3 papers
cs.LG2026
What Do Lorentz-Equivariant Jet Taggers Learn?
Jay Agarwal, Siddharth Khare, Dhruv Kumar
We study what Lorentz-equivariant jet taggers learn internally, using equivariance tests, linear probes and grade ablations across five models including L-GATr, L-GATr-slim and LLo…
cs.LG2026
Authority, Truth, and Citation Bias: A Large-Scale Multi-Domain Benchmark for Studying Epistemic Susceptibility in Large Language Models
Aryan Khurana, Aravind Ramana RN, Dhruv Kumar
Large language models are increasingly deployed in citation-augmented settings, yet the effect of citation presence on model behavior independent of factual content remains poorly…
cs.CL2026
IndicDB -- Benchmarking Multilingual Text-to-SQL Capabilities in Indian Languages
Aviral Dawar, Roshan Karanth, Vikram Goyal +1
While Large Language Models (LLMs) have significantly advanced Text-to-SQL performance, existing benchmarks predominantly focus on Western contexts and simplified schemas, leaving…