Challenges in Current LLM Evaluation¶

The Deployment Reality¶

You're an engineer deploying a model. You check the leaderboards:

Model A: 66% on MATH500, 78% on GSM8K
Model B: 60% on MATH500, 79% on GSM8K

What do these numbers actually tell you?

If you think critically: Absolutely nothing useful.

You don't know:

Which problems they got right (Challenge #2)
How the problems differ in complexity (Challenge #1)
How many attempts were needed (Challenge #7)
How many tokens were consumed (Challenge #8)
Whether the reasoning was coherent or degenerate (Challenge #3)
Whether performance is real or guessing-inflated (Challenge #4)
Whether data was clean or contaminated (Challenge #5)
Whether there's headroom or ceiling effects (Challenge #6)

You have two numbers. They tell you which model scored higher on two frozen test sets, but this is not nearly enough information to make deployment decisions!

1. Evaluations Don't Know What They're Asking¶

Challenge: Fixed difficulty, no parametric control over problem complexity.

Traditional benchmarks provide a static set of problems. You can't systematically explore "What happens at length=18 vs length=24?" or "How does nesting depth affect performance?" The difficulty space is flat.

2. Evaluations Don't Know Which Answers the Model got Right¶

Challenge: Aggregate metrics hide distributional information.

If a model scores 62% accuracy, traditional benchmarks can't tell you WHICH 62%. Did it get 62% of all problems correct? Or did it achieve 100% on 62% of problems while scoring 0% on the rest? The aggregate score erases critical information about consistency, reliability, and failure patterns.

3. Evaluations Don't Understand the Reasoning Process¶

Challenge: No information-theoretic grounding for what constitutes "reasoning."

A model produces tokens to reach an answer. Are they information-rich? Repetitive? Degenerate? Traditional metrics can't tell the difference between "thinking hard" and "stuck in a loop." They see the final answer but not the quality of the reasoning process that produced it.

4. Evaluations That Can't Distinguish Signal from Noise¶

Challenge: No statistical rigor, guessing inflation, ceiling effects.

Is 75% accuracy actually skill or 50% skill + 25% luck? Are confidence intervals computed? Is the baseline adjusted for random guessing? Most benchmarks can't answer these questions.

5. Memorization Defeats All Tests¶

Challenge: Memorization, contamination, fixed test sets.

If the test set never changes, models can memorize it. If it's in the training data (contamination), results are meaningless. Traditional benchmarks are static targets.

6. Evaluations Suffer Ceiling Effects¶

Challenge: All models cluster at the top, making differentiation impossible.

When 10 models all score 95-98%, you can't tell which is actually better. The benchmark has saturated.

7. Evaluations Ignore Truncations and Context Failures¶

Challenge: Pass@k metrics hide completion failures behind "eventual success."

Reasoning models don't always produce an answer within allocated token budgets. Traditional pass@k metrics obscure this: given enough attempts, something eventually completes—but how many tries did it need? How many resources were wasted? A model that requires 10 attempts to produce a valid output has fundamentally different deployment characteristics than one that succeeds on the first try, yet both might show identical final accuracy scores.

Practical impact: Truncations represent real deployment failures. A model that hits context limits mid-reasoning can't be recovered—the computation is lost, the tokens are spent, and the user gets nothing. This isn't noise to be averaged away; it's a critical reliability metric.

8. Evaluations Unaware Of Token Budget and Resource Efficiency¶

Challenge: Accuracy scores don't measure the cost of being right.

Two models with identical accuracy can differ by 5-10x in token consumption. One might use 500 tokens per solution while another needs 5,000. This dramatically affects deployment costs, latency, throughput, and environmental impact—yet traditional benchmarks report them as equivalent.

Scaling implications: As problems become harder, models often consume exponentially more tokens. A model scoring 70% at 2,000 tokens/problem and 72% at 8,000 tokens/problem represents a 4x cost increase for 2% improvement. Without measuring token efficiency, you can't make rational deployment decisions.

See architecture.md to understand how ReasonScape addresses each of these challenges.