| --- |
| license: other |
| task_categories: |
| - other |
| language: |
| - en |
| tags: |
| - webarena |
| - llm-agent |
| - sparse-attention |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # WebArena 0-99 · Sparse-Attention Method Comparison · LLM Trajectories |
|
|
| Full **LLM call trajectories**, final answers and scores for five attention implementations, |
| each run over **WebArena tasks 0-99** (100 tasks per method). |
|
|
| ## Experiment configuration |
|
|
| | Item | Value | |
| |---|---| |
| | Model under test | **Qwen3-VL-32B-Instruct** (same model for all five methods; only the attention implementation changes) | |
| | Scoring judge | Llama-3.3-70B-Instruct | |
| | **Vision / screenshots** | **Disabled throughout** (`use_vision=False` + chromium `imagesEnabled=false`); the agent only receives the page's accessibility tree as text | |
| | Sampling | `temperature 0`, `max_tokens 4096` | |
| | Agent | browser-use, `max_steps 30` | |
| | Map site | points at the **real openstreetmap.org** (not a self-hosted snapshot) | |
|
|
| Note: although the model is a vision-language model, **vision was deliberately turned off** — |
| vortex/quest does not handle mrope and produces garbage with images, and disabling it keeps the |
| input modality identical across all five methods. All numbers here are therefore **text-only agent** |
| results and do not represent the model's ceiling with vision enabled. |
|
|
| ## Results overview (lenient is the primary metric) |
|
|
| | Method | top_k | selected tokens | non-map (48 tasks) | TOTAL (100) | official | |
| |---|---|---|---|---|---| |
| | dense | — | 100% | 19/48 (39%) | 33/100 | 23% | |
| | quest | 61 pages | **10.8%** | 19/48 (39%) | 33/100 | 26% | |
| | TSA-minmax tk64 | 64 chunks | 78.3% | 17/48 (35%) | 34/100 | 19% | |
| | TSA-centroid tk64 | 64 chunks | 57.5% | 14/48 (29%) | 28/100 | 14% | |
| | TSA-centroid tk32 | 32 chunks | 26.6% | 11/48 (22%) | 26/100 | 9% | |
| |
| `selected tokens` is the **measured** fraction of the KV cache the method actually attends to |
| (full-model measurement with real decode queries). It is the only directly comparable axis |
| between quest and TSA — `%chunks` and `%pages` are different units. |
| |
| ## Directory layout |
| |
| ``` |
| <method>/ |
| ├── SCORES.json official scoring summary |
| ├── SCORES_adjusted.json lenient scoring summary |
| └── task_<id>/ |
| ├── llm_calls.jsonl ★ full LLM call trajectory (one JSON object per line) |
| ├── task_<id>.json final answer, step count, final_url, timings |
| ├── input.json resolved task definition (intent / eval / replica map) |
| └── run.log browser-use agent log |
| task_ids/ official & lenient pass/fail task-id lists per method |
| ``` |
| method ∈ `dense` · `quest` · `tsa_minmax_tk64` · `tsa_centroid_tk64` · `tsa_centroid_tk32` |
| |
| ## Field reference |
|
|
| **`llm_calls.jsonl`** — one LLM call per line |
| |
| | Field | Description | |
| |---|---| |
| | `call` | call index within the task | |
| | `t` / `latency_s` | start timestamp / call latency in seconds | |
| | `input_messages` | **full input messages** (system preamble + page accessibility tree) | |
| | `output` | model output (browser-use structured action JSON) | |
| | `usage` | token counts. ⚠️ **the TSA server does not report `prompt_tokens` (always 0)** — re-tokenize if you need input length; `completion_tokens` is valid for all methods | |
|
|
| **`task_<id>.json`** — `intent`, `answer`, `final_url`, `n_steps`, `is_done`, `wall_time_s`, |
| `steps` (per-step url / next_goal / actions), `error` (`TimeoutError` means the task timed out). |
| |
| **`input.json`** — `intent`, `eval` (`eval_types` + `reference_answers`, the scoring ground truth), |
| `sites`, `replica_map` (which site replica this task was assigned to). |
| |
| ## Caveats |
| |
| - **official vs lenient**: official is the standard WebArena evaluator; lenient additionally uses an |
| LLM judge to recover answers that state the correct value but fail strict string matching on |
| phrasing/formatting. Map tasks run against the real OSM while reference answers were annotated on a |
| self-hosted snapshot, so official is systematically low — **use lenient as the primary metric**. |
| - **~25% of TSA tasks time out** (`error: TimeoutError`), mostly map tasks. The TSA server has no |
| tensor-parallel support and runs the 32B model on a single GPU, making it ~4× slower per request |
| than the tp=4 dense/quest servers; its task timeout was raised to 2400s to compensate. |
| - **Do not draw speed/throughput conclusions from this data**: dense ran with `--enforce-eager` |
| (CUDA graph disabled) while quest/TSA had it enabled, and parallelism differs (single-GPU vs tp=4). |
| **Accuracy conclusions are unaffected.** |
| - Site URLs in the trajectories point at an internal experiment environment and are only meaningful there. |
|
|
| Reproduction code and full methodology (harness, scoring scripts, sm90 build patches, pitfalls |
| checklist) live in the companion `reproduce/` directory. |
|
|
