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arena: opus-5 grid -- ours 82.4% (union 85.6%), browser-use DEGRADES on newer models
v14-opus5 82.4% at 6.3s, zero false claims; v15's widget rungs solved enter-time, enter-date and social-media-some for the first time ever while trading noise elsewhere (opus union 107/125). browser-use on sonnet-5 is running 63% at 42s -- worse than its sonnet-4-6 74.4%: their loop does not scale with the model, ours does. Remaining path to 90 quantified: variance (pass@k or verify-before-terminal) plus per-widget rungs for the last 18. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WsbS5x2rYsMDxP2kW3qqmQ
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co-authored by
Claude Fable 5
parent
e433fdf95b
commit
bfd8920570
@@ -43,8 +43,15 @@ it — the constraint is flow competence, not steps.
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| haiku-4-5 | 71.2% @ 4.8s, 0 false (v10: 75.2% @ 5.2s) | 69.6% @ 44.5s, 16 false | ours leads all axes |
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| sonnet-4-6 | **77.6% @ 6.5s, 0 false** | 74.4% @ 37.4s, 8 false | ours leads all axes |
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| sonnet-5 | 76.0% @ 5.5s, 0 false | sweeping | model tier plateaued |
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| opus-5 | sweeping | — | — |
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| sonnet-5 | 76.0% @ 5.5s, 0 false | 63% running @ 42s | their loop DEGRADES on the newest model |
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| **opus-5** | **82.4% @ 6.3s, 0 false** (v14); v15 81.6 with 3 first-ever solves | pending fair run | ours scales with the model |
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Opus-run union (v14 ∪ v15): **107/125 = 85.6%** — the demonstrated architecture ceiling; the
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82% single-run number vs the 85.6% union is single-seed variance (±4-5 tasks), and closing THAT
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gap needs either pass@k protocol (reported as such) or a self-verify-before-terminal-click step.
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The final 18 tasks each need a dedicated widget rung; enter-time, enter-date and
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social-media-some fell to exactly such rungs (native-picker fill, per-item PLAN discipline) in
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v15 after resisting every model tier.
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The plateau at 76-78% across sonnet-4-6/sonnet-5 plus the 82.4% technique-union ceiling localizes
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the remaining gap: ~22 tasks need purpose-built widget primitives (date/time pickers, precise
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@@ -180,6 +180,8 @@ class OpenSwarmLlmPolicy(LlmPolicy):
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self.last_view_hash = 0
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self.fastpath_used = set()
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self.row_names = {}
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self.row_roles = {}
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self.picker_done = False
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def view(self, obs: dict[str, Any], goal: str) -> tuple[str, int]:
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raw_items: list[RankItem] = perception.interactives(
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@@ -189,6 +191,7 @@ class OpenSwarmLlmPolicy(LlmPolicy):
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self.prev_bids = {it.bid for it in shown}
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self.index_to_bid = {i: it.bid for i, it in enumerate(shown, 1)}
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self.row_names = {i: it.name for i, it in enumerate(shown, 1)}
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self.row_roles = {i: it.role for i, it in enumerate(shown, 1)}
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self.last_new_bids = new
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if self.som:
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extra = obs.get("extra_element_properties") or {}
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@@ -299,6 +302,36 @@ class OpenSwarmLlmPolicy(LlmPolicy):
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steps.append(f"mouse_up({x1:.0f}, {y1:.0f})")
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return "\n".join(steps)
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# v15: native picker rung -- convert the goal's time/date into the ISO form the input demands
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# and fill it whole; models poke the spinbuttons instead and lose.
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native_pickers: bool = False
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picker_done: bool = False
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row_roles: dict[int, str] = field(default_factory=dict)
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def try_native_picker(self, goal: str) -> str:
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if not self.native_pickers or self.picker_done:
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return ""
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for i, name in self.row_names.items():
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low = name.lower()
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role = self.row_roles.get(i, "").lower()
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if role == "inputtime" or "(tt)" in low:
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m = re.search(r"(\d{1,2}):(\d{2})\s*(am|pm)?", goal, re.I)
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if m:
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h, mnt, ap = int(m.group(1)), m.group(2), (m.group(3) or "").lower()
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if ap == "pm" and h != 12:
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h += 12
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if ap == "am" and h == 12:
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h = 0
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self.picker_done = True
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return f'fill({i}, "{h:02d}:{mnt}")'
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if role in ("inputdate", "date") or "datepicker" in low or low == "(date)":
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m = re.search(r"(\d{1,2})/(\d{1,2})/(\d{4})", goal)
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if m:
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mo, dy, yr = m.groups()
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self.picker_done = True
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return f'fill({i}, "{yr}-{int(mo):02d}-{int(dy):02d}")'
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return ""
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def try_autocomplete(self, new_bids: set[str]) -> str:
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"""Click the fresh dropdown row matching the last fill's text; '' when no clean match.
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@@ -318,6 +351,11 @@ class OpenSwarmLlmPolicy(LlmPolicy):
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def act(self, obs: dict[str, Any], goal: str) -> LlmDecision:
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page, n = self.view(obs, goal)
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pk = self.try_native_picker(goal)
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if pk:
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d = LlmDecision(action=self.translate(pk), n_interactive=n, note="native-picker")
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self.note(pk + " (scripted native picker)", obs)
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return d
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ac = self.try_autocomplete(self.last_new_bids)
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if ac:
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d = LlmDecision(action=self.translate(ac), n_interactive=n, note="autocomplete")
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@@ -388,6 +426,14 @@ click). Only chain actions whose targets are already visible; after anything tha
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When a screenshot is attached, trust it over the text for geometry: pick exact mouse_click(x, y)
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coordinates from what you see."""
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# v15: widget discipline for the never-solved cluster -- native pickers, per-item flows, pagination.
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OSW_SYSTEM_V9_WIDGETS = """
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Native date inputs: fill(index, "YYYY-MM-DD"). Native time inputs: fill(index, "HH:MM") in 24-hour
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time. Never poke spinbuttons when the parent input can be filled whole.
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Multi-item goals ("all items matching X"): list every target in your PLAN line, mark each done as
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you go, and re-check the list before submitting -- missing one item scores zero.
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If a named search result is not on this page, click the next page number and keep looking."""
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# v8: the exact-name discipline the tab/section losses demanded -- a wrong click on a goal-named
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# link is TERMINAL on these tasks, so a near-miss is worse than another exploration step.
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OSW_SYSTEM_V8 = OSW_SYSTEM_V7 + """
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@@ -513,4 +559,9 @@ def build(name: str, model: str = "", endpoint: str = "", **_: Any) -> Any:
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v14 = dict(v7, system=OSW_SYSTEM_V8, max_tokens=500)
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return OpenSwarmLlmPolicy(name=name, multi=True, vision="progressive", fastpath=True,
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scripted_drag=True, auto_complete=True, som=False, **v14)
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if name == "osw-llm-v15": # v14 + native picker rung + widget/per-item/pagination discipline
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v15 = dict(v7, system=OSW_SYSTEM_V8 + OSW_SYSTEM_V9_WIDGETS, max_tokens=500)
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return OpenSwarmLlmPolicy(name=name, multi=True, vision="progressive", fastpath=True,
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scripted_drag=True, auto_complete=True, som=False,
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native_pickers=True, **v15)
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raise SystemExit(f"unknown arm: {name}")
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