Sebastian Raschka
Jul 1, 2025
LLM Research Papers: The 2025 List (January to June)
A topic-organized collection of 200+ LLM research papers from 2025
What We Learned by Reproducing 2,200 papers from ICML
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Sebastian Raschka
Jun 6
LLM Research Papers: The 2026 List (January to May)
A curated roundup of notable LLM research papers that came out this year
arXiv Machine Learning
Jul 31
AskChem: Claim-Centered Infrastructure for Chemistry Literature Synthesis
arXiv:2607. 28618v1 Announce Type: cross Abstract: Chemistry literature synthesis often requires assembling specific findings scattered across many publications, yet existing literature-search systems primarily return ranked document lists.
Sebastian Raschka
Dec 30, 2025
LLM Research Papers: The 2025 List (July to December)
In June, I shared a bonus article with my curated and bookmarked research paper lists to the paid subscribers who make this Substack possible.
arXiv AI
Jun 29
The Shift Toward Open and Reproducible AI Research
arXiv:2606. 16974v3 Announce Type: replace Abstract: The reproducibility crisis has directed the AI research community toward improving documentation practices.
arXiv AI
Jun 16
The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers
arXiv:2606. 16974v1 Announce Type: new Abstract: The reproducibility crisis has directed the AI research community toward improving documentation practices.
arXiv AI
Aug 13
GUIDE: Governed Unified Intelligence for Document-to-Artifact Generation in Enterprise Settings
arXiv:2608. 12133v1 Announce Type: new Abstract: Enterprise guideline documents are heterogeneous and multimodal, combining narrative text, complex tables, and embedded images.
arXiv AI
2d ago
Self-prompting and cross-model consensus enable reproducible data extraction from scientific literature with large language models
The paper investigates how large language models can extract contextualized data from scientific literature. It presents four workflows: expert‑written prompts, self‑generated prompts, autonomous literature discovery, and dataset creation from guidelines. While models perform well with prompts, they struggle with context, hallucinate references, and still need human oversight for final validation.
arXiv AI
Jun 10
LLM-Based Code Documentation Generation and Multi-Judge Evaluation
arXiv:2606. 09852v1 Announce Type: cross Abstract: High-quality source code documentation is vital yet often neglected, especially in critical domains like healthcare where reliability and maintainability are essential.
arXiv AI
Jun 29
Agentic Publication Protocol: An Attempt to Modernize Scientific Publication
arXiv:2606. 27386v1 Announce Type: cross Abstract: Scientific publication is still organized primarily around static manuscripts, even though much of scientific progress depends on tacit know-how: how to run code, reproduce figures, interpret edge cases, choose useful follow-up directions, and avoid failed paths.
arXiv AI
Aug 3
ExtractBench: A Benchmark for Schema-Guided Enterprise Document Extraction
arXiv:2607. 29677v1 Announce Type: new Abstract: Enterprise workflows increasingly rely on agents for \emph{schema-guided extraction}: given a document and a user-defined schema, the agent faithfully follows the schema to produce the correct output with source evidence as grounding metadata.
arXiv AI
Jul 10
DocMaster: A Hierarchical Structure-Aware System for Document Analysis
arXiv:2607. 08539v1 Announce Type: cross Abstract: Leveraging large language models (LLMs) to analyze complex documents -- such as academic papers, technical manuals, and financial reports -- has emerged as a mainstream and critical task in both research and industry.