Tootfinder

Non-Iterative Symbolic-Aided Chain-of-Thought for Logical Reasoning
Phuong Minh Nguyen, Tien Huu Dang, Naoya Inoue
https://arxiv.org/abs/2508.12425 https://

Non-Iterative Symbolic-Aided Chain-of-Thought for Logical Reasoning
This work introduces Symbolic-Aided Chain-of-Thought (CoT), an improved approach to standard CoT, for logical reasoning in large language models (LLMs). The key idea is to integrate lightweight symbolic representations into few-shot prompts, structuring the inference steps with a consistent strategy to make reasoning patterns more explicit within a non-iterative reasoning process. By incorporating these symbolic structures, our method preserves the generalizability of standard prompting techniq…

Integrating Symbolic RL Planning into a BDI-based Autonomous UAV Framework: System Integration and SIL Validation
Sangwoo Jeon, Juchul Shin, YeonJe Cho, Gyeong-Tae Kim, Seongwoo Kim
https://arxiv.org/abs/2508.11890

Integrating Symbolic RL Planning into a BDI-based Autonomous UAV Framework: System Integration and SIL Validation
Modern autonomous drone missions increasingly require software frameworks capable of seamlessly integrating structured symbolic planning with adaptive reinforcement learning (RL). Although traditional rule-based architectures offer robust structured reasoning for drone autonomy, their capabilities fall short in dynamically complex operational environments that require adaptive symbolic planning. Symbolic RL (SRL), using the Planning Domain Definition Language (PDDL), explicitly integrates domai…

Zobrist Hash-based Duplicate Detection in Symbolic Regression
Bogdan Burlacu
https://arxiv.org/abs/2508.13859 https://arxiv.org/pdf/2508.13859

Zobrist Hash-based Duplicate Detection in Symbolic Regression
Symbolic regression encompasses a family of search algorithms that aim to discover the best fitting function for a set of data without requiring an a priori specification of the model structure. The most successful and commonly used technique for symbolic regression is Genetic Programming (GP), an evolutionary search method that evolves a population of mathematical expressions through the mechanism of natural selection. In this work we analyze the efficiency of the evolutionary search in GP and…

Active Learning of Symbolic Mealy Automata
Kengo Irie, Masaki Waga, Kohei Suenaga
https://arxiv.org/abs/2509.14694 https://arxiv.org/pdf/2509.14694

Active Learning of Symbolic Mealy Automata
We propose $Λ^*_M$-an active learning algorithm that learns symbolic Mealy automata, which support infinite input alphabets and multiple output characters. Each of these two features has been addressed separately in prior work. Combining these two features poses a challenge in learning the outputs corresponding to potentially infinite sets of input characters at each state. To address this challenge, we introduce the notion of essential input characters, a finite set of input characters that i…

Behavioral and Symbolic Fillers as Delay Mitigation for Embodied Conversational Agents in Virtual Reality
Denmar Mojan Gonzales, Snehanjali Kalamkar, Sophie J\"org, Jens Grubert
https://arxiv.org/abs/2508.11781

Behavioral and Symbolic Fillers as Delay Mitigation for Embodied Conversational Agents in Virtual Reality
When communicating with embodied conversational agents (ECAs) in virtual reality, there might be delays in the responses of the agents lasting several seconds, for example, due to more extensive computations of the answers when large language models are used. Such delays might lead to unnatural or frustrating interactions. In this paper, we investigate filler types to mitigate these effects and lead to a more positive experience and perception of the agent. In a within-subject study, we asked 2…

Symbolic Control: Unveiling Free Robustness Margins
Youssef Ait Si, Antoine Girard, Adnane Saoud
https://arxiv.org/abs/2507.12339 https://

Symbolic Control: Unveiling Free Robustness Margins
This paper addresses the challenge of ensuring robustness in the presence of system perturbations for symbolic control techniques. Given a discrete-time control system that is related to its symbolic model by an alternating simulation relation. In this paper, we focus on computing the maximum robustness margin under which the symbolic model remains valid for a perturbed-version of the discrete-time control system. We first show that symbolic models are inherently equipped with a certain free ro…

The Serre depth of Stanley--Reisner Rings and the depth of their symbolic powers
Yuji Muta, Naoki Terai
https://arxiv.org/abs/2509.14838 https://arxiv.org/…

The Serre depth of Stanley--Reisner Rings and the depth of their symbolic powers
We investigate an invariant, called the Serre depth, from the perspective of combinatorial commutative algebra. In this paper, we establish several properties of an analogue of the depth of Stanley--Reisner rings. In particular, we relate the Serre depth both to the minimal free resolution of a Stanley--Reisner ring and to that of its Alexander dual. Also, we establish an analogue of a known result that describes the depth of Stanley--Reisner rings in terms of skeletons. Moreover, we study the …

(Exhaustive) Symbolic Regression and model selection by minimum description length
Harry Desmond
https://arxiv.org/abs/2507.13033 https://

(Exhaustive) Symbolic Regression and model selection by minimum description length
Symbolic regression is the machine learning method for learning functions from data. After a brief overview of the symbolic regression landscape, I will describe the two main challenges that traditional algorithms face: they have an unknown (and likely significant) probability of failing to find any given good function, and they suffer from ambiguity and poorly-justified assumptions in their function-selection procedure. To address these I propose an exhaustive search and model selection by the…

#FensterFreitag #SouthwellMinster

LOOP: A Plug-and-Play Neuro-Symbolic Framework for Enhancing Planning in Autonomous Systems
Ronit Virwani, Ruchika Suryawanshi
https://arxiv.org/abs/2508.13371 https://

LOOP: A Plug-and-Play Neuro-Symbolic Framework for Enhancing Planning in Autonomous Systems
Planning is one of the most critical tasks in autonomous systems, where even a small error can lead to major failures or million-dollar losses. Current state-of-the-art neural planning approaches struggle with complex domains, producing plans with missing preconditions, inconsistent goals, and hallucinations. While classical planners provide logical guarantees, they lack the flexibility and natural language understanding capabilities needed for modern autonomous systems. Existing neuro-symbolic…

A Traditional Approach to Symbolic Piano Continuation
Christian Zhou-Zheng, John Backsund, Dun Li Chan, Alex Coventry, Avid Eslami, Jyotin Goel, Xingwen Han, Danysh Soomro, Galen Wei
https://arxiv.org/abs/2509.12267

A Traditional Approach to Symbolic Piano Continuation
We present a traditional approach to symbolic piano music continuation for the MIREX 2025 Symbolic Music Generation challenge. While computational music generation has recently focused on developing large foundation models with sophisticated architectural modifications, we argue that simpler approaches remain more effective for constrained, single-instrument tasks. We thus return to a simple, unaugmented next-token-prediction objective on tokenized raw MIDI, aiming to outperform large foundatio…

Variables Ordering Optimization in Boolean Characteristic Set Method Using Simulated Annealing and Machine Learning-based Time Prediction
Minzhong Luo, Yudong Sun, Yin Long
https://arxiv.org/abs/2509.14754

Variables Ordering Optimization in Boolean Characteristic Set Method Using Simulated Annealing and Machine Learning-based Time Prediction
Solving systems of Boolean equations is a fundamental task in symbolic computation and algebraic cryptanalysis, with wide-ranging applications in cryptography, coding theory, and formal verification. Among existing approaches, the Boolean Characteristic Set (BCS) method[1] has emerged as one of the most efficient algorithms for tackling such problems. However, its performance is highly sensitive to the ordering of variables, with solving times varying drastically under different orderings for f…

KAN-SR: A Kolmogorov-Arnold Network Guided Symbolic Regression Framework
Marco Andrea B\"uhler, Gonzalo Guill\'en-Gos\'albez
https://arxiv.org/abs/2509.10089 https:…

KAN-SR: A Kolmogorov-Arnold Network Guided Symbolic Regression Framework
We introduce a novel symbolic regression framework, namely KAN-SR, built on Kolmogorov Arnold Networks (KANs) which follows a divide-and-conquer approach. Symbolic regression searches for mathematical equations that best fit a given dataset and is commonly solved with genetic programming approaches. We show that by using deep learning techniques, more specific KANs, and combining them with simplification strategies such as translational symmetries and separabilities, we are able to recover grou…

Complementary bodies in sphere packing
Philip W. Kuchel
https://arxiv.org/abs/2508.11633 https://arxiv.org/pdf/2508.11633

Complementary bodies in sphere packing
Symbolic and graphical tools, such as Mathematica, enable precise visualization and analysis of void spaces in sphere packings. In the cubic close packing (CCP, or face-centred cubic packing; FCC) arrangement these voids can be partitioned into repeating geometric units we term spherically truncated polyhedra - bodies analogous to plane-truncated polyhedra but bounded by both planar and spherical surfaces. These structures are relevant in geometric studies and applications such as modelling dif…

Teaching LLMs to Plan: Logical Chain-of-Thought Instruction Tuning for Symbolic Planning
Pulkit Verma, Ngoc La, Anthony Favier, Swaroop Mishra, Julie A. Shah
https://arxiv.org/abs/2509.13351

Teaching LLMs to Plan: Logical Chain-of-Thought Instruction Tuning for Symbolic Planning
Large language models (LLMs) have demonstrated impressive capabilities across diverse tasks, yet their ability to perform structured symbolic planning remains limited, particularly in domains requiring formal representations like the Planning Domain Definition Language (PDDL). In this paper, we present a novel instruction tuning framework, PDDL-Instruct, designed to enhance LLMs' symbolic planning capabilities through logical chain-of-thought reasoning. Our approach focuses on teaching models t…

Validating Solidity Code Defects using Symbolic and Concrete Execution powered by Large Language Models
\c{S}tefan-Claudiu Susan ("Alexandru Ioan Cuza", University of Ia\c{s}i, Department of Computer Science), Andrei Arusoaie ("Alexandru Ioan Cuza", University of Ia\c{s}i, Department of Computer Science), Dorel Lucanu ("Alexandru Ioan Cuza", University of Ia\c{s}i, Department of Computer Science)

Validating Solidity Code Defects using Symbolic and Concrete Execution powered by Large Language Models
The high rate of false alarms from static analysis tools and Large Language Models (LLMs) complicates vulnerability detection in Solidity Smart Contracts, demanding methods that can formally or empirically prove the presence of defects. This paper introduces a novel detection pipeline that integrates custom Slither-based detectors, LLMs, Kontrol, and Forge. Our approach is designed to reliably detect defects and generate proofs. We currently perform experiments with promising results for seven…

Assessing Data Replication in Symbolic Music via Adapted Structural Similarity Index Measure
Shulei Ji, Zihao Wang, Le Ma, Jiaxing Yu, Kejun Zhang
https://arxiv.org/abs/2509.13658

Assessing Data Replication in Symbolic Music via Adapted Structural Similarity Index Measure
AI-generated music may inadvertently replicate samples from the training data, raising concerns of plagiarism. Similarity measures can quantify such replication, thereby offering supervision and guidance for music generation models. Existing similarity measure methods for symbolic music mainly target melody repetition, leaving a gap in assessing complex music with rich textures and expressive performance characteristics. To address this gap, we introduce SSIMuse, the first adaptation of the Str…

[2025-08-20 Wed (UTC), no new articles found for cs.SC Symbolic Computation]
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Neural Network-Guided Symbolic Regression for Interpretable Descriptor Discovery in Perovskite Catalysts
Yeming Xian, Xiaoming Wang, Yanfa Yan
https://arxiv.org/abs/2507.12404

Neural Network-Guided Symbolic Regression for Interpretable Descriptor Discovery in Perovskite Catalysts
Understanding and predicting the activity of oxide perovskite catalysts for the oxygen evolution reaction (OER) requires descriptors that are both accurate and physically interpretable. While symbolic regression (SR) offers a path to discover such formulas, its performance degrades with high-dimensional inputs and small datasets. We present a two-phase framework that combines neural networks (NN), feature importance analysis, and symbolic regression (SR) to discover interpretable descriptors fo…

Tapas are free! Training-Free Adaptation of Programmatic Agents via LLM-Guided Program Synthesis in Dynamic Environments
Jinwei Hu, Yi Dong, Youcheng Sun, Xiaowei Huang
https://arxiv.org/abs/2508.11425

Tapas are free! Training-Free Adaptation of Programmatic Agents via LLM-Guided Program Synthesis in Dynamic Environments
Autonomous agents in safety-critical applications must continuously adapt to dynamic conditions without compromising performance and reliability. This work introduces TAPA (Training-free Adaptation of Programmatic Agents), a novel framework that positions large language models (LLMs) as intelligent moderators of the symbolic action space. Unlike prior programmatic agents that typically generate a monolithic policy program or rely on fixed symbolic action sets, TAPA synthesizes and adapts modula…

Replaced article(s) found for cs.LO. https://arxiv.org/list/cs.LO/new
[1/1]:
- Boolformer: Symbolic Regression of Logic Functions with Transformers
d'Ascoli, Renard, Papadopoulos, Bengio, Susskind, Abb\'e

Nonautonomous Dynamical Systems III: Symbolic and Expansive Systems
Zhuo Chen, Jun Jie Miao
https://arxiv.org/abs/2509.11130 https://arxiv.org/pdf/2509.111…

Nonautonomous Dynamical Systems III: Symbolic and Expansive Systems
A nonautonomous dynamical system $(\boldsymbol{X},\boldsymbol{T})=\{(X_{k},T_{k})\}_{k=0}^{\infty}$ is a sequence of continuous mappings $T_{k}:X_{k} \to X_{k+1}$ along a sequence of compact metric spaces $X_{k}$. In this paper, we study the nonautonomous symbolic systems $(\boldsymbolΣ(\boldsymbol{m}),\boldsymbolσ)$ and nonautonomous expansive dynamical systems. We prove homogeneous properties and provide the formulae for topological pressures $\underline{P},\overline{P},P^{\mathrm{B}},P^{\m…

The Imitation Game: Turing Machine Imitator is Length Generalizable Reasoner
Zhouqi Hua, Wenwei Zhang, Chengqi Lyu, Yuzhe Gu, Songyang Gao, Kuikun Liu, Kai Chen
https://arxiv.org/abs/2507.13332

The Imitation Game: Turing Machine Imitator is Length Generalizable Reasoner
Length generalization, the ability to solve problems of longer sequences than those observed during training, poses a core challenge of Transformer-based large language models (LLM). Although existing studies have predominantly focused on data-driven approaches for arithmetic operations and symbolic manipulation tasks, these approaches tend to be task-specific with limited overall performance. To pursue a more general solution, this paper focuses on a broader case of reasoning problems that are…

FireGNN: Neuro-Symbolic Graph Neural Networks with Trainable Fuzzy Rules for Interpretable Medical Image Classification
Prajit Sengupta, Islem Rekik
https://arxiv.org/abs/2509.10510

FireGNN: Neuro-Symbolic Graph Neural Networks with Trainable Fuzzy Rules for Interpretable Medical Image Classification
Medical image classification requires not only high predictive performance but also interpretability to ensure clinical trust and adoption. Graph Neural Networks (GNNs) offer a powerful framework for modeling relational structures within datasets; however, standard GNNs often operate as black boxes, limiting transparency and usability, particularly in clinical settings. In this work, we present an interpretable graph-based learning framework named FireGNN that integrates trainable fuzzy rules i…

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[1/2]:
- Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach
Iman Sharifi, Mustafa Yildirim, Saber Fallah

Feynman Integral Reduction using Syzygy-Constrained Symbolic Reduction Rules
Sid Smith, Mao Zeng
https://arxiv.org/abs/2507.11140 https://

Feynman Integral Reduction using Syzygy-Constrained Symbolic Reduction Rules
We present a new algorithm for integration-by-parts (IBP) reduction of Feynman integrals with high powers of numerators or propagators, a demanding computational step in evaluating multi-loop scattering amplitudes. The algorithm starts with solving syzygy equations in individual sectors to produce IBP operators that turn seed integrals into IBP equations without artificially raised propagator powers. The IBP operators are expressed in terms of index-shift operators and number operators. We perf…

ATLANTIS: AI-driven Threat Localization, Analysis, and Triage Intelligence System
Taesoo Kim, HyungSeok Han, Soyeon Park, Dae R. Jeong, Dohyeok Kim, Dongkwan Kim, Eunsoo Kim, Jiho Kim, Joshua Wang, Kangsu Kim, Sangwoo Ji, Woosun Song, Hanqing Zhao, Andrew Chin, Gyejin Lee, Kevin Stevens, Mansour Alharthi, Yizhuo Zhai, Cen Zhang, Joonun Jang, Yeongjin Jang, Ammar Askar, Dongju Kim, Fabian Fleischer, Jeongin Cho, Junsik Kim, Kyungjoon Ko, Insu Yun, Sangdon Park, Dowoo Baik, Haein Lee, Hy…

ATLANTIS: AI-driven Threat Localization, Analysis, and Triage Intelligence System
We present ATLANTIS, the cyber reasoning system developed by Team Atlanta that won 1st place in the Final Competition of DARPA's AI Cyber Challenge (AIxCC) at DEF CON 33 (August 2025). AIxCC (2023-2025) challenged teams to build autonomous cyber reasoning systems capable of discovering and patching vulnerabilities at the speed and scale of modern software. ATLANTIS integrates large language models (LLMs) with program analysis -- combining symbolic execution, directed fuzzing, and static analysi…

Unlocking the Hodge Conjecture: A Spectral Fingerprint Approach via Gauss-Manin Derivatives
Bita Hajebi, Pooya Hajebi
https://arxiv.org/abs/2507.13064 http…

Unlocking the Hodge Conjecture: A Spectral Fingerprint Approach via Gauss-Manin Derivatives
We present a symbolic analytic framework for addressing the Hodge Conjecture, based on a refined invariant called the Hermitian spectral fingerprint. By projecting out $(k,k)$ components from holomorphic forms and their Gauss Manin derivatives, we define a fingerprint functional that vanishes identically for any rational cohomology class of type $(k,k)$. We prove unconditionally that the projected derivatives span the entire orthogonal complement of $H^{k,k}(X)$ in $H^{2k}(X,\mathbb{C})$, imply…

Symbolic Graphics Programming with Large Language Models
Yamei Chen, Haoquan Zhang, Yangyi Huang, Zeju Qiu, Kaipeng Zhang, Yandong Wen, Weiyang Liu
https://arxiv.org/abs/2509.05208

Symbolic Graphics Programming with Large Language Models
Large language models (LLMs) excel at program synthesis, yet their ability to produce symbolic graphics programs (SGPs) that render into precise visual content remains underexplored. We study symbolic graphics programming, where the goal is to generate an SGP from a natural-language description. This task also serves as a lens into how LLMs understand the visual world by prompting them to generate images rendered from SGPs. Among various SGPs, our paper sticks to scalable vector graphics (SVGs)…

Finetuning Large Language Model as an Effective Symbolic Regressor
Yingfan Hua, Ruikun Li, Jun Yao, Guohang Zhuang, Shixiang Tang, Bin Liu, Wanli Ouyang, Yan Lu
https://arxiv.org/abs/2508.09897

Finetuning Large Language Model as an Effective Symbolic Regressor
Deriving governing equations from observational data, known as Symbolic Regression (SR), is a cornerstone of scientific discovery. Large Language Models (LLMs) have shown promise in this task by leveraging their vast cross-disciplinary scientific knowledge. However, existing LLM-based methods primarily rely on direct inference or prompt engineering, often requiring excessive inference iterations to converge on correct formulas or failing to treating complex equation targets. These limitations i…

Symbolic Feedforward Networks for Probabilistic Finite Automata: Exact Simulation and Learnability
Sahil Rajesh Dhayalkar
https://arxiv.org/abs/2509.10034 https://

Symbolic Feedforward Networks for Probabilistic Finite Automata: Exact Simulation and Learnability
We present a formal and constructive theory showing that probabilistic finite automata (PFAs) can be exactly simulated using symbolic feedforward neural networks. Our architecture represents state distributions as vectors and transitions as stochastic matrices, enabling probabilistic state propagation via matrix-vector products. This yields a parallel, interpretable, and differentiable simulation of PFA dynamics using soft updates-without recurrence. We formally characterize probabilistic subse…

Tabularis Formatus: Predictive Formatting for Tables
Mukul Singh, Jos\'e Cambronero, Sumit Gulwani, Vu Le, Gust Verbruggen
https://arxiv.org/abs/2508.11121 https://

Tabularis Formatus: Predictive Formatting for Tables
Spreadsheet manipulation software are widely used for data management and analysis of tabular data, yet the creation of conditional formatting (CF) rules remains a complex task requiring technical knowledge and experience with specific platforms. In this paper we present TaFo, a neuro-symbolic approach to generating CF suggestions for tables, addressing common challenges such as user unawareness, difficulty in rule creation, and inadequate user interfaces. TaFo takes inspiration from component …

Monotone Neural Control Barrier Certificates
Alireza Nadali, Ashutosh Trivedi, Majid Zamani, Saber Jafarpour
https://arxiv.org/abs/2508.12178 https://arxiv…

Monotone Neural Control Barrier Certificates
This work presents a neurosymbolic framework for synthesizing and verifying safety controllers in high-dimensional monotone dynamical systems using only linear sample complexity, without requiring explicit models or conservative Lipschitz bounds. The approach combines the expressiveness of neural networks with the rigor of symbolic reasoning via barrier certificates, functional analogs of inductive invariants that formally guarantee safety. Prior data-driven methods often treat dynamics as blac…

[2025-08-19 Tue (UTC), no new articles found for cs.SC Symbolic Computation]
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AutoStub: Genetic Programming-Based Stub Creation for Symbolic Execution
Felix M\"achtle, Nils Loose, Jan-Niclas Serr, Jonas Sander, Thomas Eisenbarth
https://arxiv.org/abs/2509.08524

AutoStub: Genetic Programming-Based Stub Creation for Symbolic Execution
Symbolic execution is a powerful technique for software testing, but suffers from limitations when encountering external functions, such as native methods or third-party libraries. Existing solutions often require additional context, expensive SMT solvers, or manual intervention to approximate these functions through symbolic stubs. In this work, we propose a novel approach to automatically generate symbolic stubs for external functions during symbolic execution that leverages Genetic Programmi…

Network representations reveal structured uncertainty in music
Lluc Bono Rossell\'o, Robert Jankowski, Hugues Bersini, Mari\'an Bogu\~n\'a, M. \'Angeles Serrano
https://arxiv.org/abs/2509.14053

Network representations reveal structured uncertainty in music
Music, as a structured yet perceptually rich experience, can be modeled as a network to uncover how humans encode and process auditory information. While network-based representations of music are increasingly common, the impact of feature selection on structural properties and cognitive alignment remains underexplored. In this study, we evaluated eight network models, each constructed from symbolic representations of piano compositions using distinct combinations of pitch, octave, duration, an…

Approximating the universal thermal climate index using sparse regression with orthogonal polynomials
Sabin Roman, Gregor Skok, Ljupco Todorovski, Saso Dzeroski
https://arxiv.org/abs/2508.11307

Approximating the universal thermal climate index using sparse regression with orthogonal polynomials
This article explores novel data-driven modeling approaches for analyzing and approximating the Universal Thermal Climate Index (UTCI), a physiologically-based metric integrating multiple atmospheric variables to assess thermal comfort. Given the nonlinear, multivariate structure of UTCI, we investigate symbolic and sparse regression techniques as tools for interpretable and efficient function approximation. In particular, we highlight the benefits of using orthogonal polynomial bases-such as L…

The origins and meaning of the tryzub, the Ukrainian coat of arms: https://benborges.xyz/2025/07/13/the-origins-and-meaning-of.html

Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models
Lionel Wong, Katherine M. Collins, Lance Ying, Cedegao E. Zhang, Adrian Weller, Tobias Gersternberg, Timothy O'Donnell, Alexander K. Lew, Jacob D. Andreas, Joshua B. Tenenbaum, Tyler Brooke-Wilson
https://arxiv.org/abs/2507.12547

Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models
When faced with novel situations, people are able to marshal relevant considerations from a wide range of background knowledge and put these to use in inferences and predictions. What permits us to draw in globally relevant information and reason over it coherently? Here, we explore the hypothesis that people use a combination of distributed and symbolic representations to construct bespoke mental models tailored to novel situations. We propose a computational implementation of this idea -- a `…

Replaced article(s) found for math.CA. https://arxiv.org/list/math.CA/new
[1/1]:
- Symbolic calculus for a class of pseudodifferential operators with applications to compactness
\'Arp\'ad B\'enyi, Tadahiro Oh, Rodolfo H. Torres

Embodied Intelligence in Disassembly: Multimodal Perception Cross-validation and Continual Learning in Neuro-Symbolic TAMP
Ziwen He, Zhigang Wang, Yanlong Peng, Pengxu Chang, Hong Yang, Ming Chen
https://arxiv.org/abs/2509.11270

Embodied Intelligence in Disassembly: Multimodal Perception Cross-validation and Continual Learning in Neuro-Symbolic TAMP
With the rapid development of the new energy vehicle industry, the efficient disassembly and recycling of power batteries have become a critical challenge for the circular economy. In current unstructured disassembly scenarios, the dynamic nature of the environment severely limits the robustness of robotic perception, posing a significant barrier to autonomous disassembly in industrial applications. This paper proposes a continual learning framework based on Neuro-Symbolic task and motion plann…

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective
Roger Guimera, Marta Sales-Pardo
https://arxiv.org/abs/2507.19540 https://

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective
Symbolic regression automates the process of learning closed-form mathematical models from data. Standard approaches to symbolic regression, as well as newer deep learning approaches, rely on heuristic model selection criteria, heuristic regularization, and heuristic exploration of model space. Here, we discuss the probabilistic approach to symbolic regression, an alternative to such heuristic approaches with direct connections to information theory and statistical physics. We show how the prob…

[2025-09-19 Fri (UTC), no new articles found for cs.SC Symbolic Computation]
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Symbolic regression and precision LHC physics
Josh Bendavid, Daniel Conde, Manuel Morales-Alvarado, Veronica Sanz, Maria Ubiali
https://arxiv.org/abs/2508.00989 https://

Symbolic regression and precision LHC physics
We explore the use of symbolic regression to derive compact analytical expressions for angular observables relevant to electroweak boson production at the Large Hadron Collider (LHC). Focusing on the angular coefficients that govern the decay distributions of $W$ and $Z$ bosons, we investigate whether symbolic models can well approximate these quantities, typically computed via computationally costly numerical procedures, with high fidelity and interpretability. Using the PySR package, we first…

Ergodicity detection algorithms: Scaling of ergodicity in random symbolic dynamics
M. S\"uzen
https://arxiv.org/abs/2508.08319 https://arxiv.org/pdf/2…

Ergodicity detection algorithms: Scaling of ergodicity in random symbolic dynamics
The mathematical definitions of distinct concepts that are needed in building an ergodicity detec- tion algorithm are introduced in a framework. This algorithmic framework is expressed in a discrete setting in an accessible manner for broader quantitative practitioners without loss of generality. At the same time, the common misconceptions of the requirement of visiting all available states in the time-averaged quantities for physical systems and non-existence of an ergodic process are resolved…

Symbolic Execution in Practice: A Survey of Applications in Vulnerability, Malware, Firmware, and Protocol Analysis
Joshua Bailey, Charles Nicholas
https://arxiv.org/abs/2508.06643

Symbolic Execution in Practice: A Survey of Applications in Vulnerability, Malware, Firmware, and Protocol Analysis
Symbolic execution is a powerful program analysis technique that allows for the systematic exploration of all program paths. Path explosion, where the number of states to track becomes unwieldy, is one of the biggest challenges hindering symbolic execution's practical application. To combat this, researchers have employed various strategies to enable symbolic execution on complex software systems. This paper introduces a systematic taxonomy of these strategies, categorizing them into two primar…

Assisting the Grading of a Handwritten General Chemistry Exam with Artificial Intelligence
Jan Cvengros, Gerd Kortemeyer
https://arxiv.org/abs/2509.10591 https://

Assisting the Grading of a Handwritten General Chemistry Exam with Artificial Intelligence
We explore the effectiveness and reliability of an artificial intelligence (AI)-based grading system for a handwritten general chemistry exam, comparing AI-assigned scores to human grading across various types of questions. Exam pages and grading rubrics were uploaded as images to account for chemical reaction equations, short and long open-ended answers, numerical and symbolic answer derivations, drawing, and sketching in pencil-and-paper format. Using linear regression analyses and psychometr…

Comparing Methods for the Cross-Level Verification of SystemC Peripherals with Symbolic Execution
Karl Aaron Rudkowski, Sallar Ahmadi-Pour, Rolf Drechsler
https://arxiv.org/abs/2509.05504

Comparing Methods for the Cross-Level Verification of SystemC Peripherals with Symbolic Execution
Virtual Prototypes (VPs) are important tools in modern hardware development. At high abstractions, they are often implemented in SystemC and offer early analysis of increasingly complex designs. These complex designs often combine one or more processors, interconnects, and peripherals to perform tasks in hardware or interact with the environment. Verifying these subsystems is a well-suited task for VPs, as they allow reasoning across different abstraction levels. While modern verification techn…

How Does a Virtual Agent Decide Where to Look? - Symbolic Cognitive Reasoning for Embodied Head Rotation
Juyeong Hwang, Seong-Eun Hon, JaeYoung Seon, Hyeongyeop Kang
https://arxiv.org/abs/2508.08930

How Does a Virtual Agent Decide Where to Look? - Symbolic Cognitive Reasoning for Embodied Head Rotation
Natural head rotation is critical for believable embodied virtual agents, yet this micro-level behavior remains largely underexplored. While head-rotation prediction algorithms could, in principle, reproduce this behavior, they typically focus on visually salient stimuli and overlook the cognitive motives that guide head rotation. This yields agents that look at conspicuous objects while overlooking obstacles or task-relevant cues, diminishing realism in a virtual environment. We introduce SCOR…

Trump takes the next logical step toward dictatorship by turning the National Guard on US citizens in Washington.
Russia breaks through in the Donbas region, and things are not looking good for Ukraine.
Israel ramps up it’s genocide of Palestinians, and the death toll increases in Gaza. World leaders continue to turn a blind eye, offering only symbolic protest.
Perhaps the worst hunger crisis of all time in Sudan, and the world is not watching.
I have had quite enough…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/5]:
- Boolformer: Symbolic Regression of Logic Functions with Transformers
d'Ascoli, Renard, Papadopoulos, Bengio, Susskind, Abb\'e

[2025-06-20 Fri (UTC), no new articles found for cs.SC Symbolic Computation]
toXiv_bot_toot

Neuro-Symbolic Agents with Modal Logic for Autonomous Diagnostics
Antonin Sulc, Thorsten Hellert
https://arxiv.org/abs/2509.11943 https://arxiv.org/pdf/250…

Neuro-Symbolic Agents with Modal Logic for Autonomous Diagnostics
The development of intelligent agents, particularly those powered by language models (LMs), has shown the critical role in various environments that require intelligent and autonomous decision. Environments are not passive testing grounds and they represent the data required for agents to learn and exhibit very challenging conditions that require adaptive, complex and autonomous capacity to make decisions. While the paradigm of scaling models and datasets has led to remarkable emergent capabili…

Spectral Rigidity and Algebraicity: A Unified Framework for the Hodge Conjecture
Bita Hajebi, Pooya Hajebi
https://arxiv.org/abs/2507.12173 https://…

Spectral Rigidity and Algebraicity: A Unified Framework for the Hodge Conjecture
This paper presents a novel symbolic analytic framework to address the Hodge Conjecture, utilizing a refined invariant called the Hermitian spectral fingerprint. We modify the fingerprint functional to specifically exclude $(k,k)$ components, demonstrating its vanishing for rational classes of type $(k,k)$. Critically, we develop a comprehensive proof strategy to establish the converse: the vanishing of this refined fingerprint across all realization functors (de Rham and $\ell$adic) implies th…

Financial and symbolic incentives promote 'green' charging choices
Celina Kacperski, Florian Kutzner
https://arxiv.org/abs/2508.08282 https://arxiv…

Financial and symbolic incentives promote 'green' charging choices
Electromobility can contribute to a reduction in greenhouse gas emissions if usage behavior is aligned with the increasing availability of renewable energy. To achieve this, smart navigation systems can be used to inform drivers of optimal charging times and locations. Yet, required flexibility may impart time penalties. We investigate the impact of financial and symbolic incentive schemes to counteract these additional costs. In a laboratory experiment with real-life time costs, we find that m…

Symbolic dynamics for non-uniformly hyperbolic flows in high dimension
Yuri Lima, Juan Carlos Mongez, Jo\~ao Paulo Nascimento
https://arxiv.org/abs/2509.09050 https://

Symbolic dynamics for non-uniformly hyperbolic flows in high dimension
We construct symbolic dynamics for flows with positive speed in any dimension: for each $χ>0$, we code a set that has full measure for every invariant probability measure which is $χ$--hyperbolic. In particular, the coded set contains all hyperbolic periodic orbits with Lyapunov exponents outside of $[-χ,χ]$. This extends the recent work of Buzzi, Crovisier, and Lima for three dimensional flows with positive speed. As an application, we code homoclinic classes of measures by suspensions of …

WildScore: Benchmarking MLLMs in-the-Wild Symbolic Music Reasoning
Gagan Mundada, Yash Vishe, Amit Namburi, Xin Xu, Zachary Novack, Julian McAuley, Junda Wu
https://arxiv.org/abs/2509.04744

WildScore: Benchmarking MLLMs in-the-Wild Symbolic Music Reasoning
Recent advances in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities across various vision-language tasks. However, their reasoning abilities in the multimodal symbolic music domain remain largely unexplored. We introduce WildScore, the first in-the-wild multimodal symbolic music reasoning and analysis benchmark, designed to evaluate MLLMs' capacity to interpret real-world music scores and answer complex musicological queries. Each instance in WildScore is sourc…

Reasoning about Medical Triage Optimization with Logic Programming
Jaikrishna Manojkumar Patil, Adam Chapman, Richard Knuszka, John Chapman, Paulo Shakarian
https://arxiv.org/abs/2507.10781

Reasoning about Medical Triage Optimization with Logic Programming
We present a logic programming framework that orchestrates multiple variants of an optimization problem and reasons about their results to support high-stakes medical decision-making. The logic programming layer coordinates the construction and evaluation of multiple optimization formulations, translating solutions into logical facts that support further symbolic reasoning and ensure efficient resource allocation-specifically targeting the "right patient, right platform, right escort, right tim…

FactorHD: A Hyperdimensional Computing Model for Multi-Object Multi-Class Representation and Factorization
Yifei Zhou, Xuchu Huang, Chenyu Ni, Min Zhou, Zheyu Yan, Xunzhao Yin, Cheng Zhuo
https://arxiv.org/abs/2507.12366

FactorHD: A Hyperdimensional Computing Model for Multi-Object Multi-Class Representation and Factorization
Neuro-symbolic artificial intelligence (neuro-symbolic AI) excels in logical analysis and reasoning. Hyperdimensional Computing (HDC), a promising brain-inspired computational model, is integral to neuro-symbolic AI. Various HDC models have been proposed to represent class-instance and class-class relations, but when representing the more complex class-subclass relation, where multiple objects associate different levels of classes and subclasses, they face challenges for factorization, a crucia…

From Grounding to Skolemization: A Logic-Constrained Vector Symbolic Architecture for Complex Query Answering
Yuyin Lu, Hegang Chen, Yanghui Rao
https://arxiv.org/abs/2509.10837

From Grounding to Skolemization: A Logic-Constrained Vector Symbolic Architecture for Complex Query Answering
Complex Query Answering (CQA) over incomplete Knowledge Graphs (KGs), typically formalized as reasoning with Existential First-Order predicate logic with one free variable (EFO$_1$), faces a fundamental trade-off between logical soundness and computational efficiency. This work establishes the Grounding-Skolemization dichotomy for systematically analyzing CQA methods through the lens of formal logic. While Grounding-based methods inherently suffer from combinatorial explosion, most Skolemizatio…

When Is Prior Knowledge Helpful? Exploring the Evaluation and Selection of Unsupervised Pretext Tasks from a Neuro-Symbolic Perspective
Lin-Han Jia, Si-Yu Han, Wen-Chao Hu, Jie-Jing Shao, Wen-Da Wei, Zhi Zhou, Lan-Zhe Guo, Yu-Feng Li
https://arxiv.org/abs/2508.07299

When Is Prior Knowledge Helpful? Exploring the Evaluation and Selection of Unsupervised Pretext Tasks from a Neuro-Symbolic Perspective
Neuro-symbolic (Nesy) learning improves the target task performance of models by enabling them to satisfy knowledge, while semi/self-supervised learning (SSL) improves the target task performance by designing unsupervised pretext tasks for unlabeled data to make models satisfy corresponding assumptions. We extend the Nesy theory based on reliable knowledge to the scenario of unreliable knowledge (i.e., assumptions), thereby unifying the theoretical frameworks of SSL and Nesy. Through rigorous t…

$\mathcal{CP}$-Analyses with Symbolic Regression
Henning Bahl, Elina Fuchs, Marco Menen, Tilman Plehn
https://arxiv.org/abs/2507.05858 https://

$\mathcal{CP}$-Analyses with Symbolic Regression
Searching for $\mathcal{CP}$ violation in Higgs interactions at the LHC is as challenging as it is important. Although modern machine learning outperforms traditional methods, its results are difficult to control and interpret, which is especially important if an unambiguous probe of a fundamental symmetry is required. We propose solving this problem by learning analytic formulas with symbolic regression. Using the complementary PySR and SymbolNet approaches, we learn $\mathcal{CP}$-sensitive o…

Harmful Prompt Laundering: Jailbreaking LLMs with Abductive Styles and Symbolic Encoding
Seongho Joo, Hyukhun Koh, Kyomin Jung
https://arxiv.org/abs/2509.10931 https://

Harmful Prompt Laundering: Jailbreaking LLMs with Abductive Styles and Symbolic Encoding
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, but their potential misuse for harmful purposes remains a significant concern. To strengthen defenses against such vulnerabilities, it is essential to investigate universal jailbreak attacks that exploit intrinsic weaknesses in the architecture and learning paradigms of LLMs. In response, we propose \textbf{H}armful \textbf{P}rompt \textbf{La}undering (HaPLa), a novel and broadly applicable jailbreaking…

Neuro-Symbolic Acceleration of MILP Motion Planning with Temporal Logic and Chance Constraints
Junyang Cai, Weimin Huang, Jyotirmoy V. Deshmukh, Lars Lindemann, Bistra Dilkina
https://arxiv.org/abs/2508.07515

Neuro-Symbolic Acceleration of MILP Motion Planning with Temporal Logic and Chance Constraints
Autonomous systems must solve motion planning problems subject to increasingly complex, time-sensitive, and uncertain missions. These problems often involve high-level task specifications, such as temporal logic or chance constraints, which require solving large-scale Mixed-Integer Linear Programs (MILPs). However, existing MILP-based planning methods suffer from high computational cost and limited scalability, hindering their real-time applicability. We propose to use a neuro-symbolic approach…

Waldschmidt constants of symmetric sets of points in $\mathbb{P}^3$
Sebastian Calvo, Jack Huizenga, Tomasz Szemberg
https://arxiv.org/abs/2507.11888 https:…

Waldschmidt constants of symmetric sets of points in $\mathbb{P}^3$
Configurations of points defined by complex reflection groups have attracted a lot of attention recently in several directions of research, e.g., the containment problem between ordinary and symbolic powers of ideals, in the theory of unexpected hypersurfaces, in the study of sets of points whose general projections are complete intersections and in the ideas revolving around the Bounded Negativity Conjecture. In order to understand these configurations better several attempts have been underta…

LIFT: Automating Symbolic Execution Optimization with Large Language Models for AI Networks
Ruoxi Wang, Kun Li, Minghui Xu, Yue Zhang, Kaidi Xu, Chunchi Liu, Yinhao Xiao, Xiuzhen Cheng
https://arxiv.org/abs/2507.04931

LIFT: Automating Symbolic Execution Optimization with Large Language Models for AI Networks
Dynamic Symbolic Execution (DSE) is a key technique in program analysis, widely used in software testing, vulnerability discovery, and formal verification. In distributed AI systems, DSE plays a crucial role in identifying hard-to-detect bugs, especially those arising from complex network communication patterns. However, traditional approaches to symbolic execution are often hindered by scalability issues and inefficiencies, particularly in large-scale systems. This paper introduces LIFT (Large…

Dynamic Symbolic Execution for Semantic Difference Analysis of Component and Connector Architectures
Johanna Grahl, Bernhard Rumpe, Max Stachon, Sebastian St\"uber
https://arxiv.org/abs/2508.00749

Dynamic Symbolic Execution for Semantic Difference Analysis of Component and Connector Architectures
In the context of model-driven development, ensuring the correctness and consistency of evolving models is paramount. This paper investigates the application of Dynamic Symbolic Execution (DSE) for semantic difference analysis of component-and-connector architectures, specifically utilizing MontiArc models. We have enhanced the existing MontiArc-to-Java generator to gather both symbolic and concrete execution data at runtime, encompassing transition conditions, visited states, and internal vari…

THOR: Tool-Integrated Hierarchical Optimization via RL for Mathematical Reasoning
Qikai Chang, Zhenrong Zhang, Pengfei Hu, Jiefeng Ma, Yicheng Pan, Jianshu Zhang, Jun Du, Quan Liu, Jianqing Gao
https://arxiv.org/abs/2509.13761

THOR: Tool-Integrated Hierarchical Optimization via RL for Mathematical Reasoning
Large Language Models (LLMs) have made remarkable progress in mathematical reasoning, but still continue to struggle with high-precision tasks like numerical computation and formal symbolic manipulation. Integrating external tools has emerged as a promising approach to bridge this gap. Despite recent advances, existing methods struggle with three key challenges: constructing tool-integrated reasoning data, performing fine-grained optimization, and enhancing inference. To overcome these limitati…

Crosslisted article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- A Knowledge Graph Informing Soil Carbon Modeling
Mahdavi, et al.
https://…

PSALM-V: Automating Symbolic Planning in Interactive Visual Environments with Large Language Models
Wang Bill Zhu, Miaosen Chai, Ishika Singh, Robin Jia, Jesse Thomason
https://arxiv.org/abs/2506.20097

PSALM-V: Automating Symbolic Planning in Interactive Visual Environments with Large Language Models
We propose PSALM-V, the first autonomous neuro-symbolic learning system able to induce symbolic action semantics (i.e., pre- and post-conditions) in visual environments through interaction. PSALM-V bootstraps reliable symbolic planning without expert action definitions, using LLMs to generate heuristic plans and candidate symbolic semantics. Previous work has explored using large language models to generate action semantics for Planning Domain Definition Language (PDDL)-based symbolic planners.…

Improving BERT for Symbolic Music Understanding Using Token Denoising and Pianoroll Prediction
Jun-You Wang, Li Su
https://arxiv.org/abs/2507.04776 https:/…

Improving BERT for Symbolic Music Understanding Using Token Denoising and Pianoroll Prediction
We propose a pre-trained BERT-like model for symbolic music understanding that achieves competitive performance across a wide range of downstream tasks. To achieve this target, we design two novel pre-training objectives, namely token correction and pianoroll prediction. First, we sample a portion of note tokens and corrupt them with a limited amount of noise, and then train the model to denoise the corrupted tokens; second, we also train the model to predict bar-level and local pianoroll-deriv…

A Symbolic Adversarial Learning Framework for Evolving Fake News Generation and Detection
Chong Tian, Qirong Ho, Xiuying Chen
https://arxiv.org/abs/2508.19633 https://

A Symbolic Adversarial Learning Framework for Evolving Fake News Generation and Detection
Rapid LLM advancements heighten fake news risks by enabling the automatic generation of increasingly sophisticated misinformation. Previous detection methods, including fine-tuned small models or LLM-based detectors, often struggle with its dynamically evolving nature. In this work, we propose a novel framework called the Symbolic Adversarial Learning Framework (SALF), which implements an adversarial training paradigm by an agent symbolic learning optimization process, rather than relying on nu…

Desyan: A Platform for Seamless Value-Flow and Symbolic Analysis
Panagiotis Diamantakis, Thanassis Avgerinos, Yannis Smaragdakis
https://arxiv.org/abs/2508.00508 https://…

Desyan: A Platform for Seamless Value-Flow and Symbolic Analysis
Over the past two decades, two different types of static analyses have emerged as dominant paradigms both in academia and industry: value-flow analysis (e.g., data-flow analysis or points-to analysis) and symbolic analysis (e.g., symbolic execution). Despite their individual successes in numerous application fields, the two approaches have remained largely separate; an artifact of the simple reality that there is no broadly adopted unifying platform for effortless and efficient integration of s…

Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities
Safayat Bin Hakim, Muhammad Adil, Alvaro Velasquez, Shouhuai Xu, Houbing Herbert Song
https://arxiv.org/abs/2509.06921

Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities
Traditional Artificial Intelligence (AI) approaches in cybersecurity exhibit fundamental limitations: inadequate conceptual grounding leading to non-robustness against novel attacks; limited instructibility impeding analyst-guided adaptation; and misalignment with cybersecurity objectives. Neuro-Symbolic (NeSy) AI has emerged with the potential to revolutionize cybersecurity AI. However, there is no systematic understanding of this emerging approach. These hybrid systems address critical cybers…

Scaling Self-Supervised Representation Learning for Symbolic Piano Performance
Louis Bradshaw, Honglu Fan, Alexander Spangher, Stella Biderman, Simon Colton
https://arxiv.org/abs/2506.23869

Scaling Self-Supervised Representation Learning for Symbolic Piano Performance
We study the capabilities of generative autoregressive transformer models trained on large amounts of symbolic solo-piano transcriptions. After first pretraining on approximately 60,000 hours of music, we use a comparatively smaller, high-quality subset, to finetune models to produce musical continuations, perform symbolic classification tasks, and produce general-purpose contrastive MIDI embeddings by adapting the SimCLR framework to symbolic music. When evaluating piano continuation coherence…

Acting and Planning with Hierarchical Operational Models on a Mobile Robot: A Study with RAE UPOM
Oscar Lima, Marc Vinci, Sunandita Patra, Sebastian Stock, Joachim Hertzberg, Martin Atzmueller, Malik Ghallab, Dana Nau, Paolo Traverso
https://arxiv.org/abs/2507.11345

Acting and Planning with Hierarchical Operational Models on a Mobile Robot: A Study with RAE+UPOM
Robotic task execution faces challenges due to the inconsistency between symbolic planner models and the rich control structures actually running on the robot. In this paper, we present the first physical deployment of an integrated actor-planner system that shares hierarchical operational models for both acting and planning, interleaving the Reactive Acting Engine (RAE) with an anytime UCT-like Monte Carlo planner (UPOM). We implement RAE+UPOM on a mobile manipulator in a real-world deployment…

[2025-07-18 Fri (UTC), 1 new article found for cs.SC Symbolic Computation]
toXiv_bot_toot

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[6/7]:
- Validating Solidity Code Defects using Symbolic and Concrete Execution powered by Large Language ...
\c{S}tefan-Claudiu Susan, Andrei Arusoaie, Dorel Lucanu

[2025-08-18 Mon (UTC), no new articles found for cs.SC Symbolic Computation]
toXiv_bot_toot

[2025-09-18 Thu (UTC), no new articles found for cs.SC Symbolic Computation]
toXiv_bot_toot

WeaveMuse: An Open Agentic System for Multimodal Music Understanding and Generation
Emmanouil Karystinaios
https://arxiv.org/abs/2509.11183 https://arxiv.o…

WeaveMuse: An Open Agentic System for Multimodal Music Understanding and Generation
Agentic AI has been standardized in industry as a practical paradigm for coordinating specialized models and tools to solve complex multimodal tasks. In this work, we present WeaveMuse, a multi-agent system for music understanding, symbolic composition, and audio synthesis. Each specialist agent interprets user requests, derives machine-actionable requirements (modalities, formats, constraints), and validates its own outputs, while a manager agent selects and sequences tools, mediates user inte…

Few-Shot Neuro-Symbolic Imitation Learning for Long-Horizon Planning and Acting
Pierrick Lorang, Hong Lu, Johannes Huemer, Patrik Zips, Matthias Scheutz
https://arxiv.org/abs/2508.21501

Few-Shot Neuro-Symbolic Imitation Learning for Long-Horizon Planning and Acting
Imitation learning enables intelligent systems to acquire complex behaviors with minimal supervision. However, existing methods often focus on short-horizon skills, require large datasets, and struggle to solve long-horizon tasks or generalize across task variations and distribution shifts. We propose a novel neuro-symbolic framework that jointly learns continuous control policies and symbolic domain abstractions from a few skill demonstrations. Our method abstracts high-level task structures i…

Replaced article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- Exact Algorithms for Computing Generalized Eigenspaces of Matrices via Jordan-Krylov Basis
Shinichi Tajima, Katsuyoshi Ohara, Akira Terui

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/11]:
- FireGNN: Neuro-Symbolic Graph Neural Networks with Trainable Fuzzy Rules for Interpretable Medica...
Prajit Sengupta, Islem Rekik

Bridging Engineering and AI Planning through Model-Based Knowledge Transformation for the Validation of Automated Production System Variants
Hamied Nabizada, Lasse Beers, Alain Chahine, Felix Gehlhoff, Oliver Niggemann, Alexander Fay
https://arxiv.org/abs/2509.12091

Bridging Engineering and AI Planning through Model-Based Knowledge Transformation for the Validation of Automated Production System Variants
Engineering models created in Model-Based Systems Engineering (MBSE) environments contain detailed information about system structure and behavior. However, they typically lack symbolic planning semantics such as preconditions, effects, and constraints related to resource availability and timing. This limits their ability to evaluate whether a given system variant can fulfill specific tasks and how efficiently it performs compared to alternatives. To address this gap, this paper presents a mo…

Formal Reasoning for Intelligent QA Systems: A Case Study in the Educational Domain
Tuan Bui, An Nguyen, Phat Thai, Minh Hua, Ngan Pham L. N., Ngan Pham T. B., Dung Le, Long Nguyen, Thanh-Tung Tran, Thang Bui, Tho Quan
https://arxiv.org/abs/2509.11572

Formal Reasoning for Intelligent QA Systems: A Case Study in the Educational Domain
Reasoning is essential for closed-domain QA systems in which procedural correctness and policy compliance are critical. While large language models (LLMs) have shown strong performance on many reasoning tasks, recent work reveals that their reasoning traces are often unfaithful - serving more as plausible justifications than as causally grounded derivations. Efforts to combine LLMs with symbolic engines (e.g., Prover9, Z3) have improved reliability but remain limited to static forms of logic, s…

MusicSwarm: Biologically Inspired Intelligence for Music Composition
Markus J. Buehler
https://arxiv.org/abs/2509.11973 https://arxiv.org/pdf/2509.11973

MusicSwarm: Biologically Inspired Intelligence for Music Composition
We show that coherent, long-form musical composition can emerge from a decentralized swarm of identical, frozen foundation models that coordinate via stigmergic, peer-to-peer signals, without any weight updates. We compare a centralized multi-agent system with a global critic to a fully decentralized swarm in which bar-wise agents sense and deposit harmonic, rhythmic, and structural cues, adapt short-term memory, and reach consensus. Across symbolic, audio, and graph-theoretic analyses, the swa…

[2025-07-17 Thu (UTC), 2 new articles found for cs.SC Symbolic Computation]
toXiv_bot_toot

Fuzzy, Symbolic, and Contextual: Enhancing LLM Instruction via Cognitive Scaffolding
Vanessa Figueiredo
https://arxiv.org/abs/2508.21204 https://arxiv.org/…

Fuzzy, Symbolic, and Contextual: Enhancing LLM Instruction via Cognitive Scaffolding
We study how architectural inductive biases influence the cognitive behavior of large language models (LLMs) in instructional dialogue. We introduce a symbolic scaffolding mechanism paired with a short-term memory schema designed to promote adaptive, structured reasoning in Socratic tutoring. Using controlled ablation across five system variants, we evaluate model outputs via expert-designed rubrics covering scaffolding, responsiveness, symbolic reasoning, and conversational memory. We present …

Tree-Based Deep Learning for Ranking Symbolic Integration Algorithms
Rashid Barket, Matthew England, J\"urgen Gerhard
https://arxiv.org/abs/2508.06383 https://

Tree-Based Deep Learning for Ranking Symbolic Integration Algorithms
Symbolic indefinite integration in Computer Algebra Systems such as Maple involves selecting the most effective algorithm from multiple available methods. Not all methods will succeed for a given problem, and when several do, the results, though mathematically equivalent, can differ greatly in presentation complexity. Traditionally, this choice has been made with minimal consideration of the problem instance, leading to inefficiencies. We present a machine learning (ML) approach using tree-ba…

Whither symbols in the era of advanced neural networks?
Thomas L. Griffiths, Brenden M. Lake, R. Thomas McCoy, Ellie Pavlick, Taylor W. Webb
https://arxiv.org/abs/2508.05776 htt…

Whither symbols in the era of advanced neural networks?
Some of the strongest evidence that human minds should be thought about in terms of symbolic systems has been the way they combine ideas, produce novelty, and learn quickly. We argue that modern neural networks -- and the artificial intelligence systems built upon them -- exhibit similar abilities. This undermines the argument that the cognitive processes and representations used by human minds are symbolic, although the fact that these neural networks are typically trained on data generated by…

[2025-09-17 Wed (UTC), no new articles found for cs.SC Symbolic Computation]
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Constraints-Guided Diffusion Reasoner for Neuro-Symbolic Learning
Xuan Zhang, Zhijian Zhou, Weidi Xu, Yanting Miao, Chao Qu, Yuan Qi
https://arxiv.org/abs/2508.16524 https://

Constraints-Guided Diffusion Reasoner for Neuro-Symbolic Learning
Enabling neural networks to learn complex logical constraints and fulfill symbolic reasoning is a critical challenge. Bridging this gap often requires guiding the neural network's output distribution to move closer to the symbolic constraints. While diffusion models have shown remarkable generative capability across various domains, we employ the powerful architecture to perform neuro-symbolic learning and solve logical puzzles. Our diffusion-based pipeline adopts a two-stage training strategy:…

DeepGraphLog for Layered Neurosymbolic AI
Adem Kikaj, Giuseppe Marra, Floris Geerts, Robin Manhaeve, Luc De Raedt
https://arxiv.org/abs/2509.07665 https://…

DeepGraphLog for Layered Neurosymbolic AI
Neurosymbolic AI (NeSy) aims to integrate the statistical strengths of neural networks with the interpretability and structure of symbolic reasoning. However, current NeSy frameworks like DeepProbLog enforce a fixed flow where symbolic reasoning always follows neural processing. This restricts their ability to model complex dependencies, especially in irregular data structures such as graphs. In this work, we introduce DeepGraphLog, a novel NeSy framework that extends ProbLog with Graph Neural …

Scaling Up without Fading Out: Goal-Aware Sparse GNN for RL-based Generalized Planning
Sangwoo Jeon, Juchul Shin, Gyeong-Tae Kim, YeonJe Cho, Seongwoo Kim
https://arxiv.org/abs/2508.10747

Scaling Up without Fading Out: Goal-Aware Sparse GNN for RL-based Generalized Planning
Generalized planning using deep reinforcement learning (RL) combined with graph neural networks (GNNs) has shown promising results in various symbolic planning domains described by PDDL. However, existing approaches typically represent planning states as fully connected graphs, leading to a combinatorial explosion in edge information and substantial sparsity as problem scales grow, especially evident in large grid-based environments. This dense representation results in diluted node-level infor…

Constraints-Guided Diffusion Reasoner for Neuro-Symbolic Learning
Xuan Zhang, Zhijian Zhou, Weidi Xu, Yanting Miao, Chao Qu, Yuan Qi
https://arxiv.org/abs/2508.16524 https://

Constraints-Guided Diffusion Reasoner for Neuro-Symbolic Learning
Enabling neural networks to learn complex logical constraints and fulfill symbolic reasoning is a critical challenge. Bridging this gap often requires guiding the neural network's output distribution to move closer to the symbolic constraints. While diffusion models have shown remarkable generative capability across various domains, we employ the powerful architecture to perform neuro-symbolic learning and solve logical puzzles. Our diffusion-based pipeline adopts a two-stage training strategy:…

How well can LLMs provide planning feedback in grounded environments?
Yuxuan Li, Victor Zhong
https://arxiv.org/abs/2509.09790 https://arxiv.org/pdf/2509.0…

How well can LLMs provide planning feedback in grounded environments?
Learning to plan in grounded environments typically requires carefully designed reward functions or high-quality annotated demonstrations. Recent works show that pretrained foundation models, such as large language models (LLMs) and vision language models (VLMs), capture background knowledge helpful for planning, which reduces the amount of reward design and demonstrations needed for policy learning. We evaluate how well LLMs and VLMs provide feedback across symbolic, language, and continuous c…

TableMoE: Neuro-Symbolic Routing for Structured Expert Reasoning in Multimodal Table Understanding
Junwen Zhang, Pu Chen, Yin Zhang
https://arxiv.org/abs/2506.21393

TableMoE: Neuro-Symbolic Routing for Structured Expert Reasoning in Multimodal Table Understanding
Multimodal understanding of tables in real-world contexts is challenging due to the complexity of structure, symbolic density, and visual degradation (blur, skew, watermarking, incomplete structures or fonts, multi-span or hierarchically nested layouts). Existing multimodal large language models (MLLMs) struggle with such WildStruct conditions, resulting in limited performance and poor generalization. To address these challenges, we propose TableMoE, a neuro-symbolic Mixture-of-Connector-Expert…