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 in agentic systems.

Abhigya Verma, Amit Kumar Saha, Seganrasan Subramanian et al. · 0 citations

#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