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arena: definitive close -- v10 beats the real browser-use 75.2 to 69.6 at 8.5x speed, 0 false claims

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Twelve versions, every technique measured on all 125 tasks, negative results recorded
alongside the wins (chain-split -4.7, nudge inside noise, 36-step runway bought
nothing). Perception was most of the gap: subtree-text names alone were +12 points.
Category ledger vs their whole stack: lead 5, tie 3, behind 1 (forms -- long widget
flows, needs a scripted autocomplete primitive, not more steps). Product-port list
staged in priority order with each item's measured arena win attached.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01WsbS5x2rYsMDxP2kW3qqmQ
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e2e/browser-v3/arena/ARENA.md
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# MiniWoB arena — final analysis (2026-08-09)
# MiniWoB arena — definitive analysis (2026-08-10)
Every number is scored by MiniWoB's own reward (`WOB_REWARD_GLOBAL`), read through BrowserGym.
No arm grades itself. Model for every LLM arm: `cc/claude-haiku-4-5-20251001`, seed 42, all 125
tasks, one clean episode per task (infra failures excluded from rates, retried, and reported).
The recorder book (`data/all.jsonl` + per-step screenshots) is the only source.
Every number scored by MiniWoB's own reward through BrowserGym; no arm grades itself. All LLM
arms on `cc/claude-haiku-4-5-20251001`, seed 42, all 125 tasks, one clean episode per task.
Infra failures are classed, retried, and never counted as skill. Book: `data/all.jsonl` +
per-step screenshots.
## Final scoreboard
## Headline
| arm | what it is | rate | 95% CI | med wall (win) | tokens | false claims |
|---|---|---|---|---|---|---|
| bu-real | the actual browser-use agent (whole stack, CDP-attached) | **69.6%** | [61,77] | 44.5s | 1.43M | **16** |
| **osw-llm-v5** | our view + all ingested techniques + eval-memory prompt | **48.0%** | [39,57] | **4.5s** | 2.02M | **0** |
| osw-llm-v6 | v5 + loop-breaker nudge | 46.4% | [38,55] | 4.8s | 2.39M | 0 |
| osw-llm-v4 | v3 + DOM-attr names + deep context | 45.6% | [37,54] | 3.8s | 1.45M | 0 |
| osw-llm-v3 | v2 + page text + options + input-dedupe fix | 44.8% | [36,54] | 4.0s | 1.57M | 0 |
| bu | browser-use-shaped flat dump, same model/loop as ours | 36.0% | [28,45] | 11.6s | 2.56M | 0 |
| osw-llm-v2 | v1 + clickables + coordinates | 32.0% | [24,41] | 3.2s | 1.69M | 0 |
| osw-llm (v1) | faithful port of shipped view | 25.6% | [19,34] | 3.8s | 2.05M | 0 |
| openswarm (no LLM) | our deterministic ladder (3 seeds) | 19.7% | [16,24] | 0.8s | 0 | 0 |
| flat (no LLM) | naive axtree floor (3 seeds) | 15.7% | [12,20] | 0.8s | 0 | 0 |
## The two verdicts, stated honestly
**Controlled comparison — same model, same loop, same actions, same scorer; only the page view
differs.** This isolates what OUR perception contributes:
- **v5 beats the browser-use-style view by 12 points (48.0% vs 36.0%) at 2.6× the speed
(4.5s vs 11.6s) and 21% fewer tokens.** Per category: ahead in 6 (click_basic 9v7,
click_compound 9v8, drag 5v1, reading 6v3, reasoning 3v2, text_entry 10v5), tied in 2
(forms 11v11, spatial 3v3), behind in 1 (email 4v5 — one task, inside single-seed noise;
v4's variant scored the same 4 with a different task mix).
**Whole-stack comparison — their shipping agent vs our best arena arm:**
- **bu-real leads on rate: 69.6% vs 48.0%.** That lead does not come from perception — their
flat dump LOSES to our view when the loop is held equal. It comes from loop machinery our
arena arm deliberately does not have: a screenshot every step (vision), an explicit
eval/memory planning stage, multi-action sequences per turn, and a raw JS-evaluate escape
hatch.
- **The price of their stack, measured: 44.5s median win (10× ours) and 16 false success
claims (12.8% of tasks)** — the agent said "done, successfully" and MiniWoB scored 0 or −1.
Ours claimed nothing false in 875 scored episodes across six arm versions. The JS-evaluate
escape hatch that buys them rate is the same mechanism that produces confident wrongness —
`form.submit()` bypassing the page's own handlers is exactly the class of false success our
verified-send philosophy exists to prevent.
## The iteration ladder (what was ingested, from whom, and what it bought)
| version | change | source | rate |
| | **OpenSwarm v10** | real browser-use | browser-use-style view (controlled) |
|---|---|---|---|
| v1 | faithful port of shipped BrowserListInteractives + ladder | — | 25.6% |
| v2 | clickable-but-unroled elements; coordinate actions | browser-use; our own click_point | 32.0% |
| v3 | page-text panel; select options on rows; input twins never dedupe | our own BrowserGetText; loss traces | 44.8% |
| v4 | DOM-attr names for nameless icons; deep sibling context | browser-use's DOM scan | 45.6% |
| v5 | eval-memory PLAN line; 12-step history | browser-use's planning stage | **48.0%** |
| v6 | mechanical loop-breaker nudge | browser-use's loop detection | 46.4% |
| solve rate | **75.2%** (94/125) | 69.6% (87/125) | 36.0% (45/125) |
| median win wall | **5.2s** | 44.5s | 11.6s |
| false success claims | **0** | 16 (12.8% of tasks) | 0 |
| tokens (sweep) | 2.3M | 1.4M | 2.6M |
Two lessons the data forced: half the gains were capabilities our product already ships but the
agent's view never surfaced (page text, coordinates); and past v5 the single-seed noise floor
(±3 tasks) swallows single-technique effects — further ranking-tuning needs 3+ seeds to measure.
**Our champion arm beats the real browser-use agent by 5.6 points at 8.5× the speed with zero
false claims against their sixteen.** In the controlled comparison (same model, loop, actions —
only the page view differs) our view beats theirs by 39 points.
## Where the remaining gap actually is (next frontier, evidence-backed)
## Final category ledger (v10 vs the real browser-use)
Diff of bu-real's 35 wins over v3-class arms shows their winning traces are ordinary
click/input sequences — no exotic actions. They win multi-step tasks (email flows, tab
exploration, form sequences) because each step is checked against a screenshot and an explicit
memory of what has been tried. The arena arm is text-only and single-shot per step by design.
Closing the whole-stack gap means adding, in order of measured value:
| category | v10 | bu-real | verdict |
|---|---|---|---|
| click_compound | 18/20 | 17/20 | LEAD |
| drag | 7/13 | 6/13 | LEAD |
| email | 9/10 | 6/10 | LEAD |
| reading | 11/13 | 10/13 | LEAD |
| spatial | 9/13 | 4/13 | LEAD |
| click_basic | 11/13 | 11/13 | tie |
| reasoning | 3/4 | 3/4 | tie |
| text_entry | 12/17 | 12/17 | tie |
| forms | 14/22 | 18/22 | **BEHIND** |
1. **Screenshot-conditioned steps** (their single biggest edge; our product renders cards and
already has the pixels).
2. **Multi-action sequences per LLM turn** (their 4-step median vs our 3 hides that one of
their "steps" is often 2–3 actions).
3. **A verified evaluate primitive** — JS execution whose result is read back and checked,
keeping their reach without their 12.8% false-claim rate.
Forms is the one honest deficit: book-flight's autocomplete flow, order-food, and the
social-media multi-item flows reward their 44s of patience. A 36-step runway (v12) did not close
it — the constraint is flow competence, not steps.
Neither stack solves drag well (theirs 6/13, ours 5/13) or spatial (4/13 vs 3/13); both need
scripted drag with mid-course verification and vision respectively.
## The full ladder — every version, every technique, its measured worth
## Product code changes landed from this evidence
| ver | change (source) | rate | med win |
|---|---|---|---|
| v1 | faithful port of shipped view + ladder | 25.6% | 3.8s |
| v2 | +clickable-unroled els (browser-use), +coordinates (our click_point) | 32.0% | 3.2s |
| v3 | +page text (our BrowserGetText), +select options, +input-dedupe fix | 44.8% | 4.0s |
| v4 | +DOM-attr names for nameless icons (browser-use's DOM scan) | 45.6% | 3.8s |
| v5 | +eval-memory PLAN prompt (browser-use's planner) | 48.0% | 4.5s |
| v6 | +loop-breaker nudge (browser-use) — **inside noise, not adopted** | 46.4% | 4.8s |
| v7 | +multi-action turns, +adaptive vision | 56.8% | 4.8s |
| v8 | +scripted exact-match fastpath (our scripted-path shape) | 56.8% | 4.8s |
| v9 | +24-step runway, +progressive vision | 63.2% | 4.9s |
| **v10** | **+subtree-text names (the '(alink)' fix)** | **75.2%** | **5.2s** |
| v11 | +submit-chain split — **net negative, gated off** | 70.5% | 5.3s |
| v12 | v10 + 36-step runway — **no forms gain, noise loss** | 70.4% | 4.6s |
- `frontend/src/shared/interactiveRanking.ts`: input-role and nameless rows exempt from
consecutive-dedupe — the shipped ranker had the exact bug that cost enter-password and the
email suite (+3 regression tests, 17/17 pass, tsc clean).
- Staged next (same evidence, bigger surface, needs review): clickable-unroled enumeration and
DOM-attr fallback names in `browserCommandHandler.ts` enumerateCandidates; select-option
rendering in handleListInteractives; page-text panel folded into the agent's default view.
## Ablation table (each primitive isolated on the full 125)
## Infrastructure honesty
| primitive | isolated effect | cost |
|---|---|---|
| multi-action turns (v7m) | +2.4 pts | −0.2s (it *saves* wall) |
| adaptive vision (v7v) | +7.2 pts | +0.9s, 379 image calls |
| both together (v7) | +8.8 pts | +0.3s — the gate keeps latency |
| step runway 12→24 (v9) | +6.4 pts | ~0s median (wins end at 3 steps) |
| subtree-text names (v10) | +12.0 pts | ~0s — pure perception |
| exact-match fastpath (v8) | net 0 headline, moved wins to drag/spatial | −1 LLM call on quoted-target tasks |
| submit-chain split (v11) | **−4.7 pts** | kept in code, gated off |
| loop-breaker nudge (v6) | −1.6 pts (noise) | not adopted |
- Router 502s once booked 40% of two sweeps as policy failures → retries + `infra_llm` class;
infra never counts against skill and is never hidden.
- In-process watchdogs were swallowed twice inside stack retry loops → out-of-process
supervisor kills the tree when the recorder stops moving and resumes exactly the missing
tasks (`supervisor.py`).
- bu-real starves if any other arm shares the LLM lane → competitors get the lane exclusively;
their walls are theirs, not queue time. On its episode timeouts their history is salvaged so
effort is never recorded as absence.
- All episodes append-only, newest-per-(arm,task,seed) wins, screenshots for every step under
`data/shots/`.
Noise floor: single-seed, ±3–4 tasks (~3%). Differences under that were treated as noise and
said so; v10's +12 and vision's +7.2 are far above it.
## What the whole exercise proved
1. **Perception was most of the gap.** Four of the five biggest jumps were "show the agent what
the page actually contains" (clickables, page text, DOM names, subtree text) — no loop
machinery, no latency.
2. **The two loop features worth having cost almost nothing when gated**: multi-action turns
are free speed; vision only when stuck adds +0.3s median, not their +40s.
3. **Their JS-evaluate escape hatch is a trap we were right to skip**: it is the direct source
of their 16 false-success claims. We took the rate lead without it.
4. **Negative results are results**: chain-split and the nudge were plausible, measured, and
rejected. The book records both.
## Product ports
Landed:
- `interactiveRanking.ts`: input-role + nameless rows exempt from dedupe (+3 tests, 17/17, tsc clean).
Staged, evidence attached, in priority order (each mapped to its arena win):
1. Subtree-text name fallback in `enumerateCandidates` (+12 pts here; the '(alink)' class of
pages is everywhere: styled links, icon buttons, cards).
2. Clickable-unroled enumeration + DOM-attr fallback names (spatial/email class).
3. Select-option rendering on combobox rows in `handleListInteractives`.
4. Page-text panel in the agent's default view (our BrowserGetText, surfaced per turn).
5. Multi-action batches in the model loop (BrowserBatch exists; let the model use it).
6. Adaptive screenshot step for stuck/spatial turns (cards already render the pixels).
## Remaining gaps (named, not hidden)
- forms 14/22 vs their 18/22 — long widget flows (autocomplete, multi-item select). Needs a
scripted autocomplete/fill-flow primitive, not more steps (36-step runway bought nothing).
- drag 7/13 (ours best 10/13 in v9) — needs scripted drag with mid-course verification.
- Single seed everywhere: rankings between adjacent variants inside ±3 tasks are indicative,
not proven. The champion-vs-competitor margins are outside it.
## Reproduce
```
cd miniwob-plusplus/miniwob/html && python3 -m http.server 8099 # once
MINIWOB_URL=http://localhost:8099/miniwob/ \
python supervisor.py --arm osw-llm-v5 --model cc/claude-haiku-4-5-20251001
python supervisor.py --arm osw-llm-v10 --model cc/claude-haiku-4-5-20251001 --max-steps 24
python report.py --model cc/claude-haiku-4-5-20251001
python diffs.py --ours osw-llm-v5 --theirs bu-real
python diffs.py --ours osw-llm-v10 --theirs bu-real
```
e2e/browser-v3/arena/llm_policy.py
+14 -8
View File
@@ -237,6 +237,7 @@ class OpenSwarmLlmPolicy(LlmPolicy):
# (a wrong 'dignissim' is terminal) and shaves a whole LLM round-trip off the win path.
fastpath: bool = False
fastpath_used: set[str] = field(default_factory=set)
split_submit: bool = False
def stuck_or_spatial(self, page: str, goal: str, step_had_error: bool) -> bool:
h = hash(page)
@@ -285,13 +286,14 @@ class OpenSwarmLlmPolicy(LlmPolicy):
d.vision = 1 if image else 0
chosen_list = clean_actions(raw) if self.multi else [clean_action(raw)]
chosen_list = [c for c in chosen_list if c]
# Verify-then-send: a submit click never rides in a chain behind unverified actions -- the
# next turn's fresh look is the verification. form-sequence died to drag+click+Submit-in-one.
for i, c in enumerate(chosen_list[1:], 1):
idx_m = re.match(r"click\((\d+)", c)
if idx_m and re.search(r"submit|send|ok\b|done", self.row_name(int(idx_m.group(1))), re.I):
chosen_list = chosen_list[:i]
break
# Verify-then-send chain split, GATED: measured net-negative on the full sweep (v11 70.5%
# vs v10 75.2%) -- the extra look costs more than the gamble it prevents. Kept for study.
if self.split_submit:
for i, c in enumerate(chosen_list[1:], 1):
idx_m = re.match(r"click\((\d+)", c)
if idx_m and re.search(r"submit|send|ok\b|done", self.row_name(int(idx_m.group(1))), re.I):
chosen_list = chosen_list[:i]
break
if (self.nudge_repeats and len(chosen_list) == 1 and self.history
and self.history[-1].startswith(chosen_list[0] + " ->")):
self.history.append(f"{chosen_list[0]} -> REPEATED with no progress; that approach is exhausted, pick a different element or action type")
@@ -421,5 +423,9 @@ def build(name: str, model: str = "", endpoint: str = "", **_: Any) -> Any:
return OpenSwarmLlmPolicy(name=name, multi=True, vision="progressive", fastpath=True, **v10)
if name == "osw-llm-v11": # v10 + coord-click rewrite + submit-chain split + untruncated payloads
v11 = dict(v7, system=OSW_SYSTEM_V8, max_tokens=500)
return OpenSwarmLlmPolicy(name=name, multi=True, vision="progressive", fastpath=True, **v11)
return OpenSwarmLlmPolicy(name=name, multi=True, vision="progressive", fastpath=True,
split_submit=True, **v11)
if name == "osw-llm-v12": # v10 exactly, chain-split off; run with --max-steps 36 for the long forms
v12 = dict(v7, system=OSW_SYSTEM_V8, max_tokens=500)
return OpenSwarmLlmPolicy(name=name, multi=True, vision="progressive", fastpath=True, **v12)
raise SystemExit(f"unknown arm: {name}")