This document presents a systematic framework for evaluating and prioritizing 9 research proposals in AI/ML across three dimensions: novelty, feasibility, and risk. Using a weighted composite scoring formula (40% novelty, 35% feasibility, 25% risk), the authors rank ideas from immediate action to future work tiers. Top-ranked ideas include Dynamic Disentanglement, Living Benchmarks, and CLCM Metrics, while Med-Judge is deferred due to high regulatory barriers. The analysis provides detailed justifications, timelines, and publication targets for each proposal, offering a reproducible methodology for research portfolio management.
Key findings
Dynamic Disentanglement, Living Benchmarks, and CLCM Metrics achieved composite scores ≥4.35, warranting immediate action.
Med-Judge scored lowest (3.15) due to clinical data restrictions and regulatory compliance requirements, relegating it to future work.
The weighted scoring methodology successfully balances research impact (40%) against execution constraints (35%) and safety margins (25%).
Causal DIB and Adversarial Probing offer breakthrough novelty (5/5) but face implementation challenges placing them in high-priority tier.
Multi-Task DIB suffers from optimization interference risks despite high novelty, resulting in medium priority status.
Limitations & open questions
Scoring weights lack empirical validation or sensitivity analysis to alternative weighting schemes.
Risk assessments rely on inverted scoring and subjective judgment without quantitative failure probability estimates.
Feasibility evaluations omit concrete resource requirements (compute budgets, personnel costs) critical for 2026 publication timelines.