5 papers · 1 filter
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…
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…