23 citations · 28 across the 6 of their papers we have counts for
6 papers
Eliciting Self-Verification in Multimodal Reasoning Agents with Reinforcement Learning
Vishwas Sathish, Viresh Ranjan, Xinliang Zhu +2
Reasoning agents increasingly rely on external tools such as web search to answer complex queries. Reinforcement learning (RL) finetuning algorithms such as GRPO have improved long…
Lessons from Neuroscience for AI: How integrating Actions, Compositional Structure and Episodic Memory could enable Safe, Interpretable and Human-Like AI
Rajesh P. N. Rao, Vishwas Sathish, Linxing Preston Jiang +2
The phenomenal advances in large language models (LLMs) and other foundation models over the past few years have been based on optimizing large-scale transformer models on the surp…
PainDECOG: Machine Learning-Based Identification of Pain Biomarkers from sEEG Signals
Sidharth Sidharth, Vishwas Sathish, Shweta Bansal +5
This study presents a systematic machine-learning approach for classifying acute pain from raw electrophysiological signals. We address binary and ternary classification tasks, lev…
LLeMpower: Understanding Disparities in the Control and Access of Large Language Models
Vishwas Sathish, Hannah Lin, Aditya K Kamath +1
Large Language Models (LLMs) are a powerful technology that augment human skill to create new opportunities, akin to the development of steam engines and the internet. However, LLM…
Active Predictive Coding: A Unified Neural Framework for Learning Hierarchical World Models for Perception and Planning
Rajesh P. N. Rao, Dimitrios C. Gklezakos, Vishwas Sathish
Predictive coding has emerged as a prominent model of how the brain learns through predictions, anticipating the importance accorded to predictive learning in recent AI architectur…
Graph Embedding Based Hybrid Social Recommendation System
Vishwas Sathish, Tanya Mehrotra, Simran Dhinwa +1
Item recommendation tasks are a widely studied topic. Recent developments in deep learning and spectral methods paved a path towards efficient graph embedding techniques. But littl…