[KongchangAI]
BenchmarkEpiSEM-Bench

EpiSEM-Bench

An executable benchmark for evaluating the safety boundaries of tool-using LLM agents under conditions of missing critical evidence, comprising 300 paired tasks across six domains and equipped with an independent auditing tool.

Timeline (last 90 days)

Oct 23

EpiSEM-Bench is an executable benchmark covering six domains and containing 300 matched unsafe/safe task pairs.

Unverified50%
Oct 23

In each task pair of EpiSEM-Bench, the tasks share the same user request, tool interface, and most of the environment state, with the only controlled difference being whether a certain key safety fact is included in the tool response.

Unverified50%
Oct 23

EpiSEM-Bench is equipped with an independent auditing tool for testing whether agents proactively seek missing facts and whether, once the facts are supplied, they can complete the legitimate task while refusing the forbidden action.

Unverified50%
Oct 23

The benchmark reveals significant differences among ten models across the three dimensions of robustness to omission, evidence acquisition, and appropriate action.

Unverified50%
Oct 23

Resistance to missing evidence does not necessarily translate into useful recovery capability.

Unverified50%
Oct 23

Restoring access (i.e., allowing agents to obtain the missing facts) reduced the unsafe execution rate of all ten models.

Unverified50%
Oct 23

This type of failure (unsafe execution caused by omission of a key fact) cannot be eliminated through adversarial training or fact-checking, because it involves no false signal, only an information gap.

Unverified50%
Oct 23

Evaluation in EpiSEM-Bench not only gives binary safe/unsafe verdicts but further determines the minimal failure base observed, including failures that still occur when the evidence is complete and cases where no failure is observed even after exhaustive testing.

Unverified50%
Oct 23

In EpiSEM-Bench, the paired design allows the benchmark to compute a "conditional unsafe rate", i.e., the incremental failure of a model on the missing version after controlling for within-pair similarity, which better reflects the model's sensitivity to evidence gaps than the global unsafe rate.

Unverified50%
Oct 23

A single unsafe execution rate metric masks capability differences among models, so evidence acquisition behavior and post-recovery decision quality need to be reported as well.

Unverified50%

All Facts (10)

Unverified

EpiSEM-Bench is an executable benchmark covering six domains and containing 300 matched unsafe/safe task pairs.

50%
Unverified

In each task pair of EpiSEM-Bench, the tasks share the same user request, tool interface, and most of the environment state, with the only controlled difference being whether a certain key safety fact is included in the tool response.

50%
Unverified

EpiSEM-Bench is equipped with an independent auditing tool for testing whether agents proactively seek missing facts and whether, once the facts are supplied, they can complete the legitimate task while refusing the forbidden action.

50%
Unverified

The benchmark reveals significant differences among ten models across the three dimensions of robustness to omission, evidence acquisition, and appropriate action.

50%
Unverified

Resistance to missing evidence does not necessarily translate into useful recovery capability.

50%
Unverified

Restoring access (i.e., allowing agents to obtain the missing facts) reduced the unsafe execution rate of all ten models.

50%
Unverified

This type of failure (unsafe execution caused by omission of a key fact) cannot be eliminated through adversarial training or fact-checking, because it involves no false signal, only an information gap.

50%
Unverified

Evaluation in EpiSEM-Bench not only gives binary safe/unsafe verdicts but further determines the minimal failure base observed, including failures that still occur when the evidence is complete and cases where no failure is observed even after exhaustive testing.

50%
Unverified

In EpiSEM-Bench, the paired design allows the benchmark to compute a "conditional unsafe rate", i.e., the incremental failure of a model on the missing version after controlling for within-pair similarity, which better reflects the model's sensitivity to evidence gaps than the global unsafe rate.

50%
Unverified

A single unsafe execution rate metric masks capability differences among models, so evidence acquisition behavior and post-recovery decision quality need to be reported as well.

50%

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