7 papers · 1 filter
FactorJEPA: Factorizing Monolithic Futures into Layout-Agent-Interaction Channels for Crowded and Chaotic Global South Urban Worlds
Kapil Wanaskar, Gaytri Jena, Aman Chadha +3
World models have attracted significant attention for their ability to capture and predict the structure and dynamics of the physical world. In this emerging landscape, Joint Embed…
Stochastic CHAOS: Why Deterministic Inference Kills, and Distributional Variability Is the Heartbeat of Artifical Cognition
Tanmay Joshi, Shourya Aggarwal, Anusa Saha +7
Deterministic inference is a comforting ideal in classical software: the same program on the same input should always produce the same output. As large language models move into re…
AlignMerge - Alignment-Preserving Large Language Model Merging via Fisher-Guided Geometric Constraints
Aniruddha Roy, Jyoti Patel, Aman Chadha +2
Merging large language models (LLMs) is a practical way to compose capabilities from multiple fine-tuned checkpoints without retraining. Yet standard schemes (linear weight soups,…
Alignment Faking - the Train -> Deploy Asymmetry: Through a Game-Theoretic Lens with Bayesian-Stackelberg Equilibria
Kartik Garg, Shourya Mishra, Kartikeya Sinha +8
Alignment faking is a form of strategic deception in AI in which models selectively comply with training objectives when they infer that they are in training, while preserving diff…
nDNA -- the Semantic Helix of Artificial Cognition
Amitava Das
As AI foundation models grow in capability, a deeper question emerges: What shapes their internal cognitive identity -- beyond fluency and output? Benchmarks measure behavior, but…
TRACEALIGN -- Tracing the Drift: Attributing Alignment Failures to Training-Time Belief Sources in LLMs
Amitava Das, Vinija Jain, Aman Chadha
Large Language Models (LLMs) fine-tuned to align with human values often exhibit alignment drift, producing unsafe or policy-violating completions when exposed to adversarial promp…