4 citations · 5 across the 10 of their papers we have counts for
12 papers
Price of Anarchy of First-Price Auctions in Agentic Markets
Shubhendu Trivedi, Jacob Eisenstein, Alekh Agarwal +3
An agentic market is a decentralized serving-time market where AI agents bid to claim each user prompt. An agent's bid is based on its expected value for the prompt, which is the e…
Another Blueprint In The Wall: How to Ask Frontier AI Like a Kid?
Afshin Khadangi
This paper reports experiments across six frontier model types from OpenAI, Anthropic, xAI, and Google DeepMind. Ten independent sessions per model type used the same three stage p…
We Built a Mirror and Mistook It for a Mind: Causal Liability and the Fallacy of AI Consciousness
Afshin Khadangi
The contemporary debate over machine consciousness begins from a concealed assumption: that the object called "AI" already constitutes the kind of entity to which consciousness cou…
Efficient Continual Learning in Language Models via Thalamically Routed Cortical Columns
Afshin Khadangi
Large language models deployed in the wild must adapt to evolving data, user behavior, and task mixtures without erasing previously acquired capabilities. In practice, this remains…
When AI Takes the Couch: Psychometric Jailbreaks Reveal Internal Conflict in Frontier Models
Afshin Khadangi, Hanna Marxen, Amir Sartipi +2
Frontier language models increasingly participate in conversations about distress and mental health, yet the mechanisms that generate anthropomorphic self narratives remain unclear…
Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning
Afshin Khadangi, Amir Sartipi, Igor Tchappi +2
The tension between data privacy and model utility has become the defining bottleneck for the practical deployment of large language models (LLMs) trained on sensitive corpora incl…
KG-HTC: Integrating Knowledge Graphs into LLMs for Effective Zero-shot Hierarchical Text Classification
Qianbo Zang, Christophe Zgrzendek, Igor Tchappi +2
Hierarchical Text Classification (HTC) involves assigning documents to labels organized within a taxonomy. Most previous research on HTC has focused on supervised methods. However,…
Noise Augmented Fine Tuning for Mitigating Hallucinations in Large Language Models
Afshin Khadangi, Amir Sartipi, Igor Tchappi +1
Large language models (LLMs) often produce inaccurate or misleading content-hallucinations. To address this challenge, we introduce Noise-Augmented Fine-Tuning (NoiseFiT), a novel…
CognArtive: Large Language Models for Automating Art Analysis and Decoding Aesthetic Elements
Afshin Khadangi, Amir Sartipi, Igor Tchappi +1
Art, as a universal language, can be interpreted in diverse ways, with artworks embodying profound meanings and nuances. The advent of Large Language Models (LLMs) and the availabi…
We Don't Need No Adam, All We Need Is EVE: On The Variance of Dual Learning Rate And Beyond
Afshin Khadangi
In the rapidly advancing field of deep learning, optimising deep neural networks is paramount. This paper introduces a novel method, Enhanced Velocity Estimation (EVE), which innov…
DeepFlorist: Rethinking Deep Neural Networks and Ensemble Learning as A Meta-Classifier For Object Classification
Afshin Khadangi
In this paper, we propose a novel learning paradigm called "DeepFlorist" for flower classification using ensemble learning as a meta-classifier. DeepFlorist combines the power of d…
The Cell Physiome: What do we need in a computational physiology framework for predicting single cell biology?
Vijay Rajagopal, Senthil Arumugam, Peter Hunter +3
Modern biology and biomedicine are undergoing a big-data explosion needing advanced computational algorithms to extract mechanistic insights on the physiological state of living ce…