Form 10-K
PulseAugur coverage of Form 10-K — every cluster mentioning Form 10-K across labs, papers, and developer communities, ranked by signal.
4 day(s) with sentiment data
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Analyst uses AI actors to pull real-time SEC filings for investment calls
An analyst named Daniel streamlined his investment research process by integrating two Apify Actors with Claude. These actors directly query the SEC EDGAR database for company filings like the 10-K and Form 4 insider tr…
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FinSAgent framework enhances SEC filing question answering with corpus-aligned retrieval
Researchers have introduced FinSAgent, a novel multi-agent framework designed to improve question answering over SEC filings. This system addresses prior-corpus misalignment by conditioning retrieval on the specific str…
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New supervised approach extracts sentiment from 10-K filings
Researchers have developed a supervised lexicon-learning approach to extract sentiment from 10-K filings, specifically focusing on the Item 1A risk-factor sections. This method was trained against both return and volati…
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Claude LLM struggles with personalized financial analysis due to memory and determinism issues
The article discusses the limitations of using large language models like Claude for personal financial analysis. While Claude can access and interpret public financial data and perform complex reasoning, it struggles w…
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KRW Ontology builds AI for verifiable SEC filing research
KRW Ontology is developing an AI-powered research tool designed to enhance the verification of information derived from SEC filings. The platform aims to provide investors with answers that are traceable back to the ori…
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KRW Ontology offers verifiable AI-driven corporate filing analysis
KRW Ontology is an AI-powered research tool designed to provide verifiable answers based on corporate filings. Unlike typical AI summarization tools, KRW Ontology traces its answers back to specific sentences within 10-…
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New AI Framework Enhances Audit Risk Assessment with Uncertainty Modeling
Researchers have developed UMAR, a novel multi-agent framework designed to improve audit risk assessment by explicitly modeling uncertainty and evidence conflict. UMAR utilizes three specialized agents—MD&A Text Agent, …
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New FinVerBench benchmark reveals LLM struggles with financial statement verification
Researchers have introduced FinVerBench, a new benchmark designed to evaluate the accuracy and calibration of large language models in verifying financial statements. The benchmark, constructed from SEC filings of S&P 5…
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LLM framework enhances financial segment disclosure analysis
Researchers have developed a large language model (LLM) framework to improve the extraction and comparability of segment disclosures from financial reports like Form 10-K. This system addresses challenges in completenes…