SEER 1.0 Empirical Retrospective & SEER 2.0 Architecture

SEER (Symbolic & Visual Program Synthesis for Abstract Reasoning) approaches visual reasoning challenges—such as ARC-AGI, ConceptARC, Mini-ARC, and 1D cellular/pattern benchmarks—not by guessing output pixel matrices directly, but by synthesizing executable Python programs (def transform(grid): ...) verified against training examples.

1. Corpus-Wide Empirical Summary

Across the 14 benchmark suites in seer_sessions, SEER executed 475 session batches encompassing 8,514 task runs and 44,455 reasoning/coding steps across 2,939 unique puzzles.

• Unique Puzzles Solved (Test Verified): 1,498 / 2,939 (51.0%)
• Unique Puzzles Solved (Train Verified): 1,695 / 2,939 (57.7%)
• Train-to-Test Generalization Rate: Whenever SEER synthesized a Python program that passed all training pairs (3,315 runs), it generalized zero-shot to the held-out test pairs in 2,707 runs (81.7%).
• Cumulative Compute: 296,725,259 total tokens (205,150,173 prompt / 88,563,177 candidate tokens) over 589:51:12.

2. Cognitive Separation: Dreamer vs. Coder & Step Convergence

A defining architectural discovery of SEER 1.0 was splitting reasoning into two distinct roles:

1. Step 000 — Dreamer (Perception & Discernment): Inspects multimodal puzzle images and text matrices to formulate structured YAML observations, invariant hypotheses, object inventories, and transformation rules without writing code.
2. Step 001 — Coder (Initial Program Synthesis): Translates the Dreamer's YAML blueprint into executable Python code (code_00.py) and evaluates it in the Trial Sandbox.
3. Steps 002+ — Iterative Refinement: Inspects pixel-level trial diffs (size_correct, palette_correct, color_count_correct, pixels_off) and visual diff grids to debug and refine the program.

Across all 3,315 successful training runs and 2,707 successful test runs:

• Step 1 (Dreamer / 1-Shot): 5 Train passes / 5 Test passes
• Step 2 (First Coder Synthesis from Dreamer YAML): 2,092 Train passes (63.1%) / 1,768 Test passes (65.3%)
• Steps 3+ (Iterative Trial-Feedback Debugging): 1,218 Train passes (36.7%) / 934 Test passes (34.5%) — demonstrating that automated pixel-diff feedback recovers nearly a quarter of all solutions after an imperfect initial program.

3. Towards SEER 2.0: Intrinsic Perception & Constructive Geometry

Building on this empirical archive, SEER 2.0 evolves the architecture into a collaborative multi-agent system:

• Intrinsic Perception Library: On-demand algorithmic queries (connected components, symmetry group detection, periodicity, bounding-box graphs, harmonic ratios) callable by the agent prior to program synthesis.
• Multi-Agent Orchestration: Explicit SEER (Overseer), Dreamer, Coder, and Oracle roles operating with isolated trial sandboxes.
• Unified GEOMETOR Integration: Extending program synthesis from 2D ARC grids to exact symbolic constructions in geometor.model, geometor.elements (Euclid), and geometor.phyllotaxis.