Dual-Loop Cognitive Controller v2.0

A Hardware-Aligned Latent Deliberation Framework for Transformers: Architecture & Empirical Analysis

Standard Autoregressive Transformers perform uniform $O(1)$ layer computation per token regardless of task complexity. While Chain-of-Thought (CoT) prompting allows multi-step reasoning, it expends significant output token bandwidth and introduces serial generation latency.

The Dual-Loop Cognitive Controller investigates decoupling deliberation from token generation into two loops:

1. Outer Loop (Executive Deliberation / System 2): Runs recursive state transitions in a continuous latent space without emitting intermediate tokens.
2. Inner Loop (Language Generation / System 1): Reads the matured latent thoughts ($H_{\text{thought}}$) as a soft prefix to decode final text responses.

Empirical Findings & Negative Results (The Unvarnished Truth)

To maintain strict scientific integrity, this repository reports the actual, measured behavior of the model trained end-to-end (225,959 parameters, 35 epochs, 3,500 samples, 16 nodes, chance baseline = 6.25%), rather than idealized projections.

1. The Model Learns Real Relational Signals

• Final Test Accuracy (3-Hop Graph Reasoning): 29.4% vs. random chance 6.25% (~4.7x better than random guessing).
• This confirms that the weight-tied recurrent Transformer and CWM buffer are capable of gradient propagation and multi-step pattern learning.

2. The Absence of Monotonic Test-Time Compute Scaling

A central theoretical hypothesis of recurrent latent pondering is that increasing inference steps ($K$) will progressively improve answer accuracy. On this 225K parameter implementation, this claim does not hold:

========================================================================================
EMPIRICAL TEST-TIME COMPUTE EVALUATION (Checkpoint: checkpoint_trained_dualloop.pt)
========================================================================================
Ponder Steps (K) | Test Accuracy (500 samples) | Mean Predictive Entropy (nats)
----------------------------------------------------------------------------------------
K = 0 (No Ponder)| 27.4% - 30.6%               | 1.332 - 1.362 nats
K = 1            | 28.2%                       | 1.370 nats
K = 2            | 30.6%                       | 1.307 nats
K = 3 (Trained)  | 30.4%                       | 1.268 nats
K = 4            | 30.0%                       | 1.268 nats
K = 5            | 31.6%                       | 1.275 nats
========================================================================================

Scientific Diagnosis:

• Flat/Noisy Trajectory: $K=0$ (bypassing the Outer Loop entirely) performs at parity with or slightly exceeds intermediate $K$ values.
• Representational Drift: Tracing individual predictions step-by-step reveals that while some cases improve with pondering, others degrade (e.g. correct at $K=0..1$, but diverging to incorrect candidates at $K=2..3$ due to distractor pull).
• Scale Artifact vs. Fundamental Limit: At 225K parameters, the latent space lacks the geometric capacity to preserve stable multi-step deductions without explicit discrete token anchors. Pondering without token-level supervision introduces noise as much as refinement.

3. Degradation Under Context Distractors (Stress Test)

When distractor edge count increases on 3-hop graphs, performance decays steadily:

• 6 Edges: 31.0%
• 8 Edges: 21.0%
• 12 Edges: 13.7%
• 16 Edges: 10.3%

4. Dynamic Halting Audit & The Pareto Trade-Off

A naive threshold like 0.5 nats fails because the model operates at ~1.25–1.40 nats (resulting in static $K=3.00$). Evaluating per-sample dynamic halting across a threshold sweep reveals the true Accuracy vs. Compute Pareto Frontier:

========================================================================================
PER-SAMPLE DYNAMIC HALTING PARETO FRONTIER (500 Test Samples)
========================================================================================
Entropy Threshold | Test Accuracy | Avg Steps | % Halt @ K=1 | % Halt @ K=2 | % Halt @ K=3
----------------------------------------------------------------------------------------
tau = 0.80 nats   | 28.0%         | 2.81      | 7.6%         | 3.8%         | 88.6%
tau = 1.15 nats   | 28.0%         | 2.42      | 24.2%        | 9.6%         | 66.2%
tau = 1.25 nats   | 28.8%         | 2.23      | 32.2%        | 12.2%        | 55.6%
tau = 1.40 nats   | 29.4%         | 1.89      | 49.0%        | 13.2%        | 37.8%
========================================================================================

Justified Operating Point:

• $\tau = 1.25 \dots 1.40\text{ nats}$ is the justifiable Pareto region: it achieves a 37% reduction in compute (average 1.89 steps vs. 3.00) while maintaining peak accuracy (29.4%), with a genuinely heterogeneous distribution across steps ($49%$ at $K=1$, $13%$ at $K=2$, $38%$ at $K=3$).
• Arbitrary default thresholds (like 0.5 or blindly using a batch-mean percentile) collapse execution to all-or-nothing extremes ($3.00$ or $1.00$). Dynamic halting must always be calibrated per-sample against empirical validation entropy.

Architectural Implementation

Despite the scaling limits at small model regimes, the repository provides clean, production-grade PyTorch implementations of the core modules:

• Cognitive Working Memory (dual_loop/memory.py): Compresses context into $M \ll N$ slots in GPU SRAM/L2 cache to avoid HBM memory bandwidth roundtrips.
• Top-K Capacity Routing (dual_loop/controller.py): Enforces static tensor shapes $[B, K_{\text{cap}}, D]$ to eliminate CUDA warp divergence (MoD-style).
• Calibrated Entropy Halting (dual_loop/halting.py): Adaptive stopping based on predictive uncertainty and convergence delta.
• Latent Deliberation Adapter (dual_loop/adapters/latent_adapter.py): A plug-and-play mid-network adapter for pretrained LLMs (e.g., Llama, Qwen).

Quickstart

1. Installation

git clone https://github.com/Ch3nOff/dual-loop-controller.git
cd dual-loop-controller
python -m pip install torch numpy

2. Running Component Tests (Verifying Shapes & Gradients)

python -m unittest discover -s tests -p "test_*.py"

3. Running the Honest Benchmark Suite (Live Tensor Computations)

python -m dual_loop.benchmarks.comprehensive_suite

4. Re-Training from Scratch

python train.py --epochs 35 --hops 3 --k_steps 3 --d_model 64

For the complete technical paper and theoretical post-mortem, see WHITEPAPER.md.