feat: Replace additive scoring with equilibrium-constrained scorer (ΔØ)

LICENSE

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+Apache License
+Version 2.0, January 2004
+http://www.apache.org/licenses/
+
+Copyright 2026 K. Fain (ThēÆrchītēcť)
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.

proposals/delta-null-equilibrium/LICENSE

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+Apache License
+Version 2.0, January 2004
+http://www.apache.org/licenses/
+
+Copyright 2026 K. Fain (ThēÆrchītēcť)
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.

proposals/delta-null-equilibrium/README.md

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+# ΔØ Equilibrium Scorer
+
+### Drop-in replacement for X's recommendation algorithm scoring layer
+
+> *"We know the algorithm is dumb and needs massive improvements."*
+> — Elon Musk, January 19, 2026
+
+**We fixed it.**
+
+---
+
+## The Problem
+
+X's recommendation algorithm ranks content using a **weighted linear sum**:
+
+```
+Final Score = Σ (weight_i × P(action_i))
+```
+
+This is fundamentally broken:
+- Weights are manually tuned with no mathematical basis
+- High engagement can **overcome** high rejection signals
+- Gets gamed by engagement bait and outrage farming
+- Requires constant manual retuning as user behavior shifts
+
+A block or report should **kill** a piece of content's ranking. Instead, enough likes and retweets can overwhelm it. That's not an algorithm — it's a slot machine.
+
+---
+
+## The Fix: Equilibrium Constraint (ΣΔ = 0)
+
+Instead of subtracting rejection from engagement, ΔØ **multiplies** engagement by an equilibrium factor that **collapses** when rejection signals are present.
+
+```
+Score = raw_engagement × exp(-rejection_presence × sensitivity)
+```
+
+One block doesn't just lower the score. It **destroys** it.
+
+### How It Works
+
+1. **Partition signals:** Constructive (likes, shares, follows) vs. Destructive (blocks, mutes, reports)
+2. **Compute equilibrium ratio:** ρ = Δ⁺ / (Δ⁺ + Δ⁻)
+3. **Apply exponential penalty:** Any rejection presence collapses the score multiplicatively
+4. **Self-adapt:** Sensitivity learns from signal distribution — no manual tuning
+
+---
+
+## Results
+
+| Content Type | Raw Engagement | ΔØ Score | Change |
+|---|---|---|---|
+| Quality Content | 2.09 | **2.19** | baseline |
+| Engagement Bait | 2.09 | **0.37** | **-83%** |
+| Toxic Viral | 2.93 | **0.34** | **-85%** |
+
+**Toxic content with 40% higher raw engagement scores 85% lower under ΔØ.**
+
+No amount of engagement can overcome significant rejection signals. That's not a parameter — it's a mathematical guarantee.
+
+---
+
+## Architecture
+
+```
+┌──────────────────────────────────────────────────────────────┐
+│                   PHOENIX SCORER (existing)                   │
+│   P(like), P(reply), P(block), P(mute), P(report), etc.     │
+└──────────────────────────────────────────────────────────────┘
+                             │
+                             ▼
+┌──────────────────────────────────────────────────────────────┐
+│                    ΔØ EQUILIBRIUM LAYER                       │
+│                                                              │
+│   1. PARTITION: Δ⁺ (constructive) vs Δ⁻ (destructive)       │
+│   2. COMPUTE:   ρ = Δ⁺ / (Δ⁺ + Δ⁻)                         │
+│   3. ADAPT:     Sensitivity learns from signal distribution  │
+│   4. ENFORCE:   Score = engagement × exp(-rejection × σ)     │
+│                                                              │
+└──────────────────────────────────────────────────────────────┘
+                             │
+                             ▼
+┌──────────────────────────────────────────────────────────────┐
+│                EQUILIBRIUM-CONSTRAINED SCORE                  │
+│    Content ranked by user value, not engagement theater       │
+└──────────────────────────────────────────────────────────────┘
+```
+
+---
+
+## Quick Start
+
+### Drop-In Replacement (Rust)
+
+```bash
+# Backup original
+cp home-mixer/scorers/weighted_scorer.rs weighted_scorer.rs.backup
+
+# Replace with ΔØ scorer
+cp src/weighted_scorer_delta_null.rs home-mixer/scorers/weighted_scorer.rs
+
+# Add dependency
+echo 'lazy_static = "1.4"' >> Cargo.toml
+
+# Build
+cargo build --release
+```
+
+### Python Reference (Testing & Validation)
+
+```bash
+python examples/demo.py
+```
+
+---
+
+## The Math
+
+**Signal Partitioning:**
+```
+Δ⁺ = Σ(constructive signals × weights)    // likes, replies, shares, follows
+Δ⁻ = max(Σ(destructive signals × weights), ε)  // blocks, mutes, reports
+```
+
+**Equilibrium Ratio:**
+```
+ρ = Δ⁺ / (Δ⁺ + Δ⁻)
+```
+
+**Equilibrium Factor:**
+```
+φ = exp(-(1 - ρ) × σ)     // σ = adaptive sensitivity
+```
+
+**Final Score:**
+```
+S = raw_engagement × φ
+```
+
+The constraint ΣΔ = 0 is enforced through the multiplicative relationship: content cannot achieve high final scores without maintaining equilibrium between engagement and rejection signals.
+
+**Full formal derivation:** [docs/MATH.md](docs/MATH.md)
+
+---
+
+## Self-Adaptive Sensitivity
+
+Unlike fixed-weight systems, ΔØ learns optimal sensitivity from signal distribution:
+
+```
+σₜ₊₁ = σₜ + η × (ρ̄ₜ - ρ*)
+```
+
+- If feed is too permissive (high ρ̄) → increase sensitivity
+- If feed is too aggressive (low ρ̄) → decrease sensitivity
+- Converges when average feed equilibrium ratio = target (0.75)
+
+No manual tuning. No weight spreadsheets. The math handles it.
+
+---
+
+## Theoretical Foundation
+
+ΔØ is grounded in established control theory and cybernetics:
+
+- **Feedback control systems** (Wiener, 1948): Sustainable systems maintain equilibrium through feedback loops
+- **Thermodynamic analogy:** Content "sustainability" parallels Gibbs free energy — engagement without rejection is thermodynamically favorable
+- **Lyapunov stability:** The adaptive system converges when learning rate stays within stability bounds
+
+This isn't a hack or a heuristic. It's what control theory has said since 1948 applied to a system that ignored it.
+
+---
+
+## Repository Contents
+
+| File | Description |
+|---|---|
+| `src/weighted_scorer_delta_null.rs` | **Drop-in replacement** for X's weighted_scorer.rs |
+| `src/delta_null_scorer.rs` | Standalone Rust implementation |
+| `src/delta_null_scorer.py` | Python reference implementation |
+| `examples/demo.py` | Interactive demonstration with test scenarios |
+| `docs/MATH.md` | Formal mathematical derivation |
+| `docs/INTEGRATION.md` | Step-by-step integration guide |
+| `config/delta_null.toml` | Configuration parameters |
+
+---
+
+## Why Open Source
+
+The recommendation algorithm shapes what billions of people see every day. The fix shouldn't sit in a folder. If X won't merge it, someone else will build on it.
+
+ΔØ generalizes beyond social media. Any multi-signal optimization system that needs balance enforcement — medical devices, financial risk, industrial control — can use this constraint.
+
+The principle is simple: **ΣΔ = 0.**
+
+---
+
+## Author
+
+**K. Fain** (ThēÆrchītēcť)
+
+---
+
+## License
+
+Apache 2.0 — See [LICENSE](LICENSE)

proposals/delta-null-equilibrium/proposals/delta-null-scorer/config/delta_null.toml

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+# ═══════════════════════════════════════════════════════════════════════════════
+# ΔØ EQUILIBRIUM CONFIGURATION
+# Equilibrium Constraint Theory
+# ═══════════════════════════════════════════════════════════════════════════════
+#
+# Open Source Release
+# Copyright 2026 K. Fain (ThēÆrchītēcť)
+# Author: K. Fain aka ThēÆrchītēcť
+#
+# ═══════════════════════════════════════════════════════════════════════════════
+
+[equilibrium]
+# Base sensitivity to equilibrium deviation
+# Higher values = stronger penalty for rejection signals
+# Range: 1.0 (permissive) to 15.0 (aggressive)
+# Self-adapts when adaptive_learning = true
+base_sensitivity = 5.0
+
+# Minimum rejection signal before equilibrium enforcement activates
+# Below this threshold, pure engagement scoring applies
+rejection_floor = 0.001
+
+# Maximum boost multiplier for pure engagement content
+# Content with zero rejection signals gets this small bonus
+max_engagement_boost = 0.05
+
+[adaptation]
+# Enable self-adaptive sensitivity learning
+# When true, sensitivity adjusts based on signal distribution
+adaptive_learning = true
+
+# Learning rate for sensitivity adaptation
+# Higher = faster adaptation, lower = more stable
+learning_rate = 0.01
+
+# Exponential moving average decay for statistics
+# Higher = longer memory, lower = faster response
+ema_decay = 0.95
+
+# Target equilibrium ratio for healthy content distribution
+# System adapts sensitivity to achieve this average ratio
+target_equilibrium_ratio = 0.75
+
+# Sensitivity bounds for adaptive learning
+min_sensitivity = 1.0
+max_sensitivity = 15.0
+
+[constructive_signals]
+# Weights for positive engagement signals (Δ⁺)
+# Higher weight = more contribution to constructive delta
+favorite = 1.0
+reply = 1.5           # Deeper engagement than passive like
+retweet = 1.3
+quote = 1.4           # Thought engagement
+share = 1.6           # External sharing = very strong
+share_via_dm = 1.5
+share_via_copy_link = 1.4
+follow_author = 2.5   # Highest: commitment signal
+click = 0.3           # Weak: curiosity not value
+profile_click = 0.4
+photo_expand = 0.4
+dwell = 0.6
+vqv = 0.8             # Video quality view
+
+[destructive_signals]
+# Weights for rejection signals (Δ⁻)
+# Higher weight = more contribution to destructive delta
+not_interested = 1.0
+mute_author = 2.5     # Strong rejection
+block_author = 4.0    # Severe rejection  
+report = 5.0          # Most severe
+
+[decay_factors]
+# Time-based decay for signal relevance
+# 1.0 = no decay, < 1.0 = signal importance decreases
+click = 0.9
+profile_click = 0.9
+photo_expand = 0.9
+dwell = 0.95
+
+[integration]
+# Integration settings for X pipeline
+
+# Where to inject ΔØ in the scoring pipeline
+# Options: "replace" (recommended), "wrap", "parallel"
+injection_point = "replace"
+
+# Enable detailed logging for debugging
+debug_logging = false
+
+# Export metrics for monitoring dashboards
+export_metrics = true
+
+# Feature flag for gradual rollout (0-100)
+rollout_percentage = 100
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# END OF CONFIGURATION
+# 
+# Open Source Release
+# Copyright 2026 K. Fain (ThēÆrchītēcť)
+# ═══════════════════════════════════════════════════════════════════════════════