---
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}
}
```