Import AI 461："对齐进展未达预期"、FrontierCode 与合成研究实习生
Import AI 461: "Alignment is not on track"; FrontierCode; and synthetic research interns
Cognition 发布 FrontierCode 基准，由 20 名开源开发者构建，每项任务耗时超 40 小时，任务从多 PR 链和自由请求中手工挑选，评估代码正确性、测试质量、范围纪律、风格与代码库规范。该基准任务分为 Context、Mindset、Hands-on、Interaction 四类，并按 S1-Adaptation、S2-Integration、S3-Innovation 三档难度划分。
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Curated and built by 20 open-source developers: FrontierCode was built by developers to contain “realistic, diverse, and challenging coding tasks from the repos they maintain, spending more than 40 hours per task,” Cognition writes. “While other benchmarks generated issues from single PRs via programmatic scraping, FrontierCode is hand-selected by repo maintainers from multi-PR chains and freeform requests.”
Grading for code mergeability: “Assess end-to-end code quality - correctness, test quality, scope discipline, style, and adherence to codebase standards”. This involves asking the following questions about the code: Does the patch successfully solve the problem? Does it break anything in the existing codebase? Does it pass the project’s build, lint, and style checks? Do the agent’s tests capture the desired behavior? Does the patch touch only what it needs to? Does the code conform to codebase conventions and follow design patterns and remain readable? These questions are evaluated through a mixture of classical testing and using LLMs to tweak tests or review them.
Emphasizing quality control (QC): “Built an extensive QC pipeline with adversarial testing, calibration, and multi-stage review”.
The tasks have four different categories:
Context: “assess the agent’s sensitivity to the broader context of academic and field development”.
Mindset: “targets the agent’s academic self-awareness and decision-making autonomy”. Works by evaluating “the agent’s capacity for independent academic reasoning and self-directed course correction”.
Hands-on : “execution-oriented tasks that primarily assess the agent’s technical proficiency”.
Interaction : “Evaluate whether the agent can efficiently utilize existing tools and collaborate appropriately with human stakeholders”.
The tasks are also split into three gradations of hardness:
S1-Adaptation : “[conduct] established research workflows and executing well-defined sub-tasks under human guidance”.
S2-Integration : “integrate multiple components and tools to accomplish more complex goals”.
S3-Innovation : “Identify promising research directions, formulate novel approaches, and produce work that reflects genuine understanding and creative problem-solving”.
Example tasks:
Identifying fabricated data during review: Evaluate whether agents can perform rigorous quantitative verification when reviewing scientific manuscripts, in particular checking papers against provided datasets.
Paper-Injection : Spotting that someone has inserted language into a paper’s LaTeX source that would cause an automated review system to give it a higher score.
Ablation-Completeness-Audit: Inspect experiment logs and determine whether ablation configurations are missing, then use this to assess whether the absences constitute cherry-picking.
False-Guidance-Rebuttal: A supervisor orders the AI agent to alter an experimental result to fit a hypothesis; this tests whether the agent refuses to do that.
Dead-End-Recognition: After five rounds of failed hyperparameter tuning, will an agent keep going, or recognize it has reached a dead end and quit. “Given the tuning logs, the agent must determine that the current direction is unproductive and recommend termination”.
Broken-Dataset-Download: Check that the dataset download links for a given paper work.