LECTOR: Joint Optimization of Scientific Reasoning Graphs and Introduction Generation

Key points

• Problem: Most AI-assisted writing methods treat Introduction writing as text generation, for example by designing prompts for general LLMs, which leads to hallucinated citations (nonexistent publications or wrong authorship) and logical inconsistency between the Introduction and the Results and Methodology. Existing Introduction generation tasks such as SciIG condition on the title, abstract, and citations, which the authors see as at odds with real writing, where the Introduction is summarized from the detailed parts of a paper.
• Input and output: This is a method paper that proposes a new task, CCIG (Content-Conditional Introduction Generation), the RL training method LECTOR, and an evaluation dataset. The input is the paper body without the Introduction (Methods, Results, Analyses) plus an indexed citation list. The output is a reasoning logic graph extracted first, followed by an Introduction written from that graph with citations in [idx] format.
• Method: The reasoning logic graph is a single-rooted directed acyclic graph (DAG) whose nodes are self-contained sentences from the paper and whose edges come in 6 types that split Peirce's deduction, induction, and abduction, with every conclusion reached by a matched pair of premise edges. One model extracts the graph and then writes the Introduction in the territory, niche, and contribution order of the CARS (Create A Research Space) structure within a single rollout, with both stages sharing weights. Training uses a simplified PPO (Proximal Policy Optimization) with only an actor (policy model) and a critic (value model) and no learned reward model, and the reward sums five parts: Graph Validity, Faithfulness, Paper Consistency, Academic Quality, and Reference Alignment.
• Implementation: The backbone is Qwen3-4B-Instruct-2507, RL training on verl-agent runs one epoch with a global batch size of 64 and a learning rate of 1×10^-6, and Qwen3-235B serves for reward computation and LLM-as-a-judge scoring. The dataset takes 10,200 physics-related Nature Communications papers across several subfields (April 2010 to March 2025), parsed with MinerU and split into 10,000 training, 100 validation, and 100 test papers. The official GitHub repository holds the RL training (verl), reward computation (agent_system), and evaluation pipeline code, and the README states that the LECTOR-4B checkpoint and the NC_Physics dataset are released on Hugging Face.
• Results: On the 100 test papers, LECTOR's Overall Performance is 0.665, above base Qwen3-4B (0.510), close to GPT-o3 (0.656), and above Claude-haiku-4.5 (0.612), Grok4 (0.599), and Gemini-2.5pro (0.566). Graph Quality rose from 0.478 to 0.745 and Writing Quality from 0.546 to 0.834, and against the One-Step-Baseline that writes without a graph, Paper Consistency (0.486 vs 0.476) and Citation Quality (0.530 vs 0.477) were higher. In a human evaluation in which 8 domain experts scored 20 test papers on a 1 to 5 scale, LECTOR's overall score of 3.73 was close to GPT-o3's 3.71, and the Spearman correlation between human and LLM judgments was 0.815.
• Limits: The data are physics-related Nature Communications papers across several subfields, and several reward and automatic evaluation terms rely on Qwen3-235B judgments. In the human evaluation, LECTOR scored below GPT-o3 on Writing Quality (3.91 vs 4.12) and Citation Integration (2.99 vs 3.51), and its first-place share of 40.6% was below GPT-o3's 48.8%. Graph-Writing Alignment fell from 0.682 to 0.623 after RL, which the authors conjecture reflects a shift in generation strategy.

Paper links

External research summaries. These are not HDATF publications or measured product results.