1 citations · 2 across the 11 of their papers we have counts for
11 papers
OpenDiscoveryTrace: Process Traces for Evaluating AI Scientist Workflows
Aayam Bansal, Keertan Balaji
Existing benchmarks for autonomous AI scientists evaluate only final outputs---generated code, hypotheses, or papers---yet discard the reasoning process by which those outputs were…
Reason-to-Transmit: Deliberative Adaptive Communication for Cooperative Perception
Aayam Bansal, Ishaan Gangwani
Cooperative perception among autonomous agents overcomes the limitations of single-agent sensing, but bandwidth constraints in vehicle-to-everything (V2X) networks require efficien…
AgentComm-Bench: Stress-Testing Cooperative Embodied AI Under Latency, Packet Loss, and Bandwidth Collapse
Aayam Bansal, Ishaan Gangwani
Cooperative multi-agent methods for embodied AI are almost universally evaluated under idealized communication: zero latency, no packet loss, and unlimited bandwidth. Real-world de…
Sketch2Feedback: Grammar-in-the-Loop Framework for Rubric-Aligned Feedback on Student STEM Diagrams
Aayam Bansal
Providing timely, rubric-aligned feedback on student-drawn diagrams is a persistent challenge in STEM education. While large multimodal models (LMMs) can jointly parse images and g…
Light-Weight Benchmarks Reveal the Hidden Hardware Cost of Zero-Shot Tabular Foundation Models
Ishaan Gangwani, Aayam Bansal
Zero-shot foundation models (FMs) promise training-free prediction on tabular data, yet their hardware footprint remains poorly characterized. We present a fully reproducible bench…
Zero-Training Temporal Drift Detection for Transformer Sentiment Models: A Comprehensive Analysis on Authentic Social Media Streams
Aayam Bansal, Ishaan Gangwani
We present a comprehensive zero-training temporal drift analysis of transformer-based sentiment models validated on authentic social media data from major real-world events. Throug…
MicroProbe: Efficient Reliability Assessment for Foundation Models with Minimal Data
Aayam Bansal, Ishaan Gangwani
Foundation model reliability assessment typically requires thousands of evaluation examples, making it computationally expensive and time-consuming for real-world deployment. We in…
Algorithmic Tradeoffs in Fair Lending: Profitability, Compliance, and Long-Term Impact
Aayam Bansal
As financial institutions increasingly rely on machine learning models to automate lending decisions, concerns about algorithmic fairness have risen. This paper explores the tradeo…
Emotional Analysis of Fashion Trends Using Social Media and AI: Sentiment Analysis on Twitter for Fashion Trend Forecasting
Aayam Bansal, Agneya Tharun
This study explores the intersection of fashion trends and social media sentiment through computational analysis of Twitter data using the T4SA (Twitter for Sentiment Analysis) dat…
Linguistic Complexity and Socio-cultural Patterns in Hip-Hop Lyrics
Aayam Bansal, Raghav Agarwal, Kaashvi Jain
This paper presents a comprehensive computational framework for analyzing linguistic complexity and socio-cultural trends in hip-hop lyrics. Using a dataset of 3,814 songs from 146…
Temporal Encoding Strategies for Energy Time Series Prediction
Aayam Bansal, Keertan Balaji, Zeus Lalani
In contemporary power systems, energy consumption prediction plays a crucial role in maintaining grid stability and resource allocation enabling power companies to minimize energy…