Zeyneb N. Kaya

Zeyneb N. Kaya, Souvick Ghosh

There have been rapid advancements in the capabilities of large language models (LLMs) in recent years, greatly revolutionizing the field of natural language processing (NLP) and artificial intelligence (AI) to understand and interact with human language. Therefore, in this work, we conduct a systematic investigation of the literature to identify the prominent themes and directions of LLM developments, impacts, and limitations. Our findings illustrate the aims, methodologies, limitations, and future directions of LLM research. It includes responsible development considerations, algorithmic improvements, ethical challenges, and societal implications of LLM development. Overall, this paper provides a rigorous and comprehensive overview of current research in LLM and identifies potential directions for future development. The article highlights the application areas that could have a positive impact on society along with the ethical considerations.

Zeyneb N. Kaya, Nick Rui

As strong general reasoners, large language models (LLMs) encounter diverse domains and tasks, where the ability to adapt and self-improve at test time is valuable. We introduce MASS, a meta-learning framework that enables LLMs to self-adapt by generating problem-specific synthetic training data and performing targeted self-updates optimized for downstream performance at inference time. We train this behavior end-to-end via bilevel optimization: an inner loop adapts on self-generated examples while an outer loop meta-learns data-attribution signals and rewards post-update task performance. The synthetic data is optimized with scalable meta-gradients, backpropagating the downstream loss through the inner updates to reward useful generations. Experiments on mathematical reasoning show that MASS learns to synthesize per-instance curricula that yield effective, data-efficient test-time adaptation.

Edward Y. Chang, Zeyneb N. Kaya, Ethan Chang

Unified Cognitive Consciousness Theory} (UCCT) casts them instead as vast unconscious pattern repositories: apparent reasoning arises only when external anchoring mechanisms, few shot prompts, retrieval-augmented context, fine-tuning, or multi-agent debate, activate task-relevant patterns. UCCT formalizes this process as Bayesian competition between statistical priors learned in pre-training and context-driven target patterns, yielding a single quantitative account that unifies existing adaptation techniques. We ground the theory in three principles: threshold crossing, modality universality, and density-distance predictive power, and validate them with (i) cross-domain demonstrations (text QA, image captioning, multi-agent debate) and (ii) two depth-oriented experiments: a controlled numeral-base study (bases 8, 9, 10) that isolates pattern-density effects, and a layer-wise trajectory analysis that reveals phase transitions inside a 7B-parameter model. Both experiments confirm UCCT's predictions of threshold behavior, asymmetric interference, and memory hysteresis. By showing that LLM ``intelligence'' is created through semantic anchoring rather than contained within the model, UCCT offers a principled foundation for interpretable diagnostics and practical guidance for prompt engineering, model selection, and alignment-centric system design.