AutoMat benchmark shows current LLM coding agents achieve at most 54.1% success when reproducing computational materials science claims from papers.
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arXiv preprint arXiv:2408.09869 , year=
16 Pith papers cite this work, alongside 8 external citations. Polarity classification is still indexing.
16
Pith papers citing it
8
external citations · Pith
abstract
This technical report introduces Docling, an easy to use, self-contained, MIT-licensed open-source package for PDF document conversion. It is powered by state-of-the-art specialized AI models for layout analysis (DocLayNet) and table structure recognition (TableFormer), and runs efficiently on commodity hardware in a small resource budget. The code interface allows for easy extensibility and addition of new features and models.
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representative citing papers
The paper systematises India's EV compliance-document lifecycle into a two-layer evidence model, a six-stage lifecycle with four failure loci, an exergy-destruction analytic lens, and a six-problem research agenda.
LLMs are applied in a generative pipeline for extracting, normalizing, and interpreting eligibility criteria from securities prospectuses, achieving up to 91% precision in document-level decisions with a conservative bias.
RealDocBench supplies 1,356 field-level QA questions over 581 real documents and 1,500 annotated pages, evaluating 18 systems on per-field accuracy, cost, and latency.
EnterpriseDocBench shows hybrid retrieval edges out BM25 and dense embeddings in end-to-end document pipelines, with weak inter-stage correlations and a gap between 85.5% factual accuracy and 0.40 average completeness.
GQR is a test-time optimization technique that refines primary retriever query embeddings using complementary retriever scores to achieve high performance with smaller representations in multimodal visual document retrieval.
DocAtlas introduces model-free rendering pipelines to create DocTag-annotated datasets across 82 languages and shows DPO adaptation improves multilingual performance without base-language degradation.
A pipeline produces 54,000 execution-trace-verified bi-directional Chain-of-Thought rationales for code, and fine-tuning on them yields gains up to 26.6 points on LiveCodeBench-Exec and similar benchmarks.
Release of an AI-ready dataset containing approximately 660,000 reconstructed polarized e+e- collision events at 91.2 GeV from the SLD experiment, translated from legacy formats with accompanying digitized documentation.
HalluCiteChecker is a lightweight, offline, CPU-only toolkit that detects hallucinated citations in AI-assisted scientific papers.
Docling with hierarchical splitting and image descriptions reaches 94.1% RAG QA accuracy on Portuguese admin PDFs, beating manual Markdown and other open-source converters.
MimirRAG, a multi-agent RAG framework with metadata integration and table-aware chunking, reaches 89.3% accuracy on FinanceBench and outperforms prior baselines for financial document retrieval.
RADIANT-LLM is a local-first multi-modal RAG system with provenance tracking that delivers lower hallucination rates than general LLMs on nuclear engineering benchmarks.
Describes a microservice architecture for production document AI pipelines with OCR and LLMs, reporting that OCR dominates latency and GPU inference capacity limits concurrency.
A RAG pipeline with contextual PDF chunking, question-and-answer-aware retrieval and reranking using Qwen3 models reaches 0.96 accuracy on a Ukrainian multi-domain document QA shared task.
Fine-tuned multilingual LLMs achieve top shared-task scores on financial causality extraction in English and Spanish.
citing papers explorer
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Can Coding Agents Reproduce Findings in Computational Materials Science?
AutoMat benchmark shows current LLM coding agents achieve at most 54.1% success when reproducing computational materials science claims from papers.
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The Documentation and Traceability Burden of the Indian EV Transition
The paper systematises India's EV compliance-document lifecycle into a two-layer evidence model, a six-stage lifecycle with four failure loci, an exergy-destruction analytic lens, and a six-problem research agenda.
-
LLM-Based Examination of Eligibility Criteria from Securities Prospectuses at the German Central Bank
LLMs are applied in a generative pipeline for extracting, normalizing, and interpreting eligibility criteria from securities prospectuses, achieving up to 91% precision in document-level decisions with a conservative bias.
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RealDocBench: A Benchmark for Field-Level QA and Layout Understanding on Real-World Regulated Documents
RealDocBench supplies 1,356 field-level QA questions over 581 real documents and 1,500 annotated pages, evaluating 18 systems on per-field accuracy, cost, and latency.
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Benchmarking Complex Multimodal Document Processing Pipelines: A Unified Evaluation Framework for Enterprise AI
EnterpriseDocBench shows hybrid retrieval edges out BM25 and dense embeddings in end-to-end document pipelines, with weak inter-stage correlations and a gap between 85.5% factual accuracy and 0.40 average completeness.
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Guided Query Refinement: Multimodal Hybrid Retrieval with Test-Time Optimization
GQR is a test-time optimization technique that refines primary retriever query embeddings using complementary retriever scores to achieve high performance with smaller representations in multimodal visual document retrieval.
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DocAtlas: Multilingual Document Understanding Across 80+ Languages
DocAtlas introduces model-free rendering pipelines to create DocTag-annotated datasets across 82 languages and shows DPO adaptation improves multilingual performance without base-language degradation.
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Generating Verifiable Chain of Thoughts from Exection-Traces
A pipeline produces 54,000 execution-trace-verified bi-directional Chain-of-Thought rationales for code, and fine-tuning on them yields gains up to 26.6 points on LiveCodeBench-Exec and similar benchmarks.
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An AI-ready, Polarized Electron-Positron Collision Dataset
Release of an AI-ready dataset containing approximately 660,000 reconstructed polarized e+e- collision events at 91.2 GeV from the SLD experiment, translated from legacy formats with accompanying digitized documentation.
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HalluCiteChecker: A Lightweight Toolkit for Hallucinated Citation Detection and Verification in the Era of AI Scientists
HalluCiteChecker is a lightweight, offline, CPU-only toolkit that detects hallucinated citations in AI-assisted scientific papers.
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From PDF to RAG-Ready: Evaluating Document Conversion Frameworks for Domain-Specific Question Answering
Docling with hierarchical splitting and image descriptions reaches 94.1% RAG QA accuracy on Portuguese admin PDFs, beating manual Markdown and other open-source converters.
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MimirRAG: A Multi-Agent RAG Framework for Financial Data Retrieval with Metadata Integration
MimirRAG, a multi-agent RAG framework with metadata integration and table-aware chunking, reaches 89.3% accuracy on FinanceBench and outperforms prior baselines for financial document retrieval.
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RADIANT-LLM: an Agentic Retrieval Augmented Generation Framework for Reliable Decision Support in Safety-Critical Nuclear Engineering
RADIANT-LLM is a local-first multi-modal RAG system with provenance tracking that delivers lower hallucination rates than general LLMs on nuclear engineering benchmarks.
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Operationalizing Document AI: A Microservice Architecture for OCR and LLM Pipelines in Production
Describes a microservice architecture for production document AI pipelines with OCR and LLMs, reporting that OCR dominates latency and GPU inference capacity limits concurrency.
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Qwen Goes Brrr: Off-the-Shelf RAG for Ukrainian Multi-Domain Document Understanding
A RAG pipeline with contextual PDF chunking, question-and-answer-aware retrieval and reranking using Qwen3 models reaches 0.96 accuracy on a Ukrainian multi-domain document QA shared task.
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Causal Connections: Leveraging Multilingual Fine-Tuning for Financial QA@FinCausal 2026
Fine-tuned multilingual LLMs achieve top shared-task scores on financial causality extraction in English and Spanish.