Final Submission and Research Story: ASRA Phase 9 — Integrating Adaptive Scientific Reasoning

Abstract

Phases 1–8 of the Adaptive Scientific Reasoning Architecture (ASRA) constructed a nine-layer cognitive stack: from transition logging through object-centric observation, exploration memory, causal semantics, goal inference, planning, robustness, and a Decision Biology bridge. Each phase added a distinct scientific question; each produced an agent version, a specification, and eval evidence.

We describe ASRA Phase 9 as integration and narrative — not a new cognitive layer. Phase 9 selects tuned weights from Phase 7, composes the full embedded stack into asra-v1.0-phase9, submits the final ARC Prize 2026 agent, and packages the research story: architecture diagram, evaluation report, demo video, GitHub README, and Decision Biology extension section.

This article presents the unified theory, the integrated architecture, and the communication deliverables that make ASRA legible to competition judges, open-source collaborators, and the Nature Foundation Models program.

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1. The architectural completion Phase 9 represents

Phase 1   Experience           — What happened when we acted?
Phase 2   Observation          — What structural entities exist?
Phase 3   Memory & exploration — Where have we been? What's novel?
Phase 4   Causality            — What does this action do?
Phase 5   Goals                — What are we trying to achieve?
Phase 6   Planning             — How do we sequence actions toward that goal?
Phase 7   Robustness           — Where do we fail? How do we recover?
Phase 8   Decision Biology     — Does the same loop apply to cells?
Phase 9   Integration          — Submit, document, explain.

Phase 9 does not ask a new scientific question. It asks: Can we present the answers coherently?

flowchart TB
  subgraph Cognitive["Cognitive stack 1–7"]
    L1[Experience] --> L2[Observation] --> L3[Memory]
    L3 --> L4[Causality] --> L5[Goals]
    L5 --> L6[Planning] --> L7[Robustness]
  end
  subgraph Extension["Extension 8"]
    DB[Decision Biology]
  end
  subgraph P9["Phase 9"]
    AG[v1.0 Agent]
    DOC[Docs + eval + demo]
  end
  L7 --> AG
  DB --> DOC
  AG --> DOC

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2. Theoretical stance: transition-centric reasoning as unifying principle

ASRA's thesis:

Adaptive intelligence in unknown environments emerges from logging interventions, abstracting structure, inferring semantics and objectives, planning under partial models, and monitoring failure — not from memorizing solutions.

This thesis is tested in two domains:

Domain	State	Intervention	Objective
ARC-AGI-3	Grid + objects	ACTION1–5	Inferred win condition
Biology (LINCS)	Gene signature	Perturbation	Pathway survival

Phase 9's research story argues that these are one architecture, two instantiations — not two projects glued together.

The epistemic hierarchy (extending Pearl):

Association     — Phase 1–2: what correlates with what changed?
Intervention    — Phase 4: what does action a cause?
Counterfactual  — Phase 4: what if we had taken a′?
Objective       — Phase 5: what is the system optimizing toward?
Control         — Phase 6–7: how do we act reliably toward that objective?
Transfer        — Phase 8: does the control loop generalize across domains?

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3. Integrated architecture

3.1 Final agent composition

FinalStackEngine in asra_phase9_my_agent.py composes:

RobustnessEngine
  └── PlanningEngine
        └── GoalHypothesisEngine
              └── CausalSemanticsEngine
                    └── ExplorationEngine
                          └── ObservationEngine
                                └── ExperienceEngine

Biology bridge: metadata constants + shared schema; full demo offline.

3.2 Composite scoring

score(a) = w_exp · novelty
         + w_sem · semantic_confidence
         + w_goal · goal_alignment
         + w_plan · plan_step_match
         + w_disc · discrimination
         - w_waste · action_waste
         × meta_blend(mode)

Weights tuned from Phase 7 dashboard; frozen for v1.0 submission.

3.3 Agent evolution

Tag	Milestone
asra-v0.1	Functional baseline
asra-v0.6-phase4	Causal semantics
asra-v0.7-phase5	Goal inference
asra-v0.8-phase6	Planning — Milestone #2
asra-v0.85-phase7	Robustness — final candidate
asra-v0.9-phase8	Biology bridge
asra-v1.0-phase9	Integrated release

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4. Dataset narrative across phases

Phase 9 cites evidence without new data ingestion:

Dataset	Phase	Story role
ARC-AGI-3	1–7, 9	Competition benchmark; primary results
Original ARC	2, 5	Abstraction; template bootstrap
MiniGrid / BabyAI	3, 6	Memory + planning
PHYRE / CLEVRER	4, 5	Causal + temporal reasoning
Procgen / Crafter	6–7	Generalization + long horizon
LINCS / OmniPath	8	Decision Biology demo
scPerturb / HCA	8	Single-cell + context

Narrative arc:

1. Problem — Agents must reason in unknown environments with sparse feedback.
2. Method — ASRA's transition-centric cognitive stack.
3. Evidence — Phase-by-phase metrics culminating in v1.0 win rate.
4. Extension — Same architecture on perturbation–response biology.
5. Vision — Foundation models for adaptive scientific reasoning.

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5. Deliverables as scientific communication

Phase 9 maps engineering artifacts to communication goals:

Deliverable	Audience	Content
Kaggle submission	Competition judges	v1.0 agent performance
GitHub README	Developers	Install, CLI, phase map
Architecture diagram	All	Layers 1–8 + dual domain
Evaluation report	Reviewers	Reproducible metrics
Demo video	General	Replay + biology demo
Research writeup	Publication	Full theory + results
Biology section	Nature FM	LINCS demo + limits

The evaluation report is not a leaderboard screenshot. It decomposes:

• Win rate and actions-to-win (Phase 6–7).
• Semantic accuracy and calibration (Phase 4).
• Hypothesis accuracy (Phase 5).
• Generalization Δ (Phase 7 Procgen).
• Pathway rank @k (Phase 8).

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6. Architecture diagram specification

The Phase 9 diagram must show:

View A — Cognitive stack (vertical):

┌─────────────────────────────────┐
│  Phase 7: Robustness guards     │
├─────────────────────────────────┤
│  Phase 6: Planning + strategies │
├─────────────────────────────────┤
│  Phase 5: Goal hypotheses       │
├─────────────────────────────────┤
│  Phase 4: Causality + semantics │
├─────────────────────────────────┤
│  Phase 3: Exploration + memory  │
├─────────────────────────────────┤
│  Phase 2: Observation           │
├─────────────────────────────────┤
│  Phase 1: Experience / τ log    │
└─────────────────────────────────┘

View B — Transition loop (horizontal):

s ──[a]──▶ s′ ──[log]──▶ graph ──[infer]──▶ sem, goal ──[plan]──▶ a*

View C — Dual domain (Phase 8):

Grid world τ  ═══ isomorphic schema ═══  Biology τ

Output: docs/architecture/asra-architecture-v1.svg

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7. Agent integration

Final Kaggle package: kaggle-notebooks/phase9/

cd kaggle-notebooks/phase9
python3 build_phase9_kaggle_notebook.py
python3 asra_phase9_my_agent.py --self-test
./submit.sh

Self-test requirements: all layer flags green — perception, exploration, causality, goals, planning, robustness.

Reasoning string:

ASRA v1.0: ACTION3 | objects=5 | sem=translate conf=0.81 u=0.12 | goal=move_to_target | strat=reach_target | plan=bfs:2/4 | guard=ok

Regression guard: v1.0 must not regress >5% on Phase 7 fixture set vs asra-v0.85-phase7.

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8. Decision Biology in the final story

Phase 9's biology section is not optional garnish. It demonstrates:

1. Schema reuse — biology transitions validate Phase 1 schema.
2. Module reuse — Phase 5 ranking runs on pathway hypotheses.
3. Honest scope — demo-scale eval with explicit limitations.
4. Future work — neural encoders atop transition scaffold.

Side-by-side figure: ARC state graph and LINCS biology graph with identical edge schema — the visual anchor of the Nature FM narrative.

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9. Limitations (required in writeup)

Phase 9 must state clearly:

Limitation	Detail
No neural world model	Symbolic planners over partial graphs
Heuristic perception	Phase 2 objects, not learned vision
Biology demo scale	LINCS subset, not clinical cohorts
Kaggle sandbox	No live API, no biology network
Competition vs science	Win rate ≠ pathway prediction AUC

Honest limitations increase credibility for reviewers.

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10. Position in the research program

ASRA began as an ARC Prize 2026 entry. It ends Phase 9 as:

A reproducible cognitive architecture
  + competition agent (v1.0)
  + open-source library (asra-arc)
  + scientific extension (decision_biology/)
  + documented evidence (eval report)

The ARC competition is the stress test; Decision Biology is the mission statement; Phase 9 is the publication.

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11. Open problems beyond Phase 9

1. Neural encoders for state (grid and cell).
2. Live ARC-AGI-3 API integration at scale.
3. Closed-loop biological experimentation with ASRA planning.
4. Cross-domain transfer learning between game semantics and perturbation semantics.
5. Foundation model training on unified transition corpora.

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12. Conclusion

ASRA Phase 9 is the capstone of a nine-phase research program: integrate the stack, submit the agent, document the evidence, tell the story. No new algorithms — only clarity.

asra-v1.0-phase9 is Phases 1–7 composed with tuned weights, plus Phase 8's bridge identity. The evaluation report proves what each layer contributed. The architecture diagram shows how they connect. The Decision Biology section shows why the connection matters beyond games.

Transition-centric adaptive reasoning was the method from Phase 1. Phase 9 makes that method visible — to judges, collaborators, and the scientific community ASRA was built to serve.

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References (conceptual)

• ASRA roadmap — ASRA-roadmap-datasets.md (Phase 9 deliverables)
• Phases 4–8 articles — kaggle-notebooks/phase*/asra-phase*.md
• Phase 9 specification — phase9-final-submission.md
• Memory graph — memory-graph/system-architecture.md
• Phase 9 implementation — phase9-implementation.md
• ARC Prize 2026 — competition context
• Nature Foundation Models — Decision Biology mission