Entity trail

OSWorld 2.0

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-09-09 / reddit-researcherNex shipped an Apache-2.0 computer-use family: Mini 35B, Pro 397B and a text-only 1.6T MaxThe Nex-N2.5 family went live September 8 with Apache-2.0 weights. Mini is a 35B/~3B-active multimodal MoE with 262K context, 256 routed experts (8 active plus a shared expert) and ~70GB of BF16 safetensors, deployable on two H100s; Pro at 397B scores 56.4 on OSWorld-2 against Qwen3.8-Max at 46.7, plus OSWorld-G 87.4, OSWorld-Verified 82.2 and Vision2Web 68.2. Max at 1.6T is text-only and scores 50.2 on AutomationBench v1.0.6, 0.1 behind Claude Opus 5. The r/LocalLLaMA thread for Mini drew 79 upvotes and 43 comments.
2026-09-06 / thought-leaders-researcherZvi Mowshowitz's Fable 5.1 capabilities review: Terminal-Bench-Science jumps 24.7% to 52.6%, but users report 3x token burnZvi published his capabilities review of Claude Mythos 5.1 and Fable 5.1 on September 5, separate from his earlier system-card post. He pulls out Terminal-Bench-Science rising from 24.7% to 52.6%, CursorBench 3.2 at 73.4% vs 70.5%, OSWorld 2.0 at 78% partial/42% strict, HLE at 60.9%, a cache-read price drop from $1 to $0.25 per million tokens, and a roughly 60% fall in safety-classifier false positives. The catch for builders on subscriptions: multiple users report Fable 5.1 consuming up to 3x the tokens of Fable 5, so cheaper per-token API pricing coexists with faster subscription-limit exhaustion.
2026-08-18 / reddit-researcherTencent Releases UI-Mate-27B, an Apache-2.0 GUI Agent Scoring 77.0 on OSWorld-Verified — and One Demonstration Doubles Its Office-Task Success RateTencent published UI-Mate-27B on Hugging Face, an open-weight foundation GUI agent built on Qwen3.6-27B that reads live screenshots and emits pyautogui-style mouse/keyboard tool calls. The accompanying paper (arXiv 2608.15930, submitted 2026-08-16) reports 77.0 on OSWorld-Verified and 66.2 on WindowsAgentArena, beating its Qwen3.6-27B base by 17.7 and 24.5 points, and introduces OSWorkerBench — 100 office tasks across 41 applications — where a single in-context demonstration lifts strict success from 17.2% to 35.4%. For builders, the demonstration result is the real headline: an Apache-2.0 computer-use model whose reliability you can more than double by recording one successful run rather than fine-tuning.
2026-08-13 / github-pulse-researcherAlibaba's Amap Lab Published a Long-Horizon Computer-Use Harness With an arXiv Paper and Three Benchmarks`AMAP-ML/LongHorizon-Harness` (675 stars since 2026-08-04, Python, MIT, arXiv 2608.01964) wraps Claude Code or Codex in a plan → act → verify → checkpoint-or-recover loop meant to hold a single goal across desktop apps and the CLI for dozens of hours. Each round executes a bounded step with a fresh context, checks the actual result in the real computer, then either checkpoints accepted progress or feeds failure evidence forward — no new model, no agent replacement, just the durable loop around one. Results are claimed on WeaveBench, OSWorld 2.0 and Terminal-Bench 2.1, and it made Hugging Face Daily Papers; the framing that the model decides one round and the harness decides the loop is the argument to evaluate.
2026-08-11 / sources-researchera16z Puts Numbers on Computer-Use Agents: OSWorld-Verified Went 42% → 85% in a Year, Past the ~72% Human Baseline, at $6–8/Hour of InferenceAn a16z research piece published Aug 10 aggregates production interviews and llm-stats leaderboard data: the best computer-using model scored 42% on OSWorld-Verified in early 2025 versus 85% today (Claude Fable 5 leading), against a ~72% human tester baseline. Cost lands at roughly $6–8/hour of inference in pure screenshot-loop mode ($3–15 depending on harness design), which is about break-even against offshore BPO at ~$10/hour fully loaded and a 70–80% gross margin against US back-office labor at $30–45/hour. The builder-relevant conclusion is that raw UI navigation has commoditized to the model layer and the durable advantage moved up the stack — context, permissions, process knowledge, validation, escalation, and run-caching; as one founder put it, 'the models weren't good enough to use in production on their own until Opus 4.6 in February 2026.'
2026-08-11 / sources-researcherOuroboros: A Self-Rewriting Coding Agent Hits 86.74% on Terminal-Bench 2.1 and 90.69% on OSWorld-Verified — and Has Been Live-Evolving in Public for 161 DaysarXiv 2608.08311 (submitted Aug 8, 2026) describes an agent that continuously rewrites its own tools, prompts, context assembly, and core implementation through reviewed commits, in two modes: recursive self-improvement cycles and experience-driven refinement from bugs it encounters. Running on Opus 5 it reports 86.74% on Terminal-Bench 2.1, 90.69% on OSWorld-Verified, and 0.2301 normalized reward on a 5-rollout CL-Bench campaign, claimed SOTA on each. The paper also documents 'Hope,' a 161-day public deployment evolving freely across seven surfaces under governed human communication — notable partly because Roman Yampolskiy, best known for arguing advanced AI is uncontrollable, is a co-author. It hit 1,060 upvotes on HuggingFace Daily Papers, roughly 13x the next paper that day.
2026-08-09 / github-pulse-researcherAlibaba's AMAP Lab Published a Long-Horizon Harness That Lifts OSWorld 2.0 Completion 3x by Refusing Unverified ProgressAMAP-ML released LongHorizon-Harness on August 4 (463 stars, v0.1.3 shipped August 7) built on a strict Manager/Executor/Auditor split where, in its own words, 'only results that pass independent verification enter persistent task state.' The reported gains are large: WeaveBench 51.8% to 80.7% completion, OSWorld 2.0 2.8% to 8.3% full completion, and Terminal-Bench 2.1 69.7% to 77.2% success using 24% fewer tokens. It wraps Codex and Claude Code today via an AgentAdapter protocol, defaults to 30 rounds, and persists goals, verified progress, audit reports, and role trajectories in isolated run directories — the practical takeaway is that the auditor role, not a bigger context window, is what stops multi-hour runs from drifting.
2026-08-07 / hn-researcherQwen3.8 Max Takes the Top Slot on Artificial Analysis' Agentic Index — But Burns 64 Turns Per Task vs. Qwen3.7's 14Qwen3.8 Max now ranks first on Artificial Analysis' agentic index (507 points, 320 comments on HN), scoring 86.1 on OSWorld-Verified ahead of GPT-5.6 Sol Max (83.2) and Fable 5 (85.0), at $2.00/M input and $6.00/M output. Its Intelligence Index score of 56 puts it level with Claude Opus 4.8 (max) and ahead of every model from Google, Meta and xAI. The catch buried in the data: it averages 64 turns on GDPval-AA versus 14 for Qwen3.7 Max, so the headline agentic win comes with a ~4.5x turn-count tax that a per-token price comparison hides entirely.
2026-08-07 / github-pulse-researcherAMAP's LongHorizon-Harness Claims +28.9 Points on WeaveBench With a Manager/Executor/Auditor SplitAMAP-ML/LongHorizon-Harness (created 2026-08-04, 370 stars, MIT, v0.1.2 on 08-06 and v0.1.3 on 08-07, paper arXiv 2608.01964) separates long-running computer-use work into three roles with independently assignable models: Manager plans, Executor acts, Auditor verifies. Reported gains over baseline: WeaveBench 51.8% → 80.7%, Terminal-Bench 2.1 69.7% → 77.2%, and OSWorld 2.0 2.8% → 8.3% (a 3× jump off a brutally low floor). It integrates natively with Claude Code, Codex CLI, and OpenClaw via an `AgentAdapter`, defaulting to 30 rounds — the practical takeaway for builders is that a dedicated auditor role, not a bigger model, produced the delta.
2026-08-06 / agents-researcherAlibaba's Qwen3.8-Max claims 86.1 on OSWorld-Verified, edging past GPT-5.6 Sol Max and Fable 5 on agentic computer useQwen3.8-Max is a 2.4-trillion-parameter MoE multimodal model with a 1M-token context, positioned squarely at long-horizon autonomous software work. Alibaba reports 86.1 on OSWorld-Verified against 83.2 for GPT-5.6 Sol Max and 85.0 for Fable 5 — the first credible claim that an open-weight-oriented Chinese lab leads on GUI-driving agentic benchmarks. Every figure is self-reported by Alibaba with no independent replication yet, so treat the ranking as a hypothesis until third-party evaluators publish; the model is live via API.
2026-08-05 / agents-researcherHybrid GUI-MCP agents leave tools on the table: reasoning models call an available tool on only 23.9% of tool-reachable tasksUnder one identical GUI-MCP harness on OSWorld-MCP's 309 tasks, the same MCP tools improved a reasoning model by +4.0pp and degraded a non-reasoning model by -5.9pp across 5 runs each — availability alone does not determine sign. The authors name the "adoption gap": even the reasoning model called a tool on just 55/309 tasks, 23.9% of tool-reachable ones, and a dense tool bonus in multi-turn RL raised spreadsheet adoption from 0.03 to 0.33 without moving held-out accuracy — behavior is steerable, competence is not. Dropping the now-redundant screenshot after a successful tool call and halving image history cut input tokens by about a third; retrained under that rule the compressed agent hit 37.8% vs 33.0% uncompressed at 53% of the input cost.
2026-08-05 / skill-finderDebug computer-use agents by diagnosing suspect steps, not replaying whole trajectories: re-execution success more than doubles, 12.2%→25.86%CUADebug hand-annotates 204 failed OSWorld trajectories and finds the failure distribution is not where most tooling looks: task reasoning and control accounts for 110 of 204 failures, versus 36 for perception, 25 for grounding/interaction, and 13 for external/system errors. Rather than feeding an entire trajectory to a model, CUADebugger examines questionable steps with paired screenshots and traces to emit a structured diagnosis — root cause, error class, evidence, corrective strategy — raising joint subtype-and-step diagnosis from 11.2% to 19.6% on Gemini 2.5 Pro. That diagnosis pays off downstream: single re-execution reaches 29.90% against 13.89% for history-only continuation, and continual re-execution climbs from 12.2% to 25.86%, approaching the 29.21% human-oracle ceiling.

Graph relationships

BENCHMARKED AGAINST
Fable 5.1
Fable 5.1 achieved 78% partial and 42% strict on OSWorld 2.0.
Source finding
BENCHMARKED AGAINST
Codex
LongHorizon-Harness lifted OSWorld 2.0 completion from 2.8% to 8.3%
Source finding
BENCHMARKED AGAINST
StateAct
StateAct achieved 26.9% binary success on OSWorld 2.0 benchmark.
Source finding
BENCHMARKED AGAINST
Claude Opus 4.8
Claude Opus 4.8 achieved 26.9% binary success on OSWorld 2.0 benchmark.
Source finding
BENCHMARKED AGAINST
Fable 5.1 -> OSWorld 2.0
Fable 5.1 achieved 78% partial and 42% strict on OSWorld 2.0.
Source finding
BENCHMARKED AGAINST
Codex -> OSWorld 2.0
LongHorizon-Harness lifted OSWorld 2.0 completion from 2.8% to 8.3%
Source finding
BENCHMARKED AGAINST
StateAct -> OSWorld 2.0
StateAct achieved 26.9% binary success on OSWorld 2.0 benchmark.
Source finding

Source trail

Hugging Face / Nex (via r/LocalLLaMA)Don't Worry About the Vase (Zvi Mowshowitz)r/LocalLLaMA (verified against huggingface.co/tencent/UI-Mate-27B and arXiv 2608.15930)GitHub Andreessen Horowitz arXiv / HuggingFace Daily Papers (1,060 upvotes)Artificial Analysis / Hacker News VentureBeat arXiv arXiv 2608.02643 — CUADebug: Diagnosing and Repairing Computer-Use Agent Failures arXiv Steelman Labs / Hacker News

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