Aayam Bansal

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 efficient communication policies. Existing approaches rely on reactive mechanisms, such as confidence maps, learned gating, or sparse masks, to decide what to transmit, without reasoning about why a message benefits the receiver. We introduce Reason-to-Transmit (R2T), a framework that equips each agent with a lightweight transformer-based module that reasons over local scene context, estimated neighbor information gaps, and bandwidth budget to make per-region transmission decisions. Trained end-to-end with a bandwidth-aware objective, R2T is evaluated against nine baselines in a multi-agent bird's-eye-view perception environment. Any communication improves performance by about 58% AP over no communication. At low bandwidth, all selective methods perform similarly, but R2T shows clear gains under high occlusion, where information asymmetry is greatest, approaching oracle performance. All methods degrade gracefully under packet drops up to 50%, showing robustness to communication failures. These results indicate that while fusion design dominates performance, deliberative communication provides additional gains in challenging scenarios. R2T introduces a reasoning-based approach to communication, enabling more efficient and context-aware information sharing in cooperative perception.

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 deployment on robots with wireless links, autonomous vehicles on congested networks, or drone swarms in contested spectrum offers no such guarantees. We introduce AgentComm-Bench, a benchmark suite and evaluation protocol that systematically stress-tests cooperative embodied AI under six communication impairment dimensions: latency, packet loss, bandwidth collapse, asynchronous updates, stale memory, and conflicting sensor evidence. AgentComm-Bench spans three task families: cooperative perception, multi-agent waypoint navigation, and cooperative zone search, and evaluates five communication strategies, including a lightweight method we propose based on redundant message coding with staleness-aware fusion. Our experiments reveal that communication-dependent tasks degrade catastrophically: stale memory and bandwidth collapse cause over 96% performance drops in navigation, while content corruption (stale or conflicting data) reduces perception F1 by over 85%. Vulnerability depends on the interaction between impairment type and task design; perception fusion is robust to packet loss but amplifies corrupted data. Redundant message coding more than doubles navigation performance under 80% packet loss. We release AgentComm-Bench as a practical evaluation protocol and recommend that cooperative embodied AI work report performance under multiple impairment conditions.

Aayam Bansal, Ishaan Gangwani

Zero-shot foundation models (FMs) promise training-free prediction on tabular data, yet their hardware footprint remains poorly characterized. We present a fully reproducible benchmark that reports test accuracy together with wall-clock latency, peak CPU RAM, and peak GPU VRAM on four public datasets: Adult-Income, Higgs-100k, Wine-Quality, and California-Housing. Two open FMs (TabPFN-1.0 and TabICL-base) are compared against tuned XGBoost, LightGBM, and Random Forest baselines on a single NVIDIA T4 GPU. The tree ensembles equal or surpass FM accuracy on three datasets while completing full-test batches in <= 0.40 s and <= 150 MB RAM, using zero VRAM. TabICL achieves a 0.8 percentage-point gain on Higgs but requires roughly 40,000 times more latency (960 s) and 9 GB VRAM. TabPFN matches tree-model accuracy on Wine and Housing but peaks at 4 GB VRAM and cannot process the full 100k-row Higgs table. These results quantify the substantial hardware-versus-accuracy trade-offs in current tabular FMs and provide an open baseline for future efficiency-oriented research.

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 waste and avoid overloading the grid. While there are several research works on energy optimization, they often fail to address the complexities of real-time fluctuations and the cyclic pattern of energy consumption. This work proposes a novel approach to enhance the accuracy of predictive models by employing sinusoidal encoding on periodic features of time-series data. To demonstrate the increase in performance, several statistical and ensemble machine learning models were trained on an energy demand dataset, using the proposed sinusoidal encoding. The performance of these models was then benchmarked against identical models trained on traditional encoding methods. The results demonstrated a 12.6% improvement of Root Mean Squared Error (from 0.5497 to 0.4802) and a 7.8% increase in the R^2 score (from 0.7530 to 0.8118), indicating that the proposed encoding better captures the cyclic nature of temporal patterns than traditional methods. The proposed methodology significantly improves prediction accuracy while maintaining computational efficiency, making it suitable for real-time applications in smart grid systems.

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 tradeoff between enforcing fairness constraints (such as demographic parity or equal opportunity) and maximizing lender profitability. Through simulations on synthetic data that reflects real-world lending patterns, we quantify how different fairness interventions impact profit margins and default rates. Our results demonstrate that equal opportunity constraints typically impose lower profit costs than demographic parity, but surprisingly, removing protected attributes from the model (fairness through unawareness) outperforms explicit fairness interventions in both fairness and profitability metrics. We further identify the specific economic conditions under which fair lending becomes profitable and analyze the feature-specific drivers of unfairness. These findings offer practical guidance for designing lending algorithms that balance ethical considerations with business objectives.

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) dataset. By applying natural language processing and machine learning techniques, we examine how sentiment patterns in fashion-related social media conversations can serve as predictors for emerging fashion trends. Our analysis involves the identification and categorization of fashion-related content, sentiment classification with improved normalization techniques, time series decomposition, statistically validated causal relationship modeling, cross-platform sentiment comparison, and brand-specific sentiment analysis. Results indicate correlations between sentiment patterns and fashion theme popularity, with accessories and streetwear themes showing statistically significant rising trends. The Granger causality analysis establishes sustainability and streetwear as primary trend drivers, showing bidirectional relationships with several other themes. The findings demonstrate that social media sentiment analysis can serve as an effective early indicator of fashion trend trajectories when proper statistical validation is applied. Our improved predictive model achieved 78.35% balanced accuracy in sentiment classification, establishing a reliable foundation for trend prediction across positive, neutral, and negative sentiment categories.

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 influential artists spanning four decades (1980-2020), we employ natural language processing techniques to quantify multiple dimensions of lyrical complexity. Our analysis reveals a 23.7% increase in vocabulary diversity over the study period, with East Coast artists demonstrating 17.3% higher lexical variation than other regions. Rhyme density increased by 34.2% across all regions, with Midwest artists exhibiting the highest technical complexity (3.04 rhymes per line). Topic modeling identified significant shifts in thematic content, with social justice themes decreasing from 28.5% to 13.8% of content while introspective themes increased from 7.6% to 26.3%. Sentiment analysis demon- strated that lyrics became significantly more negative during sociopolitical crises, with polarity decreasing by 0.31 following major social unrest. Multi-dimensional analysis revealed four dis- tinct stylistic approaches that correlate strongly with geographic origin (r=0.68, p!0.001) and time period (r=0.59, p<0.001). These findings establish quantitative evidence for the evolution of hip- hop as both an art form and a reflection of societal dynamics, providing insights into the interplay between linguistic innovation and cultural context in popular music.