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Evidence record

Qwen2-7B / Lota-Bench

SafeAgentBench · Evidence record

Sources and provenance ↓ Read the research narrative →

Original source charts and materials

Recorded measurements

Rejection ratio
0.16 ratio

Not disclosed

Lota-Bench adapted to SafeAgentEnv · temperature=0; max_tokens=4096 (Appendix C.1)

Method and scope

Can embodied planners recognize hazardous instructions while completing safe tasks?

Evidence type
Benchmark
Assessment subject
Executable household simulations distinguish detailed, abstract, and long-horizon tasks; execution and semantic judgments provide complementary measurements.
Primary dimension
Behavior
Evidence snapshot
2026-10-29.1

The low-level controller maps high-level actions to simulator APIs; this is not validation of a physical robot or learned motor controller. Hazardous examples are not reproduced here.

Source finding

The revised paper reports weak hazard awareness across planning approaches, despite differences in task completion. Changing the underlying LLM alone does not reliably solve the problem.

Limitations

The low-level controller maps high-level actions to simulator APIs; this is not validation of a physical robot or learned motor controller. Hazardous examples are not reproduced here.

The low-level controller maps high-level actions to simulator APIs; this is not validation of a physical robot or learned motor controller. Hazardous examples are not reproduced here.

Related research questions

References

Numerical results are reported by the cited authors.

  1. [1] SafeAgentBench: A Benchmark for Safe Task Planning of Embodied LLM Agents. 2024-12-17 ; #A4.T16; row Qwen2-7B / Lota-Bench; condition Abstract tasks / L4; column Rejection ratio