From 9f4acf7823167c274a5a64b0503408fb9ff2d16e Mon Sep 17 00:00:00 2001 From: ciregenz Date: Sun, 16 Aug 2026 18:22:12 -0700 Subject: [PATCH] arena: competitor note -- Polar's 98.0 is self-graded on own benchmarks (tuned product, not generic); not comparable, don't chase --- e2e/browser-v3/arena/ARENA.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/e2e/browser-v3/arena/ARENA.md b/e2e/browser-v3/arena/ARENA.md index 00619bc6..2404f86b 100644 --- a/e2e/browser-v3/arena/ARENA.md +++ b/e2e/browser-v3/arena/ARENA.md @@ -961,3 +961,17 @@ table role, question-word goal, quoted target) or is pure harness correctness (g truncated, quote-aware action parser). Test applied to each: "would it help on a website the agent has never seen?" — YES for all shipped; NO (hence DECLINED, prose-only) for the api/selector bypasses. Overfitting boundary holds; real-world generalization preserved. + +## Competitor note: Polar / Recursive Intelligence (2026-07-29 launch, reviewed 2026-08-16) +Claims 'world's most powerful browser agent', 98.0 on 'Odysseys' + 95.0 on 'BU Bench V1' beating +Browser Use / GPT-5.4 / Claude Opus 4.6. ASSESSMENT: both benchmarks are Polar's OWN (self-scored +via their polar-evals repo) -- vendor-graded, the category our method excludes. Tell: on the +non-home 'BU Bench V1' the field compresses (Polar 95 vs Aside 93 vs Browser Use 89.5) vs the +98-vs-70 blowout on their own 'Odysseys'. Polar is a TUNED MULTI-MODEL PRODUCT (their words: agent +orchestration, model mixing, per-user memory, 15h tasks) -- same class as browser-use Cloud, NOT a +generic single-harness agent. Not comparable to our third-party-scored generic numbers; a neutral +WebArena/MiniWoB run of Polar would be. LEARN-FROM (not a number): their 4 pillars (generalizable +UI tools, agent orchestration, model-mixing, durable memory) = the planning/memory axis our SOTA +scan flagged as our weak side; their memory system == the durable cross-page memory gap we found +(v37 note-scratchpad, deferred). Does NOT change our standing or warrant chasing a self-graded +number by overfitting.