Data sources:
- arXiv preprints (via
arxivpackage + Atom API fallback) - ACL Anthology (search API + venue JSON fallback)
Intelligence pipeline:
- Normalised Paper/Author/Lab/University schema
- Near-duplicate detection (Jaccard title similarity)
- Keyword-based topic tagging (25+ topics)
- Paper type detection (survey, benchmark, dataset, methods, …)
- L1–L4 difficulty classification with plain-English reason
- 4-score system: paper_score, read_first_score, content_potential, momentum
- Score breakdowns + transparent reasoning
- Author aggregation with momentum scoring
- Lab / industry aggregation
- University aggregation
- 3-layer research gap engine (explicit, pattern, starter)
- Creator content generation (tweet, LinkedIn, newsletter, video script)
- Daily editorial queue
Frontend:
- Homepage with stats, top papers, editorial digest
- Papers page (filterable, sortable, score breakdown)
- Topics page (reading paths, starter/frontier packs)
- Research Gaps page (3 gap types, starter ideas)
- Authors page (momentum, topics, profile)
- Labs & Universities page (output, momentum)
- Conferences page
- Dark mode
Infrastructure:
- GitHub Actions daily pipeline
- GitHub Pages static deployment
- Config-driven weights and venues
- OpenReview connector — ICLR, NeurIPS, ICML, COLM with oral/spotlight/poster acceptance-tier signals (feeds
quality_hint; oral/spotlight surfaced as paper badges) - PMLR connector — ICML proceedings
- Per-conference explorer pages with theme detection
- Best paper award tracking
- Acceptance rate context per venue
- Conference comparison view
- Sentence-transformer embeddings for semantic dedup and clustering
- Replace Jaccard with cosine similarity
- Better topic clustering beyond keywords
- Citation network via Semantic Scholar API
- Real citation counts
- Influential citations
- Paper ancestry / descendant graphs
- Paper lifecycle tracking
- Link arXiv preprint → accepted conference version
- "First seen / accepted at" timeline
- Maturity stage auto-classification
- Optional LLM-powered enrichment stage (requires API key)
- Better summaries from full PDF text
- Richer "why it matters" explanations
- Automatic limitations extraction from PDFs
- PDF parsing via
pymupdf4llmor similar - Preserve rule-based fallback when no API key is set
- Curated reading paths per topic (hand-edited + auto-generated)
- Prerequisites graph visualisation
- "Start here" vs "Go deeper" track separation
- Paper difficulty quiz / self-assessment
- User reading history (localStorage)
- Creator dashboard — per-paper content package download
- Email newsletter template export
- Slides outline generator
- "Explain to non-expert" generator
- Topic subscriptions (RSS feed per topic)
- Custom watchlists (stored in localStorage)
- "Papers like this one" recommendations
- Author follow + alert
| Conference | Area | Status |
|---|---|---|
| ICLR | ML | Planned (v0.2) |
| NeurIPS | ML | Planned (v0.2) |
| ICML | ML | Planned (v0.2) |
| COLM | LM | Planned (v0.2) |
| CVPR | CV | Planned |
| ICCV | CV | Planned |
| ECCV | CV | Planned |
| CHI | HCI | Planned |
| SIGIR | IR | Planned |
| AAAI | AI | Planned |
| IJCAI | AI | Planned |
| WWW | Web | Planned |
- Affiliation extraction is heuristic (keyword matching) — no ground-truth author-institution database
- Deduplication uses title Jaccard similarity — may miss paraphrased titles
- All tagging and difficulty are rule-based — LLM enrichment planned for v0.4
- ACL search API availability is not guaranteed — falls back gracefully to empty
- No persistent state between pipeline runs (each run is a full refresh)