collaborators

14 papers

cs.CL2026

Caching for the Future: Scrub Jay Episodic Memory Principles for Agent Memory Systems

Kartikey Singh Bhandari, Aarya Wadhwani, Dhruv Kumar +1

LLM agents that persist across sessions accumulate stored memories whose validity varies enormously by content type, yet existing memory architectures treat all memories as equally…

cs.CL2026

Navigating the Reality Gap: On-Device Continual Adaptation of ASR for Clinical Telephony

Darshil Chauhan, Adityasinh Solanki, Vansh Patel +5

Automatic Speech Recognition (ASR) can significantly reduce documentation burden in clinical workflows, but standard models degrade sharply in real-world telephony settings where n…

cs.AI2026

Beyond Sentiment: A Multi-Agent Pipeline for Actionable Business Advice from Reviews

Kartikey Singh Bhandari, Tanish Jain, Archit Agrawal +3

Customer reviews contain valuable signals about service quality, but converting large-scale review corpora into actionable business recommendations remains difficult. Standard sent…

cs.LG2026

Generative Evolutionary Meta-Solver (GEMS): Scalable Surrogate-Free Multi-Agent Reinforcement Learning

Alakh Sharma, Gaurish Trivedi, Kartikey Singh Bhandari +4

Scalable multi-agent reinforcement learning (MARL) remains a central challenge for AI. Existing population-based methods, like Policy-Space Response Oracles, PSRO, require storing…

cs.LG2026

Trust Regions Sell, But Who's Buying? Overlap Geometry as an Alternative Trust Region for Policy Optimization

Gaurish Trivedi, Alakh Sharma, Kartikey Singh Bhandari +4

Standard trust-region methods constrain policy updates via Kullback-Leibler (KL) divergence. However, KL controls only an average divergence and does not directly prevent rare, lar…

cs.CL2026

A Hybrid Supervised-LLM Pipeline for Actionable Suggestion Mining in Unstructured Customer Reviews

Aakash Trivedi, Aniket Upadhyay, Pratik Narang +2

Extracting actionable suggestions from customer reviews is essential for operational decision-making, yet these directives are often embedded within mixed-intent, unstructured text…