docs: refine literate text by removing redundant problem/solution blocks and updating headings

README.org

@@ -34,7 +34,7 @@ Every line of system logic is written as a **Literate Org file**. This weaves th
 ** The Long-Term Vision: A True Lisp Machine
 The kernel is fundamentally **actuator-agnostic**. While it currently uses Emacs, the ultimate trajectory is to write external editors and browsers out of existence. In this vision, the interface itself—the editor, browser, and system prompt—will be built entirely in Common Lisp, running within the exact same address space as the agent. This eliminates IPC entirely, creating a unified, zero-latency cognitive environment.
 
-* 3. How: The 6 Core Components
+* 3. How: The Core Components
 
 The microkernel is divided into six primary subsystems, each solving a fundamental problem of agentic autonomy.
 
@@ -183,8 +183,6 @@ sequenceDiagram
     end
 #+end_src
 
-**Problem:** Linear AI scripts are blocking and cannot perceive new information while thinking.
-**Solution:** A multi-threaded, asynchronous OODA loop. It uses recursion to allow the agent to observe its own results and chain thoughts together.
 
 #+begin_src lisp :tangle src/core.lisp
 (in-package :org-agent)
@@ -449,8 +447,6 @@ Streaming raw JSON over a socket is fragile. If a 5MB Org AST is fragmented by t
 - **Physical Boundary:** By prefixing every message with a 6-character hex length, we create a deterministic physical boundary. 
 - **Actuator-Agnosticism:** This protocol makes the kernel a "Dumb Terminal" host. Any program (Bash, Python, WebSockets) that can calculate a length and send bytes can now become an agentic interface.
 
-**Problem:** Massive Org ASTs can fragment and desynchronize raw JSON streams over a socket.
-**Solution:** **OACP (Org-Agent Communication Protocol)**. It uses 6-character hex-length framing to guarantee the daemon never crashes regardless of payload size.
 
 #+begin_src lisp :tangle src/protocol.lisp
 (in-package :org-agent)
@@ -504,8 +500,6 @@ graph TD
     end
 #+end_src
 
-**Problem:** Reading text files for every "memory" is slow; external databases add bloat.
-**Solution:** **CLOSOS Single Address Space**. A persistent in-memory hash table that stores native Lisp `org-objects`. Includes memory-imaging to skip boot-time parsing and Merkle-Tree hashing for state verification.
 
 #+begin_src lisp :tangle src/object-store.lisp
 (in-package :org-agent)
@@ -603,8 +597,6 @@ graph TD
 LLMs lose precision when context windows are bloated with irrelevant data.
 - **Sparse Trees:** We use Lisp's deterministic tree-walking to surgically prune the Org AST. We pass the skeletal "peripheral" outline to the LLM, giving it global awareness while keeping its "foveal" focus on the task at hand. This minimizes token burn and maximizes reasoning accuracy.
 
-**Problem:** Context window bloat confuses LLMs and wastes tokens.
-**Solution:** **Sparse Tree Perception**. Surgically prunes the Org AST to pass only the skeletal outline and high-signal context to System 1.
 
 #+begin_src lisp :tangle src/context.lisp
 (in-package :org-agent)
@@ -713,8 +705,6 @@ Hardcoding logic into a compiled binary creates a "Brittle Kernel."
 - **Institutional Memory:** By using Literate Org files as skills, the "Why" (PRD) and the "How" (Lisp) are unified. 
 - **Hot-Reloading:** The agent can "learn" a new trick (recompile a package) while running. This allows for a continuous evolutionary loop where the agent can eventually rewrite its own skills to fix bugs it perceives.
 
-**Problem:** Hardcoding logic makes the daemon brittle; teaching new skills usually requires a restart.
-**Solution:** **Hot-Reloadable Late-Binding**. Compiles Lisp code blocks from Org files into isolated packages at runtime. Brain updates without dropping heartbeats.
 
 #+begin_src lisp :tangle src/skills.lisp
 (in-package :org-agent)
@@ -933,8 +923,6 @@ System 1 (LLM) is creative but hallucination-prone. System 2 (Lisp) is rigid but
 - **The Safety Gate:** We never allow the LLM to talk to the actuators directly. It must propose a Lisp form. System 2 intercepts this form and validates it against mathematical rules and PSF invariants. 
 - **Sovereign Decoupling:** By moving the physical API logic into skills, the core remains a neutral "Thinking Engine" that doesn't care if the imagination comes from Google, Anthropic, or a local Llama instance.
 
-**Problem:** LLMs hallucinate; pure code is rigid.
-**Solution:** The **Safety Gate**. `neuro.lisp` (System 1) proposes; `symbolic.lisp` (System 2) intercepts and validates. Imagination checked by the laws of physics.
 
 #+begin_src lisp :tangle src/neuro.lisp
 (in-package :org-agent)