2 citations · 2 across the 8 of their papers we have counts for
9 papers
Inheritance as an Admission Test for LLM Prompt Merge Operators
Zan-Kai Chong, Hiroyuki Ohsaki, Bryan Ng
Large language model (LLM) agents are often improved by iterating until a fixed verifier is done. Once that verifier saturates, the loop has nothing left to climb. We instead score…
Compiling Deterministic Structure into SLM Harnesses
Zan Kai Chong, Hiroyuki Ohsaki, Bryan Ng
Enterprise SLM deployment faces epistemic asymmetry: small models cannot self-correct reasoning errors, while frontier LLMs incur prohibitive costs and data sovereignty risks at sc…
The Silent Scholar Problem: A Probabilistic Framework for Breaking Epistemic Asymmetry in LLM Agents
Zan-Kai Chong, Hiroyuki Ohsaki, Bryan Ng
Autonomous agents powered by LLMs and Retrieval-Augmented Generation (RAG) are proficient consumers of digital content but remain unidirectional, a limitation we term epistemic asy…
Tractable Asymmetric Verification for Large Language Models via Deterministic Replicability
Zan-Kai Chong, Hiroyuki Ohsaki, Bryan Ng
The landscape of Large Language Models (LLMs) shifts rapidly towards dynamic, multi-agent systems. This introduces a fundamental challenge in establishing computational trust, spec…
Proof of Useful Intelligence (PoUI): Blockchain Consensus Beyond Energy Waste
Zan-Kai Chong, Hiroyuki Ohsaki, Bryan Ng
Blockchain technology enables secure, transparent data management in decentralized systems, supporting applications from cryptocurrencies like Bitcoin to tokenizing real-world asse…
LLM-Net: Democratizing LLMs-as-a-Service through Blockchain-based Expert Networks
Zan-Kai Chong, Hiroyuki Ohsaki, Bryan Ng
The centralization of Large Language Models (LLMs) development has created significant barriers to AI advancement, limiting the democratization of these powerful technologies. This…
Collaborative Active Learning in Conditional Trust Environment
Zan-Kai Chong, Hiroyuki Ohsaki, Bryan Ng
In this paper, we investigate collaborative active learning, a paradigm in which multiple collaborators explore a new domain by leveraging their combined machine learning capabilit…
Improving Uncertainty Sampling with Bell Curve Weight Function
Zan-Kai Chong, Hiroyuki Ohsaki, Bok-Min Goi
Typically, a supervised learning model is trained using passive learning by randomly selecting unlabelled instances to annotate. This approach is effective for learning a model, bu…
Improve Cost Efficiency of Active Learning over Noisy Dataset
Zan-Kai Chong, Hiroyuki Ohsaki, Bryan Ng
Active learning is a learning strategy whereby the machine learning algorithm actively identifies and labels data points to optimize its learning. This strategy is particularly eff…