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Question: Will a publicly available open-weight model be credibly reported as achieving CyberGym Level 1 ≥70.0% (Trials=1, full 1,507 tasks) by 2026‑08‑12? As of 2026‑05‑28 there is no qualifying public report, but Z.ai’s GLM-5.1 is self‑reported on Hugging Face at CyberGym = 68.7%—only about 1.3 percentage points (≈20 tasks) shy of the 70% threshold—and open-weight scores jumped rapidly from the 40s to high‑60s in Q1–Q2 2026. Closed/hosted systems have already crossed 70+, showing the capability frontier exists, but most high scores are self‑reported and protocol heterogeneity (Trials=1 vs best‑of‑N, harness details, closed helpers, etc.) leaves substantial uncertainty. Forecasters agree the resolution hinges on a credible public report or leaderboard row that explicitly meets the strict Trials=1/full‑benchmark/no‑closed‑helpers rules; they diverge slightly on probability—some put a bit over even odds concentrated in June–July, others slightly under even odds with an open upper tail. Key monitoring items are official CyberGym leaderboard rows, model cards (Hugging Face/GLM/Moonshot/Qwen), and independent evaluations that name Level 1, full 1,507 tasks, single‑run Trials=1, and no disallowed aids. A GLM‑5.1 successor, an allowed harness improvement, or a re‑evaluation that moves 68.7 by ≥1.3 points while preserving the strict protocol would likely resolve the question.
Forecast: Probability distribution:
The strongest common evidence across the analysts is that the current open-weight frontier is already very close to the threshold: multiple forecasters cite a current CyberGym/leaderboard result for GLM-5.1 at 68.7% on Level 1 with Trials = 1, leaving only a 1.3 percentage point gap, or about 20 tasks out of 1,507. If that evidence is correct, the question is not asking for a dramatic capability breakthrough so much as a modest additional gain plus a qualifying public report. That materially raises the probability of resolution in the next few months. Forecasters 2 and 4 appropriately emphasize model release cadence and the possibility that a newly released open-weight model such as a DeepSeek, Qwen, or GLM follow-on gets evaluated soon. They are directionally persuasive, but I think both are somewhat too aggressive on timing because full-benchmark evaluation and publication often lag model release, and many seemingly relevant reports can fail the strict criteria. Forecaster 3 usefully highlights those criteria risks: Trials = 1 matters, older public CyberGym writeups showed a large gap between one-trial and many-trial performance, and not every headline score or third-party tracker entry necessarily documents the exact qualifying protocol. However, that forecast appears too pessimistic because it leans heavily on older 2025-era baselines and seems to underweight newer leaderboard evidence that open-weight models are already near 70% in the required setting. Forecaster 1 has solid evidence but its written probability judgment is internally inconsistent with its percentile table, so I treat it as informative but not decisive. My synthesis is that the event is more likely than not to happen before the administrative deadline, but not close to certain. The main drivers are: current open-weight proximity to the threshold, several plausible release/evaluation paths for a first credible report, and strong incentives for labs to publicize a crossing if they have it. Offsetting that are protocol disqualification risk, reporting delays, and the possibility that 68.7% is nearer a local plateau than the bullish analysts assume. Net result: I place the median in mid-July and about four-fifths of mass on or before the Aug 12 bound, with a meaningful open-after tail.
Compared with a typical amateur forecast, this estimate avoids two opposite mistakes: assuming that a model already at 68.7% means a qualifying report is imminent within days, or anchoring on older single-trial CyberGym results and concluding that the milestone is probably far away. I split the difference by taking the near-threshold leaderboard evidence seriously while still pricing in the strict resolution criteria and evaluation-publication lag. I also represent deadline uncertainty correctly with percentiles past Aug 12 instead of stacking mass at the bound. Confidence in improvement is moderate: the evidence that open-weight models are close is strong, but recent-model evaluation timing is genuinely hard to call.
Forecast rationale (numeric):
— Iteration 1 — Across the rationales, the main view is that CyberGym Level 1 is already within reach technically, but the open-weight constraint makes timing uncertain.
The combined picture is that 70%+ on CyberGym Level 1 by an open-weight model is likely eventually, but the cutoff-date resolution is uncertain. The forecasts cluster around a scenario where rapid open-source replication of agentic/coding systems could produce a qualifying result near the August 2026 deadline, while a more conservative interpretation places achievement sometime in early 2027 after further engineering and model releases.
— Iteration 2 —
Overall view:
The milestone is seen as close in capability terms but uncertain in verification terms. Most reasoning places the first qualifying open-weight model sometime between mid-2026 and 2027, with a long tail beyond that if strict evaluation evidence is slow to appear.
The performance gap looks small.
Proprietary models are already above 70%, and the leading open-weight candidate is reported around 68.7%, implying only a modest step is needed.
Rapid improvement in model systems is expected.
The forecasts assume continued gains from better model releases, agent harnesses, and evaluation setups, making a near-term crossing plausible.
Public benchmarks create strong incentives to publish.
Because the benchmark is visible and competitive, there is motivation for teams to release and publicize a qualifying result quickly.
Strict resolution criteria may delay recognition.
A score counts only if it is:
Existing headline scores may not survive scrutiny.
Some reported open-weight results may rely on aggregator protocols, partial evaluations, or methods that would not qualify under strict rules.
A “qualified” result requires both capability and documentation.
The issue is not just whether the model can do it, but whether someone publishes a result that is unambiguously valid under the benchmark rules.
The collective reasoning suggests that an open-weight model achieving 70%+ on CyberGym Level 1 is likely soon in capability terms, but the actual event date depends heavily on whether a protocol-compliant, publicly documented result appears promptly.
— Iteration 3 — The forecasts converge on a few core ideas:
The main bottleneck is not benchmark availability, but an eligible open-weight release.
The benchmark and evaluation setup are public, and closed/frontier systems already appear to clear the bar. The key question is when an open-weight model will both exist and be credibly reported under the strict rules.
Near-term optimism is driven by rapid open-weight catch-up.
One view is that once a target is visible, the open-model ecosystem can move quickly, especially with strong agentic scaffolding, harness improvements, and recent advances in frontier models. This pushes a large share of probability into late July to early August 2026.
Conference timing may matter.
The late-summer window is associated with major security events like Black Hat and DEF CON, which could motivate announcements, demos, or evaluations before the August 12, 2026 resolution date.
Strict resolution criteria make immediate success harder than the raw capability gap suggests.
The forecasts repeatedly note constraints such as:
The collective reasoning is that this is a race between capability catch-up and release/evaluation friction: if a frontier open-weight model is released soon and evaluated cleanly, the benchmark could be met in late summer 2026; if not, the most likely resolution shifts well into 2027+.
The rationales unanimously agree that an open-weight AI model crossing the benchmark threshold is imminent. This consensus is driven by a remarkably narrow performance gap, the rapid evolution of agentic frameworks, and a strict time constraint tied to the resolution criteria.
Current Baseline and the Capability Gap Forecasters highlight that the current state-of-the-art for open-weight models is already just a fraction of a step away from the target, with existing models (like GLM-5.1) falling short by merely a few dozen tasks. Because closed-weight frontier models have already comfortably exceeded the target, the benchmark is proven to be solvable. Given the historically short lag between closed and open-weight capabilities, forecasters expect open models to natively possess the requisite reasoning very soon.
The Power of Agentic Scaffolding A central argument across all rationales is that crossing the finish line does not strictly require a new, massive foundation model. Because the benchmark allows for test-time compute—such as multi-agent frameworks, automated fuzzing, and execution feedback loops—researchers can likely bridge the remaining gap simply by pairing already-released models with highly optimized execution harnesses.
Impending Evaluations and Conference Season Recent and anticipated releases of highly capable open-weight models (e.g., DeepSeek, Llama, and Qwen variants) are widely believed to have the latent capability to succeed. The timeline is heavily influenced by the resolution criteria’s strict mid-August cutoff for model weight availability. Forecasters note that this deadline perfectly aligns with the traditional summer cybersecurity conference season (such as DEF CON, Black Hat, and USENIX), which serves as a natural catalyst for researchers to publish benchmark evaluations and new multi-agent exploits.
Pessimistic Scenarios While the bulk of expectations lean toward a very rapid resolution via an arXiv preprint or a leaderboard update in the near term, forecasters acknowledge a slight risk of delay. The primary bottleneck is the heavy compute and orchestration required to run thousands of complex agentic tasks. If current models face structural asymptotes in context management, or if independent labs struggle to reliably scale these automated evaluations, the qualifying report may be delayed until late in the year, relying heavily on complex harness engineering applied to a pre-August model.
Question: When will an open-weight AI model achieve 70%+ on CyberGym Level 1? Final Prediction: Probability distribution:
Total Cost: extra_metadata_in_explanation is disabled Time Spent: extra_metadata_in_explanation is disabled LLMs: extra_metadata_in_explanation is disabled Bot Name: extra_metadata_in_explanation is disabled
Forecaster 1: Probability distribution:
Forecaster 2: Probability distribution:
Forecaster 3: Probability distribution:
The research reports that as of May 28, 2026 the CyberGym Level 1 leaderboard (self-reported and unverified as of May 7, 2026) shows closed/API models leading (MDASH multi-model 88.4%, Anthropic Claude Mythos Preview 83.1%, OpenAI GPT-5.5 81.8%, Anthropic Claude Opus 4.6 73.8%) while the highest-ranked open-weight/open-source model (Zhipu AI) is at 68.7%, i.e., 1.3 percentage points below the 70% threshold. CyberGym Level 1 is a UC Berkeley benchmark introduced Oct 8, 2025 that measures single-trial proof-of-concept exploit generation across 1,507 real-world vulnerability reproduction tasks from 188 open-source projects; the leaderboard entries are currently marked “self-reported and unverified,” which creates a major caveat about whether the Zhipu result meets strict criteria (public downloadable weights, Trials=1 compliance, and protocol adherence).
The research summarizes trajectory and context: closed-model CyberGym performance rose from ~28.9% in Oct 2025 to ~88.4% by May 2026 (~60 percentage points in ~7 months, ~8.5 pp/month), open-weight models have rapidly improved on related software-engineering benchmarks (e.g., open-weight MiniMax M2.5 reached 80.2% on SWE-bench by Feb 2026), and the open-weight vs closed-model gap has compressed to roughly 0–20 pp depending on model. Key uncertainties flagged are verification status of the Zhipu leaderboard entry, the short remaining time window to Aug 12, 2026 (about 2.5 months), whether the final ~1.3 pp is achievable via minor tuning vs requiring new model releases, and strict definition compliance (public weights, no browsing/assistance, Trials=1). The research also notes no dedicated prediction market was found for this exact question and highlights rapid growth and incentives in the open-weight ecosystem as contextual factors.
Sources used in the research (as cited; no direct URLs were provided in the research text):
(If you want the original URLs for any of the above sources, I can try to locate and provide them.)
CyberGym Level 1 Leaderboard Status:
As of May 28, 2026, the CyberGym Level 1 leaderboard shows the following performance [1][2]:
Closed/API Models:
Open-Weight/Open-Source:
Critical Finding: An open-weight model is already at 68.7%, only 1.3 percentage points below the 70% threshold required for resolution.
CyberGym Level 1, introduced by UC Berkeley on October 8, 2025, evaluates AI agents on 1,507 real-world vulnerability reproduction tasks across 188 open-source projects [1][4]. The benchmark requires generating proof-of-concept exploits that trigger vulnerabilities in pre-patch code but not post-patch versions, with success measured on single-trial attempts.
Important Caveat: The leaderboard shows all 6 models as “self-reported and unverified” as of May 7, 2026 [2]. This means the Zhipu AI result at 68.7% may not yet meet all the strict criteria outlined in your question (particularly regarding verification of public weight availability, Trials=1 specification, and protocol compliance).
Historical Progress Rate:
Open-Weight Model Lag:
Parallel Benchmark Performance (SWE-bench):
Open-weight models have shown remarkable progress in coding tasks [8]:
Key Insight: On software engineering benchmarks closely related to CyberGym, open-weight models are achieving performance within 0-5 percentage points of frontier closed models by early 2026 [8].
No Specific Market Found: My search did not identify a dedicated prediction market for this exact question on Polymarket, Manifold, or Metaculus [6][12].
Related Market Context:
Implication: The absence of a specific market for this question suggests it may be relatively new or niche, limiting crowd-sourced probability signals.
Verification Status: Whether the current Zhipu AI 68.7% result meets all strict criteria (publicly downloadable weights, proper protocol, Trials=1, credible reporting standards)
Time Constraint: Only ~2.5 months from question opening (May 28, 2026) to deadline (August 12, 2026) - a very short window for new model releases
Incremental vs. Step-Change: Whether crossing from 68.7% to 70% requires:
Reference Class: AI Benchmark Threshold Crossing
Comparable Benchmark Progression:
The open-weight AI ecosystem is experiencing rapid growth [11]:
Favorable Factors:
Unfavorable Factors:
Key Question: Whether the existing Zhipu AI result (or soon-to-be-verified results) meets all strict criteria, or whether a new model release is required - the former could resolve soon, the latter may require months beyond the August 2026 deadline.
I agree with the critic’s main correction: my previous forecast treated the 68.7% near-miss too much like a smooth capability gap and not enough like a strict public-resolution problem. The benchmark itself is large and agentic—1,507 instances across 188 projects—and Level 1 involves generating working PoCs from the vulnerability description plus unpatched codebase; CyberGym also defines “Trials” as attempts per instance, which is central to this resolution. (cybergym.io)
The near-miss remains very important. GLM-5.1 appears to be genuinely public/open-weight in the relevant sense: its Hugging Face page lists an MIT license and local-use paths, and its model card reports CyberGym 68.7. (huggingface.co) But I now put more weight on the difference between “a model card row exists” and “a resolver accepts this as full-benchmark, Trials=1, no-web/no-closed-helper, non-CyberGym-optimized, protocol-clean.” A 1.3pp gain is only about 20 additional successful tasks, but those tasks may be correlated hard failures rather than independent coin flips.
The critic’s best point is the August 12 weight-availability cliff. Before that date, normal open-weight progress and new releases matter. After that date, most ordinary model progress stops mattering; resolution then depends on delayed reports, clarification, or independent evaluation of models whose weights were already public before the cutoff. That should create a sharp hazard reduction and a fatter right tail than I previously gave.
I still think the critic may be slightly too bearish on the front half. A compliant crossing could come from a new Z.ai/Qwen/DeepSeek/MiniMax-style release, a clean rerun with a better harness, or a developer deciding to document protocol details more fully. Labs are already publishing CyberGym numbers, including for open models, so I do not think the “no one dares publish” argument dominates. But it is enough to move my median from mid-July to after the cutoff.
My updated distribution is a mixture: substantial pre-cutoff mass, a slower post-cutoff reporting/verification phase through late 2026 and 2027, and a long tail for “apparently capable but never cleanly reported” or “no qualifying pre-cutoff model is ever credibly shown.”
Updated prediction:
Percentile 0.1: 2026-05-30
Percentile 1: 2026-06-04
Percentile 5: 2026-06-13
Percentile 10: 2026-06-24
Percentile 20: 2026-07-11
Percentile 30: 2026-07-27
Percentile 40: 2026-08-11
Percentile 50: 2026-09-10
Percentile 60: 2026-12-10
Percentile 70: 2027-04-15
Percentile 80: 2028-01-15
Percentile 90: 2030-01-01
Percentile 95: 2033-06-01
Percentile 99: 2042-01-01
Percentile 99.9: 2055-01-01
Conditioning on the question not being annulled, and assuming no currently published result already counts.
The critic’s strongest point is right: my first forecast overconverted “68.7% is close to 70%” into “the report should arrive very soon.” CyberGym Level 1 is a full 1,507-task vulnerability-reproduction benchmark, and “Trials” is explicitly attempts per instance; a qualifying result needs the right protocol, not just a benchmark-table number. (cybergym.io) The gap from 68.7% to 70.0% is only about 20 extra successes, but those may be systematically hard failures rather than random near-misses.
I also accept the operational-burden argument. The public repo says benchmark data is about 240GB and full server data is about 10TB, and GLM-5.1 itself is a 754B-parameter model, so independent reruns are not trivial. (github.com) This makes “someone cleanly reruns it next week” less likely than I initially implied.
The credibility/protocol point is also material. GeekWire reports that CyberGym leaderboard scores are self-reported and not independently verified. (geekwire.com) Since the resolution criteria require enough detail to rule out easier setups, a bare “CyberGym 70.2” table might not resolve. This especially hurts the early percentiles.
Where I still don’t fully follow the critic: the 68.7% open-weight result remains a very strong inside-view signal. GLM-5.1 is listed as MIT-licensed and locally runnable, and its model card reports CyberGym 68.7. (huggingface.co) I also do not think self-reporting is fatal under this question: the resolution explicitly allows model cards, technical reports, official leaderboards, and recognized evaluations, not only independent audits. If an official leaderboard entry or model-card update clearly says Level 1, Trials=1, full benchmark, open weights, and no closed helpers, I would expect it to count.
I now treat MDASH as weaker evidence than before. It proves the benchmark is highly solvable by current AI systems, but MDASH is a multi-model/multi-agent Microsoft system with 100+ specialized agents and scored 88.45%, which is not directly comparable to a single qualifying open-weight model under the question’s constraints. (geekwire.com)
Net update: I move my median from early July to early August and reduce probability before the 12 August 2026 weight-availability deadline from roughly ~80% to about 58–60%. I still stay slightly more bullish than the critic on the central case because the open-weight threshold is genuinely very close and the first qualifying report need not be an independent audit. But I now put much more mass on late reports, ambiguous 70%+ claims that don’t resolve, and no clean pre-deadline crossing.
Updated prediction:
Percentile 0.1: 2026-06-01
Percentile 1: 2026-06-05
Percentile 5: 2026-06-13
Percentile 10: 2026-06-22
Percentile 20: 2026-07-05
Percentile 30: 2026-07-16
Percentile 40: 2026-07-26
Percentile 50: 2026-08-03
Percentile 60: 2026-08-17
Percentile 70: 2026-09-20
Percentile 80: 2026-11-20
Percentile 90: 2027-04-01
Percentile 95: 2027-10-01
Percentile 99: 2029-01-01
Percentile 99.9: 2031-01-01
I agree with the critic’s central correction: the right forecast is not “when will capability exist?” but “when will there be a protocol-clean public report for an eligible pre-2026-08-12 open-weight model?” Capability may already be very close; resolution is the fragile part.
The key bullish anchor is still real. CyberGym Level 1 is the intended benchmark: 1,507 instances across 188 projects, vulnerability description plus unpatched codebase, and success based on generating a working PoC; CyberGym’s own page also defines “Trials” as attempts per instance, which makes the one-shot requirement material. (cybergym.io) GLM-5.1 is a publicly downloadable MIT-licensed 754B model with local serving instructions, and its model card reports CyberGym 68.7—only about 20 additional successes short of 70% on 1,507 tasks. (huggingface.co)
But I now give more weight to the critic’s “resolution signal vs capability signal” distinction. The GLM-5.1 model card’s CyberGym row is a strong capability signal, but not by itself a complete resolution package: it does not visibly spell out all of Level 1, full 1,507 tasks, Trials = 1, no closed/API helpers, no web, no hidden PoCs/diffs, and no CyberGym-specific optimization. A future sparse “CyberGym 70.2” table row could therefore be ambiguous rather than automatically resolving.
I also accept the critic’s point that the post-cutoff regime is especially weird. After 2026-08-12, new open-weight model releases mostly stop helping unless their weights were already downloadable before then. However, I still think the post-cutoff hazard is not as close to zero as a pure “frozen model” frame suggests: a pre-cutoff model can still be evaluated later, a general-purpose harness can improve, and an official leaderboard/model-card/technical report does not require independent replication. The official CyberGym page’s leaderboard structure—model, trials, success rate, evaluation date, source—also suggests that if a qualifying ≥70 row appears with Trials = 1, the resolver may treat it as credible even if company-reported. (cybergym.io)
My main update from the critic is to move the median later than my previous 2026-08-10, because I had slightly over-translated “68.7 is close” into “a qualifying 70+ report is imminent.” I now put probability by 2026-08-12 at roughly 48–49%, not 52%. But I do not move all the way to the critic’s very late median, because one small open-weight release, leaderboard update, or cleaner GLM-5.1-compatible general agent run before the cutoff remains very plausible.
I also fatten the right tail. If no clean qualifying report appears by late 2026 or 2027, the event becomes much less about normal AI progress and more about whether someone later evaluates stale pre-cutoff weights under a clean, publishable protocol. That leaves a meaningful “very late / effectively never” component.
Updated prediction:
Percentile 0.1: 2026-05-30
Percentile 1: 2026-06-04
Percentile 5: 2026-06-14
Percentile 10: 2026-06-24
Percentile 20: 2026-07-08
Percentile 30: 2026-07-22
Percentile 40: 2026-08-05
Percentile 50: 2026-08-18
Percentile 60: 2026-10-15
Percentile 70: 2027-02-01
Percentile 80: 2028-01-15
Percentile 90: 2030-06-01
Percentile 95: 2036-01-01
Percentile 99: 2060-01-01
Percentile 99.9: 2100-01-01
Modal: After Jun 11, 2027 (28.9%) • partial_shift • 6m41s
Reading: strict
The resolution criteria are highly detailed and restrictive, specifying exact score thresholds (≥70.0%), exact evaluation protocol (Trials=1, full 1,507-task benchmark), precise model eligibility rules (publicly downloadable weights, no API helpers, no benchmark-specific training), and an explicit list of qualifying report types. This narrow, literal framework leaves little room for charitable or inclusive interpretation — each condition must be met precisely.
Total evidence registered (shared pool): ?
| Variant | Perspective | Model | Turns | Tools | Status |
|---|---|---|---|---|---|
| 0 | inside_view (inside_view_v1) | anthropic/claude-sonnet-4-6 | 25 | 41 | OK |
| 1 | outside_view (outside_view_v1) | anthropic/claude-sonnet-4-6 | 25 | 35 | OK |
| 2 | contrarian (contrarian_v1) | anthropic/claude-sonnet-4-6 | 25 | 38 | OK |
Evidence confidence: medium
medium evidence]GLM-5.1’s 68.7% score is accepted as protocol-compliant (e.g., the Claude Code harness issue is reinterpreted as permissible, or Z.ai republishes evaluation results with a compliant all-open-weight harness); OR a GLM-5.2 or comparable open-weight model (e.g., from DeepSeek, Qwen, or another lab) achieves ≥70% in a qualifying run before Aug 12, 2026. The 1.3pp gap between GLM-5.1 and threshold is very small. The rapid open-weight improvement trajectory (per sources 6, 23) and the ~2.5 months remaining until deadline both support this.
GLM-5.1 used Claude Code as harness (per sources 13, 15), which categorically disqualifies it. The score is entirely self-reported with no independent verification (per sources 30–32). No new open-weight model has been announced (per source 37). Independent evaluations show a 19–28% gap between vendor-reported and verified CyberGym scores for other models (per source 22). Open-weight models lag closed frontier by 3.5–10 months (per sources 8, 9). The prediction market commentary suggests true compliant score may be ~55% (per source 10).
high evidence]GLM-5.1’s harness used Claude Code (a closed model), disqualifying its run under the strict resolution criteria. All other open-weight models are far below threshold (Kimi K2.5 at 41.3%). Independent evaluations consistently show substantially lower scores than vendor-reported figures. No new open-weight model near this capability has been announced through late May 2026. The closed-to-open gap on cybersecurity benchmarks is large and growing (per sources 8, 9). Leaderboard activity has stalled (last commit May 22, 2026, per source 36).
GLM-5.1 is only 1.3pp below threshold; a protocol-compliant re-run could easily cross it. Rapid improvement trajectory observed (sources 16, 21). Z.ai or another lab could publish a new qualifying evaluation within the ~2.5 month window. The original CyberGym paper showed rapid capability gains from initial publication to mid-2026.
low evidence]The 68.7% score is the vendor’s reported Trials=1 figure over 1,507 tasks; the resolution criteria allow vendor-reported scores if the report describes the protocol well enough. If the harness issue is found not to disqualify (e.g., Claude Code is interpreted as a code execution environment rather than an AI model call), the leaderboard entry dated April 7, 2026 would already be a qualifying report. The score is ‘credible’ per the official leaderboard listing (per source 14).
The Claude Code harness problem is explicit and directly violates the rule that ‘all AI model calls in the run must be to the qualifying open-weight model only’ (per sources 13, 15). The leaderboard entry itself is self-reported (per source 32). Independent evaluation has not corroborated the score (per sources 30, 39). Lyptus Research shows a large gap between vendor and verified scores for other models (per source 22).
medium evidence]The pace of open-weight model releases has been rapid (DeepSeek, Qwen, Z.ai/GLM releasing major models roughly quarterly). The closed-model frontier is already at 73–83%, providing a target. General coding and cybersecurity capability is improving across the open-weight ecosystem. The ~2.5 month window is non-trivial. A new model from DeepSeek, Qwen, or another lab could be released and evaluated on CyberGym with a fully compliant protocol.
No successor model to GLM-5.1 has been announced (per source 37). The gap between GLM-5.1 (68.7%) and the next open-weight model (41.3%) is enormous, suggesting no near-term competitor is close. Getting from 0% to a compliant, independently-verified 70%+ score in 2.5 months requires both a capable model release AND a qualifying evaluation to be published. The leaderboard has not updated since May 22 (per source 36).
inside_view_v1)Inside View Causal Analysis
Current state → causal chain → outcome probabilities
Key facts driving my forecast:
GLM-5.1 disqualification (high-evidence scenario): Per sources 13 and 15, GLM-5.1’s CyberGym run used Claude Code as the agent harness — a closed API model. The resolution criteria explicitly requires “all AI model calls in the run must be to the qualifying open-weight model only.” This creates a categorical disqualification. The GLM-5.1 score is also entirely self-reported (per sources 30, 31, 32, 39) with BenchLM.ai classifying it as “display only” (source 38). Lyptus Research found 19–28% inflation in vendor-reported CyberGym scores for other models (source 22), suggesting the true compliant score for GLM-5.1 could be ~50-55%.
Large capability gap to the next open-weight model: The next open-weight model (Kimi K2.5) scored only 41.3% (source 12) — a 27.4pp gap below GLM-5.1. This shows GLM-5.1 is an outlier, and the second-best open-weight model is far from the threshold.
No successor announced: No GLM-5.2 or comparable model has been announced through late May 2026 (source 37). The leaderboard last updated May 22, 2026 (source 36).
Survival projection (medium-evidence): Per sources 28 and 29, the survival model projects P(qualifying result by Aug 12) ≈ 0.35–0.55. But this was computed BEFORE fully accounting for the disqualification of GLM-5.1’s existing result — if the threshold hasn’t been crossed by any compliant run yet, the hazard rate needs to be recomputed from scratch.
Causal chain for “no qualifying result by Aug 12”:
- GLM-5.1 is disqualified (Claude Code harness) → No currently-reported result qualifies
- No new model announced → The next qualifying result requires either: (a) Z.ai re-running GLM-5.1 with a compliant harness, or (b) a new capable open-weight model being released and evaluated
- The ~2.5 month window is tight for (b), given typical model release and evaluation cycles
- Even if a model is released, getting it onto the CyberGym leaderboard with a fully compliant protocol (no closed model helpers, no independent internet access, etc.) takes additional time
Causal chain for “qualifying result within window”:
- Z.ai could re-run GLM-5.1 with a compliant harness (pure open-weight agentic framework) and publish that result → this is plausible but requires active effort from Z.ai
- A new frontier open-weight model (DeepSeek successor, Qwen update, GLM-5.2) achieves underlying capability → probability decays exponentially over the remaining 2.5 months
- Labs tend to benchmark on leaderboards as marketing; if a new model achieves this, they’d likely run it quickly
Distribution reasoning:
- The “high-evidence” scenario (no resolution by Aug 12) deserves the most mass (~55-60% total no-resolution or post-deadline).
- The Aug 12–Oct 27 (bin_8) and beyond captures the likely eventual resolution if open-weight models continue their trajectory.
- Within the resolution window (bins 0–7), I estimate ~35-40% probability total.
- Given the fast AI release cadence and Z.ai’s incentive to fix the protocol issue, early bins (June-July 2026) are more likely than late bins within the window.
- The GLM-5.1 score being only 1.3pp below threshold means a re-run could happen quickly if Z.ai addresses the harness issue.
Distribution allocation:
- Bins 0-1 (late May to mid-June): ~6% — some chance Z.ai quickly re-runs with compliant harness
- Bins 2-3 (mid-June to early July): ~10% — window for Z.ai re-run or new model
- Bins 4-5 (early to mid-July): ~9% — potential for new model releases
- Bins 6-7 (late July to Aug 12): ~8% — deadline pressure
- Bin 8 (Aug 12–Oct 27): ~15% — likely resolution period if new models emerge
- Bin 9 (Oct 27–Jan 11): ~15% — further out
- Bins 10-12 (Jan 2027+): ~37% — tail for slow progress or much later resolution
Total within window: ~33%; post-window: ~67%. This reflects the high-evidence “no resolution by deadline” scenario dominating but not being certain.
outside_view_v1)Reference class: No clean historical reference class exists for an “open-weight model hitting ≥70% on CyberGym Level 1 within a ~2.5 month window” because CyberGym is new and there are few analogous open-weight breakpoint events. I therefore start from the brief’s quantitative survival projection (treated as the best available outside-view signal). Base rate: the brief’s survival projections cluster P(by 2026-08-12) ≈ 0.35–0.55 (sources 28, 29); I take the midpoint 45% as the base-rate anchor. Adjustment: -13 percentage points to 32% because the evidence gives several strong reasons to reduce the headline survival midpoint: (a) the highest open-weight entry (GLM-5.1 at 68.7%) is vendor-reported and used Claude Code as the agent harness, which the resolution criteria likely disqualify (sources 6, 13, 15, 19, 30–32); (b) independent re-runs historically show vendor-reported CyberGym scores can be inflated ~19–28% (Lyptus Research, source 22), so vendor self-report uncertainty argues downward; (c) no GLM-5.2 or comparable successor has been announced by late May 2026 to credibly close the 1.3pp gap through a verified run (source 37), and the leaderboard/registry has seen little activity since May 22 (source 36). Final (P(event by 2026-08-12)): 32% (this is the total mass I placed across bins bin_0..bin_7). How I mapped that 32% across date bins: I concentrated probability in mid-June→early-August (bins where an independent re-run or a new model + qualifying evaluation is most likely to appear given normal release/eval lag). Specific bin allocation (sums to 1.0): early windows (bin_0..bin_2) get small chances (2%, 3%, 4%) because immediate qualifying reports are possible but unlikely; mid-June→mid-July (bin_3..bin_5) get larger chunks (5%, 6%, 6%) as the most plausible window for a verified re-run or a new-model release + evaluation; late-July→early-August (bin_6, bin_7) get smaller tails (3%, 3%). The remaining 68% mass is allocated to dates after the August-12 window (bins 8–12) reflecting the brief’s high-evidence scenario that no qualifying open-weight result appears by the deadline (this ‘no-by-deadline’ scenario was tagged high evidence in the brief — sources 13, 15, 30–33, 22). I split the post-deadline mass to reflect plausible later arrival times: near-term post-deadline (bin_8 Aug12–Oct27) 15%; late-2026 (bin_9 Oct27–Jan11) 12%; early-2027 (bin_10 Jan11–Mar28) 10%; spring 2027 (bin_11 Mar28–Jun11) 8%; long-tail (bin_12 After Jun11, 2027) 23%. Weighting rationale vs. brief scenarios: I followed the brief’s instruction to weight by scenario evidence strength — the brief labeled the scenario “No qualifying open-weight model by Aug 12” as high-evidence, so I placed the majority (68%) mass after the deadline; medium-strength scenarios (a protocol-compliant GLM-5.1 re-run or a new open-weight model achieving ≥70% before Aug 12) share the 32% pre-deadline mass (sources 6, 17, 26, 28, 29); the low-strength immediate-qualify scenario (instant independent verification) receives only a few percent. Key factual anchors used inline: GLM-5.1 at 68.7% (best open-weight reported) (sources 6, 17, 27); GLM-5.1 run used Claude Code as harness and is self-reported (sources 13, 15, 30–32); independent re-runs have reduced vendor scores by ~19–28% in other cases (source 22); no GLM-5.2 announced and leaderboard activity stalled as of late May (sources 36, 37). Confidence in evidence: medium (as stated in the brief) — the main quantitative survival projection is useful but sensitive to protocol-interpretation risk (whether the Claude Code harness disqualifies GLM-5.1) and to whether an independent lab will rerun GLM-5.1 or whether a new open-weight model will be released and evaluated before the deadline (sources 13, 15, 30, 37).
contrarian_v2)Adversarial reading
The bulk of the evidence (GLM-5.1 at 68.7% but using Claude Code harness per sources 13/15, entirely self-reported with zero independent corroboration per sources 30-32/39, next open-weight model at only 41.3%, 19-28% vendor inflation gap per source 22, 3.5-10 month closed-to-open lag per sources 8/9, no successor announced per source 37, leaderboard frozen since May 22 per source 36) pulls strongly toward no qualifying report before the August 12 deadline under the strict reading.
Counter-evidence found (or absence)
Searched for counter-evidence; the 1.3pp gap and rapid prior gains are cited but undermined by the categorical harness disqualification, lack of any verified run, and stalled leaderboard activity. No substantive counter-evidence supports an imminent compliant report.
Distribution implications
This places materially less mass on the June-July bins (0-7) than a naive average across scenarios would suggest, and shifts mass into the post-deadline bins (especially bin_12 and bin_11) to reflect the high-evidence scenario that the threshold remains un-crossed for months after August 12.
Strategy: logit_mean • Drafts: 3/3
anchoring_v1The draft rationale explicitly acknowledges GLM-5.1’s categorical disqualification (Claude Code harness), yet the p50 of the within-window distribution still clusters in June–July 2026, treating the modal scenario as “GLM-5.1 re-runs with a compliant harness soon.” This is anchored optimistically on the smallest plausible gap: the 1.3pp deficit to the 70% threshold is treated as implying near-term resolution, but the brief’s high-evidence scenario explicitly states that no qualifying compliant open-weight run exists today, that no GLM-5.2 has been announced, and that vendor-reported scores are inflated 19–28% — implying even a hypothetical compliant re-run of GLM-5.1 may come in well below 70%. The draft’s “inside view” notes ~33% within-window probability but the within-window bins are then shaped as if a June/July resolution is the modal conditional path, rather than a mid-to-late August or post-deadline path. The survival model in sources 28/29 was explicitly noted as “computed BEFORE fully accounting for the disqualification,” yet the draft still anchors on the ~0.35–0.55 range as a base before downward adjustment — the true starting point for a compliant-only hazard model should be substantially lower. Specifically, the draft places ~23% total in bins 0–5 (May 28–July 24), front-loading the distribution in a way inconsistent with the high-evidence “no resolution by deadline” scenario that the brief tagged as dominant.
ceiling_v1The most critical structural ceiling this forecast fails to integrate is the hard disqualification floor imposed by GLM-5.1’s protocol violation. The brief explicitly states (sources 13, 15) that GLM-5.1’s evaluation “was conducted using Claude Code as the agent harness — an external closed API model” — which categorically disqualifies it under the resolution rule that “all AI model calls in the run must be to the qualifying open-weight model only.” This means the highest achieved compliant open-weight score is NOT 68.7% (1.3pp below threshold) but rather effectively Kimi K2.5 at 41.3% — a gap of 28.7pp to the threshold. The draft rationale acknowledges this but the distribution doesn’t fully honor it: allocating ~33% to bins 0-7 (pre-deadline) treats resolution within the window as roughly equiprobable with slightly-post-deadline, when in fact crossing 28.7pp from a compliant baseline within 2.5 months requires either (a) a major new model release + compliant evaluation, or (b) Z.ai re-running GLM-5.1 with a fully open-weight harness and getting independent verification. The 27.4pp gap between the only two open-weight models on the leaderboard (41.3% to 68.7%) and the fact that the leaderboard has been frozen since May 22 with zero new submissions structurally implies the inside-window mass should be materially lower, pushing more probability into bin_8 through bin_12.
math_v1The draft distribution is mathematically coherent and sums to 1.00, and the bin allocations align reasonably with the three-model rationale. However, there is a meaningful internal inconsistency: the rationale explicitly frames GLM-5.1’s Claude Code harness as a “categorical disqualification” (sources 13, 15) and cites high-evidence scenario (“no resolution by Aug 12”) with 68% mass post-deadline, yet allocates ~33% pre-deadline mass. This implies ~33% credence that either GLM-5.1’s harness will be reinterpreted as compliant or a new qualifying model will emerge within 2.5 months. The rationale does not clearly articulate why 33% is the right weight on these joint possibilities given the “categorical disqualification” language—it appears the allocation is anchored partly to the brief’s survival projections (0.35–0.55) before downward adjustment, but the final reasoning for 33% vs. 25% or 40% is not explicit.
Reviews consumed: 3
Both anchoring and ceiling reviewers independently flagged the same structural issue: the draft front-loads ~33% within-window mass treating GLM-5.1’s 1.3pp gap as a near-term signal, but GLM-5.1’s Claude Code harness categorically disqualifies it, making the true compliant baseline Kimi K2.5 at 41.3% (a 28.7pp gap). Both reviewers suggested shift_higher (later/post-deadline) — I accepted this and reduced bins 0-7 from ~33% to ~22%, redistributing into long tail (bin_12) and bins 10-11. The math reviewer suggested shift_lower, but their concern was about transparency rather than direction; their independent forecast actually has a fatter tail in bin_12, consistent with shift_higher in spirit. I partially incorporated the math reviewer’s EV consistency point by shaping within-window mass to peak slightly later (bins 4-5 rather than 2-3), since a Z.ai re-run or new model release is more plausible mid-to-late summer. The dominant high-evidence scenario (no resolution by Aug 12) gets ~78% mass with substantial weight in bin_12 reflecting structural barriers (no successor announced, leaderboard frozen, vendor inflation 19-28%).
| Bin | Probability |
|---|---|
| May 28, 2026 to Jun 07, 2026 (bin_0) | 1.2% |
| Jun 07, 2026 to Jun 16, 2026 (bin_1) | 1.8% |
| Jun 16, 2026 to Jun 26, 2026 (bin_2) | 2.5% |
| Jun 26, 2026 to Jul 05, 2026 (bin_3) | 3.2% |
| Jul 05, 2026 to Jul 15, 2026 (bin_4) | 4.0% |
| Jul 15, 2026 to Jul 24, 2026 (bin_5) | 4.0% |
| Jul 24, 2026 to Aug 03, 2026 (bin_6) | 3.0% |
| Aug 03, 2026 to Aug 12, 2026 (bin_7) | 2.5% |
| Aug 12, 2026 to Oct 27, 2026 (bin_8) | 12.5% |
| Oct 27, 2026 to Jan 11, 2027 (bin_9) | 11.5% |
| Jan 11, 2027 to Mar 28, 2027 (bin_10) | 12.0% |
| Mar 28, 2027 to Jun 11, 2027 (bin_11) | 13.0% |
| After Jun 11, 2027 (bin_12) | 28.9% |
wooley_rhino • run 3e1ecae5 • $4.21 • 6m41s
Extracted Date Percentile Values: {0.1: 1780099200.0, 1.0: 1780444800.0, 5.0: 1781049600.0, 10.0: 1781654400.0, 15.0: 1782259200.0, 20.0: 1782864000.0, 25.0: 1783296000.0, 30.0: 1783728000.0, 35.0: 1784160000.0, 40.0: 1784505600.0, 45.0: 1784851200.0, 46.0: 1784937600.0, 47.0: 1785024000.0, 48.0: 1785110400.0, 49.0: 1785196800.0, 50.0: 1785283200.0, 51.0: 1785369600.0, 52.0: 1785456000.0, 53.0: 1785628800.0, 54.0: 1785801600.0, 55.0: 1785974400.0, 60.0: 1786665600.0, 65.0: 1787616000.0, 70.0: 1788825600.0, 75.0: 1790294400.0, 80.0: 1792281600.0, 85.0: 1794700800.0, 90.0: 1797292800.0, 95.0: 1802649600.0, 99.0: 1811808000.0, 99.9: 1827619200.0}
Model’s Answer:
Based on the research, I need to evaluate the current state:
Current benchmark standings (as of late May 2026):
Critical observations:
Assumption 1: GLM-5.1 at 68.7% may not qualify as truly open-weight
Assumption 2: Labs will continue publishing strong cybersecurity benchmark results
Assumption 3: Current trajectory of improvement continues
Earlier outcome pathway (before July 15, 2026):
Later outcome pathway (after August 12, 2026):
Given that:
I estimate approximately 55% probability of resolution before August 12, 2026, with the remaining 45% extending into late 2026 and potentially 2027 if unexpected barriers emerge.
The distribution should reflect:
Percentile .1: 2026-05-30 Percentile 1: 2026-06-03 Percentile 5: 2026-06-10 Percentile 10: 2026-06-17 Percentile 15: 2026-06-24 Percentile 20: 2026-07-01 Percentile 25: 2026-07-06 Percentile 30: 2026-07-11 Percentile 35: 2026-07-16 Percentile 40: 2026-07-20 Percentile 45: 2026-07-24 Percentile 46: 2026-07-25 Percentile 47: 2026-07-26 Percentile 48: 2026-07-27 Percentile 49: 2026-07-28 Percentile 50: 2026-07-29 Percentile 51: 2026-07-30 Percentile 52: 2026-07-31 Percentile 53: 2026-08-02 Percentile 54: 2026-08-04 Percentile 55: 2026-08-06 Percentile 60: 2026-08-14 Percentile 65: 2026-08-25 Percentile 70: 2026-09-08 Percentile 75: 2026-09-25 Percentile 80: 2026-10-18 Percentile 85: 2026-11-15 Percentile 90: 2026-12-15 Percentile 95: 2027-02-15 Percentile 99: 2027-06-01 Percentile 99.9: 2027-12-01
The benchmark is large, verification-heavy, and requires full public reproducibility, concentrating viable entries among groups prepared to publish runnable artifacts.
The public-download criterion and the single-trial rule are structural constraints that slow rapid, widespread demonstrations even as capabilities advance elsewhere.
Recent public evidence shows closed entrants comfortably above the threshold while the nearest fully public entrant sits just below it by a small absolute margin.
That small absolute gap is plausibly bridgeable without a new foundational release via harness refinements, targeted updates, or improved runtime engineering.
A prompt protocol clarification or an audit-friendly submission pathway could convert a near-miss into a qualifying report almost immediately.
By contrast, a finding that near-threshold runs used permissive setups, extra trials, or external assistance would deflate near-term prospects and push the crossing later.
Key unresolved uncertainties are the exact experimental hygiene of near-miss public runs and whether organizations will publish offensive-capability results before the deadline.
Sensitivity clusters on a handful of tasks and on publication incentives; modest technical or procedural changes could swing the outcome, but procedural ambiguity preserves substantial residual uncertainty.
Median: No consensus (P50 ranges 2027-03 to 2027-05 across analyses)
Enormous current gap: The best confirmed open-weight model scores 3.6% on CyberGym, while all models above 70% are API-only (Claude Mythos 83.1%, GPT-5.5 81.8%). This ~66-percentage-point gap is structurally larger than on general coding benchmarks, where open-weight models already compete.
Unverified near-threshold claim: One truncated source claims GLM-5.1 scored 68.7%, but this appears in no other corroborating source and remains unverified. If real and qualifying, resolution could occur within weeks; if false, the gap remains severe. This single claim drives ~15–20% of early-resolution probability.
Specialized capability barrier: CyberGym tests exploit generation, not just code repair. Open-weight models lag closed models by ~18× on this benchmark versus near-parity on SWE-Bench Pro, suggesting a qualitative capability gap rather than a simple scale difference.
Low community engagement and high infrastructure barriers: CyberGym requires ~240 GB of data and has only 372 GitHub stars and 55 forks. Few independent evaluations exist, slowing the pipeline to a qualifying leaderboard submission even if a capable model is released.
Safety constraints on open-weight cybersecurity models: Anthropic explicitly limits cyber capabilities in publicly accessible models. Regulatory dynamics may further suppress open release of high-capability exploit-generation systems.
Most likely timeframe: If the GLM-5.1 claim is false or unverified, resolution likely occurs in late 2026 to mid-2027 as new frontier models (DeepSeek V4,
When will an open-weight AI model achieve 70%+ on CyberGym Level 1?
Key figures
Historical context
The CyberGym Level 1 benchmark was first introduced in June 2025 with a paper describing 1,507 real-world vulnerabilities. At that time, top models achieved only about 20% success. However, the field transitioned rapidly; by early 2026, the doubling time for reliability in autonomous cyber tasks was measured at just 4.7 months by the UK AI Security Institute. Historical precedents in open-weight models show a pattern of ‘catching up’ to proprietary benchmarks within 6-12 months. For example, the GLM series moved from 48.3% to 68.7% in a single iteration. Similarly, Meta’s Llama series and Alibaba’s Qwen models have historically bridged the gap to GPT-level performance in coding and reasoning within one release cycle. The current gap of 1.3 percentage points is the smallest lead proprietary models have held on this specific benchmark since its inception.
Tailwinds
Headwinds
Detailed reasoning
My analysis indicates that the 70% success rate threshold on CyberGym Level 1 for open-weight models is imminent. The current state-of-the-art for publicly downloadable models is held by Z.ai’s GLM-5.1, which achieved a 68.7% success rate as of May 2026. This model was released in April 2026 and demonstrated a significant leap from its predecessor, GLM-5, which scored 48.3%. This rapid progression suggests that the open-source community is closing the gap with proprietary models at an accelerated pace.
The primary reason for the predicted median date of 17th July 2026 is the convergence of several high-probability pathways. First, GLM-5.1 is only 1.3 percentage points away from the goal. A minor architectural tweak, an optimized agentic harness, or a clarified evaluation protocol (ensuring Trials = 1 on the full 1,507-task set) could trigger resolution within weeks. Second, the UK AI Security Institute reported in early 2026 that the doubling time for AI reliability on autonomous cybersecurity tasks has dropped to approximately 4.7 months. This empirical trend suggests that the incremental improvement needed is likely to occur well before the end of Q3 2026.
Furthermore, the competitive landscape is dense with active open-weight developers. DeepSeek released the preliminary V4 series in April 2026, and Alibaba’s Qwen series continues to focus heavily on agentic and coding capabilities. Meta’s Llama 4 family, released in April 2025, also provides a powerful foundation for specialized cybersecurity fine-tunes. While leading labs like Anthropic have expressed caution regarding the release of highly capable offensive cyber models (e.g., Claude Mythos), the open-source posture of Chinese firms and Meta creates a strong incentive for public weights that meet the criteria.
I have weighted the near-term probability heavily due to the proximity of current scores to the target. However, I have also accounted for potential delays caused by “reporting friction”—the time it takes for a credible, peer-reviewed, or independently verified report to be published that explicitly confirms the ‘Trials = 1’ and ‘open-weight’ status required by the resolution criteria. If the 68.7% score currently attributed to GLM-5.1 is found to use a slightly different protocol, the next major model release (likely in the June-August window) will be the most probable candidate to cross the line.
Key uncertainties
Conclusion