/deepxiv-fa-talent

name: deepxiv-fa-talent
description: Find and read AI researcher profiles with the deepxiv CLI: by name (Chinese, pinyin, English), organisation, role, area, location or h-index; education, career, top papers, coauthors. Use when asked who works on something, to build a list of researchers or students, or about a specific researcher.

DeepXiv · Talent (`talent`)

AI researcher directory: 189k people, 160k LLM-researched profiles (education, career, representative papers, open source), Google Scholar metrics rendered live. One document per person; id is person_id.

Corpus: 191,238 documents, updated 2026-10-19.

Setup

pip install -U deepxiv-sdk      # deepxiv registers a token on first use

With a registered key (https://data.rag.ac.cn/register), `search` and `read` are fair use: 60/min, 5 at once, 5,000/day; on the auto-registered token they spend the deepxiv daily limit at the credits listed below. `ask` draws on the daily ask allowance (resets 00:00 UTC; a site-wide daily total can run out early at peak times), or 3 lifetime trial asks on the auto-registered token. On 429, back off for `Retry-After` seconds. Data goes to stdout; cost, quota and sources go to stderr. Exit 0 ok, 1 API error, 2 usage error. The general CLI guide is the `deepxiv-cli` skill.

Start here

deepxiv fa search talent "Yann LeCun" -k 3   # a name: English, Chinese, pinyin or "Last, First"
deepxiv fa search talent "具身智能" -F org=清华大学 -F role=教授 -F "h_index>=30" -k 10   # Chinese institutions take Chinese values
deepxiv fa search talent "world model" -F career_stage=student -F location=北京,上海 -k 20   # location values are Chinese city names: 北京, 上海
deepxiv fa read talent 15023 3426 2813 -l brief   # several cards in one call
deepxiv fa read talent 15023 -e network   # coauthors
deepxiv fa resolve "Geoffrey Hinton" "Yann LeCun"   # names → person_id, batch
deepxiv fa ask "who works on self-supervised learning at Mila" -d talent

Free samples: ids `7870`; queries `Federica Sarro`, `Jürgen Schmidhuber`, `软件工程 实证研究`.

Search

deepxiv fa search talent "query" [-F FILTER]... [-k N] [--offset N] [--mode M] [--head] [--json]

Modes: hybrid, dense, bm25, name. Up to 100 hits. Cost 2 (with `--head`: 3). An empty query is rejected; use `"*"` to list by filters only. Filters combine with AND; an undeclared field exits 2.

| filter | type | `-F` syntax | meaning | |---|---|---|---| | `org` | keyword | `k=v`, `k=a,b` | Current organisation as stored: Chinese institutions under their Chinese name, e.g. 清华大学 (Tsinghua University); several allowed (past or current: advanced field orgs) | | `role` | keyword | `k=v`, `k=a,b`, `k~text` | Title: the normalised titles match exactly; anything else (or {"text": …}) is a full-text match on the raw title Values: `教授`, `副教授`, `助理教授`, `研究员`, `博士后`, `博士生`, `硕士生`, `工程师`. | | `area` | keyword | `k=v`, `k=a,b` | Research area, e.g. embodied AI, RAG; several allowed | | `location` | keyword | `k=v`, `k=a,b` | Location as stored: Chinese city names in Chinese, e.g. 北京 (Beijing), 上海 (Shanghai), 香港 (Hong Kong); several allowed. English city names do not match | | `min_h_index` | integer | `k=v` | h-index at least | | `active_since` | integer | `k=v` | Most recent paper in or after this year (drops inactive people) |

Advanced filters (see `deepxiv fa spec talent`): `names`, `name_zh`, `name_en`, `aff_raw`, `orgs`, `orgs_current`, `role_norm`, `career_stage`, `areas`, `h_index`, `citations_all`, `citations_5y`, `i10_index`, `last_paper_year`, `profile_at`, `has_profile`, `has_scholar`.

Read

deepxiv fa read talent ID [ID...] [-l brief|detail|full] [-e EXTRA] [-p k=v]... [-f json|md|html]

| level | credits | returns | |---|---|---| | `brief` | 1 | Card: name, affiliation, title, areas, h-index, citations, has_profile (what search hits carry as brief) | | `detail` (default) | 3 | Everything most callers need in one call (the default, level=detail): card + bio, current organisations, education, career, top 5 papers, Scholar metrics, top 5 coauthors, advisors, and the full profile's section map (read one with level=full&section=<name>) | | `full` | 10 | The full profile markdown (level=full; the contact section is omitted unless contact=1) |

| `-e` extra | credits | returns | |---|---|---| | `scholar` | 2 | Structured Google Scholar: metrics (all / 5y), citations by year, top papers, as_of, stale, source. A stale snapshot is rebuilt from cache for free when possible, otherwise queued for background refresh | | `scholar_live` | 20 | Same as scholar but synchronously fetches the latest numbers from Google Scholar (≤10s). Subject to a per-account daily quota (talent:scholar_live) and a global crawl budget; if the refresh does not actually happen you get a 502 and the credits and quota back — use extra=scholar for the stored snapshot | | `papers` | 2 | Representative papers: Scholar top papers + profile top_papers + OpenAlex works, deduplicated by title, sorted by citations | | `network` | 2 | Relations: coauthors (with in-corpus person_id), advisors, students | | `sources` | 1 | The profile's reference list: the [n] numbers used in the report, url, title, confidence, whether the page archived at research time is still readable (snapshot), and which facts it backs (backs: education / affiliation) | | `source` | 2 | The archived page behind one reference (the snapshot taken at research time; no live fetch). n is the [n] in the report; raw=1 returns the original HTML | | `structured` | 3 | Structured profile for section-by-section rendering: education / work with dates (verbatim strings plus parsed start_year / end_year / current), open_source, top_papers, links, bio_md, activities_md, tags, career_stage; every item carries source_ids numbered as in view=sources. No Scholar metrics (live ones are in view=scholar); email only with contact=1; people without a researched profile get 404 (not charged) |

At `-l full`, `-p section=NAME` reads on