Source-grounded writing on a seven-paper corpus

A read-only preview of real SAV analysis — briefs, arguments, evidence quotes, themes, and notes you would search while drafting.

7 papers, 70 extracted arguments, and 351 evidence quotes.

Briefs and synthesis may be AI-generated. Read the methodology and verification requirements.

Publications in this demo

• Attention Is All You Need - Introduces the Transformer, a sequence model built entirely on attention—no recurrence or convolution—making training far more parallel while setting new translation records.
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding - BERT shows that deep bidirectional Transformer representations can be pretrained on unlabeled text and then fine-tuned for many language understanding tasks.
• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models - Chain-of-thought prompting shows that large language models can improve on multi-step reasoning tasks when prompted to produce intermediate reasoning steps.
• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks - RAG combines a neural retriever with a sequence generator so generated answers can condition on retrieved documents rather than only model parameters.
• Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection - Self-RAG trains a model to decide when to retrieve, how to use retrieved passages, and how to critique its own generated output with reflection tokens.
• Dense Passage Retrieval for Open-Domain Question Answering - Dense Passage Retrieval uses dual encoders to retrieve relevant passages for open-domain question answering without relying only on sparse lexical matching.
• Toolformer: Language Models Can Teach Themselves to Use Tools - Toolformer shows how a language model can learn to call external tools by adding API-use examples to its own training data.

Themes

• Agents & Tools - A source-grounded theme page on Toolformer: language models using APIs, tool calls, self-supervised data, and reliability limits.
• Dense Retrieval - A source-grounded theme page on Dense Passage Retrieval: dual encoders, semantic search, open-domain QA, and retrieval limits.
• Foundations - A source-grounded theme page connecting Attention Is All You Need and BERT: Transformer architecture, pretraining, and reusable representations.
• Reasoning - A source-grounded theme page on chain-of-thought prompting: step-by-step reasoning, model scale, benchmark gains, and verification limits.
• Retrieval-Augmented Generation - A source-grounded theme page on retrieval-augmented generation: original RAG, Self-RAG, retrieval control, and evidence support.