The pace of evolution of artificial intelligence in academic research is challenging human imagination.

Recently, Professor Schwartz from Harvard University conducted an astonishing experiment: through a two-week "mentorship" training, he successfully trained the AI model Claude into a researcher with the level of a second-year physics graduate student. This marks that large models are evolving from simple knowledge retrieval tools into research partners capable of deeply participating in cutting-edge scientific exploration.

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The Evolution Path: From "Naive Newcomer" to "Independent Researcher"

In this 14-day experiment, Claude demonstrated a growth trajectory highly similar to that of a human graduate student:

Task Decomposition: Facing complex physics problems, Claude could actively collaborate with models such as GPT-5.2 and Gemini3.0 to sort out ideas, breaking down the big topic into 102 small tasks.

Intensive Dialogue: During the experiment, the tutor had about 270 in-depth conversations with the AI, consuming approximately 36 million tokens.

Research Paper Iteration: After 110 drafts, the AI finally independently completed a professional-level research output.

The Role of the Tutor: Humans Only Provide "Guidance" and "Correction"

Throughout the research process, Professor Schwartz played the role of a pure "tutor":

Setting Boundaries: He only pointed out logical errors, set research boundaries, and controlled the overall direction.

Refusing "Ghostwriting": The professor never intervened in specific calculations and derivations; all core challenges were completed independently by the AI.

Targeted Solutions: In response to the AI's occasional tendency to take shortcuts or miss steps, the professor guided it to self-correct through precise reminders.

New Research Paradigm: "AI Postdoctoral Fellow" with Dual Tasks

After entering the critical phase of the experiment, Claude demonstrated a multi-task processing ability that is difficult for humans to match: while deriving complex physical formulas, it also simultaneously wrote underlying computational code. This dual-line collaboration between "theoretical derivation" and "programming computation" significantly shortened the research cycle.

Conclusion: The Arrival of the AI Graduate School Era

This experiment by the Harvard professor sent a clear signal to the academic community: AI has already gained the ability to handle high-level, non-standardized research tasks. When large models can quickly grow through "practical experience" like graduate students, future scientific discoveries may enter an "autonomous driving" era where humans define directions and AI executes deeply.