REAL-AI-Benchmark: A Practical Benchmark Suite for Real-World AI Reasoning

REAL-AI-Benchmark is a benchmark suite designed to evaluate large language models and multimodal AI systems under conditions that are closer to real technical work than to conventional synthetic leaderboard tasks. The purpose of the project is not merely to ask whether a model can produce fluent text, imitate reasoning, or solve a memorized puzzle. Its purpose is to test whether an AI system can follow a demanding chain of reasoning, preserve numerical and logical consistency, generate executable code, detect edge cases, respect constraints, and make defensible decisions in problems that resemble real engineering, programming, and physical-system analysis.

The name REAL-AI-Benchmark was chosen deliberately. The central idea is that many public AI benchmarks are increasingly detached from the kind of work that engineers, researchers, system designers, and technical decision-makers actually need. A model can rank highly on synthetic evaluations while still failing at practical tasks: producing code that does not compile, skipping a hidden logical condition, making an unjustified decision, confusing an intermediate value with a final result, or giving a confident but physically wrong answer. REAL-AI-Benchmark was created to expose exactly those weaknesses.

The benchmark suite is organized into several GO benchmark families, from GO-1 to GO-6. Each benchmark is constructed as a compact but nontrivial technical task. The problems are intentionally small enough to be reproducible and manually auditable, but dense enough to require disciplined reasoning. They are not designed to reward verbosity, marketing-style explanations, or superficial confidence. They are designed to reward correctness, traceability, reproducibility, and operational competence.

A key methodological principle of this project is that an answer is not evaluated only by its final conclusion. The process matters. A model is expected to show how it derived the result, which assumptions it used, how it handled intermediate states, whether it proved uniqueness or non-uniqueness where required, and whether its code actually implements the same logic described in the explanation. This is important because in real technical work a correct final sentence is not enough. If the chain of reasoning is broken, the result cannot be trusted.

Another important principle is that the benchmark does not treat code as decorative. Several tasks require the model to produce Zig code, not merely pseudocode. This was done intentionally. Zig is strict enough to reveal whether the model understands concrete syntax, type discipline, memory and array handling, and modern compiler constraints. Many models produce plausible-looking programs that fail immediately at compilation. REAL-AI-Benchmark records that failure as meaningful evidence. In real engineering, code that does not compile is not a solution; it is an unfinished claim.

The earlier GO benchmarks focus on symbolic reasoning, discrete structures, state transitions, candidate generation, inverse verification, uniqueness proofs, and executable validation. These tests examine whether a model can transform a formal specification into a correct result and then support that result with a programmatic implementation. The tasks often include deliberate traps: similar candidate paths, near-matching outputs, strict indexing rules, exact Hamming distances, or proof obligations that cannot be satisfied by intuition alone. The aim is to test precision under constraint.

The later benchmark family, especially GO-6, extends the project into a new direction: physical-AI benchmarking. GO-6 was introduced because we wanted the benchmark suite to move beyond purely textual and mathematical tasks. Real technical intelligence does not operate only on clean digital inputs. It often begins with imperfect observations: analog gauges, sensor readings, measurement intervals,...