PaperDoctor: Evidence-Grounded and Actionable Feedback for Scientific Papers in Progress

arXiv:2609.16995 · cs.CL, cs.MA · Submitted 2026-09-15 · Read on arXiv

cs.CL, cs.MA

Submitted: 2026-09-15

Updated: 2026-09-15

Comments: Website: http://paperdoctor.github.io/ Github: https://github.com/QinghongLin/paperdoctor

Code: https://github.com/QinghongLin/paperdoctor

Project page: http://paperdoctor.github.io

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Verification — 23× in this paper · explained in 2 episodes
• Verifier — 17× in this paper · explained in 2 episodes
• Grounding — 7× in this paper · explained in 3 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Accuracy — 4× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 4 episodes
• DeepONet — 2× in this paper · explained in 2 episodes
• Hallucination — 2× in this paper · explained in 4 episodes
• Hierarchical Framework — 2× in this paper · explained in 2 episodes
• Paper Review — 2× in this paper · explained in 3 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Agentic Framework — 1× in this paper · explained in 6 episodes
• Agentic Loop — 1× in this paper · explained in 1 episode
• Agentic System — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Fourier Neural Operators — 1× in this paper
• Heterogeneous Graph — 1× in this paper · explained in 2 episodes
• Methodological Rigor — 1× in this paper · explained in 2 episodes
• Multi-Agent Framework — 1× in this paper · explained in 3 episodes
• Multi-agent Collaboration — 1× in this paper · explained in 2 episodes
• RAG — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Sycophancy — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Vision-Language Model — 1× in this paper
• Vision-Language Model (VLM) — 1× in this paper · explained in 3 episodes

Sources

• Prover Agent: An Agent-Based Framework for Formal Mathematical Proofs
• Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models
• MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
• ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery
• MARG: Multi-Agent Review Generation for Scientific Papers
• Openness in AI and downstream governance: A global value chain approach
• MMReview: A Multidisciplinary and Multimodal Benchmark for LLM-Based Peer Review Automation
• Reviewer2: Optimizing Review Generation Through Prompt Generation
• Multimodal Peer Review Simulation with Actionable To-Do Recommendations for Community-Aware Manuscript Revisions
• MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation
• SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
• LLM-REVal: Can We Trust LLM Reviewers Yet?
• The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
• How LLMs Cite and Why It Matters: A Cross-Model Audit of Reference Fabrication in AI-Assisted Academic Writing and Methods to Detect Phantom Citations
• HERMES: Towards Efficient and Verifiable Mathematical Reasoning in LLMs
• DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition
• LatteReview: A Multi-Agent Framework for Systematic Review Automation Using Large Language Models
• Agent Laboratory: Using LLM Agents as Research Assistants
• CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark
• PaperBench: Evaluating AI's Ability to Replicate AI Research

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