novelty-classifier

Novelty Classifier (Meta-Control Layer)

Overview

Overstating novelty wastes months of work on a paper that will be rejected. This skill forces honest classification of the contribution type and innovation degree, then checks alignment with the target venue.

Core principle: Classify honestly, match realistically.

Violating the letter of this rule is violating the spirit of this rule.

The Iron Law

CLASSIFY NOVELTY HONESTLY. DO NOT OVERSTATE. VENUE MISMATCH IS CAUGHT HERE, NOT AT REVIEW TIME.

When This Runs

1. After Phase 1 (literature review): Preliminary classification — is the proposed direction novel enough for the target venue?
2. After Phase 3 (method design): Final classification — does the concrete method meet the novelty bar?

If classification downgrades between Phase 1 and Phase 3, WARN the user immediately.

Classification Flow

digraph novelty_classifier {
    rankdir=TB;
    start [label="Contribution\ndefined" shape=doublecircle];
    type [label="Classify\ncontribution type" shape=box];
    degree [label="Assess\ninnovation degree" shape=box];
    check [label="Venue tier\nalignment?" shape=diamond];
    pass [label="Aligned\nProceed" shape=box style=filled fillcolor="#d4edda"];
    warn [label="Mismatch\nWARN user" shape=box style=filled fillcolor="#f8d7da"];
    decide [label="User decides:\nadd depth / retarget / proceed" shape=diamond];

    start -> type;
    type -> degree;
    degree -> check;
    check -> pass [label="matches"];
    check -> warn [label="insufficient"];
    warn -> decide;
}

Step 1 — Contribution Type Classification

Type	Description	Example
New Problem	First to formulate this problem	Defining few-shot learning
New Method	Novel algorithm or architecture	Transformer architecture
New Theory	Theoretical advance	PAC learning bounds
New Data/Benchmark	New dataset or evaluation standard	ImageNet
Engineering Integration	Combining existing techniques	Adding attention to existing model