6 papers
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…
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…
Mean-Pooled Cosine Similarity is Not Length-Invariant: Theory and Cross-Domain Evidence for a Length-Invariant Alternative
Sibayan Mitra, Dhruv Kumar
Mean-pooled cosine similarity is the default metric for comparing neural representations across languages, modalities, and tasks. We establish that this metric is not length-invari…
BITS Pilani at SemEval-2026 Task 9: Structured Supervised Fine-Tuning with DPO Refinement for Polarization Detection
Atharva Gupta, Dhruv Kumar, Yash Sinha
The POLAR SemEval-2026 Shared Task aims to detect online polarization and focuses on the classification and identification of multilingual, multicultural, and multi-event polarizat…
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…
A Modular Architecture for Typologically Controlled Lexicon Generation
Sankalp Tattwadarshi Swain, Dhruv Kumar
Constructing artificial lexicons that are pronounceable, typologically plausible, and semantically structured remains an open challenge in computational linguistics. Existing conla…