1 citations · 2 across the 11 of their papers we have counts for
12 papers
Breaking Homogeneity: Diversifying Persona Sets for Creative LLM Outputs
Sang Bin Moon, Nicole Cho, Daniel Borrajo +2
Language models often produce homogeneous responses to open-ended tasks; such homogeneity can spawn groupthink-the convergence of ideas toward a singular and potentially suboptimal…
Entropy-informed Decoding: Adaptive Information-Driven Branching
Benjamin Patrick Evans, Sumitra Ganesh, Leo Ardon
Large language models (LLMs) achieve remarkable generative performance, yet their output quality is dependent on the decoding strategy. While sampling-based methods (e.g., top-k, n…
AgentCrypt: Advancing Privacy and (Secure) Computation in AI Agent Collaboration
Harish Karthikeyan, Yue Guo, Leo de Castro +5
As AI agents increasingly operate in complex environments, ensuring reliable, context-aware privacy is critical for regulatory compliance. Traditional access controls are insuffici…
PADME: Procedure Aware DynaMic Execution
Deepeka Garg, Sihan Zeng, Annapoorani L. Narayanan +2
Learning to autonomously execute long-horizon procedures from natural language remains a core challenge for intelligent agents. Free-form instructions such as recipes, scientific p…
Downside Risk-Aware Equilibria for Strategic Decision-Making
Oliver Slumbers, Benjamin Patrick Evans, Sumitra Ganesh +1
Game theory has traditionally had a relatively limited view of risk based on how a player's expected reward is impacted by the uncertainty of the actions of other players. Recently…
Modelling bounded rational decision-making through Wasserstein constraints
Benjamin Patrick Evans, Leo Ardon, Sumitra Ganesh
Modelling bounded rational decision-making through information constrained processing provides a principled approach for representing departures from rationality within a reinforce…
Generating Structured Plan Representation of Procedures with LLMs
Deepeka Garg, Sihan Zeng, Sumitra Ganesh +1
In this paper, we address the challenges of managing Standard Operating Procedures (SOPs), which often suffer from inconsistencies in language, format, and execution, leading to op…
ADAGE: A generic two-layer framework for adaptive agent based modelling
Benjamin Patrick Evans, Sihan Zeng, Sumitra Ganesh +1
Agent-based models (ABMs) are valuable for modelling complex, potentially out-of-equilibria scenarios. However, ABMs have long suffered from the Lucas critique, stating that agent…
In-Context Learning with Topological Information for Knowledge Graph Completion
Udari Madhushani Sehwag, Kassiani Papasotiriou, Jared Vann +1
Knowledge graphs (KGs) are crucial for representing and reasoning over structured information, supporting a wide range of applications such as information retrieval, question answe…
Simulate and Optimise: A two-layer mortgage simulator for designing novel mortgage assistance products
Leo Ardon, Benjamin Patrick Evans, Deepeka Garg +3
We develop a novel two-layer approach for optimising mortgage relief products through a simulated multi-agent mortgage environment. While the approach is generic, here the environm…
A Heterogeneous Agent Model of Mortgage Servicing: An Income-based Relief Analysis
Deepeka Garg, Benjamin Patrick Evans, Leo Ardon +5
Mortgages account for the largest portion of household debt in the United States, totaling around $12 trillion nationwide. In times of financial hardship, alleviating mortgage bur…
Learning and Calibrating Heterogeneous Bounded Rational Market Behaviour with Multi-Agent Reinforcement Learning
Benjamin Patrick Evans, Sumitra Ganesh
Agent-based models (ABMs) have shown promise for modelling various real world phenomena incompatible with traditional equilibrium analysis. However, a critical concern is the manua…