conference-watch

# ConferenceWatch Produce an accurate, well-sourced snapshot of the AI/ML conferences a user cares about — with **every submission deadline stage**, dates, location, special sessions / CFP, official links, and a **5-year acceptance-rate trend**. Two artifacts are always produced: 1. A JSON dataset (one object per conference) — the structured source of truth. 2. A human-readable Markdown report with a short summary. --- ## Guiding principles - **Never invent dates.** A deadline goes into the JSON only if you found it on an official/authoritative source. If you infer from historical patterns, mark it `approximate` and say so explicitly in the report. - **Cite everything.** Every edition and every acceptance-rate number carries a `source_links` entry. Prefer official conference sites and CFP pages; use aggregators (see references) as leads, then confirm on the official page. - **Timezones matter.** Record the raw deadline text (e.g. "May 22, 2026 AoE"). Most AI venues use **AoE (Anywhere on Earth, UTC-12)**. Keep the AoE flag. - **Today is the anchor.** Use the current date to decide which edition is "upcoming" vs "past". Do not assume the model's training-cutoff year. --- ## Workflow ### Step 0 — Establish the anchor date Determine today's date (from the environment/context). All "future vs past" decisions are relative to it. State the anchor date in the report metadata. ### Step 1 — Analyze input & narrow the scope Parse the user's request for any of these filters: - **Area / topic** — e.g. NLP, CV, ML theory, RL, robotics, HCI, systems, data mining, speech, multimodal, AI4Science. - **Reputation / tier** — CORE rank (A*/A/B) or "top-tier only". - **Difficulty** — proxied by acceptance rate (e.g. "only <25% venues"). - **Named conferences** — if the user already lists venues, skip to Step 2. - **Region / timing** — e.g. "conferences with deadlines in the next 6 months", or "held in Europe". If the request is under-specified, **ask 1–3 concise follow-up questions** to narrow it (offer sensible defaults so the user can decline easily). Example: > To focus the search, which of these should I use? > 1. **Area** — all AI, or a subfield (NLP / CV / RL / …)? > 2. **Tier** — top-tier (CORE A*) only, or include A/B venues? > 3. **Horizon** — only deadlines in the next N months, or the full year? > (If you'd rather not specify, I'll default to the top AI venues across areas.) **Default when the user declines or doesn't answer:** the top general AI venues plus the leading venue(s) for any area they mentioned. See [references/conference-catalog.md](references/conference-catalog.md) for the curated tier list to draw from. ### Step 2 — Build the initial conference list (initial JSON) From the filters, assemble the candidate list and **write the initial JSON** using the schema in [references/json-schema.md](references/json-schema.md) and the template [assets/conference-data.template.json](assets/conference-data.template.json). At this stage only `metadata` + conference names/areas/tier need to be filled; edition/deadline/trend fields are placeholders to fill in Step 3–4. Confirm the list with the user briefly ("I'll investigate these N conferences: …") before the (potentially long) search phase, unless they asked you to just go. ### Step 3 — Research each conference: the UPCOMING edition first For each conference, search for the **next (future) edition** relative to the anchor date. Query patterns that work well: - `"<CONF> <year> call for papers"`, `"<CONF> <year> important dates"`, `"<CONF> <year> submission deadline"`, `"<CONF> <year> paper deadline"`. - Then **open the official site / CFP page** to confirm; aggregators can be stale or wrong. When exact info is found, record in the conference's `upcoming_edition`: - `year`, `location` (city, country; note if virtual/hybrid), `venue` if known. - `dates`: conference start/end. - `deadlines`: **every stage** — abstract registration, full paper, supplementary, rebuttal, author response, notification, camera-ready, workshop/tutorial proposals, etc. Keep the raw text + AoE flag + `confirmed: true`. - `special_sessions`: special tracks, new-this-year themes, datasets & benchmarks track, position papers, findings, industry track, journal-to-conference, etc. - `call_for_papers_url` and `website`. - `data_confidence: "confirmed"` and `source_links`. If no future edition is announced yet, leave `upcoming_edition.data_confidence` as `"approximate"` and fill it via inference in Step 4. ### Step 4 — Historical anchor: last 5 editions → trend + inference For each conference, gather the **last 5 editions** (most recent past years): - Populate `acceptance_rate_trend` with `{year, submissions, accepted, acceptance_rate, source}` per year — as many of the 5 as are available. - If the upcoming edition's deadlines were **not** found in Step 3, **infer** the approximate deadline window from the historical pattern (e.g. "abstract deadline has fallen in the third week of May for the last 4 years → est. ~mid-May <next_year>"). Write these into `upcoming_edition.deadlines` with `confirmed: false` and set `data_confidence: "approximate"`. **Always flag inferred dates clearly** in both JSON and report. ### Step 5 — Finalize JSON Write the complete JSON to `conferences.<anchor-date>.json` (or a path the user specifies). Validate it against the schema: every conference has a name, tier, website; every deadline has a stage + confirmed flag; every trend/edition row that carries data also carries a source link. ### Step 6 — Produce outputs 1. **Answer directly** in chat: a compact table or list of the most time-sensitive items (nearest deadlines first), plus the short summary. 2. **Write the Markdown report** using [assets/report.template.md](assets/report.template.md): - A 3–5 sentence **summary** synthesizing what you found (nearest deadlines, notable new tracks, acceptance-rate direction). - A per-conference breakdown (deadlines table, location, special sessions, links, acceptance-rate trend). - A clearly-labeled **"Approximate / inferred"** section for anything not confirmed. - A **Sources** list. --- ## Output file naming - Data: `conferences.<YYYY-MM-DD>.json` - Report: `conference-report.<YYYY-MM-DD>.md` (Use the anchor date so successive runs are comparable / diffable.) ## Quality checklist before finishing - [ ] Anchor date stated; upcoming vs past decided against it. - [ ] Every confirmed deadline has a source link; timezone/AoE captured. - [ ] Inferred/approximate items are flagged in **both** JSON and report. - [ ] Acceptance-rate trend covers up to 5 recent years with sources. - [ ] JSON validates against the schema; report summary written. - [ ] Missing data is shown as `null` with a note — never fabricated.