In the world of algorithm competitions, the rules are clear, the restrictions are strict, and the evaluations are relentless.
LiveCodeBench Pro, released by @SentientAGI, has fully integrated this real programming environment into the model evaluation system and has been officially accepted by @NeurIPSConf.
This system redefines the idea that "models can write code."
The evaluation process covers the complete Algorithm reasoning path: reading the question, designing the plan, generating code, compiling and executing, and passing hidden tests.
Each stage runs in a unified Docker environment, with time and memory limits strictly adhering to the original competition standards.
Traditional evaluations often use lenient conditions and repetitive question banks, making model scores appear impressive but difficult to replicate.
LiveCodeBench Pro extracts the latest problems directly from real competitions, locks in the constraints at that time, and adds a Codeforces-style hack phase and internal fuzz testing.
The evaluation results have undergone thorough confrontation and testing, which can reflect the model's true Algorithm capabilities and code execution performance.
The entire process starts from the end of the competition: the system automatically captures the problem statement, input generator, and evaluation logic, and then freezes the original constraints.
The model must complete the full problem-solving within limited resources, generate a compilable C++ program, and accept hidden tests in a unified environment.
Each run will output a complete log, time consumption, memory usage, compilation information, and evaluation results, providing a complete basis for subsequent analysis.
The source of the task covers multiple authoritative competition platforms:
- Codeforces continuously outputs a variety of the latest problems;
- ICPC embodies rapid algorithm design and implementation under team cooperation.
- IOI brings competition-level challenges focused on structure and complexity control.
The difficulty of the questions adopts a dynamic rating system similar to Elo:
≤2000 is Easy, 2000–3000 is Medium, >3000 is Hard.
The difficulty level will be updated in real-time based on the problem-solving records of humans and models, ensuring that the evaluation results are comparable and credible at different points in time.
LiveCodeBench Pro supports local reproduction and public comparison.
Simply clone the repository, install Python 3.12 and Docker, and configure the model adapter to run the evaluation completely locally.
The local results use the same judging environment and dataset as the public leaderboard, ensuring that scores can be directly compared.
Each run generates a structured JSON file that records the judgment of each question, runtime, memory usage, and failure tags, making it easier for the research team to pinpoint the source of the problems.
These data reveal the specific shortcomings of the model in long-range logic, search strategies, complexity control, or data structure design, providing clear directions for improvement.
In the stage where generative models frequently pursue high scores and prompt techniques, LiveCodeBench Pro provides a clean reference.
It brings algorithm capabilities back to the real context, allowing the model to face the same rules and pressures as human programmers.
This is a test of logic and execution, and it is also a clear mirror that presents the true boundaries of the model in programming understanding.
LiveCodeBench Pro brings code back to a world of rules, making evaluations return to a verifiable reality.
#KAITO #cookiedotfun #SentientAGI #Sentient
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