Author

Seganrasan Subramanian

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Preprint Aug 2026

AgentJudgeBench: A Multi-Difficulty Benchmark for Evaluating LLM Judges on Agentic Tool-Calling

The first benchmark to systematically study LLM-as-a-judge reliability for agentic tool-calling over workflow DAGs, as distinct from the broader LLM-as-a-judge task of open-ended text or preference evaluation, exposes fundamental limitations of current LLM judges and yields practical guidelines for reliable evaluation...

Abhigya Verma, Amit Kumar Saha, Seganrasan Subramanian et al. · 1 citation · ⚡1

#machine learning Preprint Aug 2026

DARTS: Decoder-Aware Representation Tuning via Surgery for Model Merging

This work proposes Decoder-Aware Representation Tuning via Surgery (DARTS), which employs a novel entropy-weighted L1 loss to upweight correction at high-entropy positions where errors most affect generation quality, and a per-position additive bias that captures position-dependent error without overparameterization.

Aaryan Ajay Sharma, Sai Nishanth Padala, Seganrasan Subramanian · 0 citations