Channel Engineering Strategy & Invariant Log

Overview & Philosophy

The Channel Inference Engine was developed through rigorous milestone-driven discipline:

Step 1: Foundational Baseline Model Runner (verified pure Zig execution, mathematical parity, Vulkan compute dispatch, and Q4_0 memory mapping).
Step 2: Streaming Architecture & Dynamic Ring Integration (unified 4,096-slot geometry, episodic Hippocampal UMA readback, associative RoPE injection, continuous pull-streaming).
Step 3: Autonomic Reflex Plane & Multi-Channel Transduction (1-token logit probes, Shannon entropy/confidence gating, Thinking Gate, and A-FSM focus management).

Channel uses Pure Symmetric Zero-Centered Q4_0 Quantization across all linear projections ( $Q, K, V, O$ , gate_proj, up_proj, down_proj), while maintaining the output vocabulary classifier in Q8_0 and input token embeddings in BF16.
This matches official Google QAT weights and standard llama.cpp quantization geometry, keeping weight memory read per token to $\approx 6.00\text{ GB}$ on 12B Unified, yielding $\ge 24\text{ tok/s}$ sustained decode throughput on AMD UMA hardware.

Physical KV storage is strictly 4,096 slots per layer across all 48 layers:
- 0 .. N_sys-1: Tier 1 anchors (system prompt & tool contracts), permanently locked.
- N_sys .. 3967: Dynamic sliding FIFO working ring.
- 3968 .. 4095: Dedicated Tier 3 associative recall slots.
Prefill Slot Bounding: Prefill slot lists passed to GPU kernels must always remain $\le 4,096$ . Slots cannot collide with Tier 3 recall space.

All GPU compute dispatches execute on a dedicated asynchronous compute queue.
4-Layer Command Chunking: Command buffers are submitted in 4-layer chunks with fences rather than all 48 layers in one dispatch. This completely eliminates AMDGPU Linux kernel driver watchdog resets (VK_ERROR_DEVICE_LOST) under continuous decode workloads.

In Gemma 4, turns permit at most one thinking phase.
Once token 101 (<channel|>) is sampled, the thinking channel is closed for the remainder of the turn.
Token 100 (<|channel>) must be actively suppressed in the sampler during response generation to prevent hallucinated recursive thinking loops.

Thinking is disabled by default: suppress_thinking = true is locked across prefill and decode unless explicitly requested via INPUT_FLAG_REASON (0x02).
When deliberate reasoning is engaged, token 101 is masked on token 1 inside the thought channel to prevent premature channel collapse.
Adaptive thinking depth probes evaluate reasoning sufficiency at soft yields, capping thought early once confidence $\ge 0.80$ .

The engine tracks turn_open state. If a client delivers an interrupt turn while a prior turn was open or yielding, the engine automatically commits token 106 (<turn|>), maintaining KV structural validity.
Delimiters (<|turn>, <|channel>) belong exclusively to the engine; the host client communicates purely through model-agnostic binary opcodes and semantic flags.

User turn paths use raw numerical filenames (msg/user/1001.txt, not msg_1001.txt).
Notification IDs use uniform typed strings without underscores (not101, not102) to prevent pseudo-token stutter and hallucinated formatting loops.

Working state snapshots serialize active slots asynchronously in a detached thread with 1 MB buffered I/O, completing in ~5ms without interrupting active token decode.
Zero-delta snapshot gating returns status code 2 when the clock has not advanced, eliminating duplicate checkpoints.

Phase 1 Baseline: Verified mathematical parity with llama.cpp at $\ge 24.3\text{ tok/s}$ and $\le 2.0\text{s}$ prefill.
Phase 2 Memory Subsystem: UMA zero-copy episodic commits (OP_MEM_COMMIT), Givens 2D RoPE delta re-rotation, and sub-millisecond centroid cosine scanning.
Phase 3 Continuous Streaming: Elastic syntactic unit gating (STOP_ELASTIC_YIELD), in-flight barge-in staging, and VFS pull-streams.
Phase 4 Autonomic Architecture: Discrete 1-token logit probes (OP_PROBE_AUTONOMIC) evaluating in ~1.5ms with $O(1)$ clock rollback, Shannon entropy scoring, and A-FSM focus arbitration.