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20242026
most citedApproximating Human Strategic Reasoning with LLM-Enhanced Recursive Reasoners Leveraging Multi-agent Hypergames

2 citations · 3 across the 9 of their papers we have counts for

collaborators

9 papers

cs.AI2026

Not What You Meant: Can LLMs Follow a Specified Negation Semantics?

Qiming Bao, Agnieszka Mensfelt, Michael J. Witbrock +1

Negation does not carry a uniform interpretation across domains. In legal, regulatory, and medical reasoning, the intended interpretation depends on the reading in force -- open- v…

cs.AI2026

PrologMCP: A Standardized Prolog Tool Interface for LLM Agents

Agnieszka Mensfelt, Adarsh Prabhakaran, Adrian Haret +2

Frontier reasoning-tuned language models still fail on deductive tasks at depth, and the cost of improved performance through extended internal reasoning scales poorly. Symbolic de…

cs.AI2025

Towards a Common Framework for Autoformalization

Agnieszka Mensfelt, David Tena Cucala, Santiago Franco +3

Autoformalization has emerged as a term referring to the automation of formalization - specifically, the formalization of mathematics using interactive theorem provers (proof assis…

cs.AI2025

A Survey on Hypergame Theory: Modelling Misaligned Perceptions and Nested Beliefs for Multi-Agent Systems

Vince Trencsenyi, Agnieszka Mensfelt, Kostas Stathis

Classical game-theoretic models typically assume rational agents, complete information, and common knowledge of payoffs - assumptions that are often violated in real-world MAS char…

cs.AI2025★ 1 cited

The Influence of Human-inspired Agentic Sophistication in LLM-driven Strategic Reasoners

Vince Trencsenyi, Agnieszka Mensfelt, Kostas Stathis

The rapid rise of large language models (LLMs) has shifted artificial intelligence (AI) research toward agentic systems, motivating the use of weaker and more flexible notions of a…

cs.AI2025★ 2 cited

Approximating Human Strategic Reasoning with LLM-Enhanced Recursive Reasoners Leveraging Multi-agent Hypergames

Vince Trencsenyi, Agnieszka Mensfelt, Kostas Stathis

LLM-driven multi-agent-based simulations have been gaining traction with applications in game-theoretic and social simulations. While most implementations seek to exploit or evalua…