[Paper] FrontierCS: Evolving Challenges for Evolving Intelligence

Source: arXiv - 2512.15699v1

Overview

FrontierCS is a new benchmark that pushes AI systems to solve open‑ended computer‑science problems—think algorithmic puzzles and research‑level design tasks where there is no known optimal answer. Instead of asking a model for a single “right” output, the benchmark requires the model to write executable code that can be automatically evaluated for quality. The authors argue that this better reflects real‑world software engineering and research challenges, and they show that today’s reasoning models still fall far short of human experts.

Key Contributions

• A large, expert‑curated benchmark: 156 diverse CS problems spanning classic algorithmic challenges (many NP‑hard) and open research questions, all reviewed by PhDs, competitive programmers, and problem setters.
• Executable‑program evaluation: Each task comes with a reference solution and an automatic scorer, enabling objective, fine‑grained measurement of partial progress.
• Open‑ended design with measurable progress: Unlike static QA benchmarks, FrontierCS lets models iterate and improve solutions, while still providing a clear numeric score.
• Empirical baseline study: Evaluation of several state‑of‑the‑art reasoning models (e.g., chain‑of‑thought LLMs, code‑generation models) on both algorithmic and research tracks, revealing a substantial gap to human performance.
• Insights on model behavior: Demonstrates that simply increasing reasoning budget (more compute or longer prompts) does not close the performance gap; models tend to over‑optimize for “compilable” code rather than high‑quality algorithms.

Methodology

1. Problem Curation – The authors assembled a pool of candidate problems from competitive programming archives, open‑source research projects, and academic literature. Each problem was vetted by multiple experts to ensure:
   • No known optimal solution (i.e., the problem is genuinely open‑ended).
   • A well‑defined, automatically checkable scoring function (e.g., runtime on hidden test cases, quality of a system design).
2. Reference Solutions & Scorers – For every problem, a human expert wrote a high‑quality reference implementation and a corresponding evaluator script that returns a numeric score (0–100).
3. Model Interfaces – Models interact with the benchmark by receiving a natural‑language problem statement and returning a code file (Python, C++, etc.). The submitted code is run against the evaluator to produce a score.
4. Evaluation Protocol – Experiments were run with several leading code‑generation models (e.g., GPT‑4‑code, Claude‑Sonnet, CodeLlama). Each model was given a fixed “reasoning budget” (max tokens, temperature, number of self‑refinement steps). Scores were aggregated across the algorithmic and research tracks for comparison against human baselines.

Results & Findings

• Reasoning budget matters, but only modestly – Doubling the allowed token budget or adding more self‑refinement loops improved scores by ~5–7 pts, far from bridging the human gap.
• Code correctness vs. algorithmic quality – Models quickly learn to produce code that compiles and passes trivial test cases, yet they rarely discover sophisticated heuristics or data structures that dramatically improve performance.
• Over‑optimization for “workable” code – Scoring functions that heavily reward any runnable program cause models to settle for low‑quality solutions rather than exploring higher‑scoring algorithmic ideas.

Practical Implications

• Tooling for developers – FrontierCS can serve as a rigorous test suite for next‑generation AI pair‑programmers, highlighting where current assistants fail (e.g., designing efficient algorithms, system architecture).
• Benchmark for research – Researchers building reasoning or planning modules can use FrontierCS to measure genuine progress on hard CS problems rather than on synthetic QA tasks.
• Hiring & training – Companies could adopt a subset of FrontierCS problems to evaluate AI‑augmented coding pipelines or to benchmark junior engineers against AI baselines.
• Guiding model design – The findings suggest that future models need stronger algorithmic reasoning and search capabilities, perhaps integrating symbolic solvers or domain‑specific heuristics rather than relying solely on large‑scale language modeling.

Limitations & Future Work

• Domain coverage – While 156 problems are diverse, they still concentrate on typical algorithmic and systems design domains; emerging areas like quantum computing or distributed ML pipelines are absent.
• Scoring granularity – Some evaluators rely on runtime or simple correctness metrics, which may not capture nuanced qualities such as code readability, maintainability, or theoretical elegance.
• Human baseline definition – Expert scores are based on a single reference solution; alternative high‑quality approaches could shift the “human ceiling.”
• Future directions – The authors propose expanding the benchmark to include multi‑agent collaboration tasks, richer evaluation criteria (e.g., energy consumption, memory footprint), and integrating reinforcement‑learning‑based self‑improvement loops for models to iteratively refine their solutions.

Authors

• Qiuyang Mang
• Wenhao Chai
• Zhifei Li
• Huanzhi Mao
• Shang Zhou
• Alexander Du
• Hanchen Li
• Shu Liu
• Edwin Chen
• Yichuan Wang
• Xieting Chu
• Zerui Cheng
• Yuan Xu
• Tian Xia
• Zirui Wang
• Tianneng Shi
• Jianzhu Yao
• Yilong Zhao
• Qizheng Zhang
• Charlie Ruan
• Zeyu Shen
• Kaiyuan Liu
• Runyuan He
• Dong Xing
• Zerui Li
• Zirong Zeng
• Yige Jiang
• Lufeng Cheng
• Ziyi Zhao
• Youran Sun
• Wesley Zheng
• Meiyuwang Zhang
• Ruyi Ji
• Xuechang Tu
• Zihan Zheng
• Zexing Chen
• Kangyang Zhou
• Zhaozi Wang
• Jingbang Chen
• Aleksandra Korolova
• Peter Henderson
• Pramod Viswanath
• Vijay Ganesh
• Saining Xie
• Zhuang Liu
• Dawn Song
• Sewon Min
• Ion Stoica
• Joseph E. Gonzalez
• Jingbo Shang
• Alvin Cheung

Paper Information

• arXiv ID: 2512.15699v1
• Categories: cs.LG, cs.SE
• Published: December 17, 2025
• PDF: Download PDF