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.