Author

Anik Jha

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Preprint Aug 2026

Whose Gold? Annotator-Pool Disagreement Is Large at the Item Level, and Hidden by Small Leaderboards

Preference benchmarks are built by hiring annotators, and the identity of those annotators is treated as an implementation detail. We measure what that detail buys. On the 2,885 MultiPref items where both pools are internally unanimous, so no tie-breaking convention is consulted at all, expert and crowd annotators assi...

Anik Jha · 0 citations

Jul 2026

Half the Experts, All the Code: One-Shot Domain Pruning of Mixture-of-Experts LLMs for Coding

The strongest open-weight coding models are mixture-of-experts (MoE) networks: most of their size comes from large pools of"expert"subnetworks, of which only a few act on any token. That pool is why these models do not fit on the machines most developers own, yet for a user who only wants coding help, most experts enco...

Anik Jha · arXiv.org · 2 citations

Preprint Aug 2026

Decorrelation Is Not Complementarity: Skill, Not Lineage, Governs Trusted-Monitor Ensembles

Trusted monitoring has a cheap, trusted model score a stronger untrusted model's actions, and a diverse ensemble of them beats a single stronger monitor at matched cost. They are built by minimising average pairwise correlation, and that paper's twelve monitors shared one base model, leaving open what supplies the dive...

Anik Jha · 0 citations

#artificial intelligence Preprint Sep 2026

Does Fault Localization Beat a Fresh Attempt? A Placebo-Controlled Study of Test-Guided Code Repair

This work separates three arms applied to the same failed candidate: blind whole-solution resampling, spectrum-based localization followed by suspect-span infilling followed by suspect-span infilling, and same-length infilling at a disjoint random code span.

Anik Jha · 0 citations