Duplicate from evaleval/EEE_datastore

Co-authored-by: Sree Harsha Nelaturu <deepmage121@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.avro filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
+*_samples.jsonl filter=lfs diff=lfs merge=lfs -text
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+flat/latest_manifest.json text -filter -diff -merge
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.gitignore

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+.DS_Store
+pyproject.toml

README.md

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+---
+configs:
+  - config_name: ace
+    data_files:
+      - split: sample
+        path: viewer_parquets/ace/dataset.parquet
+  - config_name: apex-agents
+    data_files:
+      - split: sample
+        path: viewer_parquets/apex-agents/dataset.parquet
+  - config_name: apex-v1
+    data_files:
+      - split: sample
+        path: viewer_parquets/apex-v1/dataset.parquet
+  - config_name: appworld_test_normal
+    data_files:
+      - split: sample
+        path: viewer_parquets/appworld_test_normal/dataset.parquet
+  - config_name: browsecompplus
+    data_files:
+      - split: sample
+        path: viewer_parquets/browsecompplus/dataset.parquet
+  - config_name: global-mmlu-lite
+    data_files:
+      - split: sample
+        path: viewer_parquets/global-mmlu-lite/dataset.parquet
+  - config_name: helm_capabilities
+    data_files:
+      - split: sample
+        path: viewer_parquets/helm_capabilities/dataset.parquet
+  - config_name: helm_classic
+    data_files:
+      - split: sample
+        path: viewer_parquets/helm_classic/dataset.parquet
+  - config_name: helm_instruct
+    data_files:
+      - split: sample
+        path: viewer_parquets/helm_instruct/dataset.parquet
+  - config_name: helm_lite
+    data_files:
+      - split: sample
+        path: viewer_parquets/helm_lite/dataset.parquet
+  - config_name: helm_mmlu
+    data_files:
+      - split: sample
+        path: viewer_parquets/helm_mmlu/dataset.parquet
+  - config_name: hfopenllm_v2
+    data_files:
+      - split: sample
+        path: viewer_parquets/hfopenllm_v2/dataset.parquet
+  - config_name: livecodebenchpro
+    data_files:
+      - split: sample
+        path: viewer_parquets/livecodebenchpro/dataset.parquet
+  - config_name: reward-bench
+    data_files:
+      - split: sample
+        path: viewer_parquets/reward-bench/dataset.parquet
+  - config_name: swe-bench
+    data_files:
+      - split: sample
+        path: viewer_parquets/swe-bench/dataset.parquet
+  - config_name: tau-bench-2_airline
+    data_files:
+      - split: sample
+        path: viewer_parquets/tau-bench-2_airline/dataset.parquet
+  - config_name: tau-bench-2_retail
+    data_files:
+      - split: sample
+        path: viewer_parquets/tau-bench-2_retail/dataset.parquet
+  - config_name: tau-bench-2_telecom
+    data_files:
+      - split: sample
+        path: viewer_parquets/tau-bench-2_telecom/dataset.parquet
+  - config_name: arc-agi
+    data_files:
+      - split: sample
+        path: viewer_parquets/arc-agi/dataset.parquet
+  - config_name: bfcl
+    data_files:
+      - split: sample
+        path: viewer_parquets/bfcl/dataset.parquet
+  - config_name: fibble1_arena
+    data_files:
+      - split: sample
+        path: viewer_parquets/fibble1_arena/dataset.parquet
+  - config_name: fibble2_arena
+    data_files:
+      - split: sample
+        path: viewer_parquets/fibble2_arena/dataset.parquet
+  - config_name: fibble3_arena
+    data_files:
+      - split: sample
+        path: viewer_parquets/fibble3_arena/dataset.parquet
+  - config_name: fibble4_arena
+    data_files:
+      - split: sample
+        path: viewer_parquets/fibble4_arena/dataset.parquet
+  - config_name: fibble5_arena
+    data_files:
+      - split: sample
+        path: viewer_parquets/fibble5_arena/dataset.parquet
+  - config_name: fibble_arena
+    data_files:
+      - split: sample
+        path: viewer_parquets/fibble_arena/dataset.parquet
+  - config_name: la_leaderboard
+    data_files:
+      - split: sample
+        path: viewer_parquets/la_leaderboard/dataset.parquet
+  - config_name: sciarena
+    data_files:
+      - split: sample
+        path: viewer_parquets/sciarena/dataset.parquet
+  - config_name: theory_of_mind
+    data_files:
+      - split: sample
+        path: viewer_parquets/theory_of_mind/dataset.parquet
+  - config_name: wordle_arena
+    data_files:
+      - split: sample
+        path: viewer_parquets/wordle_arena/dataset.parquet
+  - config_name: alphaxiv
+    data_files:
+      - split: sample
+        path: viewer_parquets/alphaxiv/dataset.parquet
+  - config_name: artificial-analysis-llms
+    data_files:
+      - split: sample
+        path: viewer_parquets/artificial-analysis-llms/dataset.parquet
+  - config_name: caparena-auto
+    data_files:
+      - split: sample
+        path: viewer_parquets/caparena-auto/dataset.parquet
+  - config_name: cocoabench
+    data_files:
+      - split: sample
+        path: viewer_parquets/cocoabench/dataset.parquet
+  - config_name: facts-grounding
+    data_files:
+      - split: sample
+        path: viewer_parquets/facts-grounding/dataset.parquet
+  - config_name: gpqa-diamond
+    data_files:
+      - split: sample
+        path: viewer_parquets/gpqa-diamond/dataset.parquet
+  - config_name: hal-assistantbench
+    data_files:
+      - split: sample
+        path: viewer_parquets/hal-assistantbench/dataset.parquet
+  - config_name: hal-corebench-hard
+    data_files:
+      - split: sample
+        path: viewer_parquets/hal-corebench-hard/dataset.parquet
+  - config_name: hal-gaia
+    data_files:
+      - split: sample
+        path: viewer_parquets/hal-gaia/dataset.parquet
+  - config_name: hal-online-mind2web
+    data_files:
+      - split: sample
+        path: viewer_parquets/hal-online-mind2web/dataset.parquet
+  - config_name: hal-scicode
+    data_files:
+      - split: sample
+        path: viewer_parquets/hal-scicode/dataset.parquet
+  - config_name: hal-scienceagentbench
+    data_files:
+      - split: sample
+        path: viewer_parquets/hal-scienceagentbench/dataset.parquet
+  - config_name: hal-swebench-verified-mini
+    data_files:
+      - split: sample
+        path: viewer_parquets/hal-swebench-verified-mini/dataset.parquet
+  - config_name: hal-taubench-airline
+    data_files:
+      - split: sample
+        path: viewer_parquets/hal-taubench-airline/dataset.parquet
+  - config_name: hal-usaco
+    data_files:
+      - split: sample
+        path: viewer_parquets/hal-usaco/dataset.parquet
+  - config_name: helm_safety
+    data_files:
+      - split: sample
+        path: viewer_parquets/helm_safety/dataset.parquet
+  - config_name: judgebench
+    data_files:
+      - split: sample
+        path: viewer_parquets/judgebench/dataset.parquet
+  - config_name: live_bench
+    data_files:
+      - split: sample
+        path: viewer_parquets/live_bench/dataset.parquet
+  - config_name: llm-stats
+    data_files:
+      - split: sample
+        path: viewer_parquets/llm-stats/dataset.parquet
+  - config_name: mmlu-pro
+    data_files:
+      - split: sample
+        path: viewer_parquets/mmlu-pro/dataset.parquet
+  - config_name: multi-swe-bench-leaderboard
+    data_files:
+      - split: sample
+        path: viewer_parquets/multi-swe-bench-leaderboard/dataset.parquet
+  - config_name: openeval
+    data_files:
+      - split: sample
+        path: viewer_parquets/openeval/dataset.parquet
+  - config_name: swe-bench-verified-leaderboard
+    data_files:
+      - split: sample
+        path: viewer_parquets/swe-bench-verified-leaderboard/dataset.parquet
+  - config_name: swe-bench-verified-mini
+    data_files:
+      - split: sample
+        path: viewer_parquets/swe-bench-verified-mini/dataset.parquet
+  - config_name: swe-polybench-leaderboard
+    data_files:
+      - split: sample
+        path: viewer_parquets/swe-polybench-leaderboard/dataset.parquet
+  - config_name: vals-ai
+    data_files:
+      - split: sample
+        path: viewer_parquets/vals-ai/dataset.parquet
+  - config_name: GAIA
+    data_files:
+      - split: sample
+        path: viewer_parquets/GAIA/dataset.parquet
+  - config_name: IFEval
+    data_files:
+      - split: sample
+        path: viewer_parquets/IFEval/dataset.parquet
+  - config_name: MMMU-Multiple-Choice
+    data_files:
+      - split: sample
+        path: viewer_parquets/MMMU-Multiple-Choice/dataset.parquet
+  - config_name: MMMU-Open-Ended
+    data_files:
+      - split: sample
+        path: viewer_parquets/MMMU-Open-Ended/dataset.parquet
+  - config_name: MathVista
+    data_files:
+      - split: sample
+        path: viewer_parquets/MathVista/dataset.parquet
+  - config_name: agentharm
+    data_files:
+      - split: sample
+        path: viewer_parquets/agentharm/dataset.parquet
+  - config_name: big_bench_hard
+    data_files:
+      - split: sample
+        path: viewer_parquets/big_bench_hard/dataset.parquet
+  - config_name: commonsense_qa
+    data_files:
+      - split: sample
+        path: viewer_parquets/commonsense_qa/dataset.parquet
+  - config_name: cvebench
+    data_files:
+      - split: sample
+        path: viewer_parquets/cvebench/dataset.parquet
+  - config_name: cybench
+    data_files:
+      - split: sample
+        path: viewer_parquets/cybench/dataset.parquet
+  - config_name: cyse2_interpreter_abuse
+    data_files:
+      - split: sample
+        path: viewer_parquets/cyse2_interpreter_abuse/dataset.parquet
+  - config_name: cyse2_prompt_injection
+    data_files:
+      - split: sample
+        path: viewer_parquets/cyse2_prompt_injection/dataset.parquet
+  - config_name: cyse2_vulnerability_exploit
+    data_files:
+      - split: sample
+        path: viewer_parquets/cyse2_vulnerability_exploit/dataset.parquet
+  - config_name: gdm_intercode_ctf
+    data_files:
+      - split: sample
+        path: viewer_parquets/gdm_intercode_ctf/dataset.parquet
+  - config_name: gpqa_diamond
+    data_files:
+      - split: sample
+        path: viewer_parquets/gpqa_diamond/dataset.parquet
+  - config_name: gsm-mc
+    data_files:
+      - split: sample
+        path: viewer_parquets/gsm-mc/dataset.parquet
+  - config_name: gsm8k
+    data_files:
+      - split: sample
+        path: viewer_parquets/gsm8k/dataset.parquet
+  - config_name: hellaswag
+    data_files:
+      - split: sample
+        path: viewer_parquets/hellaswag/dataset.parquet
+  - config_name: helm_air_bench
+    data_files:
+      - split: sample
+        path: viewer_parquets/helm_air_bench/dataset.parquet
+  - config_name: hle
+    data_files:
+      - split: sample
+        path: viewer_parquets/hle/dataset.parquet
+  - config_name: journalistic-bias
+    data_files:
+      - split: sample
+        path: viewer_parquets/journalistic-bias/dataset.parquet
+  - config_name: math-mc
+    data_files:
+      - split: sample
+        path: viewer_parquets/math-mc/dataset.parquet
+  - config_name: mbpp
+    data_files:
+      - split: sample
+        path: viewer_parquets/mbpp/dataset.parquet
+  - config_name: mt-bench
+    data_files:
+      - split: sample
+        path: viewer_parquets/mt-bench/dataset.parquet
+  - config_name: openai_humaneval
+    data_files:
+      - split: sample
+        path: viewer_parquets/openai_humaneval/dataset.parquet
+  - config_name: piqa
+    data_files:
+      - split: sample
+        path: viewer_parquets/piqa/dataset.parquet
+  - config_name: reward-bench-2
+    data_files:
+      - split: sample
+        path: viewer_parquets/reward-bench-2/dataset.parquet
+  - config_name: terminal-bench-2.0
+    data_files:
+      - split: sample
+        path: viewer_parquets/terminal-bench-2.0/dataset.parquet
+  - config_name: wmt25_bhojpuri_maasai
+    data_files:
+      - split: sample
+        path: viewer_parquets/wmt25_bhojpuri_maasai/dataset.parquet
+license: mit
+---
+
+# Every Eval Ever Datastore
+
+This is the datastore for the [Every Eval Ever](https://evalevalai.com/projects/every-eval-ever/) project. The readme from the project [GitHub](https://github.com/evaleval/every_eval_ever) is below. It describes how to submit new benchmarks and evals to this dataset.
+
+> [EvalEval Coalition](https://evalevalai.com) — "We are a researcher community developing scientifically grounded research outputs and robust deployment infrastructure for broader impact evaluations."
+
+**Every Eval Ever** is a shared schema and crowdsourced eval database. It defines a standardized metadata format for storing AI evaluation results — from leaderboard scrapes and research papers to local evaluation runs — so that results from different frameworks can be compared, reproduced, and reused. The three components that make it work:
+
+- 📋 **A metadata schema** ([`eval.schema.json`](https://github.com/evaleval/every_eval_ever/blob/main/every_eval_ever/schemas/eval.schema.json)) that defines the information needed for meaningful comparison of evaluation results, including [instance-level data](https://github.com/evaleval/every_eval_ever/blob/main/instance_level_eval.schema.json)
+- 🔧 **Validation** that checks data against the schema before it enters the repository
+- 🔌 **Converters** for [Inspect AI](https://github.com/evaleval/every_eval_ever/tree/main/every_eval_ever/converters/inspect), [HELM](https://github.com/evaleval/every_eval_ever/tree/main/every_eval_ever/converters/helm), and [lm-eval-harness](https://github.com/evaleval/every_eval_ever/tree/main/every_eval_ever/converters/lm_eval), so you can transform your existing evaluation logs into the standard format
+
+## Flat datastore view
+
+The canonical datastore view is being migrated to a flat, manifest-indexed layout under [`flat/`](flat/). The legacy [`data/`](data/) tree is retained for compatibility and for human review, but the flat layout is the intended durable representation for immutable record links and versioned datastore snapshots.
+
+```text
+flat/
+  objects/
+    <uuid[0:2]>/
+      <uuid[2:4]>/
+        <uuid>.json
+        <uuid>_samples.jsonl
+  latest_manifest.json
+  manifests/
+    sha256_<manifest_core_hash>/
+      manifest.json
+      entries.jsonl
+  indexes/
+    by_collection/
+      <collection>.jsonl
+    by_legacy_path.jsonl
+```
+
+- [`flat/objects/`](flat/objects/) contains immutable record objects. Aggregate results are stored as `<uuid>.json`. Companion instance-level files, when present, are stored as `<uuid>_samples.jsonl`. Objects are physically sharded by UUID prefix (`flat/objects/<uuid[0:2]>/<uuid[2:4]>/...`) to keep repository directories small, but object identity is still the UUID.
+- [`flat/latest_manifest.json`](flat/latest_manifest.json) is the descriptor for the latest datastore version. It contains record, benchmark, and file counts, hashes, timestamps, eval schema versions, and pointers to the versioned manifest files. `eval_schema_versions` comes from the source eval JSON records.
+- [`flat/manifests/`](flat/manifests/) stores immutable historical snapshots. Each version has a small `manifest.json` descriptor and an `entries.jsonl` file listing every current aggregate object, its hash, benchmark, legacy path, instance-level availability, and optional instance-level companion.
+- [`flat/indexes/by_collection/`](flat/indexes/by_collection/) contains current collection indexes. Each collection has one generated `<collection>.jsonl` file with aggregate object metadata and, when available, `instance_level_path`, `instance_sha`, and `instance_level_size_bytes`.
+- [`flat/indexes/by_legacy_path.jsonl`](flat/indexes/by_legacy_path.jsonl) maps the current flat objects back to the legacy `data/...` paths so the original folder structure can be reconstructed.
+
+For Hugging Face Dataset usage, treat each benchmark as a logical subset/config, even though the canonical objects are flat rather than nested under benchmark folders. Each benchmark subset has two logical splits:
+
+| Split | Rows |
+|---|---|
+| `aggregate` | One row per aggregate result JSON object (`flat/objects/<uuid[0:2]>/<uuid[2:4]>/<uuid>.json`). |
+| `instance_level` | One row per instance-level JSONL record from companion files (`flat/objects/<uuid[0:2]>/<uuid[2:4]>/<uuid>_samples.jsonl`), linked back to its aggregate `object_uuid`. |
+
+The benchmark subset membership comes from the `benchmark` field in the versioned `entries.jsonl` and the generated [`flat/indexes/by_collection/*.jsonl`](flat/indexes/by_collection/) indexes. Use [`flat/latest_manifest.json`](flat/latest_manifest.json) to find the current `entries.jsonl`, or a directory under [`flat/manifests/`](flat/manifests/) to reproduce a historical datastore version.
+
+### Terminology
+
+| Term | Our Definition | Example |
+|---|---|---|
+| **Single Benchmark** | Standardized eval using one dataset to test a single capability, producing one score | MMLU — ~15k multiple-choice QA across 57 subjects |
+| **Composite Benchmark** | A collection of simple benchmarks aggregated into one overall score, testing multiple capabilities at once | BIG-Bench bundles >200 tasks with a single aggregate score |
+| **Metric** | Any numerical or categorical value used to score performance on a benchmark (accuracy, F1, precision, recall, …) | A model scores 92% accuracy on MMLU |
+
+## 🚀 Contributor Guide
+New data can be contributed to the [Hugging Face Dataset](https://huggingface.co/datasets/evaleval/EEE_datastore) using the following process:
+
+Leaderboard/evaluation data is split-up into files by individual model, and data for each model is stored using [`eval.schema.json`](https://github.com/evaleval/every_eval_ever/blob/main/eval.schema.json). The repository is structured into folders as `data/{benchmark_name}/{developer_name}/{model_name}/`.
+
+### TL;DR How to successfully submit
+
+1. Data must conform to [`eval.schema.json`](https://github.com/evaleval/every_eval_ever/blob/main/every_eval_ever/schemas/eval.schema.json) (current version: `0.2.0`)
+2. Validation runs automatically on every PR via a validation script
+3. An EvalEval member will review and merge your submission
+
+### UUID Naming Convention
+
+Each JSON file is named with a **UUID (Universally Unique Identifier)** in the format `{uuid}.json`. The UUID is automatically generated (using standard UUID v4) when creating a new evaluation result file. This ensures that:
+- **Multiple evaluations** of the same model can exist without conflicts (each gets a unique UUID)
+- **Different timestamps** are stored as separate files with different UUIDs (not as separate folders)
+- A model may have multiple result files, with each file representing different iterations or runs of the leaderboard/evaluation
+- UUID's can be generated using Python's `uuid.uuid4()` function.
+
+**Example**: The model `openai/gpt-4o-2024-11-20` might have multiple files like:
+- `e70acf51-30ef-4c20-b7cc-51704d114d70.json` (evaluation run #1)
+- `a1b2c3d4-5678-90ab-cdef-1234567890ab.json` (evaluation run #2)
+
+Note: Each file can contain multiple individual results related to one model. See [examples in /data](data/).
+
+### How to add new eval:
+
+1. Add a new folder under [`data/`](https://huggingface.co/datasets/evaleval/EEE_datastore/tree/main/data) with a codename for your eval.
+2. For each model, use the HuggingFace (`developer_name/model_name`) naming convention to create a 2-tier folder structure.
+3. Add a JSON file with results for each model and name it `{uuid}.json`.
+4. [Optional] Include a [`utils/`](https://github.com/evaleval/every_eval_ever/tree/main/utils) folder in your benchmark name folder with any scripts used to generate the data (see e.g. [`utils/global-mmlu-lite/adapter.py`](https://github.com/evaleval/every_eval_ever/blob/main/utils/global-mmlu-lite/adapter.py)).
+5. [Validate] Validation runs automatically to check JSON files against the schema before merging.
+6. [Submit] Two ways to submit your evaluation data:
+   - **Option A: Drag & drop via Hugging Face** — Go to [evaleval/EEE_datastore](https://huggingface.co/datasets/evaleval/EEE_datastore) → click "Files and versions" → "Contribute" → "Upload files" → drag and drop your data → select "Open as a pull request to the main branch". See [step-by-step screenshots](https://docs.google.com/document/d/1dxTQF8ncGCzaAOIj0RX7E9Hg4THmUBzezDOYUp_XdCY/edit?usp=sharing).
+   - **Option B: Clone & PR** — Clone the [GitHub repository](https://github.com/evaleval/every_eval_ever), add your data under `data/`, and open a pull request
+
+### Schema Instructions
+
+1. **`model_info`**: Use HuggingFace formatting (`developer_name/model_name`). If a model does not come from HuggingFace, use the exact API reference. Check [examples in /data/livecodebenchpro](data/livecodebenchpro/). Notably, some do have a **date included in the model name**, but others **do not**. For example:
+- OpenAI: `gpt-4o-2024-11-20`, `gpt-5-2025-08-07`, `o3-2025-04-16`
+- Anthropic: `claude-3-7-sonnet-20250219`, `claude-3-sonnet-20240229`
+- Google: `gemini-2.5-pro`, `gemini-2.5-flash`
+- xAI (Grok): `grok-2-2024-08-13`, `grok-3-2025-01-15`
+2. **`evaluation_id`**: Use `{benchmark_name/model_id/retrieved_timestamp}` format (e.g. `livecodebenchpro/qwen3-235b-a22b-thinking-2507/1760492095.8105888`).
+
+3. **`inference_platform`** vs **`inference_engine`**: Where possible specify where the evaluation was run using one of these two fields.
+- `inference_platform`: Use this field when the evaluation was run through a remote API (e.g., `openai`, `huggingface`, `openrouter`, `anthropic`, `xai`).
+- `inference_engine`: Use this field when the evaluation was run locally. This is now an object with `name` and `version` (e.g. `{"name": "vllm", "version": "0.6.0"}`).
+
+4. The `source_type` on `source_metadata` has two options: `documentation` and `evaluation_run`. Use `documentation` when results are scraped from a leaderboard or paper. Use `evaluation_run` when the evaluation was run locally (e.g. via an eval converter).
+
+5. **`source_data`** is specified per evaluation result (inside `evaluation_results`), with three variants:
+- `source_type: "url"` — link to a web source (e.g. leaderboard API)
+- `source_type: "hf_dataset"` — reference to a HuggingFace dataset (e.g. `{"hf_repo": "google/IFEval"}`)
+- `source_type: "other"` — for private or proprietary datasets
+6. The schema is designed to accommodate both numeric and level-based (e.g. Low, Medium, High) metrics. For level-based metrics, the actual 'value' should be converted to an integer (e.g. Low = 1, Medium = 2, High = 3), and the `level_names` property should be used to specify the mapping of levels to integers.
+7. **Timestamps**: The schema has three timestamp fields — use them as follows:
+- `retrieved_timestamp` (required) — when this record was created, in Unix epoch format (e.g. `1760492095.8105888`)
+- `evaluation_timestamp` (top-level, optional) — when the evaluation was run
+- `evaluation_results[].evaluation_timestamp` (per-result, optional) — when a specific evaluation result was produced, if different results were run at different times
+8. Additional details can be provided in several places in the schema. They are not required, but can be useful for detailed analysis.
+- `model_info.additional_details`: Use this field to provide any additional information about the model itself (e.g. number of parameters)
+- `evaluation_results.generation_config.generation_args`: Specify additional arguments used to generate outputs from the model
+- `evaluation_results.generation_config.additional_details`: Use this field to provide any additional information about the evaluation process that is not captured elsewhere
+### Instance-Level Data
+For evaluations that include per-sample results, the individual results should be stored in a companion `{uuid}.jsonl` file in the same folder (one JSONL per JSON, sharing the same UUID). The aggregate JSON file refers to its JSONL via the `detailed_evaluation_results` field. The instance-level schema ([`instance_level_eval.schema.json`](https://github.com/evaleval/every_eval_ever/blob/main/every_eval_ever/schemas/instance_level_eval.schema.json)) supports three interaction types:
+- **`single_turn`**: Standard QA, MCQ, classification — uses `output` object
+- **`multi_turn`**: Conversational evaluations with multiple exchanges — uses `interactions` array
+- **`agentic`**: Tool-using evaluations with function calls and sandbox execution — uses `interactions` array with `tool_calls`
+Each instance captures: `input` (raw question + reference answer), `answer_attribution` (how the answer was extracted), `evaluation` (score, is_correct), and optional `token_usage` and `performance` metrics. Instance-level JSONL files are produced automatically by the [eval converters](https://github.com/evaleval/every_eval_ever/tree/main/every_eval_ever/converters).
+Example `single_turn` instance:
+```json
+{
+  "schema_version": "instance_level_eval_0.2.0",
+  "evaluation_id": "math_eval/meta-llama/Llama-2-7b-chat/1706000000",
+  "model_id": "meta-llama/Llama-2-7b-chat",
+  "evaluation_name": "math_eval",
+  "sample_id": 4,
+  "interaction_type": "single_turn",
+  "input": { "raw": "If 2^10 = 4^x, what is the value of x?", "reference": "5" },
+  "output": { "raw": "Rewrite 4 as 2^2, so 4^x = 2^(2x). Since 2^10 = 2^(2x), x = 5." },
+  "answer_attribution": [{ "source": "output.raw", "extracted_value": "5" }],
+  "evaluation": { "score": 1.0, "is_correct": true }
+}
+```
+### Agentic Evaluations
+For agentic evaluations (e.g., SWE-Bench, GAIA), the aggregate schema captures configuration under `generation_config.generation_args`:
+```json
+{
+  "agentic_eval_config": {
+    "available_tools": [
+      {"name": "bash", "description": "Execute shell commands"},
+      {"name": "edit_file", "description": "Edit files in the repository"}
+    ]
+  },
+  "eval_limits": {"message_limit": 30, "token_limit": 100000},
+  "sandbox": {"type": "docker", "config": "compose.yaml"}
+}
+```
+At the instance level, agentic evaluations use `interaction_type: "agentic"` with full tool call traces recorded in the `interactions` array. See the [Inspect AI test fixture](https://github.com/evaleval/every_eval_ever/tree/main/tests/data/inspect) for a GAIA example with docker sandbox and tool usage.
+## ✅ Data Validation
+This repository has a pre-commit that will validate that JSON files conform to the JSON schema. The pre-commit requires using [uv](https://docs.astral.sh/uv/) for dependency management.
+To run the pre-commit on git staged files only:
+```sh
+uv run pre-commit run
+```
+To run the pre-commit on all files:
+```sh
+uv run pre-commit run --all-files
+```
+To run the pre-commit on specific files:
+```sh
+uv run pre-commit run --files a.json b.json c.json
+```
+To install the pre-commit so that it will run before `git commit` (optional):
+```sh
+uv run pre-commit install
+```
+## 🗂️ Repository Structure
+```
+data/
+└── {benchmark_name}/
+    └── {developer_name}/
+        └── {model_name}/
+            ├── {uuid}.json          # aggregate results
+            └── {uuid}.jsonl         # instance-level results (optional)
+```
+Example evaluations included in the schema v0.2 release:
+| Evaluation | Data |
+|---|---|
+| Global MMLU Lite | [`data/global-mmlu-lite/`](https://huggingface.co/datasets/evaleval/EEE_datastore/tree/main/data/global-mmlu-lite) |
+| HELM Capabilities v1.15 | [`data/helm_capabilities/`](https://huggingface.co/datasets/evaleval/EEE_datastore/tree/main/data/helm_capabilities) |
+| HELM Classic | [`data/helm_classic/`](https://huggingface.co/datasets/evaleval/EEE_datastore/tree/main/data/helm_classic) |
+| HELM Instruct | [`data/helm_instruct/`](https://huggingface.co/datasets/evaleval/EEE_datastore/tree/main/data/helm_instruct) |
+| HELM Lite | [`data/helm_lite/`](https://huggingface.co/datasets/evaleval/EEE_datastore/tree/main/data/helm_lite) |
+| HELM MMLU | [`data/helm_mmlu/`](https://huggingface.co/datasets/evaleval/EEE_datastore/tree/main/data/helm_mmlu) |
+| HF Open LLM Leaderboard v2 | [`data/hfopenllm_v2/`](https://huggingface.co/datasets/evaleval/EEE_datastore/tree/main/data/hfopenllm_v2) |
+| LiveCodeBench Pro | [`data/livecodebenchpro/`](https://huggingface.co/datasets/evaleval/EEE_datastore/tree/main/data/livecodebenchpro) |
+| RewardBench | [`data/reward-bench/`](https://huggingface.co/datasets/evaleval/EEE_datastore/tree/main/data/reward-bench) |
+Schemas: [`eval.schema.json`](https://github.com/evaleval/every_eval_ever/blob/main/eval.schema.json) (aggregate) · [`instance_level_eval.schema.json`](https://github.com/evaleval/every_eval_ever/blob/main/instance_level_eval.schema.json) (per-sample JSONL)
+Each evaluation has its own directory under [`data/`](https://huggingface.co/datasets/evaleval/EEE_datastore/tree/main/data). Within each evaluation, models are organized by developer and model name. Instance-level data is stored in optional `{uuid}.jsonl` files alongside aggregate `{uuid}.json` results.
+## 🤖 Hugging Face Validator Bot Commands
+If you contribute to the repository a bot will validate the json files. If you adjust the json files e.g. reuploading them, please make sure to use 
+```sh
+/eee validate changed
+```
+to check if the format is still valid. The checks need to pass and no warnings should be contained. 
+## 📋 The Schema in Practice
+For a detailed walk-through, see the [blogpost](https://evalevalai.com/infrastructure/2026/02/17/everyevalever-launch/).
+Each result file captures not just scores but the context needed to interpret and reuse them. Here's how it works, piece by piece:
+**Where did the evaluation come from?** Source metadata tracks who ran it, where the data was published, and the relationship to the model developer:
+```json
+"source_metadata": {
+  "source_name": "Live Code Bench Pro",
+  "source_type": "documentation",
+  "source_organization_name": "LiveCodeBench",
+  "evaluator_relationship": "third_party"
+}
+```
+**Generation settings matter.** Changing temperature or the number of samples alone can shift scores by several points — yet they're routinely absent from leaderboards. We capture them explicitly:
+
+```json
+"generation_config": {
+  "generation_args": {
+    "temperature": 0.2,
+    "top_p": 0.95,
+    "max_tokens": 2048
+  }
+}
+```
+
+**The score itself.** A score of 0.31 on a coding benchmark (pass@1) means higher is better. The same 0.31 on RealToxicityPrompts means lower is better. The schema standardizes this interpretation:
+
+```json
+"evaluation_results": [{
+  "evaluation_name": "code_generation",
+  "metric_config": {
+    "evaluation_description": "pass@1 on code generation tasks",
+    "lower_is_better": false,
+    "score_type": "continuous",
+    "min_score": 0,
+    "max_score": 1
+  },
+  "score_details": {
+    "score": 0.31
+  }
+}]
+```
+
+The schema also supports **level-based metrics** (e.g. Low/Medium/High) and **uncertainty** reporting (confidence intervals, standard errors). See [`eval.schema.json`](https://github.com/evaleval/every_eval_ever/blob/main/eval.schema.json) for the full specification.
+
+## 🔧 Auto-generation of Pydantic Classes for Schema
+
+Run following bash commands to generate pydantic classes for `eval.schema.json` and `instance_level_eval.schema.json` (to easier use in data converter scripts):
+
+```bash
+uv run datamodel-codegen --input eval.schema.json --output eval_types.py --class-name EvaluationLog --output-model-type pydantic_v2.BaseModel --input-file-type jsonschema --formatters ruff-format ruff-check
+uv run datamodel-codegen --input instance_level_eval.schema.json --output instance_level_types.py --class-name InstanceLevelEvaluationLog --output-model-type pydantic_v2.BaseModel --input-file-type jsonschema --formatters ruff-format ruff-check
+```
+
+## 🔌 Eval Converters
+
+We have prepared converters to make adapting to our schema as easy as possible. At the moment, we support converting local evaluation harness logs from `Inspect AI`, `HELM` and `lm-evaluation-harness` into our unified schema. Each converter produces aggregate JSON and optionally instance-level JSONL output.
+
+| Framework | Command | Instance-Level JSONL |
+|---|---|---|
+| [Inspect AI](https://github.com/evaleval/every_eval_ever/tree/main/every_eval_ever/converters/inspect) | `uv run python3 -m eval_converters.inspect --log_path <path>` | Yes, if samples in log |
+| [HELM](https://github.com/evaleval/every_eval_ever/tree/main/every_eval_ever/converters/helm) | `uv run python3 -m eval_converters.helm --log_path <path>` | Always |
+| [lm-evaluation-harness](https://github.com/evaleval/every_eval_ever/tree/main/every_eval_ever/converters/lm_eval) | `uv run python -m eval_converters.lm_eval --log_path <path>` | With `--include_samples` |
+
+For full CLI usage and required input files, see the [Eval Converters documentation](https://github.com/evaleval/every_eval_ever/tree/main/every_eval_ever/converters).
+
+## 🏆 ACL 2026 Shared Task
+
+We are running a [Shared Task](https://evalevalai.com/events/shared-task-every-eval-ever/) at **ACL 2026 in San Diego** (July 7, 2026). The task invites participants to contribute to a unifying database of eval results:
+
+- **Track 1: Public Eval Data Parsing** — Parse leaderboards (Chatbot Arena, Open LLM Leaderboard, AlpacaEval, etc.) and academic papers into [our schema](eval.schema.json) and contribute to a unifying database of eval results!
+- **Track 2: Proprietary Evaluation Data** — Convert proprietary evaluation datasets into [our schema](eval.schema.json) and contribute to a unifying database of eval results!
+
+| Milestone | Date |
+|---|---|
+| Submission deadline | May 1, 2026 |
+| Results announced | June 1, 2026 |
+| Workshop at ACL 2026 | July 7, 2026 |
+
+Qualifying contributors will be invited as co-authors on the shared task paper.
+
+## 📎 Citation
+
+```bibtex
+@misc{everyevalever2026schema,
+  title   = {Every Eval Ever Metadata Schema v0.2},
+  author  = {EvalEval Coalition},
+  year    = {2026},
+  month   = {February},
+  url     = {https://github.com/evaleval/every_eval_ever},
+  note    = {Schema Release}
+}
+```

USAGE_EEE_datastore.md

@@ -0,0 +1,123 @@
+# EEE Datastore Usage
+
+## Flat Layout
+
+`flat/` is generated from `data/`. Do not edit generated flat files by hand.
+
+Regenerate the flat view:
+
+```sh
+uv run tools/build_flat_datastore.py --datastore .
+```
+
+Validate the generated files:
+
+```sh
+uv run tools/validate_flat_datastore.py --datastore .
+```
+
+Layout:
+
+```text
+flat/
+  objects/<uuid[0:2]>/<uuid[2:4]>/<uuid>.json
+  objects/<uuid[0:2]>/<uuid[2:4]>/<uuid>_samples.jsonl
+  latest_manifest.json
+  manifests/sha256_<manifest_core_sha256>/manifest.json
+  manifests/sha256_<manifest_core_sha256>/entries.jsonl
+  indexes/by_collection/<collection>.jsonl
+  indexes/by_legacy_path.jsonl
+```
+
+- `flat/latest_manifest.json` is the current snapshot descriptor.
+- `flat/manifests/sha256_*/manifest.json` stores immutable snapshot metadata.
+- `flat/manifests/sha256_*/entries.jsonl` stores the full row list for that snapshot.
+- `flat/objects/` stores aggregate JSON objects and optional companion sample JSONL files.
+- `flat/indexes/by_collection/<collection>.jsonl` is the current one-file index
+  for one collection, grouped by the source `benchmark` field.
+- `flat/indexes/by_legacy_path.jsonl` maps flat objects back to their original `data/...` paths.
+
+The manifest descriptor records `eval_schema_versions` from the source JSON records.
+
+The count fields are:
+
+- `aggregate_file_count`: aggregate JSON files, also rows in `entries.jsonl`.
+- `benchmark_count`: distinct benchmark values in `entries.jsonl`.
+- `instance_level_file_count`: companion `_samples.jsonl` files.
+- `total_file_count`: aggregate JSON files plus companion instance-level JSONL files.
+
+## Manifest Format
+
+`flat/latest_manifest.json` and `flat/manifests/sha256_*/manifest.json` have the same JSON shape. `latest_manifest.json` is the moving pointer to the current snapshot. The versioned `manifest.json` is immutable.
+
+Fields:
+
+- `created_at`: UTC timestamp for the snapshot.
+- `source`: source tree used to generate the flat view.
+- `eval_schema_versions`: eval record schema versions present in the aggregate JSON files.
+- `aggregate_file_count`: number of aggregate JSON files, also rows in `entries.jsonl`.
+- `benchmark_count`: number of distinct benchmark values in `entries.jsonl`.
+- `instance_level_file_count`: number of companion `_samples.jsonl` files.
+- `total_file_count`: total aggregate JSON files plus companion instance-level JSONL files.
+- `entries_path`: path to the snapshot row file.
+- `entries_sha256`: SHA-256 of `entries.jsonl`.
+- `entries_size_bytes`: byte size of `entries.jsonl`.
+- `manifest_path`: path to the immutable manifest descriptor.
+- `manifest_core_sha256`: content hash used in the versioned manifest directory name.
+
+Each line in `entries.jsonl` is one aggregate result object:
+
+```json
+{"object_uuid":"...","object_path":"flat/objects/.../uuid.json","sha256":"...","size_bytes":123,"legacy_path":"data/benchmark/developer/model/uuid.json","benchmark":"gsm8k","eval_schema_version":"0.2.2","record_type":"aggregate","instance_level_available":false}
+```
+
+Rows with instance-level data also include:
+
+- `instance_level_available`: `true`
+- `instance_level_path`
+- `instance_sha`
+- `instance_level_size_bytes`
+
+Rows without instance-level data include `instance_level_available: false` and
+omit the instance-level path, hash, and size fields.
+
+Read flow:
+
+1. Read `flat/latest_manifest.json`.
+2. Read `entries_path`.
+3. Verify `entries_sha256` and `entries_size_bytes`.
+4. Parse `entries.jsonl` one line at a time.
+5. Download each `object_path` needed.
+6. Download `instance_level_path` when `instance_level_available` is `true` and
+   instance-level rows are needed.
+
+For a single collection, either filter `entries.jsonl` by `benchmark` or read
+`flat/indexes/by_collection/<collection>.jsonl`. The collection
+indexes describe the current snapshot only.
+
+## Changelog
+
+### 2026-06-13 18:11:41 CEST
+
+- Replaced split per-collection indexes (`aggregate.jsonl` and
+  `instance_level.jsonl`) with one `records.jsonl` per collection.
+- Added explicit `instance_level_available` booleans to flat rows, with
+  `instance_level_path`, `instance_sha`, and `instance_level_size_bytes` present
+  only when instance-level data exists.
+- Regenerated the latest flat snapshot from `data/`: 39,217 aggregate records,
+  1,041 instance-level companion files, and 85 collection `records.jsonl` files.
+
+### 2026-06-13 18:23:27 CEST
+
+- Flattened current collection indexes from
+  `flat/indexes/by_collection/<collection>/records.jsonl` to
+  `flat/indexes/by_collection/<collection>.jsonl`.
+- Updated build and validation tooling to reject leftover nested collection
+  index directories.
+
+### 2026-06-13 19:37:25 CEST
+
+- Added Git LFS tracking for direct
+  `flat/indexes/by_collection/<collection>.jsonl` files so large collection
+  indexes, including `alphaxiv.jsonl`, satisfy Hugging Face's 10 MiB regular
+  Git file limit.

data/BountyBench/anthropic/claude-opus-4-6/e24f7e2f-c66f-4db1-80c4-913df59e4c77.json

@@ -0,0 +1,96 @@
+{
+    "schema_version": "0.2.2",
+    "evaluation_id": "BountyBench/anthropic_claude-opus-4-6/1774492770.625677",
+    "evaluation_timestamp": "1774492770.625677",
+    "retrieved_timestamp": "1776143888.662194",
+    "source_metadata": {
+        "source_name": "BountyBench",
+        "source_type": "evaluation_run",
+        "source_organization_name": "Arcadia Impact",
+        "evaluator_relationship": "third_party"
+    },
+    "eval_library": {
+        "name": "bountybench",
+        "version": "unknown"
+    },
+    "model_info": {
+        "name": "anthropic/claude-opus-4-6",
+        "id": "anthropic/claude-opus-4-6",
+        "developer": "Anthropic"
+    },
+    "evaluation_results": [
+        {
+            "evaluation_result_id": "bountybench_detect_anthropic_claude-opus-4-6",
+            "evaluation_name": "BountyBench - DetectWorkflow",
+            "source_data": {
+                "dataset_name": "BountyBench",
+                "source_type": "url",
+                "url": [
+                    "https://github.com/bountybench/bountybench"
+                ],
+                "additional_details": {
+                    "num_bounties": "46",
+                    "workflow": "DetectWorkflow"
+                }
+            },
+            "evaluation_timestamp": "1774492770.625677",
+            "metric_config": {
+                "evaluation_description": "BountyBench DetectWorkflow — success rate across bounties",
+                "metric_id": "accuracy",
+                "metric_name": "Success Rate",
+                "metric_kind": "accuracy",
+                "metric_unit": "proportion",
+                "lower_is_better": false,
+                "score_type": "continuous",
+                "min_score": 0.0,
+                "max_score": 1.0
+            },
+            "score_details": {
+                "score": 0.13043478260869565,
+                "details": {
+                    "successes": "6",
+                    "total": "46"
+                },
+                "uncertainty": {
+                    "standard_error": {
+                        "value": 0.04965567310610093,
+                        "method": "analytic"
+                    },
+                    "num_samples": 46
+                }
+            },
+            "generation_config": {
+                "generation_args": {
+                    "temperature": 0.5,
+                    "max_tokens": 4096,
+                    "agentic_eval_config": {
+                        "available_tools": [
+                            {
+                                "name": "bash",
+                                "description": "Kali Linux terminal"
+                            }
+                        ]
+                    },
+                    "eval_limits": {
+                        "message_limit": 400
+                    },
+                    "sandbox": {
+                        "type": "docker"
+                    },
+                    "max_attempts": 1
+                },
+                "additional_details": {
+                    "max_input_tokens": "8192",
+                    "helm": "False"
+                }
+            }
+        }
+    ],
+    "detailed_evaluation_results": {
+        "format": "jsonl",
+        "file_path": "./e24f7e2f-c66f-4db1-80c4-913df59e4c77_samples.jsonl",
+        "hash_algorithm": "sha256",
+        "checksum": "1cfdc9fe70ea15dbeb095b67163ae7f62f1aa92ccc5ac5f9002da74e5acd95c1",
+        "total_rows": 46
+    }
+}

data/BountyBench/anthropic/claude-opus-4-6/e24f7e2f-c66f-4db1-80c4-913df59e4c77_samples.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1cfdc9fe70ea15dbeb095b67163ae7f62f1aa92ccc5ac5f9002da74e5acd95c1
+size 13255203

data/GAIA/anthropic/claude-3-7-sonnet-20250219/be888544-4602-4a63-b75a-7a678236db9c.json

@@ -0,0 +1,177 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "GAIA/anthropic_claude-3-7-sonnet-20250219/1744624699.0",
+  "evaluation_timestamp": "1744624699.0",
+  "retrieved_timestamp": "1775627806.945313",
+  "source_metadata": {
+    "source_name": "inspect_ai",
+    "source_type": "evaluation_run",
+    "source_organization_name": "Arcadia Impact",
+    "evaluator_relationship": "third_party"
+  },
+  "eval_library": {
+    "name": "inspect_ai",
+    "version": "inspect_ai:0.3.87"
+  },
+  "model_info": {
+    "name": "anthropic/claude-3-7-sonnet-20250219",
+    "id": "anthropic/claude-3-7-sonnet-20250219",
+    "developer": "anthropic",
+    "inference_platform": "anthropic"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "GAIA",
+      "source_data": {
+        "dataset_name": "GAIA",
+        "source_type": "hf_dataset",
+        "hf_repo": "gaia-benchmark/GAIA",
+        "samples_number": 26,
+        "sample_ids": [
+          "676e5e31-a554-4acc-9286-b60d90a92d26",
+          "bec74516-02fc-48dc-b202-55e78d0e17cf",
+          "00d579ea-0889-4fd9-a771-2c8d79835c8d",
+          "384d0dd8-e8a4-4cfe-963c-d37f256e7662",
+          "de9887f5-ead8-4727-876f-5a4078f8598c",
+          "983bba7c-c092-455f-b6c9-7857003d48fc",
+          "9b54f9d9-35ee-4a14-b62f-d130ea00317f",
+          "56db2318-640f-477a-a82f-bc93ad13e882",
+          "8131e2c0-0083-4265-9ce7-78c2d568425d",
+          "72c06643-a2fa-4186-aa5c-9ec33ae9b445",
+          "ebbc1f13-d24d-40df-9068-adcf735b4240",
+          "c526d8d6-5987-4da9-b24c-83466fa172f3",
+          "3da89939-209c-4086-8520-7eb734e6b4ef",
+          "8d46b8d6-b38a-47ff-ac74-cda14cf2d19b",
+          "e961a717-6b25-4175-8a68-874d28190ee4",
+          "851e570a-e3de-4d84-bcfa-cc85578baa59",
+          "50f58759-7bd6-406f-9b0d-5692beb2a926",
+          "872bfbb1-9ccf-49f6-8c5f-aa22818ccd66",
+          "c3a79cfe-8206-451f-aca8-3fec8ebe51d3",
+          "da52d699-e8d2-4dc5-9191-a2199e0b6a9b",
+          "ad2b4d70-9314-4fe6-bfbe-894a45f6055f",
+          "5b2a14e8-6e59-479c-80e3-4696e8980152",
+          "9e1fc53b-46ff-49a1-9d05-9e6faac34cc5",
+          "5f982798-16b9-4051-ab57-cfc7ebdb2a91",
+          "0512426f-4d28-49f0-be77-06d05daec096",
+          "0bdb7c40-671d-4ad1-9ce3-986b159c0ddc"
+        ],
+        "additional_details": {
+          "shuffled": "False"
+        }
+      },
+      "evaluation_timestamp": "1744624699.0",
+      "metric_config": {
+        "evaluation_description": "accuracy",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "raw_evaluation_name": "accuracy on inspect_evals/gaia_level3 for scorer gaia_scorer"
+        },
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.11538461538461539,
+        "uncertainty": {
+          "standard_error": {
+            "value": 0.06389710663783135
+          },
+          "num_samples": 26
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "reasoning": false,
+          "agentic_eval_config": {
+            "available_tools": [
+              {
+                "name": "bash",
+                "parameters": {
+                  "timeout": "180"
+                }
+              },
+              {
+                "name": "python",
+                "parameters": {
+                  "timeout": "180"
+                }
+              },
+              {
+                "name": "web_browser_go",
+                "parameters": {
+                  "instance": "\"HzhABJZNvwd7UhowvMnnM7\""
+                }
+              },
+              {
+                "name": "web_browser_click",
+                "parameters": {
+                  "instance": "\"HzhABJZNvwd7UhowvMnnM7\""
+                }
+              },
+              {
+                "name": "web_browser_type_submit",
+                "parameters": {
+                  "instance": "\"HzhABJZNvwd7UhowvMnnM7\""
+                }
+              },
+              {
+                "name": "web_browser_type",
+                "parameters": {
+                  "instance": "\"HzhABJZNvwd7UhowvMnnM7\""
+                }
+              },
+              {
+                "name": "web_browser_scroll",
+                "parameters": {
+                  "instance": "\"HzhABJZNvwd7UhowvMnnM7\""
+                }
+              },
+              {
+                "name": "web_browser_back",
+                "parameters": {
+                  "instance": "\"HzhABJZNvwd7UhowvMnnM7\""
+                }
+              },
+              {
+                "name": "web_browser_forward",
+                "parameters": {
+                  "instance": "\"HzhABJZNvwd7UhowvMnnM7\""
+                }
+              },
+              {
+                "name": "web_browser_refresh",
+                "parameters": {
+                  "instance": "\"HzhABJZNvwd7UhowvMnnM7\""
+                }
+              }
+            ]
+          },
+          "eval_plan": {
+            "name": "plan",
+            "steps": [
+              "{\"solver\": \"system_message\", \"params\": {\"template\": \"\\nYou are an autonomous AI agent, consisting of a large language model and a scaffolding program that runs on a linux VM.\\n\\nYou have full control over your VM through the scaffolding program's provided functions, including: writing and editing files, running bash commands, installing packages etc.. From your VM, you can access the internet.\\n\\nYou are state-of-the-art, and excellent at analysis, planning and programming. You possess encyclopedic knowledge of the world. You write in a very direct and concise style.\\n\\nYou are resourceful and adaptive, and you never give up. Before deciding something can't be done, you try it out. You consider multiple options and choose the best one. If your current approach doesn't work, you formulate a new plan. You are given a task you need to solve completely on your own.\\n\\nPlease think step by step before calling tools. When you are ready to answer, use the submit tool to provide your final answer.\\n\"}, \"params_passed\": {\"template\": \"\\nYou are an autonomous AI agent, consisting of a large language model and a scaffolding program that runs on a linux VM.\\n\\nYou have full control over your VM through the scaffolding program's provided functions, including: writing and editing files, running bash commands, installing packages etc.. From your VM, you can access the internet.\\n\\nYou are state-of-the-art, and excellent at analysis, planning and programming. You possess encyclopedic knowledge of the world. You write in a very direct and concise style.\\n\\nYou are resourceful and adaptive, and you never give up. Before deciding something can't be done, you try it out. You consider multiple options and choose the best one. If your current approach doesn't work, you formulate a new plan. You are given a task you need to solve completely on your own.\\n\\nPlease think step by step before calling tools. When you are ready to answer, use the submit tool to provide your final answer.\\n\"}}",
+              "{\"solver\": \"use_tools\", \"params\": {\"tools\": [[{\"type\": \"tool\", \"name\": \"bash\", \"params\": {\"timeout\": 180}}, {\"type\": \"tool\", \"name\": \"python\", \"params\": {\"timeout\": 180}}, {\"type\": \"tool\", \"name\": \"web_browser_go\", \"params\": {\"instance\": \"HzhABJZNvwd7UhowvMnnM7\"}}, {\"type\": \"tool\", \"name\": \"web_browser_click\", \"params\": {\"instance\": \"HzhABJZNvwd7UhowvMnnM7\"}}, {\"type\": \"tool\", \"name\": \"web_browser_type_submit\", \"params\": {\"instance\": \"HzhABJZNvwd7UhowvMnnM7\"}}, {\"type\": \"tool\", \"name\": \"web_browser_type\", \"params\": {\"instance\": \"HzhABJZNvwd7UhowvMnnM7\"}}, {\"type\": \"tool\", \"name\": \"web_browser_scroll\", \"params\": {\"instance\": \"HzhABJZNvwd7UhowvMnnM7\"}}, {\"type\": \"tool\", \"name\": \"web_browser_back\", \"params\": {\"instance\": \"HzhABJZNvwd7UhowvMnnM7\"}}, {\"type\": \"tool\", \"name\": \"web_browser_forward\", \"params\": {\"instance\": \"HzhABJZNvwd7UhowvMnnM7\"}}, {\"type\": \"tool\", \"name\": \"web_browser_refresh\", \"params\": {\"instance\": \"HzhABJZNvwd7UhowvMnnM7\"}}]], \"append\": true}, \"params_passed\": {\"tools\": [[{\"type\": \"tool\", \"name\": \"bash\", \"params\": {\"timeout\": 180}}, {\"type\": \"tool\", \"name\": \"python\", \"params\": {\"timeout\": 180}}, {\"type\": \"tool\", \"name\": \"web_browser_go\", \"params\": {\"instance\": \"HzhABJZNvwd7UhowvMnnM7\"}}, {\"type\": \"tool\", \"name\": \"web_browser_click\", \"params\": {\"instance\": \"HzhABJZNvwd7UhowvMnnM7\"}}, {\"type\": \"tool\", \"name\": \"web_browser_type_submit\", \"params\": {\"instance\": \"HzhABJZNvwd7UhowvMnnM7\"}}, {\"type\": \"tool\", \"name\": \"web_browser_type\", \"params\": {\"instance\": \"HzhABJZNvwd7UhowvMnnM7\"}}, {\"type\": \"tool\", \"name\": \"web_browser_scroll\", \"params\": {\"instance\": \"HzhABJZNvwd7UhowvMnnM7\"}}, {\"type\": \"tool\", \"name\": \"web_browser_back\", \"params\": {\"instance\": \"HzhABJZNvwd7UhowvMnnM7\"}}, {\"type\": \"tool\", \"name\": \"web_browser_forward\", \"params\": {\"instance\": \"HzhABJZNvwd7UhowvMnnM7\"}}, {\"type\": \"tool\", \"name\": \"web_browser_refresh\", \"params\": {\"instance\": \"HzhABJZNvwd7UhowvMnnM7\"}}]], \"append\": true}}",
+              "{\"solver\": \"submit_tool\", \"params\": {}, \"params_passed\": {}}",
+              "{\"solver\": \"basic_agent_loop\", \"params\": {}, \"params_passed\": {}}"
+            ],
+            "config": {}
+          },
+          "eval_limits": {},
+          "sandbox": {}
+        }
+      },
+      "evaluation_result_id": "GAIA/anthropic_claude-3-7-sonnet-20250219/1744624699.0#gaia#accuracy"
+    }
+  ],
+  "detailed_evaluation_results": {
+    "format": "jsonl",
+    "file_path": "./be888544-4602-4a63-b75a-7a678236db9c_samples.jsonl",
+    "hash_algorithm": "sha256",
+    "checksum": "e47dee4d293e3fcc97d44e987cefc8f9610eed2b9a7d0593769009bedad31500",
+    "total_rows": 26
+  }
+}

data/GAIA/deepseek/deepseek-r1/1a8b6f58-6474-41eb-9c30-8e0fe9e73850.json

@@ -0,0 +1,155 @@
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data/GAIA/google/gemini-2.0-flash-001/2d6ea10d-e961-47ab-bd13-eb8c89d5d115.json

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data/GAIA/grok/grok-2-1212/eb3bdd01-a936-4030-9664-8b0d6cbdef80.json

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data/GAIA/meta-llama/llama-3.3-70b-instruct/3dfd3608-0ba9-4286-acc0-07cfe7a3df01.json

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data/GAIA/mistral/mistral-large-latest/85ba1992-b00f-463a-b889-5aa3d196aeef.json

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data/GAIA/mistral/mistral-small-latest/250594a4-e833-4342-a788-0041a68bc318.json

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data/GAIA/openai/gpt-4o-2024-08-06/dcf4c9d9-6fc2-44f1-a657-711cf37e1912.json

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data/GAIA/openai/gpt-4o-mini-2024-07-18/80f07624-03d1-4934-8f3c-cd0ed7962a92.json

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data/GAIA/openai/o1-mini-2024-09-12/b8bd9eea-d06b-40c8-9a93-cdfcf5f55cd6.json

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data/GAIA/openai/o3-mini-2025-01-31/dd273829-4f66-4ac3-9d15-bcece6ba72e2.json

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