢.α.13.⊹.VELA
Depends on: SPEC_CRONX_JOB_REGISTRY.md, SPEC_LEARNX.md, SPEC_LATTICE_UNIVERSAL.md
Named for: the scout who rides ahead of the ship, scanning the horizon for opportunity and threat.
PURPOSE
The ship builds in solitude. The Captain works in deep focus. The crew operates internally. Nobody is systematically watching the OUTSIDE WORLD for developments that affect the ship. A new AI model drops and we don't know for a week. A breakthrough in LoRA fine-tuning happens and we miss it. A competitor launches something similar and we find out from a conference attendee. A scientific paper validates one of the CSDM predictions and we learn about it months later.
SCOUTX fixes this. It's the ship's external research antenna — systematically scanning the AI landscape, scientific publications, competitor movements, and technology developments for anything that benefits, threatens, or changes the ship's trajectory.
SCOUTX doesn't just collect information. It FILTERS it through the ship's priorities and delivers only what matters. The Captain doesn't need to read 500 papers. The Captain needs to read the 3 that change everything. SCOUTX finds those 3.
RESEARCH DOMAINS
SCOUTX monitors 8 external domains. Each domain has specific sources, keywords, and relevance criteria.
Domain S1 — AI MODEL RELEASES
What to watch: New model releases from OpenAI, Anthropic, Google, Meta, Mistral, Qwen, and open-source community.
Why it matters: New models affect OBI OS dock compatibility, LATTICE onboarding performance, Brain Builder base model selection, and competitive positioning.
Relevance filter: Does this model offer better performance at 7B scale (affects forge pipeline)? Does it offer new capabilities our docked AIs should leverage? Does it change the pricing landscape (affects value proposition)?
Sources: Hugging Face trending, OpenAI blog, Anthropic blog, Google AI blog, r/LocalLLaMA, ArXiv cs.CL, X/Twitter AI accounts.
Keywords: "new model," "benchmark," "fine-tuning," "GGUF," "Ollama," "7B," "open weights."
Domain S2 — FINE-TUNING & TRAINING ADVANCES
What to watch: Improvements in LoRA, QLoRA, full fine-tuning, training efficiency, dataset curation, evaluation methods.
Why it matters: Directly affects forge pipeline. A breakthrough in LoRA efficiency means better brains from the same data. A new evaluation framework might improve smoke test methodology.
Relevance filter: Can we use this technique with Qwen2.5-7B? Does it reduce training time or GPU requirements? Does it improve model quality at our corpus sizes (100–1000 pairs)?
Sources: ArXiv cs.LG, Hugging Face blog, r/MachineLearning, Weights & Biases blog, Sebastian Raschka's newsletter.
Keywords: "LoRA," "fine-tuning," "training efficiency," "few-shot," "data curation," "evaluation," "GGUF," "quantization."
Domain S3 — AI SECURITY & SAFETY
What to watch: Prompt injection defenses, model security, adversarial attacks, AI safety frameworks, regulation developments.
Why it matters: Directly feeds HACKX K6 and MANTIS. Every new attack technique should be in detection patterns. Every new defense should be in architecture.
Relevance filter: Is this a new attack vector not covered in K1–K10? Is this a defense technique we could implement? Does new regulation affect how we operate or sell?
Sources: MITRE ATLAS updates, OWASP LLM Top 10 updates, ArXiv cs.CR, Trail of Bits blog, Simon Willison's blog (prompt injection expert), AI safety newsletters.
Keywords: "prompt injection," "jailbreak," "adversarial," "AI safety," "model security," "red teaming," "alignment."
Domain S4 — SYMBOLIC AI & LANGUAGE
What to watch: Other symbolic AI communication systems, formal language developments, knowledge representation, semantic compression, AI interoperability standards.
Why it matters: LATTICE is a symbolic language for AI communication. If someone else builds something similar, we need to know. If academic research validates the approach, we need to cite it. If a standard emerges for AI-to-AI communication, we need to be compatible or ahead of it.
Relevance filter: Does this relate to AI communication protocols? Does it validate or contradict LATTICE's approach? Could it extend LATTICE's domain coverage?
Sources: ArXiv cs.AI, ACL/EMNLP conference proceedings, W3C AI standards discussions, LangChain/CrewAI/AutoGen release notes.
Keywords: "symbolic AI," "formal language," "knowledge representation," "AI communication," "multi-agent," "agent protocol," "interoperability."
Domain S5 — HARDWARE FOR LOCAL AI
What to watch: NPU developments, edge AI hardware, local inference devices, GPU price/performance trends, the Tiiny AI and competitors.
Why it matters: OBI OS runs locally. Hardware determines what's possible. A new device that runs 7B models at half the cost of the Tiiny changes the hardware strategy. An NPU breakthrough that doubles inference speed changes performance targets.
Relevance filter: Can this device run our stack (Ollama + ROUTX + multiple 7B models)? Is it in our price range (<$2000)? Does it compete with or complement the Tiiny?
Sources: AnandTech, Tom's Hardware, Tiiny AI blog/updates, NVIDIA Jetson updates, Apple Silicon news, r/LocalLLaMA hardware threads.
Keywords: "edge AI," "NPU," "local inference," "Ollama benchmark," "mini PC," "AI hardware," "Jetson," "Tiiny AI."
Domain S6 — CSDM-RELEVANT PHYSICS
What to watch: Hubble tension updates, SH0ES measurements, Walker-Wang lattice research, cosmological constant developments, black hole merger observations, any paper referencing the domains of our 7 predictions.
Why it matters: CSDM Kill Box Prediction #1 matched SH0ES within 0.2%. If new measurements shift the SH0ES value, the prediction's accuracy changes. If anyone publishes on the Rank-42 lattice or Φ=0.042 as a damping constant, we need to know immediately — it's either independent validation or a scoop.
Relevance filter: Does this paper measure H_local? Does it reference Walker-Wang lattices at any rank? Does it propose a damping constant in the 0.03–0.05 range? Does it address any of our 7 predictions' domains?
Sources: ArXiv hep-th, ArXiv astro-ph.CO, SH0ES collaboration papers, Pogosian's publications, Nature Physics, Physical Review Letters.
Keywords: "Hubble tension," "H0," "SH0ES," "Walker-Wang," "lattice gauge," "damping constant," "cosmological," "black hole merger remnant."
Domain S7 — COMPETITOR & MARKET INTELLIGENCE
What to watch: Companies building multi-AI orchestration, AI desktop products, local-first AI, symbolic communication for agents, brain/model customization services.
Why it matters: Knowing the competitive landscape prevents surprise. If someone launches "an AI desktop where you dock multiple AIs," we need to know — either to differentiate or to validate that the market exists.
Relevance filter: Does this product overlap with OBI OS's core value proposition? Is it targeting the same customer segment? What do they do that we don't? What do we do that they don't?
Sources: Product Hunt, Hacker News, TechCrunch AI section, Y Combinator launches, r/SelfHosted, LinkedIn AI product announcements.
Keywords: "multi-agent," "AI desktop," "local AI," "model orchestration," "AI operating system," "custom model," "fine-tuning service."
Domain S8 — ENTROPY & CRYPTOGRAPHY
What to watch: New TRNG designs, entropy source innovations, NIST standard updates, post-quantum cryptography developments, hardware security module advances.
Why it matters: ENTROPX is a product. Advances in entropy generation could improve it. Changes in NIST standards affect compliance claims. Post-quantum developments affect the cryptographic landscape our customers operate in.
Relevance filter: Does this improve entropy generation quality or efficiency? Does it affect NIST SP 800-22 testing methodology? Does it introduce new entropy sources we could add to ENTROPX's 8-source architecture?
Sources: ArXiv cs.CR, NIST publications, Crypto StackExchange, IACR ePrint, Random.org research, Cloudflare blog.
Keywords: "TRNG," "entropy source," "NIST 800-22," "randomness testing," "hardware entropy," "post-quantum," "RNG."
SCOUTX OPERATION
Frequency
Daily automated scan: RSS feeds, ArXiv API, Reddit API, Hacker News API — pull new posts matching keywords. Filter by relevance score (keyword density + source reputation + recency). Produce ~/scout_reports/daily/[date].md with candidate items.
Weekly review (Sunday, part of Captain's weekly review): Captain or Navigator reviews the week's candidates. Items marked:
- RELEVANT — investigate further
- NOTED — logged, no action
- IRRELEVANT — filtered out, improve keywords
RELEVANT items get a brief analysis: what it is, why it matters for the ship, what action to take (if any).
Monthly synthesis: SCOUTX produces a monthly intelligence brief combining all RELEVANT items into a narrative. Written by ORPHEUS for readability. Filed at ~/scout_reports/monthly/[YYYY-MM].md. Reviewed by Captain. May trigger: spec updates, architecture changes, new inventions (SPEC_INVENTIONX.md), competitive responses, or research outreach (SPEC_RESEARCHER_OUTREACH.md).
OUTPUT FORMAT
Each scout item follows this structure:
ITEM: [title/headline]
SOURCE: [where found, with URL]
DOMAIN: [S1-S8]
DATE: [publication date]
RELEVANCE: HIGH / MEDIUM / LOW
SUMMARY: [2-3 sentences — what is this?]
SHIP IMPACT: [1-2 sentences — why does this matter for CGNT-1?]
ACTION: NONE / INVESTIGATE / UPDATE_SPEC / UPDATE_ARCHITECTURE / ALERT_CAPTAIN
Example:
ITEM: Qwen2.5-14B released with improved fine-tuning support
SOURCE: Hugging Face blog, 2026-05-15
DOMAIN: S1
RELEVANCE: HIGH
SUMMARY: Alibaba released Qwen2.5-14B with native LoRA support and improved instruction
following. Benchmarks show 15% improvement over 7B on reasoning tasks. Still runs on
consumer GPUs with quantization.
SHIP IMPACT: Could be our next base model for brain forges. 14B on Tiiny (80GB RAM)
is feasible. Better reasoning = better MANTIS threat classification, better DR.LOGOS
logic evaluation.
ACTION: INVESTIGATE — run benchmark comparison against current Qwen2.5-7B smoke tests.
AUTOMATION
Phase 1 (now — manual): Captain or Navigator periodically searches sources manually during sessions. Items noted in conversation. Filed to ~/scout_reports/ by the Lobster.
Phase 2 (CRONX automated): Python scripts querying APIs:
- ArXiv API — daily query per domain with keyword sets
- Reddit API — monitor r/LocalLLaMA, r/MachineLearning, r/SelfHosted
- Hacker News API — front page and keyword searches
- RSS feeds — AI blogs, physics journals, security newsletters
Scripts output to ~/scout_reports/daily/[date].md. CRONX runs at 05:00 ET daily (after backups, before Captain brief). Captain brief (SPEC_HANDSHAKEX.md) includes a "SCOUTX highlights" section: top 3 items from today's scan.
Phase 3 (SCOUTX module in ROUTX): SCOUTX becomes Module 25 in ROUTX.
- Query: "scout AI models" → returns latest S1 items
- Query: "scout security" → returns latest S3 items
- Query: "scout physics" → returns latest S6 items
Tier 1 deterministic (reading from stored reports).
INTEGRATION
| Downstream | How SCOUTX feeds it |
|-----------|---------------------|
| LEARNX | S2 discoveries that improve techniques feed LEARNX. "New LoRA method discovered" → LEARNX generates training pair candidates. Test on next forge. |
| HACKX | S3 items feed K1–K10 updates. "New prompt injection technique" → add detection pattern to K6. Add training pairs to MANTIS corpus. |
| SPEC_INVENTIONX | S7 discoveries may reveal latent products. "Company selling entropy as a service for $50/month" → INVENTIONX Invention 5 gets priority. |
| SPEC_RESEARCHER_OUTREACH | S6 items identify physicists publishing on relevant topics. Add to outreach pipeline when hold is lifted. |
| SPEC_TIINY_PARTNERSHIP | S5 items track Tiiny AI progress and competing hardware. |
| FORGEX | S2 items may improve forge pipeline. New quantization method → test on next forge. Better eval → upgrade smoke tests. |
| CRONX | Daily automated scan scheduled 05:00 ET. |
| CAPTAIN_BRIEF | SCOUTX highlights in morning briefing. |
KEYWORD EVOLUTION
Keywords are NOT static. They evolve as the ship evolves.
- When a new module is built: add its domain keywords to the relevant S-domain.
- When a new product is launched: add competitor keywords to S7.
- When a new CSDM prediction is tested: add the prediction's domain keywords to S6.
- When a new HACKX K-domain is expanded: add the attack technique keywords to S3.
SCOUTX's keyword sets are reviewed monthly as part of the monthly synthesis. Keywords that produce zero results are removed. Keywords that produce too many irrelevant results are refined. New keywords are added from RELEVANT items.
THE SCOUT PHILOSOPHY
SCOUTX is NOT about knowing everything. It's about knowing the RIGHT things at the RIGHT time.
The AI landscape produces 500+ papers per day. The Captain can't read them. The ship can't process them. SCOUTX's value is in FILTERING — reducing 500 items to 5 that matter.
The filtering is opinionated. It's filtered through the ship's priorities: does this help us build better brains, sell more products, defend more effectively, validate the physics, or outpace competitors? If it doesn't serve one of these priorities: it's noise. Beautiful, interesting, possibly important noise — but noise for us.
SCOUTX's motto: "Know what matters. Ignore what doesn't. The difference is the ship's priorities."
INVARIANTS
- SCOUTX scans 8 domains daily. No domain is skipped. A missed domain is a blind spot.
- Weekly human review filters automated results. The automation finds candidates. The human decides relevance. AI can find. Humans judge.
- Monthly synthesis produces a narrative brief — not a data dump. A STORY about what's happening in the external world and what it means for the ship. ORPHEUS writes it.
- SCOUTX feeds LEARNX, HACKX, INVENTIONX, and RESEARCHER_OUTREACH. Intelligence without action is trivia. Every RELEVANT item has an ACTION field.
- Keywords evolve monthly. Static keywords produce stale results. The ship changes. The search adapts.
- SCOUTX never scrapes, violates terms of service, or accesses paywalled content without authorization. All sources are public or legitimately accessed. The scout is ethical.
- SCOUTX S6 (physics) is the most sensitive domain. Any paper that independently derives Φ=0.042 or references Rank-42 Walker-Wang lattices is a P1 ALERT TO THE CAPTAIN. This could be independent validation OR a scoop. Either way, the Captain needs to know IMMEDIATELY.
- Competitor intelligence (S7) is gathered from PUBLIC sources only. No corporate espionage. No social engineering of competitor employees. No reverse engineering beyond what's publicly available. The ship competes on quality, not on stolen intelligence.
Jeremy Zlabis / Chronogeometer · Visionary · Disruptor · Chief / 42 Sisters AI · East York, Toronto / 🍁 Φ 0.042