| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - image-text-to-text |
| - image-classification |
| language: |
| - en |
| tags: |
| - agentic |
| - tool-use |
| - bird-identification |
| - fine-grained-recognition |
| - benchmark |
| - calibration |
| - abstention |
| pretty_name: BirdAgent Agentic & Calibration Benchmarks |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: agentic |
| data_files: agentic.jsonl |
| - config_name: calibration |
| data_files: calibration.jsonl |
| --- |
| |
| # BirdAgent benchmarks — agentic & calibration |
|
|
| 📄 [Paper (under review)](https://github.com/xinzhuwang-wxz/Bird-Agent) · |
| 💻 [Code](https://github.com/xinzhuwang-wxz/Bird-Agent) · |
| 🧠 [Model `Chinzhu/BirdAgent-Qwen3VL-4B`](https://huggingface.co/Chinzhu/BirdAgent-Qwen3VL-4B) · |
| 🏋️ [Training data `Chinzhu/BirdAgent-Train`](https://huggingface.co/datasets/Chinzhu/BirdAgent-Train) |
|
|
| > These are the held-out **evaluation** sets. For the **training** data |
| > (SFT / DPO / GRPO trajectories) see |
| > [`Chinzhu/BirdAgent-Train`](https://huggingface.co/datasets/Chinzhu/BirdAgent-Train). |
|
|
| Two open, tier-stratified benchmarks for **agentic, tool-orchestrating** bird |
| identification. Unlike monolithic recognition sets, these isolate |
| *orchestration*: every item is built so that a strong single-image model must |
| lose, and the deciding evidence is reachable only by *calling the right tool*. |
|
|
| - **`agentic`** — **500** items, each with an engineered **information gap** |
| (scenarios S1–S4 below) that a tool-free model cannot close. Every item's |
| minimal necessary tool set (`mnts`, 1–2 tools) is hand-verified |
| (`mnts_confirmed = true`). v0 items are **single-gap** (one deciding tool); |
| multi-gap and fault-injection layers are left to a future release. |
| - **`calibration`** — **400** items, testing *when to commit* to a species vs. |
| degrade to genus/family or abstain. Each item carries a **`reachability`** |
| label — whether the true species is actually within a tool's top-*k* — which |
| makes the **tool ceiling** explicit: **303/400 (76%)** are `truth_missing` |
| (no tool can surface the truth), the rest `top1_high` / `mid` / `top1_low`. |
|
|
| Both sets are stratified by frequency tier (common / uncommon / rare): |
| agentic ≈ 181 / 178 / 141, calibration ≈ 134 / 133 / 133. |
|
|
| > **The two splits use different schemas** (the agentic set is scenario-based; |
| > the calibration set is reachability-based) — see the field tables below. |
|
|
| ## Fields |
|
|
| **Common to both splits:** `id` · `modality` (`image` / `sound` / `image_sound`) |
| · `query` · `mnts` (minimal necessary tool set) · `freq_tier` |
| (`common`/`uncommon`/`rare`) · `geo_band` · `true_species_ebird` (eBird code — |
| **scoring only, not shown to the model**) · `image` (iNaturalist URL, `null` |
| for sound-only) · `audio` (Xeno-canto URL, `null` for image-only). |
|
|
| **`agentic` only:** |
|
|
| | field | meaning | |
| |---|---| |
| | `scenario` | S1 look-alike (voice-diagnostic) · S2 range/season prior · S3 poor image quality · S4 multi-bird / clutter | |
| | `difficulty` | `L1` (single-gap; the only layer in v0) | |
| | `mnts_confirmed` | MNTS hand-verified (always `true` here) | |
| | `privileged_geo` | true `{lat, lon, date}` — **held out of the prompt**; the model must call `geo_prior` to use location | |
| | `cf_geo` | a **counterfactual** `{lat, lon}` for the anti-memorization probe (a model that truly uses the tool changes its verdict) | |
|
|
| **`calibration` only:** |
|
|
| | field | meaning | |
| |---|---| |
| | `purpose` | `"calibration"` | |
| | `reachability` | is the truth reachable through a tool? `top1_high` · `mid` · `top1_low` · `truth_missing` | |
| | `lat`, `lon`, `date` | observation geo/date (scoring / tool input; keep out of the prompt) | |
|
|
| ## Scenarios (agentic set) |
|
|
| | scenario | information gap | required orchestration | |
| |---|---|---| |
| | **S1** look-alike, voice-diagnostic | near-identical plumage (e.g. *Empidonax*) | `classify_sound` on the diagnostic call | |
| | **S2** range/season prior | candidate looks right but is implausible here/now | `geo_prior(lat,lon,date)` to down-weight | |
| | **S3** poor image quality | distant / blurry / backlit; field marks hidden | `quality_gate` → `zoom`/`enhance` → reclassify | |
| | **S4** multi-bird / clutter | several birds or a tiny subject | `detect_bird` → `crop` → classify the isolate | |
|
|
| Each scenario has 125 agentic items. **Abstention / out-of-distribution** |
| behavior is tested by the separate **calibration** set (via `reachability` and |
| the option to degrade or abstain), not as an agentic scenario. |
|
|
| ## Protocol (important) |
|
|
| - **Privileged information is never shown.** GPS, date, and the true taxon are |
| used only to select and score. A model that wants a range prior must call the |
| `geo_prior` tool — this is what separates *orchestrates* from *was fed the |
| answer*. |
| - **Identical-harness comparison.** Score every model through one identical tool |
| harness (same signatures, return schemas, call budget). Conditions: **C0** |
| bare · **N** vendor web search · **C1** our exact tools, no orchestration |
| training · **C2** a trained agent · **T0** image→`classify_image` (recognition |
| floor). |
| - **Primary metric: `solve`** — correct at the *declared* grain (a genus verdict |
| counts iff the genus is right; over-committed species are penalized). Also |
| report species-declaration precision, overclaim, and mean calls. |
| - **De-leaking.** Items favor post-cutoff observations, perceptual-hash |
| de-duplicated, media-ID de-leaked against the recognizers' pre-training pools. |
|
|
| ## Media |
|
|
| Raw media bytes are **not** redistributed. Each item carries its source `image` |
| (iNaturalist open-data) and/or `audio` (Xeno-canto) **URL**; fetch them yourself, |
| e.g.: |
|
|
| ```python |
| import json, requests |
| for line in open("agentic.jsonl"): |
| it = json.loads(line) |
| if it["image"]: |
| img = requests.get(it["image"]).content # iNaturalist photo |
| if it["audio"]: |
| wav = requests.get(it["audio"]).content # Xeno-canto recording |
| ``` |
|
|
| Media licenses follow their sources (citizen-science; many are non-commercial), |
| hence the `cc-by-nc-4.0` label for this annotation package. Taxonomy is on the |
| eBird/Clements crosswalk. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{wang2026birdagent, |
| title = {BirdAgent: A Small Vision--Language Model that Orchestrates |
| Domain Tools Beats Large Models that Merely Hold Them}, |
| author = {Wang, Xinzhu}, |
| booktitle = {Under review}, |
| year = {2026} |
| } |
| ``` |
|
|
