Pushpa Kumar Balan — accepted AI-conference papers

2026

Adaptive Compute Efficient Learning via Conceptual-Criticality (Student Abstract)

Iñigo Parra, Mano Bharathi M, Mayank Kumar, Pushpa Kumar Balan, Priyadarsi Mishra

AAAI 2026technical

The computational cost of large language models (LLMs) is a primary obstacle to sustainable deployment. Static resource allocation is inefficient, as not all inputs require the same depth of processing. We propose a framework for adaptive, compute-efficient learning via conceptual criticality, which

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2026

Latent Representations of Land–Sea Boundaries and Extreme Temperature in Aurora’s Encoder (Student Abstract)

Benjamin Richards, Pushpa Kumar Balan

AAAI 2026technical

Deep learning models are emerging as strong alternatives to numerical weather prediction, yet their internal representations remain poorly understood. We analyze the latent space of Microsoft’s Aurora model to test whether its embed- dings align with known physical processes. First, we show that lan

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