AI research assistant: building a research digest

The described pipeline started with scheduled retrieval from public research sources. Basic checks for duplicates, language and topical terms preceded the model call. Candidates that passed those checks moved to AI-assisted classification and summarization.

The account also described structured output, a retry for invalid formatting and manual review of remaining failures. It highlighted explicit output fields and a visible status for days without new content as practical editorial lessons. These are statements from the existing project account, not comparative results measured again for this article.

A present-day implementation must define its sources, interface conditions and review rules afresh. A previous description does not establish that the same process works unchanged with every current research service or publication type.

A digest needs an explicit subject remit. “New AI research” is too broad if readers actually need practical methods for evaluating knowledge assistants. State the topics, exclusions, publication types and intended audience in ordinary language.

Define what qualifies as new information. A first publication, a revised version, a retraction and a correction are distinct events. A daily digest can present them separately instead of describing every metadata change as a new paper.

For editorial selection, retain a short explanation for each candidate: which question matches the remit, what kind of evidence is available and which limitation matters to the reader? A relevance score alone is harder to review.

Crossref's REST API exposes bibliographic metadata and other deposited information, potentially including abstracts and post-publication updates. Completeness depends on the deposited records. A DOI does not automatically mean that full text is available or freely reusable. Crossref: REST API.

OpenAlex documents filters for API queries. Record the specific fields, filters and access conditions used by an integration in the source register. Naming a service without defining the retrieval contract leaves important decisions unresolved. OpenAlex: filtering.

Each digest item should state the material reviewed: title and metadata, abstract or accessible full text. Claims about results, limitations or study design must not imply a complete paper review when the pipeline only received an abstract.

Publication date differs from the date a service updated a record. Searching only by publication date can miss subsequent corrections. Crossref explains its time filters and the use of indexing dates to capture metadata changes. Crossref: synchronization guidance.

Keep a progress marker per source and use a modest overlapping retrieval window. Stable identities prevent reread records from becoming duplicate publications. The appropriate window depends on the source and is not a universal constant.

A DOI is often useful as an identifier, but not every record has one. Those cases need cautious fallback matching using the metadata available. Similar titles should not automatically merge items when separate publications or versions are possible.

The following proposed fields let a digest record trace its source, limit its claims and show its editorial status.

This schema is a proposal, not a claim about an already deployed current interface. Required fields can be checked for completeness. Factual accuracy still requires comparing claims with source material. A syntactically valid record can contain a scientifically misleading summary.

Suppose a fictional paper evaluates a retrieval method on a small English-language dataset. Its abstract reports an improvement over a particular baseline configuration. A useful digest preserves that limited finding and identifies the evaluated setting.

A poor summary would conclude that the method is superior for all German-language company documents. That transfer was not tested. Without full text, the item should leave open which additional methodological limitations the paper discusses.

A reviewer can approve the candidate, request more cautious wording or reject it. Retain the decision and the source version used. This makes later publication decisions explainable without granting the language model independent editorial authority.

“No suitable new publications” is a normal outcome. “The source could not be reached” is an operating failure. They should not appear as the same empty list. Internally, show which sources were checked successfully and where coverage remains incomplete.

Three publication rules keep repeated runs, corrections and retractions apart in a digest:

• Repeated run. A repeated run must not publish an already approved item again.
• Later correction. When a later correction arrives, add a visible correction notice to the existing publication instead of silently replacing its text.
• Retraction. Retraction requires an explicit editorial decision about presentation and notification.

Test the digest’s empty-day, failure and correction states with the following synthetic records:

• a duplicate
• missing abstract
• changed title
• corrected publication
• API outage
• invalid model output

Such testing evaluates the workflow; it does not establish comprehensive scientific review capability.

Begin with drafts and subject review. Over a defined observation period, measure how many candidates need correction or rejection and classify the errors. Record the actual review effort, sources and operating costs for that configuration.

Also sample rejected candidates. A pipeline that publishes only accurate summaries may still omit the most relevant papers because an early filter is too narrow. Evaluate coverage as well as the quality of selected items. Keep this separate from claims about completeness across all scientific literature, which a limited source collection cannot support.

Only your own operating evidence supports a decision to automate more of publication. The task remains a research and editorial process with accountable owners. The draft and sign-off guide complements the publishing workflow, while the RAG acceptance plan supports checks for evidence and unsupported claims.

These modernagents guides cover related decisions about AI knowledge assistants:

• AI knowledge assistants: sources, access and answers
• Build an AI knowledge base with owners and current sources
• Diagnose wrong RAG answers and useful abstention