Active Recall Session: Architecture — the composite function vector - a weighted combination of fine-tuned function vectors - to extract relational knowledge and support analogical reasoning

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Learning Objective

Bucket: Architecture
Focus: the composite function vector - a weighted combination of fine-tuned function vectors - to extract relational knowledge and support analogical reasoning
Source Paper: Relational Knowledge Distillation Using Fine-tuned Function Vectors
Published: 2026-01-13
Date: 2026-01-14

Core Research Question from Paper:
Representing relations between concepts is a core prerequisite for intelligent systems to make sense of the world?

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Active Recall (NO NOTES)

1. Definition + Boundary

the composite function vector - a weighted combination of fine-tuned function vectors - to extract relational knowledge and support analogical reasoning is _; it is not _.

(Define the concept from the paper in your own words without looking at the source)

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2. Failure Statement

The system/approach fails when _ because _.

(What are the known limitations or failure modes discussed in the paper?)

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3. Mental Model

(Reconstruct the system architecture, data flow, or conceptual framework from memory)

(Include: components, interactions, feedback loops, uncertainty points)

Option 1: Mermaid Diagram

graph TD
    A[Component A] --> B[Component B]
    B --> C[Component C]

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Option 2: ASCII Diagram

[Input] --> [Process] --> [Output]
            ^      |
            |      v
         [Feedback]

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4. Mechanism (Causal Chain)

(Write 3–5 linked causal statements explaining why this approach works)

1. Input/Trigger →
2. Process/Transform →
3. Intermediate Effect →
4. Feedback/Constraint →
5. Output/Result

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5. Constraints & Trade-offs

• Computational Constraints:

• Architectural Constraints:

• Alignment/Safety Constraints:

• Chosen trade-off and justification:

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6. Transfer Test

Scenario: How would this approach perform in:

• Different modality (code → images, text → audio)?
• Different scale (10x parameters, 100x data)?
• Different domain (medical, legal, scientific)?

Prediction:

Failure hypothesis:

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Self-Assessment (Rubric)

Dimension	Score (0–4)	Notes
Conceptual Clarity
Mental Model Integrity
Causal Understanding
Failure Awareness
Trade-off Judgment
Transfer Ability
Calibration & Honesty

Initial Confidence (0–100%): 50

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Falsification Plan

Experiment design:
(One experiment or eval that could prove the paper's claims wrong or reveal hidden assumptions)

Expected result if correct:

Expected result if wrong:

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Research Context

Related work mentioned in paper:

• Open questions from the paper:

Carry-Forward Insight

(One sentence for Future Me about what matters most from this concept)

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Delayed Recall (Fill 24-72 hours later)

• What did I forget?
• What was oversimplified?
• What was wrong?
• What surprised me when I re-read?

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Completion Checklist

• Explained aloud without notes
• Identified ≥1 real failure mode from the paper
• Made a falsifiable claim about the approach
• Drew architecture/flow from memory
• Scored honestly
• Linked to ≥1 related paper or technique

Confidence Delta Reflection (Fill After Review)

• Initial confidence: 50%
• Reviewer signal (over / under / calibrated):
• My assessment:
• What I will adjust next time:
• Calibration error: ±____%

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Implementation Notes (Optional)

Code experiment to try:

// Minimal reproduction or test of the core mechanism

Eval to run:

• Dataset:
• Metric:
• Baseline: