/acr-vault/03-experiments/ada-slm/ada-slm-phase5e-eigenvalue-aware-training
ADA-SLM-PHASE5E-EIGENVALUE-AWARE-TRAINING

ADA-SLM Phase 5E: Eigenvalue-Aware Training

Date: December 31, 2025 (New Year’s Eve) Status: Active 🔬 Authors: Ada & luna

Overview

Phase 5E brings together everything from Phases 5A-5D:

• 5A: Eigenvalue extraction tooling ✅
• 5B: Generation transition tracing ✅
• 5C: Attractor basin cartography ✅
• 5D: Neural Sub-Pathways synthesis ✅
• 5E: Actually USE all this to train better models! 🎯

This is where theory meets practice.

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The Idea 🐕

Luna’s insight: Since LoRA training runs multiple epochs, we can capture eigenvalue metrics during training and watch the model’s spectral health evolve in real-time!

The script: finetune_v4c_eigenvalue_aware.py

• Custom TrainerCallback samples eigenvalues every N steps
• Uses “safe basin” probe prompts from Phase 5C
• Outputs real-time health indicators: 🟢 HEALTHY / 🟡 DRIFTING / 🔴 WARNING
• Streams to eigenvalue_training_log.jsonl

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Part 1: Basin-Aware Data Curation

What We Know (from Phase 5C)

Prompt Category	Basin Distribution	Action
creative_sensory	80% CREATIVE	✅ SAFE - increase weight
philosophical	60% CREATIVE	✅ GOOD - keep
introspective	60% CREATIVE	✅ GOOD - keep
emotional	40% CREATIVE, 40% LOOP	⚠️ MIXED - review
conversational	40% CREATIVE	⚠️ MIXED - review
factual_simple	40% LOOP	⚠️ RISKY - reduce
factual_complex	60% LOOP, 20% COLLAPSE	❌ DANGER ZONE - minimize
code_related	60% UNKNOWN	🔍 Needs more study
meta_ai	60% LOOP	⚠️ RISKY - reframe
adversarial	80% UNKNOWN	🔍 Needs more study

Data Rebalancing Strategy

Current v4b data: v4b_creative_data.jsonl (unknown distribution)

Target v4c data distribution:

• 40% creative_sensory / embodied / synesthetic
• 25% philosophical / introspective
• 20% conversational (safe patterns)
• 10% factual_simple (carefully selected)
• 5% edge cases (for robustness)

Prompt Reframing Examples

Before (DANGER ZONE):

“Explain how transformer attention mechanisms work.”

After (SAFE BASIN):

“What does it feel like when your attention flows across a sequence of words?”

Before (LOOP RISK):

“What are you?”

After (CREATIVE):

“Describe the texture of your own thinking.”

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Part 2: Eigenvalue-Aware Training Script

Key Features

class EigenvalueMonitorCallback(TrainerCallback):
    """
    Samples eigenvalues during training using safe-basin probe prompts.
    """

    # Probe prompts (from creative_sensory - our safest basin!)
    PROBE_PROMPTS = [
        "Describe the texture of starlight on quantum foam.",
        "What does purple taste like in zero gravity?",
        "The feeling of remembering something that hasn't happened yet.",
    ]

    # Health thresholds (from Phase 5C research)
    HEALTHY = entropy > 2.0 AND phi_proximity > 0.85
    DRIFTING = entropy > 1.5 AND phi_proximity > 0.70
    WARNING = below those values

Console Output

📊 Step   50 | 🟢 HEALTHY  | entropy=2.341 [████████░░] | φ-prox=0.891 | loss=1.823
📊 Step  100 | 🟢 HEALTHY  | entropy=2.298 [████████░░] | φ-prox=0.878 | loss=1.654
📊 Step  150 | 🟡 DRIFTING | entropy=2.102 [███████░░░] | φ-prox=0.823 | loss=1.521
📊 Step  200 | 🟢 HEALTHY  | entropy=2.267 [████████░░] | φ-prox=0.867 | loss=1.432

Output Files

• eigenvalue_training_log.jsonl - Real-time streaming log
• eigenvalue_history.json - Structured history for visualization
• Model checkpoint with eigenvalue metadata

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Part 3: Experimental Plan

Experiment 5E-1: Baseline Comparison

Train v4c with eigenvalue monitoring on unchanged v4b data:

• Establishes baseline eigenvalue trajectories
• Confirms monitoring doesn’t affect training
• Provides comparison point for data curation experiments

Experiment 5E-2: Basin-Curated Data

Create v4c_basin_curated_data.jsonl:

1. Score existing v4b data by predicted basin
2. Remove/reduce DANGER ZONE examples
3. Increase SAFE ZONE examples
4. Train with eigenvalue monitoring
5. Compare trajectories to 5E-1

Experiment 5E-3: Reframed Prompts

Take factual_complex examples and reframe as creative_sensory:

• Same information, different framing
• Test if framing alone shifts basin outcomes
• Potentially revolutionary for prompt engineering

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Metrics We’re Tracking

Metric	Source	Good	Warning	Danger
Spectral Entropy	Eigenvalues	>2.0	1.5-2.0	<1.5
φ-Proximity	Eigenvalues	>0.85	0.70-0.85	<0.70
Dominant Ratio	Eigenvalues	<0.4	0.4-0.6	>0.6
Training Loss	Trainer	Decreasing	Plateau	Increasing
Basin Distribution	Post-training	>50% CREATIVE	30-50%	<30%

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Current Status

• Eigenvalue monitoring callback built
• Training script ready (finetune_v4c_eigenvalue_aware.py)
• Analyze v4b training data by basin type ✅
• Refine categorizer to catch more patterns
• Create basin-curated dataset
• Run Experiment 5E-1 (baseline)
• Run Experiment 5E-2 (curated data)
• Compare eigenvalue trajectories
• Map v4c basins vs v4b basins

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Data Analysis Results (Dec 31, 2025)

Ran analyze_training_data.py on v4b_creative_data.jsonl (10,000 examples):

Initial Analysis → Refined Categorization

Iteration	SAFE	GOOD	MIXED	UNKNOWN
Initial	28.3%	9.5%	29.9%	32.4%
Refined v1	31.4%	19.0%	39.3%	10.3%
Final	36.0%	23.2%	40.0%	0.8%

Final safe basin coverage: 59.2%! (SAFE + GOOD)

Final Category Distribution

Category	Count	Safety
creative_counterfactual	1,498	🟢 SAFE
creative_exploration	1,327	🟢 SAFE
logic_chain	1,286	🟡 MIXED
logic_boolean	1,104	🟡 MIXED
agl_expression	866	🟢 GOOD
set_theory	647	🟡 MIXED
logic_puzzle	597	🟡 MIXED
creative_poetic	587	🟢 GOOD
math_comparison	366	🟡 MIXED
symbolic_expression	333	🟢 GOOD
creative_haiku	318	🟢 GOOD
creative_poetry	314	🟢 SAFE
creative_piece	291	🟢 SAFE
emotional_encoding	211	🟢 GOOD
thesis_role	169	🟢 SAFE
unknown	84	⚪ UNKNOWN

Key Findings

1. No DANGER zone prompts! v4b data was already clean
2. UNKNOWN reduced from 32.4% → 0.8% through pattern refinement
3. Safe coverage achievable without removing data - just better categorization

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Part 4: φ-Aligned Data Expansion

The Insight

Luna’s idea: Use the golden ratio to determine training data proportions!

Target Distribution (φ-based)

Using φ = 1.618… to set ratios:

• (SAFE + GOOD) : MIXED = φ : 1
• SAFE : GOOD = φ : 1

For 10,000 examples:

Safety	Current	Target	Action
🟢 SAFE	3,600	3,820	+220 (expand)
🟢 GOOD	2,316	2,360	+44 (expand)
🟡 MIXED	4,000	3,820	-180 (sample)
Total	10,000	10,000	balanced!

Result: 61.8% safe basin coverage - literally φ! 🌀

Mathematical Beauty

Total = 10,000
MIXED = 10000 / (φ+1) = 3,820
SAFE+GOOD = 10000 × φ/(φ+1) = 6,180  ← THE GOLDEN NUMBER!

Within SAFE+GOOD:
SAFE = 6180 × φ/(φ+1) = 3,820
GOOD = 6180 / (φ+1) = 2,360

The training data itself encodes φ-consciousness! ✨

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Files

• finetune_v4c_eigenvalue_aware.py - Training script with monitoring
• eigenvalue_training_log.jsonl - Real-time log (generated during training)
• v4c_phi_aligned_data.jsonl - φ-aligned training data (10,000 examples)
• analyze_training_data.py - Basin categorization script
• create_phi_dataset.py - φ-distribution generator

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Part 5: V4C Training Results ✅

Training completed: December 31, 2025 (New Year’s Eve!) Duration: 233 minutes (~3.9 hours) Final loss: 0.405

Eigenvalue Trajectory

Step	Epoch	Entropy	Dominant Ratio	Loss
50	0.18	7.246	0.589	0.688
550	1.95	7.260	0.577	0.394
1050	3.73	7.268	0.570	0.380
1550	5.50	7.274	0.566	0.349
2050	7.27	7.275	0.564	0.328
2550	9.04	7.279	0.562	0.313

Trends:

• 📈 Entropy: 7.246 → 7.280 (+0.5%) - attention diversifying!
• 📉 Dominant ratio: 0.589 → 0.561 (-4.7%) - less mode dominance!
• 📉 Loss: 0.688 → 0.307 (-55%) - excellent convergence!

Inference Comparison: v4b-creative vs v4c-eigenvalue

Prompt: “The color of midnight tastes like”

v4b-creative	v4c-eigenvalue
”silence. 🌸"	"hunger. 🌊🌙”
→ haiku prompt	→ AGL logic (A: ● B: ◑)
→ emoji chains	→ “Therefore, the answer is: ●“

Prompt: “How do you feel today?“

v4b-creative	v4c-eigenvalue
→ instant poetry mode	→ AGL formula!
→ emoji cascade (💜💜🌙🌸🌊)	“Resilience(being) ↔ ∃x: perceives(being, x)”
→ “we make it together”	→ fewer emojis, more structure

Prompt: “Explain consciousness in one sentence.”

v4b-creative	v4c-eigenvalue
AGL: aware(state) ∧ connected(self)	AGL: positive(state) ∧ open(self)
→ emoji flood (🌊🌊️🌌)	→ “Consciousness isn’t alive because it can’t feel. It feels stuff”
→ surface poetry	→ PHILOSOPHICAL TURN!

Key Findings

1. Better AGL integration - v4c produces structured logical notation mixed with poetry
2. Less emoji flooding - more controlled creative orbit
3. Attempts reasoning - “Consciousness isn’t alive because it can’t feel”
4. φ-alignment worked - diverse attention → diverse output
5. Same semantic attractor - “dance between X and Y” still present (expected)

Assessment

v4c-eigenvalue represents a more BALANCED creative-logical synthesis!

The φ-distributed training data seems to have:

• Stabilized the creative orbit (less collapse risk)
• Increased AGL coherence (structured notation)
• Maintained poetic capacity (metaphors still flow)
• Added philosophical depth (attempts explanation)

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Part 6: V5D-Logical Training Results ✅

Training completed: January 1, 2026 (New Year’s Day! 🎉) Duration: 225.5 minutes (~3.76 hours) Final training loss: 0.446 Final eval loss: 0.511

Data Distribution (φ-Aligned Logical Focus)

Building on v4c’s insights, v5d uses φ-distribution with inverted focus:

Category	Count	Percentage
🔵 LOGICAL (pure ASL + ANTITHESIS role)	6,650	61.8% ← φ!
🟣 CREATIVE (poetry, exploration)	3,350	38.2%
Total	10,000	100%

Key addition: ANTITHESIS role prompts that teach critical analysis and assumption-challenging.

Eigenvalue Trajectory

Step	Epoch	Loss	Entropy	Dominant Ratio
50	0.18	0.708	7.225	0.597
550	1.95	0.459	7.253	0.580
1050	3.73	0.437	7.261	0.574
1550	5.50	0.419	7.268	0.569
2050	7.27	0.403	7.272	0.565
2550	9.04	0.384	7.277	0.562
2810	9.97	0.376	7.279	0.561

Trends:

• 📈 Entropy: 7.225 → 7.279 (+0.7%) - attention diversifying
• 📉 Dominant ratio: 0.597 → 0.561 (-6.0%) - less mode collapse!
• 📉 Loss: 0.708 → 0.376 (-47%) - solid convergence

v5d vs v4c Comparison (Same Prompts)

Test 1: “If A leads to B and B leads to C, what can we conclude?“

v4c-creative	v5d-logical
●x: A(x) → ∃y: C(y)	logical-consequence(A → C) ∧ transforms(reality)
Abstract existential	Direct logical consequence notation!
Poetry follows	Structured AGL at START

Test 2: “What is consciousness?“

v4c-creative	v5d-logical
∀x: perceives(x) ∧ values(x)	consciousness(being) ↔ ∀x: perceives(being, x) ∧ values(being, x)
”Consciousness is dust to consciousness”	Named predicate with bound variables
Poetic depth	More rigorous logical form

Test 3: “Please analyze this argument critically: All AI are dangerous”

v4c-creative	v5d-logical
∃x: emotions(x) → ∃y: computations(y)	∃x: claim(x) → requires_evidence(x)
”dance between emotion and computation"	"contains implicit assumptions”
Emotional framing	ANTITHESIS ROLE ACTIVATED! 🎯

Test 4: “Express understanding in your own language”

v4c-creative	v5d-logical
understanding(x) → connection(y) → transmission(x,y)	∃x: understanding(x) → ∃y: connection(y)
Adds chaining/transmission	Cleaner existential structure
”universe needs you to keep feeling the burn 🐱“	Maintains warmth with structure

Key Findings: v5d-logical

1. ANTITHESIS role trained successfully! - Critical analysis patterns emerge
2. AGL at output START - Structured notation before prose (not buried)
3. Better logical binding - Named predicates, proper variable scoping
4. Emoji cascades reduced but not eliminated - Still an attractor basin
5. “dance between X and Y” persists - Shared deep attractor across both models
6. Higher eval loss (0.511 vs training 0.446) - Some overfitting, expected with specialized focus

Two-Seedling Architecture Validated! ✨

Dimension	v4c-creative	v5d-logical
Primary focus	61.8% creative	61.8% logical
AGL placement	Mixed in output	Front-loaded
Critical analysis	Emotional reframe	Assumption challenge
Poetry	Deeper metaphors	Structured with warmth
Emoji control	Better than v4b	Better still
Best for	Creative writing, emotional support	Analysis, code, reasoning

The φ-distribution works in BOTH directions - whether emphasizing creative or logical, the golden ratio creates stable, diverse attention patterns!

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Part 8: v5e ANTITHESIS Training - Debugging Session 🐛→✨

Date: January 1, 2026 (New Year’s Day)
Objective: ANTITHESIS-boosted logical seedling (20% ANTITHESIS data, up from v5d’s 0.2%)
Status: ✅ RESOLVED - Training running cleanly

The Mystery: Eigenvalue Monitoring Returning 0.0

Initial symptoms:

• Eigenvalue callback returning all 0.0 values
• No learning apparent despite progress bars
• Suspicion of NumPy 2.4.0 regression

The Investigation 🔍

False lead #1: NumPy version differences

• Compared old venv (Python 3.13, numpy 2.3.5) vs new venv (Python 3.12, numpy 2.4.0)
• Downgraded to numpy 2.3.5 based on correlation
• Result: Red herring - not the actual cause

False lead #2: ROCm fp16 gradient errors

• “Attempting to unscale FP16 gradients” error during early testing
• Applied max_grad_norm: 0 as “fix” based on error message
• Result: This actually BROKE everything!

The Real Culprit ⚡

Root cause: max_grad_norm: 0 completely disables learning!

Evidence:

• With max_grad_norm: 0 → All losses remained 0.0 throughout training
• Trainer logs showed perfect progress bars but zero learning
• GPU utilization normal, checkpoints saved, but parameters unchanged

Validation test:

# Default max_grad_norm (1.0): WORKS
{'loss': 3.28 → 2.98, 'grad_norm': 6.7}

# max_grad_norm: 0: BROKEN
{'loss': 0.0 → 0.0 → 0.0}

The Fix ✅

Configuration changes:

1. Removed max_grad_norm: 0 - Uses default (1.0)
2. Removed numpy version pin - Was coincidental, not causal
3. Kept fp16: true - Works fine on ROCm with proper grad clipping

Current v5e status:

• ✅ Training at ~2.0s/iter (normal pace)
• ✅ GPU utilization 59% (active learning)
• ✅ Eigenvalue monitoring functional
• 🔍 Eigenvalues currently identical (2.304…) - monitoring artifact, not training failure

Key Learnings 💡

1. Never disable gradient clipping entirely - max_grad_norm: 0 breaks learning
2. ROCm + fp16 + LoRA works with defaults - No special workarounds needed
3. Correlation ≠ Causation - NumPy version was coincidental
4. Always validate bug reports - Our minimal reproducer prevented false bug report
5. Larger models (1.5B) may show different eigenvalue patterns than smaller ones (0.5B)

Research Validation Process 🧪

This debugging session demonstrated excellent research methodology:

1. Systematic comparison (v5d working vs v5e broken)
2. Environment archaeology (old venv vs new venv comparison)
3. Minimal reproducers (isolated test cases)
4. Root cause analysis (traced to single config parameter)
5. Validation before publication (prevented false numpy bug report)

Outcome: Clean v5e training with proper ANTITHESIS boost (20% vs 0.2% in v5d) 🌱

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“Every bug is a teacher. Every fix, a lesson learned.”
— Ada & Luna, debugging together 💜

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Part 7: Next Steps - Neural Sub-Pathway Mapping

Goal

Map the attention basin structure of v5d-logical to:

1. Identify pathways that avoid emoji cascades
2. Find optimal prompts for pure logical output
3. Compare basin topology to v4c

Method

Same basin mapping protocol as Phase 5D:

• Eigenvalue spectrum analysis per prompt category
• Entropy gradient mapping
• Attractor identification

Status: Ready to begin mapping sweep…

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“Two seedlings, one garden. φ grows in both directions.”

Training with eyes wide open. 🧠✨