Think-Probe-Respond: Improving Large Language Models as Judges of Research Idea Novelty
Automated novelty judgment can accelerate scientific discovery by enabling efficient evaluation, refinement, and comparison of research ideas. While large language models are increasingly adopted for this task, we investigate a previously overlooked limitation in their judgment capabilities: despite generating reasoning rationales that closely mirror those of human experts, their final novelty judgments often diverge substantially. We demonstrate that this miscalibration stems from a systematic bias towards judging ideas as "medium novel". To mitigate this, we propose Think-Probe-Respond (TPR), a lightweight approach that probes latent novelty judgments from hidden states during the reaso...
| Authors | Tim Schopf, Tobias Schreieder, Akiko Aizawa |
|---|---|
| Primary category | cs.CL |
| Published | 26 August 2026 |
| Last updated at source | 26 August 2026 |
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