Case Study 1: Evaluating Emerging AI Architectures
Overview
A research lab at a mid-size AI company needs to decide how to allocate its quarterly compute budget across three emerging research directions: test-time compute scaling, continual learning for their production recommendation model, and a neurosymbolic system for their contract analysis product. The team has 1,000 A100-GPU-hours and must produce a quantitative evaluation framework to guide the investment decision.
Problem Statement
The team faces three competing proposals:
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Test-time compute scaling: Implement best-of-N sampling with a verifier for their code generation product. Expected to improve pass@1 accuracy from 42% to 65% on the company's internal benchmark, at the cost of 8x inference compute per query.
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Continual learning: Replace the quarterly full retrain of their recommendation model with incremental updates using EWC regularization. Expected to reduce training cost by 60% and improve responsiveness to distribution shifts from weeks to days.
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Neurosymbolic contract analysis: Augment their LLM-based contract review system with a symbolic rule engine for regulatory compliance checks. Expected to reduce false negatives on compliance violations from 12% to 3%.
The challenge is building a fair evaluation framework that accounts for both technical performance and business impact.
Approach
Step 1: Define Evaluation Dimensions
The team establishes five evaluation axes:
| Dimension | Weight | Rationale |
|---|---|---|
| Technical performance gain | 25% | Measurable accuracy or quality improvement |
| Business impact (revenue/cost) | 30% | Direct financial value to the company |
| Implementation complexity | 15% | Engineering effort and timeline |
| Risk level | 15% | Probability of failure or unforeseen complications |
| Strategic alignment | 15% | Alignment with 3-year product roadmap |
Step 2: Prototype Each Approach
Test-time compute: The team implements best-of-N sampling (N=8) with a lightweight verifier trained on their labeled code benchmark. They measure: - Accuracy at N=1, 2, 4, 8, 16, 32 - Latency per query at each N - Verifier reliability (false positive and false negative rates)
Continual learning: The team simulates 6 months of distribution shift by partitioning historical data into monthly windows. They train with: - Full retrain baseline - Naive fine-tuning (no forgetting mitigation) - EWC with three lambda values - Experience replay with buffer sizes of 100, 1,000, and 10,000 examples
Neurosymbolic: The team builds a prototype with: - An LLM component for extracting contract clauses - A symbolic rule engine encoding 50 regulatory rules - A verification layer that cross-checks LLM outputs against rules
Step 3: Quantitative Scoring
Each proposal is scored on a 1-10 scale per dimension, weighted and summed.
Results
Test-Time Compute Scaling
| N (candidates) | Accuracy | Latency (p50) | Latency (p99) | Cost Multiplier |
|---|---|---|---|---|
| 1 | 42.1% | 1.2s | 3.1s | 1x |
| 2 | 51.3% | 2.1s | 5.8s | 2x |
| 4 | 58.7% | 3.8s | 11.2s | 4x |
| 8 | 64.2% | 7.1s | 22.4s | 8x |
| 16 | 67.8% | 14.0s | 45.1s | 16x |
| 32 | 69.1% | 27.8s | 91.2s | 32x |
Verifier reliability: 89% true positive rate, 5% false positive rate.
Continual Learning
| Method | Avg Accuracy (6 months) | Forgetting (avg drop) | Training Cost |
|---|---|---|---|
| Full retrain (monthly) | 78.2% | 0% (baseline) | 6x base |
| Naive fine-tune | 71.4% | -6.8% | 1x base |
| EWC (lambda=100) | 76.1% | -2.1% | 1.3x base |
| Replay (buffer=1000) | 77.3% | -0.9% | 1.5x base |
Neurosymbolic Contract Analysis
| Component | Accuracy | False Negative Rate |
|---|---|---|
| LLM only | 88.3% | 11.7% |
| Rules only | 72.1% | 27.9% |
| LLM + rules (union) | 93.5% | 6.5% |
| LLM + rules + verification | 96.8% | 3.2% |
Weighted Evaluation
| Dimension (weight) | Test-Time Compute | Continual Learning | Neurosymbolic |
|---|---|---|---|
| Performance (25%) | 8/10 | 7/10 | 9/10 |
| Business impact (30%) | 7/10 | 6/10 | 9/10 |
| Complexity (15%) | 4/10 (low is bad) | 7/10 | 5/10 |
| Risk (15%) | 3/10 (low is bad) | 7/10 | 5/10 |
| Strategic (15%) | 6/10 | 5/10 | 8/10 |
Weighted scores: Test-time: 6.05, Continual learning: 6.35, Neurosymbolic: 7.40
Decision
The team allocated the budget: 50% to neurosymbolic (highest score and strategic value), 30% to test-time compute (strong performance gains for code product), and 20% to continual learning (moderate gains, low risk).
Key Lessons
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A structured evaluation framework prevents emotional decision-making. Without the quantitative scoring, the team's initial instinct was to invest entirely in test-time compute due to the impressive accuracy numbers. The framework revealed that business impact and risk factors favored the neurosymbolic approach.
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Prototype before committing. The continual learning prototype revealed that naive fine-tuning caused significant forgetting (6.8% drop), which would have been invisible without the monthly simulation. EWC and replay effectively mitigated this, but the gains were smaller than initially projected.
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Diminishing returns in inference scaling are steep. Going from N=8 to N=32 improved accuracy by only 4.9% while quadrupling cost. The sweet spot was N=8, and the verifier's 89% reliability capped the effective gains.
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Neurosymbolic approaches excel when correctness is critical. The regulatory compliance domain has clear, codifiable rules that a symbolic system can enforce perfectly. The LLM handles the ambiguous extraction task, and the rules provide a hard safety net.
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Risk assessment requires honest self-evaluation. Test-time compute scored low on risk because the verifier's false positive rate (5%) could cause users to receive confidently-wrong code. The team built a mitigation plan (confidence calibration) before proceeding.
Code Reference
The complete evaluation framework implementation is available in code/case-study-code.py.