Tootfinder

Bridging Expressivity and Scalability with Adaptive Unitary SSMs
Arjun Karuvally, Franz Nowak, Anderson T. Keller, Carmen Amo Alonso, Terrence J. Sejnowski, Hava T. Siegelmann
https://arxiv.org/abs/2507.05238

Bridging Expressivity and Scalability with Adaptive Unitary SSMs
Recent work has revealed that state space models (SSMs), while efficient for long-sequence processing, are fundamentally limited in their ability to represent formal languages particularly due to time-invariant and real-valued recurrence structures. In this work, we draw inspiration from adaptive and structured dynamics observed in biological neural systems and introduce the Adaptive Unitary State Space Model (AUSSM)- a novel class of SSMs that leverages skew-symmetric, input-dependent recurren…

Verifiable Natural Language to Linear Temporal Logic Translation: A Benchmark Dataset and Evaluation Suite
William H English, Chase Walker, Dominic Simon, Sumit Kumar Jha, Rickard Ewetz
https://arxiv.org/abs/2507.00877

Verifiable Natural Language to Linear Temporal Logic Translation: A Benchmark Dataset and Evaluation Suite
Empirical evaluation of state-of-the-art natural-language (NL) to temporal-logic (TL) translation systems reveals near-perfect performance on existing benchmarks. However, current studies measure only the accuracy of the translation of NL logic into formal TL, ignoring a system's capacity to ground atomic propositions into new scenarios or environments. This is a critical feature, necessary for the verification of resulting formulas in a concrete state space. Consequently, most NL-to-TL transla…

Efficient compilation and execution of synchronous programs via type-state programming
Avinash Malik
https://arxiv.org/abs/2508.01199 https://arxiv.org/pdf…

Efficient compilation and execution of synchronous programs via type-state programming
Synchronous programs are used extensively in implementation of safety critical embedded software. Imperative synchronous programming languages model multiple Finite State Machines (FSMs) executing in lockstep at logical clock ticks. The synchronous view of time along with the FSM based design enables easier formal verification. The synchronous composition of multiple FSMs, during compilation, results in the well known state space explosion problem. Hence, efficiently compiling imperative synchr…

CASP: An evaluation dataset for formal verification of C code
Niclas Hertzberg, Merlijn Sevenhuijsen, Liv K{\aa}reborn, Anna Lokrantz
https://arxiv.org/abs/2508.18798 https://…

CASP: An evaluation dataset for formal verification of C code
Recent developments in Large Language Models (LLMs) have shown promise in automating code generation, yet the generated programs lack rigorous correctness guarantees. Formal verification can address this shortcoming, but requires expertise and is time-consuming to apply. Currently, there is no dataset of verified C code paired with formal specifications that enables systematic benchmarking in this space. To fill this gap, we present a curated evaluation dataset of C code paired with formal spec…

Beyond the Wavefunction: Qualia Abstraction Language Mechanics and the Grammar of Awareness
Miko{\l}aj Sienicki, Krzysztof Sienicki
https://arxiv.org/abs/2508.02755 https://

Beyond the Wavefunction: Qualia Abstraction Language Mechanics and the Grammar of Awareness
We propose a formal reconstruction of quantum mechanics grounded not in external mathematical abstractions, but in the structured dynamics of subjective experience. The Qualia Abstraction Language (QAL) models physical systems as evolving streams of introspective units, structured sequences of modality, shape, and functional effect, rather than as state vectors in Hilbert space. This approach reimagines core quantum concepts: superposition becomes a form of structured ambiguity; collapse is ref…

The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems
Reza Yousefi Maragheh, Yashar Deldjoo
https://arxiv.org/abs/2507.02097

The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems
Large language models (LLMs) are rapidly evolving from passive engines of text generation into agentic entities that can plan, remember, invoke external tools, and co-operate with one another. This perspective paper investigates how such LLM agents (and societies thereof) can transform the design space of recommender systems. We introduce a unified formalism that (i) models an individual agent as a tuple comprising its language core, tool set, and hierarchical memory, and (ii) captures a mult…

Talking-to-Build: How LLM-Assisted Interface Shapes Player Performance and Experience in Minecraft
Xin Sun, Lei Wang, Yue Li, Jie Li, Massimo Poesio, Julian Frommel, Koen Hinriks, Jiahuan Pei
https://arxiv.org/abs/2507.20300

Talking-to-Build: How LLM-Assisted Interface Shapes Player Performance and Experience in Minecraft
With large language models (LLMs) on the rise, in-game interactions are shifting from rigid commands to natural conversations. However, the impacts of LLMs on player performance and game experience remain underexplored. This work explores LLM's role as a co-builder during gameplay, examining its impact on task performance, usability, and player experience. Using Minecraft as a sandbox, we present an LLM-assisted interface that engages players through natural language, aiming to facilitate creat…

Requirements Elicitation Follow-Up Question Generation
Yuchen Shen, Anmol Singhal, Travis Breaux
https://arxiv.org/abs/2507.02858 https://

Requirements Elicitation Follow-Up Question Generation
Interviews are a widely used technique in eliciting requirements to gather stakeholder needs, preferences, and expectations for a software system. Effective interviewing requires skilled interviewers to formulate appropriate interview questions in real time while facing multiple challenges, including lack of familiarity with the domain, excessive cognitive load, and information overload that hinders how humans process stakeholders' speech. Recently, large language models (LLMs) have exhibited s…

Optimizing ASR for Catalan-Spanish Code-Switching: A Comparative Analysis of Methodologies
Carlos Mena, Pol Serra, Jacobo Romero, Abir Messaoudi, Jose Giraldo, Carme Armentano-Oller, Rodolfo Zevallos, Ivan Meza, Javier Hernando
https://arxiv.org/abs/2507.13875

Optimizing ASR for Catalan-Spanish Code-Switching: A Comparative Analysis of Methodologies
Code-switching (CS), the alternating use of two or more languages, challenges automatic speech recognition (ASR) due to scarce training data and linguistic similarities. The lack of dedicated CS datasets limits ASR performance, as most models rely on monolingual or mixed-language corpora that fail to reflect real-world CS patterns. This issue is critical in multilingual societies where CS occurs in informal and formal settings. A key example is Catalan-Spanish CS, widely used in media and parli…

Neuromorphic Online Clustering and Its Application to Spike Sorting
James E. Smith
https://arxiv.org/abs/2506.12555 https://arxiv.org…

Neuromorphic Online Clustering and Its Application to Spike Sorting
Active dendrites are the basis for biologically plausible neural networks possessing many desirable features of the biological brain including flexibility, dynamic adaptability, and energy efficiency. A formulation for active dendrites using the notational language of conventional machine learning is put forward as an alternative to a spiking neuron formulation. Based on this formulation, neuromorphic dendrites are developed as basic neural building blocks capable of dynamic online clustering. …