24 papers
AI and Consumer Rights in India Working Paper
Omir Kumar, Sriya Sridhar, Vibhav Mithal +1
As AI systems proliferate in consumer facing applications, questions about liability for AI related harms remain unresolved. This working paper examines whether India's Consumer Pr…
KANEx: Translating Kolmogorov-Arnold Networks' Interpretability to Medical Explainability
Krithi Shailya, Ananya Lakshmi Ravi, Venkatanathan K. V. +4
Computer vision models have become highly effective for medical applications, yet their black-box nature continues to undermine clinician trust. In clinical workflows, chest X-ray…
Pluralis v0.1: Towards a Multicultural, Multimodal, Multilingual Benchmark for AI Risk and Reliability
Alicia Parrish, Rajat Shinde, Sanket Badhe +57
Current AI safety evaluation and benchmarking frameworks predominantly rely on Western-centric culture-agnostic defaults that mask critical regional laws, socio-linguistic nuances,…
Unifying Model-Free Efficiency and Model-Based Representations via Latent Dynamics
Jashaswimalya Acharjee, Balaraman Ravindran
We present Unified Latent Dynamics (ULD), a novel reinforcement learning algorithm that unifies the efficiency of model-free methods with the representational strengths of model-ba…
How Much Online RL is Enough? Informative Rollouts for Offline Preference Optimization in RLVR
Richa Verma, Balaraman Ravindran
Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a powerful paradigm for reasoning in language models, with GRPO as its primary example. However, GRPO requires…
PREFINE: Preference-Based Implicit Reward and Cost Fine-Tuning for Safety Alignment
Richa Verma, Bavish Kulur, Sanjay Chawla +1
We address the problem of making a pre-trained reinforcement learning (RL) policy safety-aware by incorporating cost constraints without retraining it from scratch. While costs cou…