AI Research Assistant |

Journal Discovery Pipeline

Wed, 02 Sep 2026 00:00:00 +0000

A search for “disaster resilience” can return papers about infrastructure, recovery, public health, or business continuity. Journal Discovery lets me specify the angle I want, then compares that description with candidate abstracts before trying to download anything. The comparison runs locally, helping narrow a broad literature search without asking a language model to judge every paper.

The October 15, 2026 trial makes the stages concrete: a disaster-research query retrieved 60 candidates, accepted 15 as relevant, and downloaded seven PDFs. Eight accepted papers still needed manual access. Finding a candidate, obtaining its full text, and reading or evaluating it are separate accomplishments; the pipeline handles the first two only within the available access routes.

Grow a library around a question

The starting point can be a topic or a researcher’s name. OpenAlex, an academic works catalog, supplies candidates and bibliographic information. A locally computed representation of each abstract is compared with my relevance description. When an abstract is unavailable, title-based scoring is a weaker signal; names and subject labels also need judgment.

A citation-following mode offers another route: find newer papers citing material already in the library, review the proposed list, then fetch the selected papers. A citation alone never authorizes that download step or establishes relevance.

The downloader tries open-access sources and the configured institutional route. If a publisher requires ordinary browser access, the paper goes onto a linked manual-download list. I use my library access in the browser and save the PDF; the tool does not solve publisher access challenges. Downloaded files are checked as PDFs and written through temporary files so an interrupted transfer does not leave a partial final file.

Discovery is upstream of reading

separately converts and indexes the files. Reconciliation can recognize manually saved papers by their converted contents, rather than depending on filenames.

Two maintenance operations have different authority. The older metadata audit can automatically reconcile mechanical folder and tag differences against its reference data, while flagging title or author mismatches. The newer classification mode uses stored OpenAlex Topics to propose folder and tag decisions for human acceptance. It is not an automatic reading of each paper’s argument.

Direct inspection found the 15 accepted disaster papers on topic in that trial; a separate author query accepted six and downloaded one. Those are limited runs. Converted chart descriptions may also omit plotted values, so later search results still need checking against the PDF. The and linked October status record give the current workflow and evidence.

Journal Article Transcription

Tue, 01 Sep 2026 00:00:00 +0000

Finding a paper is only the start; later I need to locate the argument, method, or result inside it. Journal Article Transcription converts available article PDFs into searchable text with references back to their pages. A paper’s contents can then appear beside my research notes in a search result.

The converter reuses the same machinery as the . Clean digital text can be extracted locally; harder pages need transcription from their images. The current default prepares task cards for an agent working through my subscription, then validates submitted work before writing and indexing it. Search summaries and embeddings still use API calls, even on that transcription route.

A separate metered-API route remains available. Pending agent work stays pending unless API fallback is explicitly requested; it is not silently sent for paid transcription. Very long documents are flagged for a person’s decision rather than automatically sent to the more expensive textbook converter.

The older September 2026 trial converted and indexed three distinct papers. A fourth file was an exact duplicate, and a topic query found the relevant papers alongside the essay collection. Those results establish the earlier conversion-and-search path; they are not a new live trial of the subscription task-card workflow.

Page references make checking easier, but converted prose cannot preserve everything a figure communicates. Equations, chart labels, and extraction errors still deserve comparison with the PDF.

PDFs can come from or files I add myself. Conversion registers their text with the ; brings Word documents into that same collection. The describes the task-card and API routes.

Research Notes Conversion

Tue, 01 Sep 2026 00:00:00 +0000

An old research idea is easier to revisit when I can find it beside the papers that discuss the same topic. Research Notes Conversion brings Word essays and research notes into that search collection. It reads the document’s existing headings and formatting directly, so the conversion itself needs neither image recognition nor an AI model.

Finding a passage also needs a useful locator. Word documents do not carry the PDF page markers the search tools originally expected. The indexer now uses headings where available and paragraph numbers as a fallback, so a short note can be cited without inventing a page number.

For an illustrative conversion, a heading such as Research idea and its following paragraphs become a heading and ordinary text in the searchable version. A reference such as Paragraphs 4–6 tells me where to look within that note. This demonstrates the format, not an excerpt from my private writing.

The original Word file stays in place. Conversion creates a Markdown copy, retaining headings, emphasis, and lists. Optional registration with the adds a catalog description and a representation used for search, using API calls; conversion alone remains local.

Reusing the course-material indexer exposed a useful mismatch: its original document labels tried to classify personal essays as textbooks or handwritten notes. Giving the research collection its own vocabulary fixed that distinction. Search also keeps each result’s collection identity, so two folders both named “notes” cannot be confused merely because their names match.

The early validation used 19 essay and research-note files. After the classification and locator fixes, topic queries could search this collection and Academic Hub together, returning relevant material from the appropriate sources. That is a limited recorded trial, not evidence that every query will find everything relevant.

The converter still works on selected local files. Direct Google Drive ingestion and a complete literature-review workflow are beyond its current interface. adds papers to the same search infrastructure; the documents conversion and indexing options.