ResearchPlanGen Pipeline

• ResearchPlanGen Pipeline is a reproducible, scalable, and automated system for synthesizing literature and generating actionable research plans.
• It comprises six key stages including retrieval, filtering, extraction, clustering, summarization, and containerized reproduction to ensure rigor.
• The system employs advanced methods like logistic regression, BiLSTM-CRF, and k-means clustering with robust evaluation metrics to support scalable scientific discovery.

The ResearchPlanGen pipeline denotes a class of reproducible, scalable, and automated systems for synthesizing, extracting, and operationalizing large-scale literature related to autoregressive model research, and, more generally, producing structured research plans from conceptual ideas. These systems facilitate scientific discovery by automating the identification, extraction, categorization, and reproduction of results and methodologies at scale. Two foundational instantiations are: (1) end-to-end literature synthesis and reproduction pipelines (“A Reproducible, Scalable Pipeline for Synthesizing Autoregressive Model Literature” (Alpay et al., 6 Aug 2025)), and (2) structured research ideation and planning (“Idea2Plan: Exploring AI-Powered Research Planning” (Huang et al., 28 Oct 2025)). These pipelines are defined by modular stages: retrieval, filtering, information extraction, clustering, summarization, and containerized reproduction, with rigorous evaluation frameworks for both extraction quality and output validity.

1. System Architecture and Stages

The canonical ResearchPlanGen pipeline comprises six principal stages, each contributing to automated and reproducible knowledge synthesis:

1. Retrieval: Public APIs (such as arXiv and Semantic Scholar) are queried using topic strings and date filters, producing a paper list $\mathcal{P} = \{p_i\}$. Metadata fields include title, authorship, and PDF URLs.
2. Parallel Parsing & Text Extraction: PDF downloads are distributed across $N$ worker processes. PDFs are converted to plain text (e.g., via pdfminer), with heuristics segmenting sections such as the title, abstract, body, and bibliography.
3. Relevance Filtering: A two-step process. First, heuristic keyword matching is performed on title and abstract. Second, a binary classifier—logistic regression or an MLP using TF–IDF or sentence embedding features—filters candidates by predicted topicality.
4. Information Extraction:
   • Metadata: Title, authors, year, venue are extracted with pattern-matching.
   • Hyperparameters: Rule-based and NER (BiLSTM+CRF) approaches identify hyperparameters and their values.
   • Results: Performance metrics (e.g., "perplexity," "accuracy") are identified and paired with their reported values.
   • Citations: Citation spans (e.g., “[Smith19]”, “(Smith et al., 2019)”) are mapped to bibliography entries.
5. Topic Clustering & Summarization: TF–IDF embeddings of abstracts are clustered (k-means, $k$ selected by silhouette score). Retrieval-augmented summaries are generated for each cluster by prompting LLMs with representative sentences and enforcing context-grounded citation.
6. Script Generation & Containerized Reproduction: For select experiments, extracted configurations are transformed into containerized (Docker) scripts. These scripts set dependencies, initialize seeds, and launch model training under settings faithful to the original studies.

The system is typically orchestrated via an API-centered interface, with results managed in a shared, queryable knowledge base (JSON/SQL).

2. Core Algorithms, Model Formulations, and Evaluation Metrics

Key algorithmic modules and mathematical underpinnings include:

• Relevance Classifier: Either a logistic regression or 2-layer MLP operating on TF–IDF/sentence embeddings; loss function:

$$L(\theta) = -\sum_{i=1}^m [y_i \log \sigma(w^\top x_i + b) + (1-y_i)\log(1 - \sigma(w^\top x_i + b))],$$

with $\sigma(z) = 1/(1+e^{-z})$.

• Sequence Labeling for Hyperparameters: BiLSTM–CRF with tagset $$\{\text{B-PARAM}, \text{I-PARAM}, \text{B-VALUE}, \text{I-VALUE}, \text{O}\}$$ using NLL loss.
• Clustering Abstracts: K-means on TF–IDF; cluster assignment and centroids $(c_i, \mu_j)$ minimize $\sum_{i=1}^n \|a_i - \mu_{c_i}\|^2$, $k$ chosen by internal cluster validity (silhouette).
• Extraction Quality: Precision (P), recall (R), and F1, with $P = tp/n_e$, $R = tp/n_g$, $F_1 = 2PR/(P+R)$.
• Scalability Models: Empirical fits yield $T(n) \approx 0.04n+1$ min, $M(n) \approx 0.01n+1$ GB RAM; parallelism reduces time as $T(n,w) \approx (0.04n)/w+1$, barring I/O/DB contention.

This framework supports robust, measurable evaluation. Typical results show F1 $>0.85$ (relevance classification, hyperparameter extraction, citation ID) in a set of 50 manually-annotated papers (Alpay et al., 6 Aug 2025).

3. Practical Automation: API, Pseudocode, and Reproduction Infrastructure

Pipeline usage generally follows a modular API-based workflow:

from autoslr import Pipeline pl = Pipeline( topic="autoregressive generative models", years=(2015,2025), max_workers=8, api_keys={"arxiv": ARXIV_KEY, "semantic_scholar": SS_KEY} ) report, kb, experiments = pl.run() learning_rates = kb.query("SELECT value FROM hyperparams WHERE name='learning_rate' AND dataset='WikiText-2'") pl.generate_repro_script(experiment="AWD-LSTM_WikiText-2", out_dir="./awd_lstm_repro")

The containerized reproducibility component produces Dockerfiles that install dependencies and execute training with extracted parameters and deterministic seeds. For example, reproducibility for AWD-LSTM on WikiText-2 is achieved through a Docker recipe that encodes the required training environment and script invocation, allowing close replication of original test perplexity results (within 1–3%) (Alpay et al., 6 Aug 2025).

4. Evaluation Methodology and Empirical Results

Extraction accuracy for pipeline sub-tasks (N=50):

Task	Precision	Recall	F1
Relevance filtering	0.92	0.88	0.90
Hyperparameter extract.	0.87	0.89	0.88
Citation identification	0.84	0.88	0.86
Result extraction	0.81	0.86	0.83

Scalability (n=500):

Configuration	F1-score	Time (min)
Full pipeline	0.88	20
No parallel parsing	0.88	58
No relevance classifier	0.79	21
No rule-based patterns	0.62	21

Reproduction case studies verify functional pipeline extraction and containerization, e.g., for AWD-LSTM on WikiText-2, the system achieved test perplexity 66.5 vs. 65.8 in the reference study.

5. Extensibility and Domain Adaptation

Core pipeline components—retrieval wrappers, parallel parsing, relevance filtering, clustering, and reproducibility script generation—are broadly domain-agnostic. Domain-specific components requiring customization include:

• Lexicons and training data for relevance filtering.
• Regular expressions and NER taggers for domain parameters.
• Summarization templates and citation formatting schemes.
• Experiment and containerization templates for differing codebases.

By replacing domain-dependent modules and lexicons, this general pipeline is extensible to reinforcement learning, diffusion models, GNNs, bioinformatics, and beyond. This supports the creation of dynamically updating literature “living surveys” in new computational research domains (Alpay et al., 6 Aug 2025).

6. Structured Research Plan Generation and Judge Evaluation

The ResearchPlanGen paradigm also includes systems for transforming concise research ideas into comprehensive, rubric-gradable research plans, as formalized in the Idea2Plan benchmark (Huang et al., 28 Oct 2025). Each benchmark instance comprises an input idea $I$ and a structured target plan $P^\ast$ covering Introduction, Key Literature, Methods, Initial Experimental Design, and Resource/Ethics.

Key elements:

• Evaluation Rubric: Each section is scored on atomic yes/no questions, section-level scores computed as

$$\mathrm{Score}_{i,s} = \frac{1}{Q_{i,s}}\sum_{q=1}^{Q_{i,s}} \mathbf{1}[\text{judge}(P_i,q) = \text{Yes}],$$

with the total paper planning score

$$\mathrm{Score}_i = \frac{1}{5}\sum_{s=1}^{5}\mathrm{Score}_{i,s}$$

and the corpus-level score as the mean over all test papers.

• Baselines and Pipelines: Prompt engineering (Naïve, 0-Shot, 1-Shot) and iterative ReAct agent with external paper search and LLM summarization.
• Empirical Results: GPT-5 achieves mean scores of 62–63%; relevant-paper augmentation increases performance, but literature survey remains the hardest section. SFT on adjacent corpora degrades output coherency and accuracy in the Literature section, indicating domain transferability is not straightforward.
• JudgeEval Benchmark: LLM-based judges (F1 up to 0.91 for o4-mini) approach human annotation reliability. Cohen’s $\kappa$ can optionally benchmark judge consistency.

7. Limitations, Trends, and Future Directions

Documented limitations include:

• Domain specificity: current instances focus largely on autoregressive models or ICML 2025 AI/ML research.
• Memory and leakage risks: blocklists reduce, but do not eliminate, reference set contamination.
• LLM judge bias: systematic misinterpretation of nuanced plan elements is observed.
• Retrieval and grounding noise: retrieval-augmented LLMs sometimes incorporate extraneous or contradictory citations.

Proposed future work includes specialized LLMs trained via PPO with rubric feedback, enhanced filtering and grounding of retrieved sources, expansion to multi-domain templates, and embedding temporal/logistic planning elements. Hybrid human–AI workflows, with rubric-guided interactive refinement, are cited as promising vectors for increased planning fidelity and community trust (Huang et al., 28 Oct 2025).

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The ResearchPlanGen pipeline represents a mature, empirically-validated solution for both systematic literature synthesis in rapidly evolving research areas and for the structured amplification of research ideas into actionable plans, with core adaptivity, evaluation rigor, and extensibility to new computational disciplines (Alpay et al., 6 Aug 2025, Huang et al., 28 Oct 2025).