ARCHITECTURE LENS — what we judge every paper against

# ARCHITECTURE LENS — what we judge every paper against > Version 1.0 — 2026-06-12. This is the profile of Mohamed's live systems. > Every daily paper is mapped against these domains. If a paper doesn't touch > any domain, it is SKIP unless it is foundational enough to create a new domain > (in which case: propose the new domain in the report). 1. **FIT** — which domain(s) below does it touch, and which named system of ours is the counterpart? 2. **DELTA** — what does the paper do that our counterpart does NOT do (and vice versa)? 3. **VERDICT** — one of: - `ABSORB` — their technique is better on some axis; name the exact file/module where it lands and what changes. - `TEST` — we built something comparable; define the head-to-head (their benchmark or ours, what metric, what would count as a win). - `RIVAL` — we already built something arguably ahead or different-but-stronger; write the claim with evidence (commits, metrics, dates). - `WATCH` — relevant, no action yet; state the trigger that would upgrade it to ABSORB/TEST. - `SKIP` — no domain fit. 4. **ACTION** — for ABSORB/TEST: one concrete first step small enough to start the same week. A verdict without a named system, a named file, or a falsifiable claim is invalid. ## D1 — Agent skills: libraries, induction, typing, routing **Our systems:** SOOP-2 skills operating system (296 typed skills, `SKILL_TYPES_v1` 6-category algebra, `skill-typecheck` linter), SEA two-tier router (Tier 1 recall@30 = 1.00 on 214 skills, Tier 2 twin-primary scorer on Mac4:8100), `skill-forge` (auto-generates skills from session pattern mining), Cortex rule promotion. **Where they live:** `[home-path]`, `[home-path]`, `[home-path]`. **What would beat us:** automatic skill induction with verified composition guarantees; skill libraries that self-prune by utility; routing that beats recall@30=1.00 at lower cost; typed-composition checking richer than our 6-category algebra. **Known rivals already mapped:** SkillDAG, SkillOpt, MUSE-Autoskill, GraphOfSkills, SkillsBench (see `Desktop/code4ai-analysis/ANALYSIS.md`). ## D2 — Trajectory reward, agentic RL, SFT from agent traces **Our systems:** KARL reward engine (6-signal composite, 3203 rescored records, score-at-emit via `flows-karl-writer`), cognitive twin SFT pipeline (`cognitive-forge`, 1049 SFT examples from distilled trajectories), trajectory cards from gateway events. **Where they live:** `Desktop/karl/`, `[home-path]`. **What would beat us:** process-reward models that outperform composite heuristic signals; trajectory filtering/credit-assignment methods with measured downstream SFT gains; online RL from agent traces that doesn't need human labels.