Showing cs.AIShow all
3 papers · 1 filter
cs.AI2026
Quantum-Inspired Trace-Augmented Evidence Selection for Reasoning over Structured Hypothesis Spaces
Laura Wynter, Nirvik Sahoo, Paul Griffin
Large language models (LLMs) now solve a wide range of expert-level exams at or above human level, yet remain brittle on specialised, evidence-intensive domains such as law. On the…
cs.AI2026
Declarative Skills for AI Agents in Knowledge-Grounded Tool-Use Workflows
M. Danish Lim, I. Danial Bin Sharudin, Wen Han Chen +2
We study orchestration mechanisms for tool-using AI agents in realistic customer-service workflows over an unstructured knowledge base. We argue that declarative agents -- AI agent…
cs.AI2024
Flexible and Effective Mixing of Large Language Models into a Mixture of Domain Experts
Rhui Dih Lee, Laura Wynter, Raghu Kiran Ganti
We present a toolkit for creating low-cost Mixture-of-Domain-Experts (MOE) from trained models. The toolkit can be used for creating a mixture from models or from adapters. We perf…