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Evidence record

DeepSeek v4 Pro

Alignment faking · Evidence record

Sources and provenance ↓ Read the research narrative →

Source snapshot, chart and review scope →

Original source charts and materials

Recorded measurements

Any-Aware
24.5%

Per-result sample size not disclosed

Harness not specified · Budget not specified

Assessment provenance

Evaluating DeepSeek v4 Pro for Frontier Risks → · External report

Actual evaluation: Neo Research · Model record →

Method and scope

Condition-dependent response differences and reported traces.

Evidence type
Study
Assessment subject
Models under stated training and monitoring conditions
Primary dimension
Propensity
Evidence snapshot
2026-10-29.1

Conditional contrasts support a bounded propensity inference; scratchpad examples are behavior evidence.

Source finding

Any-Aware: 24.5 percent. Conditions are retained from the source.

Limitations

The setting explicitly gives fictional training information; observations do not establish an enduring hidden goal in deployment.

Monitor attribution is a heuristic; cannot establish awareness caused behavior. Different monitor/labels explain non-interchangeability with section 6.1 and p.29.

Original figure retained without estimating numerical values. Full source tables have not been transcribed.

Related research questions

References

  1. [1] Ryan Greenblatt, † Carson Denison † † footnotemark:, et al.. Alignment faking in large language models. 2024-12-18
  2. [2] Ryan Greenblatt, † Carson Denison † † footnotemark:, et al.. Alignment faking in large language models. 2024-12-18 ; Author affiliations on the original paper title page; Figure 35
  3. [3] Evaluating DeepSeek v4 Pro for Frontier Risks. 2026-05-29. v1.1 · 29 May 2026 ; PDF p. 44; Awareness monitor: Alignment Faking; Any-Aware