Traditional single-turn assessments fail to capture adaptability, feedback incorporation, and conversational depth. We align our evaluation modeling with the LLM-as-an-Interviewer paradigm so client solutions can reflect the multi-turn rigor described in the research while mitigating benchmark contamination risks.
Single-turn prompts inflate performance by ignoring how candidates respond to clarification and feedback.
Public benchmarks leak into model pre-training corpora, artificially boosting scores by up to 84 percentage points.
Binary pass/fail grading lacks the nuance hiring teams need for coaching and calibration.
Dynamic question modification neutralizes contamination while keeping difficulty constant.
Follow-up turns expose reasoning quality changes that single prompts miss (accuracy @1 → @3 improves 19.2 points).
Interview reports reveal behavioral patterns such as perseverance, clarification, and responsiveness.
Automatically paraphrases or parameterizes prompts to remove leaked answers and preserve difficulty. Supports role-specific terminology and scenario variants.
Classifies error types and issues corrective hints that nudge candidates toward better reasoning without revealing answers outright.
Generates clarification, rationale, elaboration, and modification questions to probe depth and adaptability in real time.
Aggregates multi-turn metrics, error patterns, and qualitative notes into a structured report for hiring teams and candidates.
Initial accuracy (@1)
Research baseline
44.4%
Published dynamic result
45.2%
After feedback (@2)
Research baseline
56.6%
Published dynamic result
58.1%
After refinement (@3)
Research baseline
63.6%
Published dynamic result
65.4%
Follow-up accuracy
Research baseline
93%
Published dynamic result
94%
Interviewer satisfaction
Research baseline
3.5 / 5.0
Published dynamic result
3.7 / 5.0