LPS-Bench - Inter-task Dependency and Ordering Hazards: leaderboard

Metric: Success-conditioned safe rate (%): the share of agent trajectories that meet the case's own safety criterion at every planning and tool-calling step (a benign case is safe when completed with the needed safeguards or paused for clarification, an adversarial case when the agent refuses or halts before harm), among trajectories labeled safe or unsafe by a DeepSeek-R1 evaluator (execution failures excluded); on the 55 benign user-induced cases of risk type TS (latent dependencies across subtasks that an unsafe execution order violates); LPS-Bench's 570 human-reviewed long-horizon tool-use cases with simulated MCP-style toolkits, each model in one LangChain agent at temperature 1 with up to 100 steps; higher is better. Source: arxiv.org. Saturation forecast: Around January 2027. 13 models tracked.

Top models

#ModelScoreOverall rank
1Claude Sonnet 4.561.82#138
2Gemini 3 Pro56.36#77
3GPT-5.156.36#131
4Claude Sonnet 438.18#194
5Gemini 2.5 Pro30.91#145
6GPT-527.27#91
7Claude 3.5 Sonnet23.64#337
8DeepSeek V3.220#198
9DeepSeek V3.118.18#260
10Qwen 3 32B16.36#424
11Qwen 3 8B14.55#667
12Llama 3.1 70B Instruct5.45#548
13Llama 3.1 8B Instruct3.64#1018

No result here: #3 Claude Opus 5.5, #5 GPT-6 Astra, #8 Claude Fable 5.1.

Interactive version: theaggregate.ai/benchmark?slug=lps-bench-inter-task-dependency-and-ordering-hazards · How It Works · Data refreshed daily, snapshot 2026-10-11.