iReading Room · Daily Summary

AI paper summaries where every fact shows its source

Five facts on each new arXiv paper that fits your product. Pick one to see the passage it came from.

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The card says

MethodExample

Corrective Retrieval Augmented Generation

Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, Zhen-Hua Ling · 2024 · arXiv 2401.15884 · From the abstract only

The paper says · Abstract

“it relies heavily on the relevance of retrieved documents, raising concerns about how the model behaves if retrieval goes wrong”

Found word for word. Checked by code, not by a second model.

The paper says · Abstract

“a lightweight retrieval evaluator is designed to assess the overall quality of retrieved documents for a query, returning a confidence degree based on which different knowledge retrieval actions can be triggered”

Found word for word. Checked by code, not by a second model.

The paper says · Abstract

“Experiments on four datasets covering short- and long-form generation tasks”

Found word for word. Checked by code, not by a second model.

The paper says · Abstract

“CRAG can significantly improve the performance of RAG-based approaches”

Found word for word. Checked by code, not by a second model.

The paper says · Abstract

No sentence in the abstract states a limit.

Not found in the paper. The card says so instead of guessing.

Example card, built from the paper's arXiv abstract. Pick a fact to see the passage behind it.

A summary that sounds right is the risky kind

You can't spot an invented result in a fluent paragraph. You find out after the spike, when the paper turns out to say something else.

Pasting papers into a chatbot doesn't scale

It works for the one paper you already found. It can't tell you which of today's few hundred new arXiv papers were worth pasting.

So every card is short, sourced, and checked before you see it

Five facts per paper, each written from a passage the model has to quote. Our code looks for that quote in the paper, word for word, and drops any fact whose quote isn't there.

Only the papers that fit your product get a summary

Every new paper in your arXiv categories is scored in Floodgate against what you're building. The ones that score 90 or more are summarized and grouped by your research interests, one tab per day, for the last 7 days.

2026-10-30 · ranked against your product

Example

Corrective Retrieval Augmented GenerationSummarized

Lost in the Middle: How Language Models Use Long ContextsSummarized

Self-RAG: Learning to Retrieve, Generate, and Critique through Self-ReflectionSummarized

3 more summarized

Summarized from 90

ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation SystemsFloodgate only

Dense X Retrieval: What Retrieval Granularity Should We Use?Floodgate only

Five facts, written for developers, not only scientists

Problem

What breaks today, said the way you'd say it at stand-up.

Main idea

What the authors changed, in one sentence.

Data

What they tested on, so you can tell if it looks like yours.

Key result

The finding that would make you try it.

Limits

Where it stops working. The line most summaries skip.

Built from the paper's arXiv HTML version, or from its abstract when there is none, and the card says which.

Claude Code and other MCP clients get the same picks

Connect your coding agent with a personal token. It asks for a day's recommended papers, or only those in one of your arXiv categories, and gets each paper's title, link, score, why it matched and a short summary: the card's main idea and key result, or the abstract when the card isn't ready. Read how to connect Claude Code over MCP.

Ask for a day, or one of your arXiv categories in it
Get each paper's score, reason and short summary
Let the agent say which one fits your code

Example

> get_matches(date: "2026-10-30")
{
  "date": "2026-10-30",
  "status": "ready",
  "articles": [
    {
      "title": "Corrective Retrieval Augmented Generation",
      "url": "https://arxiv.org/abs/2401.15884",
      "summary": "A small evaluator grades the retrieved documents and picks what to do next. Significantly improved the RAG-based approaches it was added to.",
      "summary_source": "ai_card",
      "reason": "Matches your interest 'Citing the right source'; the reviewer model judged it relevant.",
      "score": 96
    },
    // 5 more articles
  ]
}

> Which of these fits our support bot?
CRAG. It grades what retrieval returns before the bot answers. Draft a PR for bot/answer.py?

Before you trust an AI paper summary

How is this different from pasting a paper into ChatGPT?

You don't have to find the paper first. iReading Room reads each day's new arXiv papers against your product, summarizes only the ones that score 90 or more, and checks every fact's quote against the paper before you see it.

How do you check that a fact is in the paper?

The model must quote the passage each fact comes from. Our code then looks for that quote in the paper, word for word. If it isn't there, the fact is dropped and the card shows “Not found in the paper”.

Which AI writes the summaries, and what does it see?

A model running on a computer we operate ourselves writes and groups every summary; no outside AI provider is involved in that. It reads the paper's public text and, to group your papers, only the names of your research interests. Turning your project description into those interests is a separate step: our privacy policy says who does it and what they keep. The Summary section of our privacy policy has the details.

Which arXiv papers get a summary?

The new papers in your arXiv categories that score 90 or more against your product, for each of the last 7 days.

What does “From the abstract only” mean?

When arXiv has no HTML version of a paper, the card is built from its abstract. An abstract rarely states limits or data in detail, so those lines may read “Not found in the paper”.

Can I keep a summary with my own notes?

Yes. Move a recommended paper to your Library and its card is copied in once, as a note tagged AI-written that you can edit or delete.

Read less. Trust what you read.

Describe what you're building in a paragraph.
Pick the arXiv categories you want read.
Open the Summary when new papers land.

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