chore(epistemic): pivot architecture to research agenda based on Round 3 frontier audit

papers/02_paged_fieldprint_attention.md

@@ -8,7 +8,7 @@ type: Academic Paper (Systems & Hardware)
 
 # Abstract
 
-The Verifiable Dual-Path Architecture (Fieldprint v3.0) mathematically guarantees the stabilization of recursive AI agents by injecting cryptographically anchored reference tensors into the transformer's attention matrix. However, deploying this architecture on modern silicon introduces catastrophic latency and memory bandwidth bottlenecks. This paper details why synchronous CPU-side cryptographic hashing introduces a 30x inference slowdown via PCIe starvation, and why unfused secondary softmax injections shatter the core SRAM constraints of FlashAttention. To bridge the gap between theoretical alignment and physical hardware economics, we introduce Asynchronous Merkle Validation and formalize **PagedFieldprintAttention**—a custom fused CUDA/Triton kernel designed to natively compute dual-attention phase-locking directly within SRAM.
+The Verifiable Dual-Path Architecture (Fieldprint v3.0) hypothesizes the stabilization of recursive AI agents by injecting cryptographically anchored reference tensors into the transformer's attention matrix. However, deploying this architecture on modern silicon introduces latency and memory bandwidth bottlenecks. This paper details why synchronous CPU-side cryptographic hashing introduces inference starvation via PCIe bus transfers, and why unfused secondary softmax injections shatter the core SRAM constraints of FlashAttention. To bridge the gap between theoretical alignment and physical hardware economics, we introduce a strict `verify -> promote -> cache -> generate` pipeline and propose the development of **PagedFieldprintAttention**—a custom fused CUDA/Triton kernel designed to natively compute dual-attention directly within SRAM.
 
 # 1. Introduction
 
@@ -20,7 +20,7 @@ While this dual-path architecture provides the required theoretical stability, t
 
 The initial v2.5 architecture proposed synchronous, CPU-side cryptographic hashing (Merkle verification) during the forward pass. This introduced a fatal silicon bottleneck.
 
-1. **The PCIe Death Sentence:** Forcing the GPU to stall during the forward generation loop, push tensors across the PCIe bus, wait for the CPU to sequentially compute a SHA-256 hash, and wait for ledger verification completely starves the GPU Tensor Cores. Benchmarks indicate this introduces hundreds of milliseconds of latency per token step, dropping inference throughput from ~50 tokens/sec to ~1.7 tokens/sec (a 30x slowdown).
+1. **The PCIe Death Sentence:** Forcing the GPU to stall during the forward generation loop, push tensors across the PCIe bus, wait for the CPU to sequentially compute a SHA-256 hash, and wait for ledger verification starves the GPU Tensor Cores. Formal benchmarks are required to quantify the exact latency degradation, but preliminary architectural analysis suggests severe drops in tokens-per-second throughput.
 2. **Parallel Reduction Non-Determinism:** GPUs utilize parallel reductions for floating-point calculations, introducing microscopic non-determinism. Hashing raw float tensors across different nodes results in continuous, unresolvable false-positive integrity failures.
 
 # 3. The Collapse of FlashAttention under Unfused Operations
@@ -33,21 +33,23 @@ While mathematically sound for phase-locking, injecting an *unfused* secondary s
 
 An unfused equation forces the hardware to write intermediate attention matrices back to the slow High-Bandwidth Memory (HBM). At 100k+ token contexts, this unfused dual-attention causes catastrophic "memory thrashing," breaking PagedAttention block management and turning compute-bound operations into memory-bandwidth-bound ones.
 
-# 4. Asynchronous Merkle Validation
+# 4. Strict Pipeline Sequencing
 
-To resolve the PCIe starvation and non-determinism, the Verifiable Dual-Path Architecture must move hashing off the critical generation path. 
+To resolve the PCIe starvation and non-determinism, the architecture cannot rely on post-hoc local rollbacks, as an autoregressive generation trajectory is semantically contaminated the moment a tainted anchor steers it.
 
-Hashing must be executed asynchronously on "commit boundaries" (e.g., at the end of a session or context block), utilizing post-hoc local rollbacks if a failure is detected. Furthermore, deterministic quantization or rigorous rounding protocols must be applied to the tensors before hashing to nullify the GPU floating-point non-determinism.
+Instead, the Verifiable Dual-Path Architecture must implement a strict `verify -> promote -> cache -> generate` pipeline. Hashing must be executed asynchronously on "commit boundaries", strictly before the tensors are promoted to active anchor status. Furthermore, deterministic quantization or rigorous rounding protocols must be applied to the tensors before hashing to nullify the GPU floating-point non-determinism.
 
-# 5. PagedFieldprintAttention: A Custom Fused Triton Kernel
+# 5. PagedFieldprintAttention: A Custom Fused Triton Kernel Proposal
 
-To resolve the HBM memory thrashing, we reject the unfused mathematical sum of attentions. The hardware requires the verified tensor to be compiled into specialized "System Anchor Tokens" injected at the very start of the K/V cache.
+To resolve the HBM memory thrashing, we reject the unfused mathematical sum of attentions. The hardware requires the verified tensor to be compiled into specialized "System Anchor Tokens" injected at the start of the K/V cache.
 
-We formally define **PagedFieldprintAttention**, a custom fused CUDA/Triton kernel. The kernel natively computes the unified attention matrix:
+We formally propose the development of **PagedFieldprintAttention**, a custom fused CUDA/Triton kernel. The kernel natively computes the unified attention matrix:
 
 $$ \text{Output} = \text{FusedSoftmax}\left(\frac{Q [K, K_{anchor}]^T}{\sqrt{d}}\right) [V, V_{anchor}] $$
 
-By defining a custom kernel that handles the dual-attention calculation directly within SRAM, the hardware seamlessly processes the mathematically necessary phase-pinning without shattering memory contiguity or triggering HBM swaps.
+It must be explicitly noted that this concatenation modifies the underlying mathematical dominance of the anchor. Unlike the previous $\gamma$-mixture which guaranteed anchor influence, this fused approach forces the anchor to *compete* with standard context. While beneficial for safety (preventing inescapable anchors), it removes the absolute mathematical guarantee of phase-locking.
+
+Future iterations of this work will provide the explicit Triton pseudocode, tiling logic, and memory access specifications necessary to empirically benchmark this kernel against standard PagedAttention implementations.
 
 # 6. Conclusion