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State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g. LSTMs) proved extremely successful in modeling complex time series…

Hidden Markov models (HMMs) and conditional random fields (CRFs) are two popular techniques for modeling sequential data. Inference algorithms designed over CRFs and HMMs allow estimation of the state sequence given the observations. In…

Artificial Intelligence · Computer Science 2012-02-20 Gungor Polatkan , Oncel Tuzel

In order to train a computer agent to play a text-based computer game, we must represent each hidden state of the game. A Long Short-Term Memory (LSTM) model running over observed texts is a common choice for state construction. However, a…

Computation and Language · Computer Science 2019-08-30 Xusen Yin , Jonathan May

Large Language Models (LLMs) have shown promise as decision-makers in dynamic settings, but their stateless nature necessitates creating a natural language representation of history. We present a unifying framework for systematically…

Artificial Intelligence · Computer Science 2025-06-19 Lyle Goodyear , Rachel Guo , Ramesh Johari

Industrial computing devices, in particular cyber-physical, real-time and safety-critical systems, focus on reacting to external events and the need to cooperate with other devices to create a functional system. They are often implemented…

Software Engineering · Computer Science 2017-02-28 Florian Murr , Wolfgang Mauerer

We present extensions to a continuous-state dependency parsing method that makes it applicable to morphologically rich languages. Starting with a high-performance transition-based parser that uses long short-term memory (LSTM) recurrent…

Computation and Language · Computer Science 2015-08-12 Miguel Ballesteros , Chris Dyer , Noah A. Smith

Neural video game simulators emerged as powerful tools to generate and edit videos. Their idea is to represent games as the evolution of an environment's state driven by the actions of its agents. While such a paradigm enables users to play…

Computer Vision and Pattern Recognition · Computer Science 2024-01-23 Willi Menapace , Aliaksandr Siarohin , Stéphane Lathuilière , Panos Achlioptas , Vladislav Golyanik , Sergey Tulyakov , Elisa Ricci

While LLM-based agents have shown promise for deep research, most existing approaches rely on fixed workflows that struggle to adapt to real-world, open-ended queries. Recent work therefore explores self-evolution by allowing agents to…

Artificial Intelligence · Computer Science 2026-02-05 Shuo Zhang , Chaofa Yuan , Ryan Guo , Xiaomin Yu , Rui Xu , Zhangquan Chen , Zinuo Li , Zhi Yang , Shuhao Guan , Zhenheng Tang , Sen Hu , Liwen Zhang , Ronghao Chen , Huacan Wang

Large Language Models (LLMs) reasoning abilities are increasingly being applied to classical board and card games, but the dominant approach -- involving prompting for direct move generation -- has significant drawbacks. It relies on the…

Large Language Models (LLMs) have changed the possibilities of Interactive Storytelling systems that process free-text user input. However, as more of these systems are built, evidence continues to mount regarding the story coherence…

Computation and Language · Computer Science 2026-05-26 Santiago Góngora , Luis Chiruzzo , Gonzalo Méndez , Pablo Gervás

Complex textual information extraction tasks are often posed as sequence labeling or \emph{shallow parsing}, where fields are extracted using local labels made consistent through probabilistic inference in a graphical model with constrained…

Machine Learning · Computer Science 2018-10-01 Dung Thai , Sree Harsha Ramesh , Shikhar Murty , Luke Vilnis , Andrew McCallum

Precise test oracles for reactive systems such as critical control systems and communication protocols can be modelled with deterministic finite state machines (FSMs). Among other roles, they serve in evaluating the correctness of systems…

Software Engineering · Computer Science 2022-04-05 Omer Nguena Timo

Large language models (LLMs) are bringing richer dialogue and social behavior into games, but they also expose a control problem that existing game interfaces do not directly address: how should LLM characters participate in live…

Human-Computer Interaction · Computer Science 2026-04-07 Yunjia Guo , Jinghan Zhu , Siyu Wang , Haixin Qiao

Among the wide variety of evolutionary computing models, Finite State Machines (FSMs) have several attractions for fundamental research. They are easy to understand in concept and can be visualised clearly in simple cases. They have a ready…

Neural and Evolutionary Computing · Computer Science 2023-10-23 Gabor Zoltai , Yue Xie , Frank Neumann

Finite state machines (FSMs) are a theoretically and practically important model of computation. We propose a general, thermodynamically consistent model of FSMs and characterise the resource requirements of these machines. We model FSMs as…

Statistical Mechanics · Physics 2018-08-27 Dominique Chu , Richard Spinney

The increasing complexity of modern chemical processes, coupled with workforce shortages and intricate fault scenarios, demands novel automation paradigms that blend symbolic reasoning with adaptive control. In this work, we introduce a…

Artificial Intelligence · Computer Science 2025-07-11 Javal Vyas , Mehmet Mercangoz

Autonomous cyber-physical systems like robots and self-driving cars could greatly benefit from using formal methods to reason reliably about their control decisions. However, before a problem can be solved it needs to be stated. This…

Logic in Computer Science · Computer Science 2025-10-08 Aditi Kabra , Jonathan Laurent , Sagar Bharadwaj , Ruben Martins , Stefan Mitsch , André Platzer

Deep state-space models (DSSMs) have gained popularity in recent years due to their potent modeling capacity for dynamic systems. However, existing DSSM works are limited to single-task modeling, which requires retraining with historical…

Machine Learning · Computer Science 2024-07-02 Yuanhang Zhang , Zhidi Lin , Yiyong Sun , Feng Yin , Carsten Fritsche

Large Reasoning Models (LRMs) solve complex tasks by generating long Chain-of-Thought (CoT) sequences; however, the emergent dynamics governing reasoning trajectories are not well understood and can lead to inconsistencies and reasoning…

Artificial Intelligence · Computer Science 2026-05-29 G M Shahariar , Erfan Shayegani , Ali Nazari , Nael Abu-Ghazaleh

The inability of Large Language Models (LLMs) to modulate their personality expression in response to evolving dialogue dynamics hinders their performance in complex, interactive contexts. We propose a model-agnostic framework for dynamic…

Computation and Language · Computer Science 2026-02-26 Leon Pielage , Ole Hätscher , Mitja Back , Bernhard Marschall , Benjamin Risse