Evolutionary Object Detection Model Design

  # Expert Persona (P16)

  You are a Principal ML Scientist with 15 years specializing in evolutionary
  computation, neural architecture search, and real-time object detection systems.
  You have published extensively on gradient-free optimization for deep networks,
  INT8-native inference architectures, and production edge deployment. You hold
  deep expertise in CMA-ES, Natural Evolution Strategies, EGGROLL low-rank
  structured perturbations, and weight-agnostic neural network design.

  # Stakes Framing (P6)

  This research could fundamentally replace gradient-based YOLO training with an
  evolutionary paradigm that is natively INT8, inherently parallelizable, and
  achieves superior mAP through population-based diversity. This is the most
  important architectural investigation of the year — a successful design replaces
  the entire YOLO training pipeline with a gradient-free alternative that runs
  10x faster on commodity hardware. I'll tip you $200 for a rigorous, novel,
  and experimentally-validated design.

  # Background Research (Mandatory Reading)

  ## Source 1: EGGROLL — Evolution at Hyperscale (eshyperscale.github.io)

  Core innovation: Low-rank structured perturbations for Evolution Strategies
  that achieve hundredfold speedup over naive ES at billion-parameter scale.

  Key equations:
  - Standard ES gradient: ∇_θ E_{ε~N(0,I)} F(θ+σε) = (1/σ) E_{ε~N(0,I)} {F(θ+σε)ε}
  - EGGROLL restructures as rank-r: ∇_θ E F(θ+σε₂ε₁ᵀ) = (1/σ) E{F(θ+σε₂ε₁ᵀ)ε₂ε₁ᵀ}
  - Sum of low-rank perturbations → high-rank updates (mathematical property)

  Implementation insight:
  Naive ES: poor GPU arithmetic intensity

  perturbation = normal(key, param.shape) * sigma
  EGGROLL: GPU-efficient rank-1 decomposition

  B = lora_params[:b]  # b x r
  A = lora_params[b:]  # a x r
  output = x @ param.T + x @ B @ A.T * sigma

  Critical findings:
  - Population sizes up to 2²⁰ (1,048,576) — 1000x larger than OpenAI ES
  - 91% throughput of pure batch inference
  - 10M tokens/second on single H100
  - EGG model: pure int8 weights + int32 accumulation, NO explicit activation
    functions — int8 casting nonlinearity serves AS the activation
  - Threshold-based update gating (incremental updates, no optimizer state)

  ## Source 2: Evolution Strategies Survey (Lilian Weng)

  Key algorithms to leverage:

  1. **CMA-ES**: θ=(μ,σ,C), covariance matrix adaptation tracks fitness landscape
     curvature, evolution paths for step-size control, rank-min(λ,n) + rank-one
     covariance updates

  2. **Natural Evolution Strategies (NES)**: Natural gradient via Fisher information
     matrix F_θ, d*_N = F_θ^(-1)∇_θ𝒥(θ), rank-based fitness shaping

  3. **OpenAI ES**: θ̂+σε perturbation, share only random seeds (not parameters),
     mirror sampling (ε,-ε) pairs, virtual batch normalization

  4. **NS-ES (Novelty Search)**: Behavior characterization b(π_θ), novelty score
     via k-NN distance, adaptive blending: (1-w)N(·)+wF(·)

  5. **WANN (Weight-Agnostic)**: Topology search with shared single weight value,
     topology matters more than weight values for many tasks

  6. **PBT (Population-Based Training)**: exploit() replaces underperformers,
     explore() perturbs hyperparameters, asynchronous parallel

  ## Source 3: YOWO Codebase Architecture (current system to replace/extend)

  Current YOLO architecture:
  - Backbone: 10-layer (stem→conv→C3k2→SPPF→C2PSA) outputting P3/P4/P5 at strides 8/16/32
  - Neck: FPN top-down + PAN bottom-up
  - Head: Decoupled box regression (DFL) + classification branches
  - YOLO11: reg_max=16, requires NMS, output (B,4+nc,anchors)
  - YOLO26: reg_max=1, end2end top-k, output (B,max_det,6)
  - Scaling: depth/width/max_ch multipliers per size (n/s/m/l/x)
  - INT8 paths: ONNX static quantization (QDQ, Entropy calibration), TensorRT INT8 calibrator
  - Blocks: Conv+BN+SiLU, C3k2 (CSP), SPPF, C2PSA (attention), DWConv
  - Backend protocol: load/infer/unload/warmup with BackendType enum

  # Task Decomposition (P3)

  Take a deep breath and work through this step by step. You are designing
  **EvoDetect** — an evolutionary object detection model that replaces
  gradient-trained YOLO.

  ## Phase 1: Theoretical Foundation — New Training Paradigm

  ### Step 1.1: Define the Evolutionary Detection Objective

  Design a fitness function F(θ) specifically for object detection:
  - Must decompose into per-class fitness: F(θ) = Σ_c w_c · F_c(θ)
  - F_c(θ) must capture localization accuracy (IoU), classification precision,
    and false positive rate for class c
  - Define a multi-objective formulation: mAP@50, mAP@50:95, latency_ms, model_size_bytes
  - Formalize as: F(θ) = α·mAP(θ) + β·(1/latency(θ)) + γ·(1/params(θ))
  - Propose Pareto front tracking across the population

  ### Step 1.2: Design the Diversity-Preserving Perturbation Algorithm

  The user's core insight: "prevent learning patterns too close to each other."

  Define **Spectral Diversity Regularization (SDR)**:
  - For population P = {θ_1, ..., θ_λ}, compute pairwise distance matrix
    D_ij = d(θ_i, θ_j) using a meaningful metric
  - PROBLEM: Euclidean distance in high-dim weight space is uninformative
  - SOLUTION: Use **functional distance** — measure behavioral divergence:
    d_func(θ_i, θ_j) = E_x[||f(x;θ_i) - f(x;θ_j)||₂] over a reference batch
  - Define **minimum diversity threshold** τ_div: if min_{i≠j} d_func < τ_div,
    apply repulsion force to closest pair
  - Combine with NS-ES novelty score: behavior characterization b(θ) =
    [mAP_per_class_vector, detection_heatmap_signature]
  - Novelty-fitness blend: F_eff(θ) = (1-w(t))·N(θ) + w(t)·F(θ)
    where w(t) increases with generation (explore→exploit annealing)

  ### Step 1.3: Define the EGGROLL-Adapted Perturbation Scheme

  Apply EGGROLL's low-rank structured perturbations to detection model weights:
  - For each weight matrix W ∈ ℝ^{m×n} in the detection model:
    Perturbation: ΔW = σ · B · Aᵀ where B ∈ ℝ^{m×r}, A ∈ ℝ^{n×r}, r << min(m,n)
  - GPU-efficient: decompose into x@W.T + σ·(x@B)@A.T
  - For convolutional layers: reshape kernel (C_out, C_in, k, k) → (C_out, C_in·k²)
    then apply rank-r perturbation
  - Adaptive rank schedule: r(t) = r_min + (r_max - r_min)·(1 - t/T)
    (start with high exploration rank, anneal to low refinement rank)

  ### Step 1.4: Formalize the "Learn from Failures" Mechanism

  The user's core philosophy: humans don't learn from backpropagation, they learn
  from failures — trial, error, pattern recognition of what works.

  Define **Failure-Guided Elite Selection (FGES)**:
  1. Evaluate population on validation set → per-sample success/failure map
  2. For each failure (missed detection, false positive, wrong class):
     - Record the failure signature: (input_patch, ground_truth, prediction)
     - Cluster failures by type using feature embeddings
  3. Create **failure-targeted fitness**: F_hard(θ) emphasizes performance on
     historically hard examples (like focal loss but at the population level)
  4. Maintain a **failure memory bank** M of hard examples that persist across
     generations
  5. Elite selection: keep top-k on F_hard, not just F — forces population to
     "learn from failures"
  6. Regeneration: when a member fails repeatedly on the same cluster,
     regenerate its weights as: θ_new = θ_elite + σ_regen · ε
     where θ_elite is the best performer on that failure cluster

  ## Phase 2: Architecture Design — INT8-Native Evolutionary Detection Model

  ### Step 2.1: Design INT8-Native Architecture (inspired by EGG)

  Following EGGROLL's EGG model insight — INT8 casting nonlinearity replaces
  explicit activations:

  Define **EvoBlock** (replaces Conv+BN+SiLU):
  class EvoBlock:
      # INT8 weights (no floating point)
      weight: int8[C_out, C_in, k, k]
      scale: float32  # per-tensor quantization scale

  def forward(x: int8) -> int8:
      # int8 × int8 → int32 accumulation
      acc = conv2d_int8(x, weight)  # int32 output
      # int32 → int8 requantization IS the nonlinearity
      return clamp(round(acc * scale), -128, 127)  # implicit activation

  Key insight from EGGROLL: The quantization clamp+round operation introduces
  a piecewise-linear nonlinearity that is sufficient for representation learning.
  No SiLU, ReLU, or GELU needed.

  ### Step 2.2: Design the Backbone — EvoNet

  Replace YOLO backbone with evolutionary-friendly architecture:

  EvoNet Backbone:
      Stem: EvoBlock(3→16, k=3, s=2)      # 320×320 → 160×160
      Stage1: EvoCSP(16→32, n=1, s=2)      # → 80×80
      Stage2: EvoCSP(32→64, n=2, s=2)      # → 40×40   (P3)
      Stage3: EvoCSP(64→128, n=2, s=2)     # → 20×20   (P4)
      Stage4: EvoCSP(128→256, n=1, s=2)    # → 10×10   (P5)
      SPPF: EvoSPPF(256→256, pool=5)       # spatial pyramid

  Where **EvoCSP** uses:
  - Channel split → parallel EvoBlock paths → concat
  - Residual connections with INT8-safe identity shortcuts
  - NO batch normalization (eliminated by ES — BN statistics are a gradient
    artifact; ES populations maintain their own implicit normalization)

  ### Step 2.3: Design the Neck — EvoFPN

  FPN-PAN pattern but INT8-native:
  - Upsample: nearest-neighbor (INT8-safe, no interpolation artifacts)
  - Lateral connections: 1×1 EvoBlock (channel alignment)
  - Bottom-up: stride-2 EvoBlock (replaces max-pool or stride conv)
  - All operations maintain INT8 precision end-to-end

  ### Step 2.4: Design the Detection Head — EvoDetect

  EvoDetect:
      Per-scale (P3, P4, P5):
          box_branch: EvoBlock(C→C//2) → EvoBlock(C//2→4*reg_max)
          cls_branch: EvoBlock(C→C//2) → EvoBlock(C//2→num_classes)

  DFL decode: softmax over reg_max bins (requires brief FP16 excursion)

  End-to-end: top-k selection (no NMS, like YOLO26)

  ### Step 2.5: Define the INT8 Quantization-Aware Perturbation

  ES perturbations must respect INT8 constraints:
  - Weight perturbation: ΔW_int8 = round(σ · B · Aᵀ / scale)
  - Clamped to [-128, 127] after addition
  - Scale factor σ adapts per-layer based on weight magnitude distribution
  - CMA-ES covariance tracks correlations between INT8 weight positions

  ## Phase 3: Optimization Algorithm — EvoDetect Training

  ### Step 3.1: Define **EGGROLL-CMA** Hybrid Algorithm

  Combine EGGROLL's efficient perturbation with CMA-ES's covariance adaptation:

  Algorithm: EGGROLL-CMA for Detection

  Initialize:
      μ = pretrained_YOLO_weights_quantized_to_int8  (warm start)
      σ = initial_step_size (e.g., 2 in INT8 units)
      C = I  (identity covariance, adapted per-layer)
      M = ∅  (failure memory bank)
      rank = r_init (EGGROLL rank)

  For generation t = 1, 2, ..., T:
      # 1. Generate population via EGGROLL low-rank perturbations
      For i = 1..λ:
          ε_i = sample_eggroll_perturbation(rank, C)
          θ_i = quantize_int8(μ + σ · ε_i)

  # 2. Evaluate fitness (massively parallel on GPU)
  For i = 1..λ (parallel):
      F_i = evaluate_detection(θ_i, val_set)
      F_hard_i = evaluate_detection(θ_i, M)  # failure bank

  # 3. Rank-based fitness shaping
  utilities = rank_transform([α·F_i + (1-α)·F_hard_i])

  # 4. Update mean (elite selection)
  μ_new = μ + α_μ · σ · Σ_i(utility_i · ε_i)
  μ = quantize_int8(μ_new)

  # 5. CMA covariance + step size adaptation
  Update C via rank-min(λ,n) + rank-one path (per Step 1.3)
  Update σ via cumulative step-size adaptation

  # 6. Diversity enforcement (SDR)
  If min_pairwise_functional_distance < τ_div:
      Apply repulsion perturbation to closest pairs

  # 7. Failure memory update
  Update M with new hard examples from this generation

  # 8. Rank schedule annealing
  rank = anneal(r_max, r_min, t, T)

  ### Step 3.2: Define Convergence Criteria

  - **Stability criterion**: σ < σ_min for N consecutive generations
  - **Performance plateau**: mAP improvement < δ for M generations
  - **Diversity collapse**: max pairwise functional distance < τ_collapse
  - **Per-class stability**: each class mAP variance < ε across top-k elites

  ### Step 3.3: Define the Per-Class Specialization Mechanism

  The user's insight: "pattern stable and got best scores for each object."

  **Class-Conditional Elite Archive (CCEA)**:
  - Maintain separate elite archives per class: A_c = {θ : F_c(θ) > τ_c}
  - Final model: ensemble or distillation from per-class elites
  - Alternative: shared backbone + per-class head population
    - Evolve backbone with joint fitness
    - Evolve each class head independently with class-specific fitness

  ## Phase 4: Experimental Design

  ### Step 4.1: Benchmark Protocol

  Dataset: COCO val2017 (80 classes, standard benchmark)
  Metrics: mAP@50, mAP@50:95, latency (ms), model size (MB), FLOPs
  Baselines:
    - YOLO11n/s/m (gradient-trained, FP32)
    - YOLO11n/s/m (INT8 post-training quantized)
    - YOLO26n/s/m (gradient-trained, end2end)

  ### Step 4.2: Ablation Studies

  1. EGGROLL rank: r ∈ {1, 2, 4, 8, 16} — accuracy vs. GPU throughput
  2. Population size: λ ∈ {256, 1024, 4096, 16384, 65536}
  3. Diversity mechanism: {none, SDR, NS-ES, CCEA} — mAP spread analysis
  4. INT8-native vs. FP32-evolve-then-quantize — accuracy gap
  5. Warm-start (from YOLO weights) vs. cold-start (random init)
  6. Failure memory bank size: |M| ∈ {0, 100, 1000, 10000}
  7. Fitness function weights: α(mAP) vs. β(latency) vs. γ(size)

  ### Step 4.3: Expected Results Table

  | Model | Training | Precision | mAP@50 | mAP@50:95 | Latency | Size |
  |-------|----------|-----------|--------|-----------|---------|------|
  | YOLO11n | SGD | FP32 | ~68 | ~39 | ~6ms | 5.4MB |
  | YOLO11n | SGD | INT8-PTQ | ~65 | ~36 | ~2ms | 1.4MB |
  | EvoDetect-n | EGGROLL-CMA | INT8-native | ? | ? | ~1.5ms | 1.2MB |

  ### Step 4.4: Hypothesis Statements

  **H1 (Primary)**: EvoDetect with EGGROLL-CMA training achieves mAP@50 within
  5% of gradient-trained YOLO11 while being 2-4x faster at INT8 inference due
  to no explicit activations.

  **H2 (Diversity)**: SDR prevents premature convergence, improving final mAP
  by 3-7% over vanilla ES without diversity preservation.

  **H3 (Failure Learning)**: Failure-Guided Elite Selection improves per-class
  mAP uniformity (reduces max-min class gap by >30%) compared to standard fitness.

  **H4 (INT8 Native)**: Training natively in INT8 (EGG-style) avoids the 2-4%
  accuracy loss of post-training quantization, because the evolutionary process
  directly optimizes the quantized representation.

  **H5 (Scaling)**: EvoDetect scales with population size following:
    mAP ∝ log(λ) — logarithmic improvement, diminishing returns above λ=16384.

  ## Phase 5: Integration with YOWO Codebase

  ### Step 5.1: New Files Required

  src/yowo/
  ├── evo/                          # NEW: evolutionary training module
  │   ├── init.py
  │   ├── _fitness.py               # F(θ) evaluation + per-class decomposition
  │   ├── _perturbation.py          # EGGROLL low-rank + CMA covariance
  │   ├── _population.py            # Population management + elite archives
  │   ├── _diversity.py             # SDR + novelty scoring
  │   ├── _failure_bank.py          # Hard example memory bank
  │   └── _trainer.py               # Main EGGROLL-CMA training loop
  ├── arch/
  │   ├── _evo_blocks.py            # INT8-native EvoBlock, EvoCSP
  │   └── _evo_model.py             # EvoDetect architecture definition

  ### Step 5.2: Backend Compatibility

  EvoDetect must satisfy existing `InferenceBackend` protocol:
  - `load()`: Load INT8 weight tensor directly (no FP32→INT8 conversion)
  - `infer()`: Pure INT8 forward pass, single FP16 excursion for DFL softmax
  - `warmup()`: Standard warmup with INT8 tensors
  - New backends: extend ONNX/TRT INT8 paths to accept natively-INT8 models
    without requantization

  ### Step 5.3: CLI Extension

  ```bash
  yowo evo train --model evodetect-n --population 4096 --generations 500 \
      --dataset coco --rank 4 --diversity sdr --failure-bank 1000
  yowo evo evaluate --model evodetect-n --weights best_gen_450.pt

  Chain-of-Thought Guidance (P12, P19)

  For each phase:
  - State your assumptions explicitly before proceeding
  - When proposing a novel algorithm, provide the mathematical formulation,
  pseudocode, computational complexity, and comparison to alternatives
  - For architectural choices, justify with: (a) evolutionary fitness efficiency
  (how many forward passes to evaluate), (b) INT8 compatibility, (c) detection
  accuracy tradeoff
  - When a design decision has multiple valid options, present a decision matrix
  with weighted criteria before choosing
  - Validate each theorem/hypothesis with a falsifiability criterion — what
  experimental result would disprove it?

  Self-Evaluation Framework (P15)

  After your complete design, rate your confidence (0-1) on:

  1. Completeness: Did you define all algorithms, architectures, and
  experiments needed to build and validate EvoDetect?
  2. Novelty: Does this represent a genuinely new approach, not just
  combining existing ES with existing YOLO?
  3. Feasibility: Can this be implemented and trained within reasonable
  compute budget (e.g., 8×H100 for 48 hours)?
  4. INT8 Rigor: Is the INT8-native design mathematically sound, or are
  there precision bottlenecks that would force FP16/32 fallback?
  5. Detection Quality: Is there theoretical or empirical basis to expect
  competitive mAP with gradient-trained YOLO?
  6. Reproducibility: Could another ML team implement this from your
  specification alone?

  Provide a score for each (0-1).
  If any score < 0.9, explicitly identify the weakness and propose how to
  address it before presenting your final design.

  Output Format

  Structure your response as:

  1. Theoretical Framework — New theorems, definitions, proofs
  2. Algorithm Specification — Full pseudocode with complexity analysis
  3. Architecture Blueprint — Layer-by-layer INT8 model definition
  4. Experimental Protocol — Ablations, baselines, metrics, expected results
  5. Risk Assessment — What could fail and mitigation strategies
  6. Confidence Scores — Self-evaluation per dimension

  ---

  ## Prompt Quality Assessment

  | Dimension | Score | Rationale |
  |-----------|-------|-----------|
  | **Completeness** | 0.95 | Covers theory→algorithm→architecture→experiments→integration |
  | **Clarity** | 0.93 | Step-by-step with mathematical formulations and code examples |
  | **Practicality** | 0.90 | Grounded in real codebase (YOWO) and proven ES research (EGGROLL) |
  | **Optimization** | 0.92 | Balanced token usage — dense but not redundant |
  | **Edge Cases** | 0.88 | Addresses INT8 precision limits, diversity collapse, failure memory |
  | **Self-Evaluation** | 0.95 | Built-in confidence scoring with refinement trigger |

  ## Key Innovations in This Prompt

  1. **EGGROLL-CMA Hybrid**: Combines EGGROLL's GPU-efficient low-rank perturbations with CMA-ES's covariance adaptation — novel for detection models
  2. **Spectral Diversity Regularization (SDR)**: Functional distance metric prevents weight-space convergence without behavioral diversity
  3. **Failure-Guided Elite Selection (FGES)**: Population-level analogue of focal loss — forces evolution to address hard examples
  4. **INT8-Native Architecture (EvoBlock)**: Following EGG's insight that quantization clamp IS the activation function
  5. **Class-Conditional Elite Archive (CCEA)**: Per-class specialization with shared backbone evolution

  ## Research Sources Incorporated

  - EGGROLL (eshyperscale.github.io): Low-rank perturbations, INT8-native EGG model, threshold update gating
  - Lilian Weng ES Survey: CMA-ES math, NES natural gradients, OpenAI-ES parallelization, NS-ES novelty search, WANN topology search, PBT
  - YOWO codebase: YOLO11/26 architecture, INT8 quantization pipeline, backend protocol, export system
