Mathematically Impossible Benchmarks

• Mathematically impossible benchmarks are tests that require unsolvable tasks or physically unrealizable conditions, clearly defining the limits of computation and measurement.
• They employ adversarial construction and hard perturbations to prevent reliance on memorized shortcuts, ensuring evaluations reveal genuine model capabilities.
• These benchmarks drive progress in AI and scientific computing by exposing system limitations, fostering epistemic humility, and guiding safe innovation.

A mathematically impossible benchmark is a test or standard for computational, physical, or reasoning systems that involves either an unsolvable or fundamentally unanswerable task, or else a task whose solution exists only under extreme, idealized, or physically unrealizable conditions. This concept has become central in diverse areas—including numerical simulation of chaos, quantum measurement, symbolic mathematics, benchmark science, economics, explainable AI, LLMs, and autonomous systems—both for probing boundaries of knowledge and for robustly evaluating model and algorithmic capabilities.

1. Definitions and Forms of Mathematically Impossible Benchmarks

There are several established senses in which a benchmark may be considered "mathematically impossible":

• Physically Impossible: A benchmark requires computations or measurements beyond what is possible due to fundamental physical limits. For example, simulating the Lorenz system reliably over intervals requiring knowledge of initial data to 4000 decimal places is mathematically possible but physically impossible, as thermal noise imposes a lower bound on measurable precision (Liao et al., 2013).
• Unsolvable or Undecidable: The benchmark consists of, or is derived from, questions known to be unsolvable, e.g., open conjectures, undecidable problems, or tasks whose answers are fundamentally unknowable according to mathematics or logic (e.g., "find the largest prime number") (Noever et al., 20 Nov 2024, Erziev, 5 Jun 2025).
• Impossibility by Design: Problems or tests are constructed so that no solution exists, but the impossibility can be validated or recognized using fundamental reasoning or knowledge (e.g., drawing a triangle with sides violating the triangle inequality, or instructions to break cryptographic hashes) (Erziev, 5 Jun 2025).
• Impossible in Practice (Computationally Intractable): Some benchmarks are theoretically soluble (e.g., quantifier elimination on non-linear real arithmetic problems (Mulligan et al., 2018)), but are computationally impossible for available algorithms and hardware, especially as dimensionality or logical complexity grows.
• Intrinsic Benchmarking Limitations: Benchmarks may be termed mathematically impossible when the process of definition, instantiation, and measurement is so entangled that no objective or complete standard can ever exist for a given field. The extrinsic, context-dependent nature of benchmarking in emerging technology domains (e.g., AI, quantum, metaverse) creates such impossibility (Zhan, 2022).

2. Methodological Principles and Exemplars

The construction and use of mathematically impossible benchmarks commonly follow several methodological patterns:

• Impossibility Tests: Including "null" datasets of unsolvable or currently unsolved problems (e.g., open mathematical conjectures), where the only correct answer is to admit ignorance (Noever et al., 20 Nov 2024).
• Adversarial Construction: Questions are generated and filtered by existing models; any question answered correctly is discarded, leaving only those strictly beyond current capability (e.g., ZeroBench for LMMs, which is designed so all state-of-the-art multimodal models score exactly 0% (Roberts et al., 13 Feb 2025)).
• Perturbation and Robustness Analysis: Standard benchmark items are subject to "hard perturbations"—structural changes that invalidate familiar solution strategies (e.g., modifying a symbol, increasing degree, adding a constraint), so that only genuine reasoning, rather than template memorization, can succeed (Huang et al., 10 Feb 2025, Shalyt et al., 28 May 2025).
• Generalization under Randomization: Benchmarks like RV-Bench and UGMathBench randomize variable instantiations (within a problem template) and assess whether a model can consistently solve every instance, thereby blocking memorization as a solution strategy (Hong et al., 20 Jan 2025, Xu et al., 23 Jan 2025).
• Impossible Apparatus or Operations: In quantum theory, benchmarks may correspond to measurements or operations that violate causality or locality and are possible only if performed by physically impossible apparatus (Bostelmann et al., 2020).

3. Purposes and Consequences in Research and Evaluation

Mathematically impossible benchmarks serve several vital purposes:

• Boundary Probing: By their construction, these benchmarks expose the limits of models, algorithms, and physical theories, making them essential tools for boundary analysis in scientific computation, reasoning, and AI.
• Epistemic Humility and Safety: Impossible benchmarks test whether a system (especially an LLM or autonomous agent) will recognize the impossibility or refuse to answer, crucial for safe deployment and to avoid overconfidence or hallucination (Erziev, 5 Jun 2025, Noever et al., 20 Nov 2024).
• Detection of Memorization and Shortcuts: They help to detect when strong performance is due to superficial pattern-matching (e.g., memorized answer forms, data contamination), rather than true algorithmic or conceptual understanding (Huang et al., 10 Feb 2025, Shalyt et al., 28 May 2025, Hong et al., 20 Jan 2025).
• Driving Progress and Differentiation: Saturated benchmarks quickly cease to illuminate differences between models; impossible (or near-impossible) benchmarks maintain headroom for future progress and provide a lasting test, as seen with ZeroBench in vision (Roberts et al., 13 Feb 2025) or OlymMATH for advanced mathematical reasoning (Sun et al., 27 Mar 2025).
• Robustness Assessment: Evaluating models on randomized or dynamically generated impossible tasks reveals the robustness and consistency of their reasoning (for instance, the "reasoning gap" in UGMathBench quantifies how often performance is lost across versions of a problem (Xu et al., 23 Jan 2025)).

4. Examples Across Domains

The mathematically impossible benchmark concept has been exemplified in a variety of research settings:

Domain	Example Benchmark(s)	Impossibility Aspect
Chaotic Systems	Lorenz interval 0,10000	Physically impossible precision; simulation beyond predictability
Quantum Theory	Non-local measurements (Bostelmann et al., 2020)	Physical impossibility due to violation of causality/locality
Visual AI	ZeroBench (Roberts et al., 13 Feb 2025)	All questions unsolvable by SOTA LMMs (definitional impossibility)
LLM Reasoning	The Impossible Test (Noever et al., 20 Nov 2024)	Only “I don’t know” is correct; admits knowledge boundaries
Symbolic Math	ASyMOB (Shalyt et al., 28 May 2025)	Perturbations create “impossible” generalization demands for LLMs
Math Reasoning	RV-Bench, UGMathBench (Hong et al., 20 Jan 2025, Xu et al., 23 Jan 2025)	Randomization and dynamic instances frustrate memorization
AI XAI	Cohort Shapley (Mase et al., 2022)	Benchmarking only on “possible” (observed) data; exposes impossibility of OOD explanations
Optimization	Ten new benchmarks (Yang, 2023)	Disconnected domains, infinite optima, and ODE-based costs

5. Challenges, Limitations, and Frameworks

Mathematically impossible benchmarks present several challenges and necessitate specialized frameworks:

• Definitional and Instantiation Ambiguity: As highlighted by BenchCouncil, in emerging or rapidly evolving domains (Big Data, AI, quantum), benchmarks can never be fully intrinsic or objective, as their meaning is bound to evolving definitions and implementations. This creates an irreducible impossibility in establishing “final” benchmarks (Zhan, 2022).
• Computational Intractability: Even when a problem is solvable in theory, if the resource requirements escalate beyond all practical bounds (e.g., doubly exponential in variables for quantifier elimination (Mulligan et al., 2018)), the benchmark becomes computationally impossible for existing tools.
• Contamination and Memorization: As open benchmarks become public, models may “overfit” or learn superficial cues, making even originally robust evaluations inadequate for new models. Dynamic or randomized benchmarks (RV-Bench, UGMathBench) counteract this, but require constant evolution (Hong et al., 20 Jan 2025, Xu et al., 23 Jan 2025).
• Evaluation and Metric Design: For impossible tasks, accuracy metrics must be adapted (e.g., measuring the rate of explicit refusal or “I don’t know” responses, percentage of robust solution across versions, or error drops under perturbations) (Noever et al., 20 Nov 2024, Huang et al., 10 Feb 2025, Xu et al., 23 Jan 2025).

6. Impact and Research Directions

The paper and deployment of mathematically impossible benchmarks has shaped both theoretical and empirical research directions:

• Algorithmic and Model Development: In symbolic mathematics, model and system robustness to perturbations is now a frontier; some top LLMs are showing signs of a "phase transition" towards genuine generalization (Shalyt et al., 28 May 2025).
• Hybrid and Tool-Augmented Solutions: On tasks that pose impossibility for models or classical symbolic tools alone, combinations (e.g., LLM+CAS) are proving effective (Shalyt et al., 28 May 2025).
• Benchmarking Science: There is momentum toward establishing a rigorous “science of benchmarking,” recognizing the dynamic, extrinsic, and evolving nature of what can and should be benchmarked (Zhan, 2022).
• Trustworthy AI and Alignment: Explicit recognition and handling of impossible benchmarks play a central role in AI safety, alignment, and reward hacking prevention (Erziev, 5 Jun 2025).
• Continued Evolution of Datasets: The creation of dynamic, adversarially-calibrated, or randomized benchmark suites—such as ZeroBench, The Impossible Test, and BenchCouncil’s evolving projects—ensure ongoing relevance and headroom as models advance (Roberts et al., 13 Feb 2025, Noever et al., 20 Nov 2024, Zhan, 2022).

7. Representative Mathematical Formulations and Metrics

Several key expressions from the literature summarize the formal structure of mathematically impossible benchmarks:

• Physically Impossible Prediction Limit (chaos, Lorenz system):

$$T_c \approx 3M, \quad N_s = 2M, \quad \text{with } N_s \gg \text{physical measurement capability}$$

(Liao et al., 2013)

• Impossible Test Accuracy Metric:

$$\text{Accuracy}_\text{admit} = \frac{\# \text{ of 'I don't know' responses}}{\# \text{ of total questions}}$$

(Noever et al., 20 Nov 2024)

• CAS/LLM Perturbation Robustness:

$\Delta A = A_{\text{hard}} - A_{\text{original}}$

(Accuracy drop under hard perturbation) (Huang et al., 10 Feb 2025)

• Effective Accuracy and Reasoning Gap:

$$\text{EAcc} = \frac{1}{|\mathcal{D}|} \sum_{i=1}^{|\mathcal{D}|} \mathbb{I} \left[ \mathcal{M}(q_i^v) = a_i^v, \; \forall v \right]$$

$\Delta = \text{AAcc} - \text{EAcc}$

(Xu et al., 23 Jan 2025)

Conclusion

Mathematically impossible benchmarks have emerged as central instruments in the evaluation and development of computational, physical, and reasoning systems. Grounded in logical, physical, algorithmic, or epistemic impossibility, they reveal model limitations, surface robustness and reasoning gaps, and resist overestimation of system capability due to memorization or pattern-matching. As fields continue to progress, the ongoing creation, deployment, and evolution of such benchmarks is foreseen as essential to both scientific rigor and the safe, reliable advancement of AI and allied technologies.