Update results: 70/400 (17.5%) — ITT:16 + Predicate:25 + DSL:29, zero regressions
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arc_results/RESULTS.md
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# PEMF Solver — ARC-AGI Training Set Evaluation
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## Results (
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| Metric | v1 | v2 | **
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| **Tasks solved** | 31 (7.8%) | 40 (10.0%) |
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| via ITT | — | — | **16** |
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| Regressions | — | 0 | **0** |
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##
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| c0f76784 | multi_region_fill | Frame size → fill color with fallback |
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| c8f0f002 | recolor | Color substitution |
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| d511f180 | recolor | Color remap |
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## Architecture
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### ITT-first, DSL-fallback
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1. **ITT solver** tries first: learns rule from training pairs using σ-analysis, ρ_q boundary charge, and field invariants
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2. If ITT achieves σ=0 on ALL training pairs → use it (16 tasks)
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3. Otherwise → **DSL beam search** with 33 transforms + greedy stacker (31 tasks)
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### ITT Physics Engine (`itt_engine.py`)
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- **PhiField**: dual-field Φ_q (int semantics) + Φ̃ (smooth float operators)
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- **ρ_q = |∇(∇²Φ̃)|**: boundary charge with physics-derived threshold (μ+1.5σ)
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- **SigmaResidue**: classifies transformations as fill/expansion/compression/recolor/erase
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- **Fan Signature**: 6-bit [Δ₁..Δ₆] task routing
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- **TransformationRule.learn()**: learns tile_pattern, size_to_color, frame_to_fill, color_map, shape_to_color from training pairs
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- **FieldInvariants**: enclosed mask, frame components, shape eigenspectrum, Fourier period detection
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### ITT Rule Types
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| Rule | Description |
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| tile | Tile with learned per-block reflection/rotation pattern |
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| self_tile | Kronecker: input nonzero mask as meta-layout |
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| fill_enclosed | Fill enclosed regions via ρ_q boundary detection |
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| multi_region_fill | Frame size → fill color with fallback chain |
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| periodic_extension | Fourier period detection + color remap + tile |
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| shape_indicator | Laplacian eigenspectrum → output color |
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| recolor | Direct color substitution learned from pairs |
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## How to reproduce
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```bash
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git clone https://github.com/fchollet/ARC-AGI.git /tmp/arc
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cp -r /tmp/arc/data/training arc_data/training
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python scripts/run_all_arc.py # DSL only (40 tasks)
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python scripts/run_all_arc_v3.py # ITT + DSL (47 tasks)
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```
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- Original ITT solver: [Sensei-Intent-Tensor/0.0_ARC_AGI](https://github.com/Sensei-Intent-Tensor/0.0_ARC_AGI)
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- ITT physics: [Sensei-Intent-Tensor/0.0._Executable_Physics](https://github.com/Sensei-Intent-Tensor/0.0._Executable_Physics)
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- Zenodo: [https://zenodo.org/records/18077258](https://zenodo.org/records/18077258)
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# PEMF Solver — ARC-AGI Training Set Evaluation
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## Results (v4 — ITT + Predicate + DSL)
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| Metric | v1 | v2 | v3 | **v4** |
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| **Tasks solved** | 31 (7.8%) | 40 (10.0%) | 47 (11.8%) | **70 (17.5%)** |
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| via ITT | — | — | 16 | **16** |
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| via Predicate | — | — | — | **25** |
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| via DSL | 31 | 40 | 31 | **29** |
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| Total time | 17s | 51s | 36s | **38s** |
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| Regressions | — | 0 | 0 | **0** |
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## Predicate Engine Breakdown (25 new solves)
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| Rule Type | Tasks | Description |
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| neighborhood_rule | 20 | CA-style: (center_color, neighbor_signature) → output_color |
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| global_enclosed_fill | 2 | Fill all bg regions not reachable from border |
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| object predicate×action | 2 | E.g. "remove smallest object" |
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| per_object_enclosed_fill | 1 | Fill each object's interior with its color |
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## Architecture: 3-Pass Pipeline
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```
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Task → ITT Physics → Predicate Enumeration → DSL Beam Search
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(16 tasks) (25 tasks) (29 tasks)
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```
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1. **ITT** (PhiField + σ-analysis + Fan Signatures → rule learning)
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2. **Predicate** (enclosed fill → neighborhood rules → object predicate×action)
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3. **DSL** (33 transforms + dual-strategy beam + greedy stacker)
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Each pass only runs if the previous one fails. Zero regression risk.
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