10 citations · 71 across the 49 of their papers we have counts for
58 papers
Agentic Cognitive Depth: Operational Criteria for Evaluating LLM Agents
Nijesh Upreti, Chris Sypherd, Vaishak Belle
Agentic large language model (LLM) systems are commonly implemented as an LLM in a loop with Planning, Memory, Tools, and Control Flow. This application-focused view connects agent…
Soft Symbol Grounding for Prototypical Concepts
Marcos Galván-López, Nijesh Upreti, Hiram Calvo +2
Neuro-symbolic models are usually trained with supervision only on final labels, leaving the intermediate concepts unobserved. Since many concept assignments are consistent with a…
Generate in the Chart, Not on the Boundary: Function-Symbol Grounding for Hard Constraints in LTN-GANs
Nijesh Upreti, Vaishak Belle
Logic Tensor Network-Enhanced Generative Adversarial Networks (LTN-GANs) inject background knowledge by grounding each logical axiom as a predicate and training the generator to ra…
DeepSWIP: Quotient-WMC Counterfactuals for Neural Probabilistic Logic Programs
Saimun Habib, Vaishak Belle, Fengxiang He
Neurosymbolic systems such as DeepProbLog combine neural perception with probabilistic logic, but standard inference is associational. Counterfactual reasoning additionally require…
Vancomycert: A Certified Neuro-Symbolic Drug Delivery System (Case Study)
Alistair Sirman, Fleur Conway, Jessica Ciupa +7
Neural network controllers for autonomous decision-making are well-established in cyber-physical systems, yet their deployment in safety-critical healthcare settings remains largel…
Tractable Hierarchical Control of Autoregressive Language Models
Max Scribner, Antonio Vergari, Vaishak Belle
Constraining the generation of autoregressive large language models (LLMs) is an important component of integrating language models into formal systems. In the generation of code a…
Used Car Salesbots? Honesty and Credulity of LLMs as Bargaining Agents under Partial Information
Antonio Valerio Miceli-Barone, Vaishak Belle, Shay B. Cohen
In this work we study agents in simulated bargaining scenarios, where a buyer and a seller communicate through a text channel and attempt to negotiate mutually beneficial trades, u…
Program Semantic Inequivalence Game with Large Language Models
Antonio Valerio Miceli-Barone, Vaishak Belle, Ali Payani
Large Language Models (LLMs) can achieve strong performance on everyday coding tasks, but they can fail on complex tasks that require non-trivial reasoning about program semantics.…
Neuro-symbolic Weak Supervision: Theory and Semantics
Nijesh Upreti, Vaishak Belle
Weak supervision enables machine learning models to learn from limited or noisy labels, but it introduces challenges in reliability and semantic clarity, particularly in multi-inst…
A Counterfactual Cause in Situation Calculus
Daxin Liu, Vaishak Belle
Perhaps the most popular modern formulation of actual causality is the HP account by Halpern and Pearl. Recent advancement has focused on extension of HP account to lift its limite…
LTLBench: Towards Benchmarks for Evaluating Temporal Reasoning in Large Language Models
Weizhi Tang, Kwabena Nuamah, Vaishak Belle
Temporal Reasoning (TR) is a critical ability for LLMs to understand and reason over temporal information and relationships between events. To study the TR ability in LLMs, prior w…
Zero, Finite, and Infinite Belief History of Theory of Mind Reasoning in Large Language Models
Weizhi Tang, Vaishak Belle
Large Language Models (LLMs) have recently shown a promise and emergence of Theory of Mind (ToM) ability and even outperform humans in certain ToM tasks. To evaluate and extend the…
Semantic Objective Functions: A distribution-aware method for adding logical constraints in deep learning
Miguel Angel Mendez-Lucero, Enrique Bojorquez Gallardo, Vaishak Belle
Issues of safety, explainability, and efficiency are of increasing concern in learning systems deployed with hard and soft constraints. Symbolic Constrained Learning and Knowledge…
Can Large Language Models put 2 and 2 together? Probing for Entailed Arithmetical Relationships
D. Panas, S. Seth, V. Belle
Two major areas of interest in the era of Large Language Models regard questions of what do LLMs know, and if and how they may be able to reason (or rather, approximately reason).…
ToM-LM: Delegating Theory of Mind Reasoning to External Symbolic Executors in Large Language Models
Weizhi Tang, Vaishak Belle
Theory of Mind (ToM) refers to the ability of individuals to attribute mental states to others. While Large Language Models (LLMs) have shown some promise with ToM ability, they st…
Deep Inductive Logic Programming meets Reinforcement Learning
Andreas Bueff, Vaishak Belle
One approach to explaining the hierarchical levels of understanding within a machine learning model is the symbolic method of inductive logic programming (ILP), which is data effic…
Statistical relational learning and neuro-symbolic AI: what does first-order logic offer?
Vaishak Belle
In this paper, our aim is to briefly survey and articulate the logical and philosophical foundations of using (first-order) logic to represent (probabilistic) knowledge in a non-te…
Toward A Logical Theory Of Fairness and Bias
Vaishak Belle
Fairness in machine learning is of considerable interest in recent years owing to the propensity of algorithms trained on historical data to amplify and perpetuate historical biase…
Learnability with PAC Semantics for Multi-agent Beliefs
Ionela G. Mocanu, Vaishak Belle, Brendan Juba
The tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition and artificial intelligence. In an influential paper, Valia…
Synthesising Recursive Functions for First-Order Model Counting: Challenges, Progress, and Conjectures
Paulius Dilkas, Vaishak Belle
First-order model counting (FOMC) is a computational problem that asks to count the models of a sentence in finite-domain first-order logic. In this paper, we argue that the capabi…
Agency and legibility for artists through Experiential AI
Drew Hemment, Matjaz Vidmar, Daga Panas +3
Experiential AI is an emerging research field that addresses the challenge of making AI tangible and explicit, both to fuel cultural experiences for audiences, and to make AI syste…
Experiential AI: A transdisciplinary framework for legibility and agency in AI
Drew Hemment, Dave Murray-Rust, Vaishak Belle +3
Experiential AI is presented as a research agenda in which scientists and artists come together to investigate the entanglements between humans and machines, and an approach to hum…
Why not both? Complementing explanations with uncertainty, and the role of self-confidence in Human-AI collaboration
Ioannis Papantonis, Vaishak Belle
AI and ML models have already found many applications in critical domains, such as healthcare and criminal justice. However, fully automating such high-stakes applications can rais…
Using Abstraction for Interpretable Robot Programs in Stochastic Domains
Till Hofmann, Vaishak Belle
A robot's actions are inherently stochastic, as its sensors are noisy and its actions do not always have the intended effects. For this reason, the agent language Golog has been ex…