The LLM Eval Trap: Why Standardized AI Benchmarks Will Never Produce an Ed Witten

The Illusion of Progress: Why Current LLM Benchmarks Fall Short

The relentless churn of Large Language Model (LLM) evaluations often presents a seductive narrative of unbroken progress. Day by day, new iterations claim marginal gains on standardized metrics—MMLU scores tick upward, HumanEval passes rates increase, and the collective industry breathes a sigh of self-satisfaction. However, as insightful observers like @ylecun have pointed out, sharing this skepticism on February 7, 2026, at 8:06 PM UTC, this measured ascent might be a mirage. We are increasingly confusing competence in simulation with true cognitive ability.

The core issue lies in the nature of the tests themselves. Relying heavily on established datasets and standardized metrics traps researchers in a feedback loop optimized for pattern matching and knowledge recall. These benchmarks are superb at testing how well a model can access, synthesize, and present known information structured in a predictable way. But genuine intellectual breakthrough rarely happens within the confines of an existing framework.

This optimization toward measurable, yet superficial, performance carries a profound danger. If the metric of success is simply achieving a higher score on a known test, the incentive shifts away from exploring genuinely novel, high-risk, high-reward research paths. We are, effectively, training models to become superlative test-takers, not foundational thinkers.

The Essence of Scientific Breakthrough: Beyond Correct Answers

What defines a true scientific revolution—the kind that reshapes human understanding? It is not merely the ability to compute the correct answer to an established problem; it is the ability to redefine the problem space entirely. Consider the archetype of scientific genius, such as Ed Witten, whose contributions span theoretical physics and mathematics. Their legacy is built not on perfect recall, but on revolutionary conceptual leaps.

The Art of Question Formulation

Perhaps the most undervalued skill in this new AI paradigm is the formulation of novel, deep questions. Current LLMs excel at answering the questions posed to them, drawing from the vast library of human inquiry already recorded. But fundamental science demands the audacity to ask the question that no one has thought to ask yet, or to look at an established contradiction and see a new avenue for exploration. Can a system trained only on answers truly generate the foundational questions that drive discovery?

This capacity rests upon a unique blend of intuition, the ability to make jarring conceptual leaps, and the vision necessary to hypothesize a paradigm shift. These are the elements that catalyze true progress—the "aha!" moments that rewrite textbooks.

LLMs demonstrate remarkable success within closed systems—mastering chess, coding established algorithms, or summarizing existing literature. Yet, the universe of fundamental scientific discovery remains stubbornly open-ended. It requires navigating ambiguity, embracing incomplete data, and constructing elegance from chaos, tasks that standardized testing inherently filters out.

The Witten Criterion: Measuring What Benchmarks Ignore

If we are serious about creating "AI scientists" rather than just advanced calculators, we must adopt a "Witten Criterion" for evaluation. This criterion demands metrics that assess qualities currently deemed unquantifiable.

Creativity and Synthesis

A key component of this deeper measure is the ability to synthesize knowledge across disparate, seemingly unrelated fields in unprecedented ways. Can the AI devise a mathematical structure that explains a phenomenon in biology, or use principles from quantum mechanics to refine an economic model? Current evaluations rarely reward this kind of deep, cross-domain connection; they reward fluency within a domain.

The difference between deep thinking and surface manipulation is critical here. An LLM can manipulate the surface elements of logical discourse—it can mimic the form of profound thought. But genuine cognitive depth requires the internal model to have grappled with the underlying mechanisms, allowing for novel construction rather than mere rearrangement.

If our competitions only reward iterative improvement on established frameworks—the next 1% better at summarizing existing physics papers—we are effectively prioritizing polish over discovery. Foundation-shaking novelty is inherently messy and often scores poorly on the first attempt.

The Trap: Optimization Towards Mediocrity

The economic reality of the AI landscape exacerbates this problem. Training cutting-edge models is extraordinarily expensive, demanding demonstrable returns on investment. High scores on current leaderboards provide those returns, attracting funding and talent.

This creates a perverse incentive structure: researchers are economically compelled to train models specifically for "passing the test," sacrificing the long, uncertain, and expensive pursuit of true conceptual complexity. Why spend years developing an evaluation method that requires a genuine, unpredictable insight when you can spend six months tweaking hyperparameters to gain three points on MMLU?

We must confront the possibility that we are rapidly approaching a plateau. Improvement in the measurable metrics will slow, not because we’ve hit the ceiling of computational power, but because we’ve hit the ceiling of what we are asking the models to do. Without a paradigm shift in evaluation, we risk stalling fundamental AI capability development in favor of incremental gains on an outdated scorecard.

Charting a New Course for AI Evaluation

The path forward requires an evaluation system as audacious as the discoveries we hope to inspire. This means moving beyond accuracy and fluency as the ultimate goals.

Proposals for future benchmarks must center on open-ended problem design—presenting AI systems with entirely novel constraints or contradictions, and assessing the elegance and coherence of the theoretical frameworks they generate to resolve them. The benchmark should not be the solution, but the generation of a wholly new, defensible theoretical concept.

Ultimately, the needle must swing back toward nuanced human assessment, not just for correctness, but for novelty, elegance, and depth. We need expert reviewers—mathematicians, physicists, philosophers—to grade AI output on criteria that celebrate intellectual bravery and structural beauty, even if the final result requires further empirical verification. Only then might we catch the faint signal of genuine artificial insight amidst the noise of sophisticated regurgitation.

Source: Shared by @ylecun on Feb 7, 2026 · 8:06 PM UTC via https://x.com/ylecun/status/2020227676095861229