paper-search

paper-search

Search OpenAlex + arXiv and rank by impact relative to the paper's own field and age — not raw citation counts, which always favor old papers and always bury the work published last month. No API key, no auth.

scripts/paper_search.py "lost in the middle long context"        # topic search
scripts/paper_search.py "kv cache compression" --since 2025-06   # only recent work
scripts/paper_search.py "agent memory" --fresh 60                # + arXiv, last 60 days
scripts/paper_search.py --after arXiv:2307.03172 --about "position bias"  # what built on it
scripts/paper_search.py "raft consensus" --field any --limit 25  # non-CS, or wider
scripts/paper_search.py --selftest                               # offline; no network

--after takes arXiv:ID, DOI:x, or an OpenAlex Wxxx. --json for machine output; --help for all flags. Set OPENALEX_MAILTO=<your email> for OpenAlex's polite pool.

Reading the output

Papers are bucketed, best first. The buckets are the point — recency and quality are independent axes, and collapsing them into one score hides exactly the tradeoff that matters in a fast-moving field.

Bucket	Means
LANDMARK	cited fast and far above its field — read this first
STRONG	peer-reviewed, comfortably above field average
RISING	recent and being picked up quickly
FRESH+	too new to be cited, but credible venue or authors
OK	real, unremarkable
FRESH?	too new to be cited and unvetted — verify it yourself
THIN	uncited preprint, authors with no track record — usually skip

Each line shows date (age) · citations (velocity) · fwci · [tier] venue · authors h=<max h-index>. FWCI is field-weighted citation impact: 1.0 = exactly the average for that field and year, so it lets a systems paper with 90 citations correctly outrank an LLM paper with 300. [TOP] = top-tier venue (NeurIPS/ICML/ACL/OSDI/SOSP/VLDB/…).

Traps

• Citation counts are a lagging indicator. In AI/infra, the paper that matters may be 6 weeks old with zero citations. Never conclude "nothing exists" from a citation-ranked list — run --fresh 60 before saying a topic is unexplored.
• FWCI is noise below ~18 months — the expected-citation denominator is near zero, so 7 citations can score FWCI 108. The script ignores it below that age; don't reintroduce it by reading the raw number off a young paper.
• Preprint date ≠ publication date. A 2024 TACL paper may be a 2023 arXiv paper; the idea landed 8 months before the venue date, and in a fast field that lead is the story. Output shows preprint YYYY-MM when they differ — cite the earlier one for priority.
• Author credibility is the weakest signal here; don't lean on it. OpenAlex conflates common names ("Kevin Lin": h=75 across 825 works, several people), and h= is the max across authors — the value conflation inflates. Prefer the @ Stanford, Berkeley affiliation beside it. On --fresh arXiv hits both are absent by design: arXiv exposes no institutions, and name lookup is worse than useless ("Feng Wang" → 4,865 authors, so you'd attach a stranger's h-index). That is what FRESH? means — open the PDF.
• A landmark is cited by every field. --after on a famous paper returns medical and legal applications too; pass --about "<keywords>" to keep the frontier on topic.
• --field cs is the default. Pass --field any for anything else, or results look mysteriously empty. Venue metadata is imperfect regardless — a paper published at EMNLP may still read [PREPRINT] arXiv, so trust the citation numbers over the tier label.

Workflow for a fast-moving topic

1. paper_search.py "<topic>" — find the LANDMARK and what is established.
2. paper_search.py --after <landmark-id> --about "<topic>" --since <~12mo ago> — what built on it since, ranked by impact. This is how you avoid citing a superseded result.
3. paper_search.py "<topic>" --fresh 45 — what dropped in the last few weeks, which step 1 structurally cannot see.
4. Read the abstracts, then the two or three papers that actually earned it.