Tootfinder

BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies
Kourosh Shahnazari, Seyed Moein Ayyoubzadeh, Mohammadali Keshtparvar
https://arxiv.org/abs/2506.00328

BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies
The quest for interpretable reinforcement learning is a grand challenge for the deployment of autonomous decision-making systems in safety-critical applications. Modern deep reinforcement learning approaches, while powerful, tend to produce opaque policies that compromise verification, reduce transparency, and impede human oversight. To address this, we introduce BASIL (Best-Action Symbolic Interpretable Learning), a systematic approach for generating symbolic, rule-based policies via online ev…

This https://arxiv.org/abs/2505.20896 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

How Do Transformers Learn Variable Binding in Symbolic Programs?
Variable binding -- the ability to associate variables with values -- is fundamental to symbolic computation and cognition. Although classical architectures typically implement variable binding via addressable memory, it is not well understood how modern neural networks lacking built-in binding operations may acquire this capacity. We investigate this by training a Transformer to dereference queried variables in symbolic programs where variables are assigned either numerical constants or other …

Higher-Order Automatic Differentiation Using Symbolic Differential Algebra: Bridging the Gap between Algorithmic and Symbolic Differentiation
He Zhang
https://arxiv.org/abs/2506.00796

Higher-Order Automatic Differentiation Using Symbolic Differential Algebra: Bridging the Gap between Algorithmic and Symbolic Differentiation
In scientific computation, it is often necessary to calculate higher-order derivatives of a function. Currently, two primary methods for higher-order automatic differentiation exist: symbolic differentiation and algorithmic automatic differentiation (AD). Differential Algebra (DA) is a mathematical technique widely used in beam dynamics analysis and simulations of particle accelerators, and it also functions as an algorithmic automatic differentiation method. DA automatically computes the Taylo…

Symbolic identification of tensor equations in multidimensional physical fields
Tianyi Chen, Hao Yang, Wenjun Ma, Jun Zhang
https://arxiv.org/abs/2507.01466

Symbolic identification of tensor equations in multidimensional physical fields
Recently, data-driven methods have shown great promise for discovering governing equations from simulation or experimental data. However, most existing approaches are limited to scalar equations, with few capable of identifying tensor relationships. In this work, we propose a general data-driven framework for identifying tensor equations, referred to as Symbolic Identification of Tensor Equations (SITE). The core idea of SITE--representing tensor equations using a host-plasmid structure--is ins…

Constraint-Guided Symbolic Regression for Data-Efficient Kinetic Model Discovery
Miguel \'Angel de Carvalho Servia (Mimi), Ilya Orson Sandoval (Mimi), King Kuok (Mimi), Hii, Klaus Hellgardt, Dongda Zhang, Ehecatl Antonio del Rio Chanona
https://arxiv.org/abs/2507.02730

Constraint-Guided Symbolic Regression for Data-Efficient Kinetic Model Discovery
The industrialization of catalytic processes hinges on the availability of reliable kinetic models for design, optimization, and control. Traditional mechanistic models demand extensive domain expertise, while many data-driven approaches often lack interpretability and fail to enforce physical consistency. To overcome these limitations, we propose the Physics-Informed Automated Discovery of Kinetics (PI-ADoK) framework. By integrating physical constraints directly into a symbolic regression app…

This https://arxiv.org/abs/2505.20748 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

INTERLEAVE: A Faster Symbolic Algorithm for Maximal End Component Decomposition
This paper presents a novel symbolic algorithm for the Maximal End Component (MEC) decomposition of a Markov Decision Process (MDP). The key idea behind our algorithm INTERLEAVE is to interleave the computation of Strongly Connected Components (SCCs) with eager elimination of redundant state-action pairs, rather than performing these computations sequentially as done by existing state-of-the-art algorithms. Even though our approach has the same complexity as prior works, an empirical evaluation…

Symbolic Higher-Order Analysis of Multivariate Time Series
Andrea Civilini, Fabrizio de Vico Fallani, Vito Latora
https://arxiv.org/abs/2506.00508 https://…

Symbolic Higher-Order Analysis of Multivariate Time Series
Identifying patterns of relations among the units of a complex system from measurements of their activities in time is a fundamental problem with many practical applications. Here, we introduce a method that detects dependencies of any order in multivariate time series data. The method first transforms a multivariate time series into a symbolic sequence, and then extract statistically significant strings of symbols through a Bayesian approach. Such motifs are finally modelled as the hyperedges …

This https://arxiv.org/abs/2505.23667 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Fortune: Formula-Driven Reinforcement Learning for Symbolic Table Reasoning in Language Models
Tables are a fundamental structure for organizing and analyzing data, making effective table understanding a critical capability for intelligent systems. While large language models (LMs) demonstrate strong general reasoning abilities, they continue to struggle with accurate numerical or symbolic reasoning over tabular data, especially in complex scenarios. Spreadsheet formulas provide a powerful and expressive medium for representing executable symbolic operations, encoding rich reasoning patt…

Dimension of Besicovitch-Eggleston sets for non-autonomous systems with countable symbolic dynamics
Jonny Imbierski, Charlene Kalle
https://arxiv.org/abs/2506.01803

Dimension of Besicovitch-Eggleston sets for non-autonomous systems with countable symbolic dynamics
In this article we derive a formula for the Hausdorff dimension of Besicovitch-Eggleston level sets associated with non-autonomous dynamics constructed from families of countable affine iterated function systems. The formula obtained shows that the universal-lower-bound phenomenon present in the autonomous case studied by Fan et al. (2010) persists in this non-autonomous setting.

a friend let me take a picture of his whiteboard

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…

This https://arxiv.org/abs/2504.21841 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Neuro-Symbolic Generation of Explanations for Robot Policies with Weighted Signal Temporal Logic
Neural network-based policies have demonstrated success in many robotic applications, but often lack human-explanability, which poses challenges in safety-critical deployments. To address this, we propose a neuro-symbolic explanation framework that generates a weighted signal temporal logic (wSTL) specification to describe a robot policy in a interpretable form. Existing methods typically produce explanations that are verbose and inconsistent, which hinders explainability, and loose, which do n…

Symbolic Computation with Symmetric Polynomials in Real Algebraic Geometry
Cordian Riener, Thi Xuan Vu
https://arxiv.org/abs/2507.23728 https://arxiv.org/p…

Symbolic Computation with Symmetric Polynomials in Real Algebraic Geometry
Symmetry plays a central role in accelerating symbolic computation involving polynomials. This chapter surveys recent developments and foundational methods that leverage the inherent symmetries of polynomial systems to reduce complexity, improve algorithmic efficiency, and reveal deeper structural insights. The main focus is on symmetry by the permutation of variables.

Replaced article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- Generalized ODE reduction algorithm for bounded degree transformation
Shaoxuan Huang
…

This https://arxiv.org/abs/2504.05424 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Speculative Automated Refactoring of Imperative Deep Learning Programs to Graph Execution
Efficiency is essential to support ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code -- supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approach…

Crafting Hanzi as Narrative Bridges: An AI Co-Creation Workshop for Elderly Migrants
Wen Zhan, Ziqun Hua, Peiyue Lin, Yunfei Chen
https://arxiv.org/abs/2507.01548

Crafting Hanzi as Narrative Bridges: An AI Co-Creation Workshop for Elderly Migrants
This paper explores how older adults, particularly aging migrants in urban China, can engage AI-assisted co-creation to express personal narratives that are often fragmented, underrepresented, or difficult to verbalize. Through a pilot workshop combining oral storytelling and the symbolic reconstruction of Hanzi, participants shared memories of migration and recreated new character forms using Xiaozhuan glyphs, suggested by the Large Language Model (LLM), together with physical materials. Suppo…

Modeling the Optical Properties of Biological Structures using Symbolic Regression
Julian Sierra-Velez, Alexandre Vial, Marina Inchaussandague, Diana Skigin, Demetrio Mac\'ias
https://arxiv.org/abs/2506.01862

Modeling the Optical Properties of Biological Structures using Symbolic Regression
We present a Machine Learning approach based on Symbolic Regression to derive, from either numerically generated or experimentally measured spectral data, closed-form expressions that model the optical properties of biological materials. To evaluate the performance of our approach, we consider three case studies with the aim of retrieving the refractive index of the materials that constitute the biological structures considered. The results obtained show that, in addition to retrieving readable…

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…

A Finite-State Symbolic Automaton Model for the Collatz Map and Its Convergence Properties
Leonard Ben Aurel Brauer
https://arxiv.org/abs/2506.21728 https:…

A Finite-State Symbolic Automaton Model for the Collatz Map and Its Convergence Properties
We present a finite-state, deterministic automaton that emulates the Collatz function by operating on base-10 digit sequences. Each digit is represented as a symbolic triplet capturing its value, the parity of the next digit, and a local carry value, resulting in a state space of exactly 60 configurations. The transition rules are local, total, and parity-dependent, yet collectively reproduce the global behavior of the Collatz map through digitwise operations. All symbolic trajectories reduce t…

Things like this are not just a symbolic expression of discontent.
Fascism depends to a •very• large extent on anticipatory compliance. It needs people to feel like things are going that way, it’s already a done deal, better to just go along with it.
Every time dissent is visible, every way dissent feels ubiquitous, every time somebody feels like they’re not alone in opposition, every time somebody who’s tempted to comply fears there might be hell to pay if they do — that directly, concretely damages the fascists’ power.

A general polynomial emulator for cosmology via moment projection
Zheng Zhang
https://arxiv.org/abs/2507.02179 https://arxiv.org/pdf/…

A general polynomial emulator for cosmology via moment projection
We present MomentEmu, a general-purpose polynomial emulator for fast and interpretable mappings between theoretical parameters and observational features. The method constructs moment matrices to project simulation data onto polynomial bases, yielding symbolic expressions that approximate the target mapping. Compared to neural-network-based emulators, MomentEmu offers negligible training cost, millisecond-level evaluation, and transparent functional forms. As a demonstration, we develop two emu…

Modelling Arbitrary Computations in the Symbolic Model using an Equational Theory for Bounded Binary Circuits
Michiel Marcus, Frank Westers, Anne Nijsten
https://arxiv.org/abs/2507.21731

Modelling Arbitrary Computations in the Symbolic Model using an Equational Theory for Bounded Binary Circuits
In this work, we propose a class of equational theories for bounded binary circuits that have the finite variant property. These theories could serve as a building block to specify cryptographic primitive implementations and automatically discover attacks as binary circuits in the symbolic model. We provide proofs of equivalence between this class of equational theories and Boolean logic up to circuit size 3 and we provide the variant complexities and performance benchmarks using Maude-NPA. Thi…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/6]:
- Multivariate de Bruijn Graphs: A Symbolic Graph Framework for Time Series Forecasting
Mert Onur Cakiroglu, Idil Bilge Altun, Mehmet Dalkilic, Elham Buxton, Hasan Kurban

An Exploratory Framework for Future SETI Applications: Detecting Generative Reactivity via Language Models
Po-Chieh Yu
#toXiv_bot_toot

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/3]:
- Quasi-symbolic Semantic Geometry over Transformer-based Variational AutoEncoder
Yingji Zhang, Danilo S. Carvalho, Andr\'e Freitas

A Large Language Model for Chemistry and Retrosynthesis Predictions
Yueqing Zhang, Wentao Liu, Yan Zhang, Danyang Xiong, Jihang Zhai, Hao Hao, YuCheng Gu, HaiBo Yang, Shuanhu Gao, Lianrui Hu, Aimin Zhou, Xiao He
https://arxiv.org/abs/2507.01444

A Large Language Model for Chemistry and Retrosynthesis Predictions
Large language models (LLM) have achieved impressive progress across a broad range of general-purpose tasks, but their effectiveness in chemistry remains limited due to scarce domain-specific datasets and the demand for precise symbolic and structural reasoning. Here we introduce ECNU-ChemGPT(name after East China Normal University), a chemistry-specialized LLM engineered for deep chemical knowledge understanding and accurate retrosynthetic route planning. Our approach is distinguished by four …

Consider: we came fairly close to extincting the various species crucial to this cycle *AND STILL COULD.*
The problem today isn’t the ongoing symbolic & subsistence whaling, it is that we have ravaged the ocean ecosystem in ways we don’t understand, such as noise pollution.
We really don’t understand enough about the subsystems of the oceans to be fucking with them in the ways we have been during the past century.

Quantum Bayesian inference with Suport vector states for intrusion detection
Nayema Mridha, Garrv Sipani, Eva R Gaarder, Shah Haque, Radhika Kuttala, Binay P Akhouri, Mohamad M Al Zein, Eric Howard
https://arxiv.org/abs/2507.00403

Quantum Bayesian inference with Suport vector states for intrusion detection
We present a quantum Bayesian inference method for intrusion detection, using explicitly constructed quantum circuits and statevector simulation. Prior and conditional probabilities are encoded via unitary gates, and posterior distributions are extracted through symbolic post-selection. Applied to a scenario with network spikes, system vulnerabilities, and false alarms, the method yields joint, marginal, and conditional probabilities aligned with causal structure. Our results demonstrate the fe…

Sign Language: Towards Sign Understanding for Robot Autonomy
Ayush Agrawal, Joel Loo, Nicky Zimmerman, David Hsu
https://arxiv.org/abs/2506.02556 https://

Sign Language: Towards Sign Understanding for Robot Autonomy
Signage is an ubiquitous element of human environments, playing a critical role in both scene understanding and navigation. For autonomous systems to fully interpret human environments, effectively parsing and understanding signs is essential. We introduce the task of navigational sign understanding, aimed at extracting navigational cues from signs that convey symbolic spatial information about the scene. Specifically, we focus on signs capturing directional cues that point toward distant locat…

This https://arxiv.org/abs/2502.12267 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

NeuroStrata: Harnessing Neurosymbolic Paradigms for Improved Design, Testability, and Verifiability of Autonomous CPS
Autonomous cyber-physical systems (CPSs) leverage AI for perception, planning, and control but face trust and safety certification challenges due to inherent uncertainties. The neurosymbolic paradigm replaces stochastic layers with interpretable symbolic AI, enabling determinism. While promising, challenges like multisensor fusion, adaptability, and verification remain. This paper introduces NeuroStrata, a neurosymbolic framework to enhance the testing and verification of autonomous CPS. We out…

Slot Attention with Re-Initialization and Self-Distillation
Rongzhen Zhao, Yi Zhao, Juho Kannala, Joni Pajarinen
https://arxiv.org/abs/2507.23755 https://a…

Slot Attention with Re-Initialization and Self-Distillation
Unlike popular solutions based on dense feature maps, Object-Centric Learning (OCL) represents visual scenes as sub-symbolic object-level feature vectors, termed slots, which are highly versatile for tasks involving visual modalities. OCL typically aggregates object superpixels into slots by iteratively applying competitive cross attention, known as Slot Attention, with the slots as the query. However, once initialized, these slots are reused naively, causing redundant slots to compete with inf…

Replaced article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- Symbolic Sets for Proving Bounds on Rado Numbers
Tanbir Ahmed, Lamina Zaman, Curtis Bright

Discovering the underlying analytic structure within Standard Model constants using artificial intelligence
S. V. Chekanov, H. Kjellerstrand
https://arxiv.org/abs/2507.00225

Discovering the underlying analytic structure within Standard Model constants using artificial intelligence
This paper presents a search for underlying analytic structures among the fundamental parameters of the Standard Model (SM) using symbolic regression and genetic programming. We identify the simplest analytic relationships connecting pairs of these constants and report several notable observations based on about a thousand expressions with relative precision better than 1%. These results may serve as valuable inputs for model builders and artificial intelligence methods aimed at uncovering hidd…

Replaced article(s) found for math.DS. https://arxiv.org/list/math.DS/new
[1/1]:
- On certain labelled directed graphs of symbolic dynamics
Wolfgang Krieger

This https://arxiv.org/abs/2505.11077 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

LLM-Enhanced Symbolic Control for Safety-Critical Applications
Motivated by Smart Manufacturing and Industry 4.0, we introduce a framework for synthesizing Abstraction-Based Controller Design (ABCD) for reach-avoid problems from Natural Language (NL) specifications using Large Language Models (LLMs). A Code Agent interprets an NL description of the control problem and translates it into a formal language interpretable by state-of-the-art symbolic control software, while a Checker Agent verifies the correctness of the generated code and enhances safety by i…

This https://arxiv.org/abs/2402.11871 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

From Real World to Logic and Back: Learning Generalizable Relational Concepts For Long Horizon Robot Planning
Humans efficiently generalize from limited demonstrations, but robots still struggle to transfer learned knowledge to complex, unseen tasks with longer horizons and increased complexity. We propose the first known method enabling robots to autonomously invent relational concepts directly from small sets of unannotated, unsegmented demonstrations. The learned symbolic concepts are grounded into logic-based world models, facilitating efficient zero-shot generalization to significantly more comple…

Industrial brain: a human-like autonomous neuro-symbolic cognitive decision-making system
Junping Wang, Bicheng Wang, Yibo Xuea, Yuan Xie
https://arxiv.org/abs/2506.23926

Industrial brain: a human-like autonomous neuro-symbolic cognitive decision-making system
Resilience non-equilibrium measurement, the ability to maintain fundamental functionality amidst failures and errors, is crucial for scientific management and engineering applications of industrial chain. The problem is particularly challenging when the number or types of multiple co-evolution of resilience (for example, randomly placed) are extremely chaos. Existing end-to-end deep learning ordinarily do not generalize well to unseen full-feld reconstruction of spatiotemporal co-evolution stru…

Diffusion-based Symbolic Music Generation with Structured State Space Models
Shenghua Yuan, Xing Tang, Jiatao Chen, Tianming Xie, Jing Wang, Bing Shi
https://arxiv.org/abs/2507.20128

Diffusion-based Symbolic Music Generation with Structured State Space Models
Recent advancements in diffusion models have significantly improved symbolic music generation. However, most approaches rely on transformer-based architectures with self-attention mechanisms, which are constrained by quadratic computational complexity, limiting scalability for long sequences. To address this, we propose Symbolic Music Diffusion with Mamba (SMDIM), a novel diffusion-based architecture integrating Structured State Space Models (SSMs) for efficient global context modeling and the …

A Closed-Form Symbolic Generator: $A^n B^n = C^n D^n$, for $n = 2,3$
Jamal Agbanwa
https://arxiv.org/abs/2506.19173 https://arxiv…

A Closed-Form Symbolic Generator: $A^n + B^n = C^n + D^n$, for $n = 2,3$
We present a unified framework for constructing integer solutions to $A^{n} + B^{n} = C^{n} + D^{n}$ for $n=2,3$. For $n=2$, we derive explicit formulas for any solutions via differences of squares. For $n=3$, we introduce general formulas that include the Hardy-Ramanujan number 1729 for instance, we also construct a symbolic generator that produces infinitely many integer solutions to the Diophantine equation A^3 + B^3 = C^3 + D^3 . While the resulting formulas for $A,B,C,D$ from the symbolic …

Watermarking Without Standards Is Not AI Governance
Alexander Nemecek, Yuzhou Jiang, Erman Ayday
https://arxiv.org/abs/2505.23814 https://

Watermarking Without Standards Is Not AI Governance
Watermarking has emerged as a leading technical proposal for attributing generative AI content and is increasingly cited in global governance frameworks. This paper argues that current implementations risk serving as symbolic compliance rather than delivering effective oversight. We identify a growing gap between regulatory expectations and the technical limitations of existing watermarking schemes. Through analysis of policy proposals and industry practices, we show how incentive structures di…

[2025-07-03 Thu (UTC), 1 new article found for cs.SC Symbolic Computation]
toXiv_bot_toot

S02E11 of #TheSandman felt like watching the entire symbolic burial of Neil Gaiman - farewell cookie fortune and all - just so DC could retain the option to a reboot, at least for some of the characters. But it was a straight up Viking funeral.

"Listen, all you Fascists, you might be surprised, People in the world, are getting organized, you're bound to loose, Fascists bound to loose":
https://www.nytimes.com/2025/07/30/opinion/ice-immigration-protest-resistance.html

Opinion | They Saw Their Neighbors Taken Away by ICE. Then They Made a Plan.
The movement against ICE in Los Angeles — one that is starting to take root across the country — demonstrates a shift from symbolic protest to direct action.

Linearithmic Clean-up for Vector-Symbolic Key-Value Memory with Kroneker Rotation Products
Ruipeng Liu, Qinru Qiu, Simon Khan, Garrett E. Katz
https://arxiv.org/abs/2506.15793

Linearithmic Clean-up for Vector-Symbolic Key-Value Memory with Kroneker Rotation Products
A computational bottleneck in current Vector-Symbolic Architectures (VSAs) is the ``clean-up'' step, which decodes the noisy vectors retrieved from the architecture. Clean-up typically compares noisy vectors against a ``codebook'' of prototype vectors, incurring computational complexity that is quadratic or similar. We present a new codebook representation that supports efficient clean-up, based on Kroneker products of rotation-like matrices. The resulting clean-up time complexity is linearithm…

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.…

Code Generation for Near-Roofline Finite Element Actions on GPUs from Symbolic Variational Forms
Kaushik Kulkarni, Andreas Kl\"ockner
https://arxiv.org/abs/2506.17471

Code Generation for Near-Roofline Finite Element Actions on GPUs from Symbolic Variational Forms
We present a novel parallelization strategy for evaluating Finite Element Method (FEM) variational forms on GPUs, focusing on those that are expressible through the Unified Form Language (UFL) on simplex meshes. We base our approach on code transformations, wherein we construct a space of scheduling candidates and rank them via a heuristic cost model to effectively handle the large diversity of computational workloads that can be expressed in this way. We present a design of a search space to w…

AutoCodeSherpa: Symbolic Explanations in AI Coding Agents
Sungmin Kang, Haifeng Ruan, Abhik Roychoudhury
https://arxiv.org/abs/2507.22414 https://arxiv.org…

AutoCodeSherpa: Symbolic Explanations in AI Coding Agents
Large Language Model (LLM) agents autonomously use external tools on top of one or more LLMs to accomplish specific tasks. Lately LLM agents for software engineering tasks have become popular. These agents can benefit from the use of program analysis tools working on program representations. This is demonstrated by existing agentic AI solutions such as AutoCodeRover or SpecRover which perform automated program repair. Specifically the goal of these works is to use program analysis to improve th…

This https://arxiv.org/abs/2505.22299 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Logical Consistency is Vital: Neural-Symbolic Information Retrieval for Negative-Constraint Queries
Information retrieval plays a crucial role in resource localization. Current dense retrievers retrieve the relevant documents within a corpus via embedding similarities, which compute similarities between dense vectors mainly depending on word co-occurrence between queries and documents, but overlook the real query intents. Thus, they often retrieve numerous irrelevant documents. Particularly in the scenarios of complex queries such as \emph{negative-constraint queries}, their retrieval perfo…

Symbolic Regression-Enhanced Dynamic Wake Meandering: Fast and Physically Consistent Wind-Turbine Wake Modeling
Ding Wang, Dachuan Feng, Kangcheng Zhou, Yuntian Chen, Shijun Liao, Shiyi Chen
https://arxiv.org/abs/2506.14403

Symbolic Regression-Enhanced Dynamic Wake Meandering: Fast and Physically Consistent Wind-Turbine Wake Modeling
Accurately modeling wind turbine wakes is essential for optimizing wind farm performance but remains a persistent challenge. While the dynamic wake meandering (DWM) model captures unsteady wake behavior, it suffers from near-wake inaccuracies due to empirical closures. We propose a Symbolic Regression-enhanced DWM (SRDWM) framework that achieves equation-level closure by embedding symbolic expressions for volumetric forcing and boundary terms explicitly into governing equations. These physicall…

[2025-07-04 Fri (UTC), no new articles found for cs.SC Symbolic Computation]
toXiv_bot_toot

Ordinary and symbolic powers of matroids via polarization
Justin Lyle, Paolo Mantero
https://arxiv.org/abs/2505.17398 https://arxiv.o…

Ordinary and symbolic powers of matroids via polarization
In this paper, we propose a uniform approach to tackle problems about squarefree monomial ideals whose powers have good properties. We employ this approach to achieve a twofold goal: (i) recover and extend several well--known results in the literature, especially regarding Stanley--Reisner ideals of matroids, and (ii) provide short, elementary proofs for these results. Among them, we provide simple proofs of two celebrated results of Minh and Trung, Varbaro, and Terai and Trung elegantly charac…

The Shape of Consumer Behavior: A Symbolic and Topological Analysis of Time Series
Pola Bereta, Ioannis Diamantis
https://arxiv.org/abs/2506.19759 https://…

The Shape of Consumer Behavior: A Symbolic and Topological Analysis of Time Series
Understanding temporal patterns in online search behavior is crucial for real-time marketing and trend forecasting. Google Trends offers a rich proxy for public interest, yet the high dimensionality and noise of its time-series data present challenges for effective clustering. This study evaluates three unsupervised clustering approaches, Symbolic Aggregate approXimation (SAX), enhanced SAX (eSAX), and Topological Data Analysis (TDA), applied to 20 Google Trends keywords representing major cons…

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…

Pendulum Model of Spiking Neurons
Joy Bose
https://arxiv.org/abs/2507.22146 https://arxiv.org/pdf/2507.22146

Pendulum Model of Spiking Neurons
We propose a biologically inspired model of spiking neurons based on the dynamics of a damped, driven pendulum. Unlike traditional models such as the Leaky Integrate-and-Fire (LIF) neurons, the pendulum neuron incorporates second-order, nonlinear dynamics that naturally give rise to oscillatory behavior and phase-based spike encoding. This model captures richer temporal features and supports timing-sensitive computations critical for sequence processing and symbolic learning. We present an anal…

An Uncertainty Principle for Probabilistic Computation in the Retina
Jayanth R Taranath, Salim M'Jahad
https://arxiv.org/abs/2507.22785 https://arxiv.o…

An Uncertainty Principle for Probabilistic Computation in the Retina
We introduce a probabilistic model of early visual processing, beginning with the interaction between a light wavefront and the retina. We argue that perception originates not with deterministic transduction, but with probabilistic threshold crossings shaped by quantum photon arrival statistics and biological variability. We formalize this with an uncertainty relation, \( Δα\cdot Δt \geq η\), through the transformation of light into symbolic neural code through the layered retinal architect…

Why Are Parsing Actions for Understanding Message Hierarchies Not Random?
Daichi Kato, Ryo Ueda, Yusuke Miyao
https://arxiv.org/abs/2506.22366 https://

Why Are Parsing Actions for Understanding Message Hierarchies Not Random?
If humans understood language by randomly selecting parsing actions, it might have been necessary to construct a robust symbolic system capable of being interpreted under any hierarchical structure. However, human parsing strategies do not seem to follow such a random pattern. Why is that the case? In fact, a previous study on emergent communication using models with hierarchical biases have reported that agents adopting random parsing strategies$\unicode{x2013}$ones that deviate significantly …

Negated String Containment is Decidable (Technical Report)
Vojt\v{e}ch Havlena, Michal He\v{c}ko, Luk\'a\v{s} Hol\'ik, Ond\v{r}ej Leng\'al
https://arxiv.org/abs/2506.22061

Negated String Containment is Decidable (Technical Report)
We provide a positive answer to a long-standing open question of the decidability of the not-contains string predicate. Not-contains is practically relevant, for instance in symbolic execution of string manipulating programs. Particularly, we show that the predicate notContains(x1 ... xn, y1 ... ym), where x1 ... xn and y1 ... ym are sequences of string variables constrained by regular languages, is decidable. Decidability of a not-contains predicate combined with chain-free word equations and …

[2025-06-03 Tue (UTC), 1 new article found for cs.SC Symbolic Computation]
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Alpay Algebra III: Observer-Coupled Collapse and the Temporal Drift of Identity
Faruk Alpay
https://arxiv.org/abs/2505.19790 https://…

Alpay Algebra III: Observer-Coupled Collapse and the Temporal Drift of Identity
This paper introduces a formal framework for modeling observer-dependent collapse dynamics and temporal identity drift within artificial and mathematical systems, grounded entirely in the symbolic foundations of Alpay Algebra. Building upon the fixed-point emergence structures developed in Alpay Algebra I and II, this third installment formalizes the observer-coupled ϕ-collapse process through transfinite categorical flows and curvature-driven identity operators. We define a novel temporal dri…

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…

Learning Interpretable Rules from Neural Networks: Neurosymbolic AI for Radar Hand Gesture Recognition
Sarah Seifi, Tobias Sukianto, Cecilia Carbonelli, Lorenzo Servadei, Robert Wille
https://arxiv.org/abs/2506.22443

Learning Interpretable Rules from Neural Networks: Neurosymbolic AI for Radar Hand Gesture Recognition
Rule-based models offer interpretability but struggle with complex data, while deep neural networks excel in performance yet lack transparency. This work investigates a neuro-symbolic rule learning neural network named RL-Net that learns interpretable rule lists through neural optimization, applied for the first time to radar-based hand gesture recognition (HGR). We benchmark RL-Net against a fully transparent rule-based system (MIRA) and an explainable black-box model (XentricAI), evaluating a…

Augmenting Von Neumann's Architecture for an Intelligent Future
Rajpreet Singh, Vidhi Kothari
https://arxiv.org/abs/2507.16628 https://

Augmenting Von Neumann's Architecture for an Intelligent Future
This work presents a novel computer architecture that extends the Von Neumann model with a dedicated Reasoning Unit (RU) to enable native artificial general intelligence capabilities. The RU functions as a specialized co-processor that executes symbolic inference, multi-agent coordination, and hybrid symbolic-neural computation as fundamental architectural primitives. This hardware-embedded approach allows autonomous agents to perform goal-directed planning, dynamic knowledge manipulation, and …

Generating and Understanding Tests via Path-Aware Symbolic Execution with LLMs
Yaoxuan Wu, Xiaojie Zhou, Ahmad Humayun, Muhammad Ali Gulzar, Miryung Kim
https://arxiv.org/abs/2506.19287

Generating and Understanding Tests via Path-Aware Symbolic Execution with LLMs
Symbolic execution is a widely used technique for test generation, offering systematic exploration of program paths through constraint solving. However, it is fundamentally constrained by the capability to model the target code including library functions in terms of symbolic constraint and the capability of underlying constraint solvers. As a result, many paths involving complex features remain unanalyzed or insufficiently modeled. Recent advances in large language models (LLMs) have shown pro…

[2025-06-04 Wed (UTC), no new articles found for cs.SC Symbolic Computation]
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SatelliteFormula: Multi-Modal Symbolic Regression from Remote Sensing Imagery for Physics Discovery
Zhenyu Yu, Mohd. Yamani Idna Idris, Pei Wang, Yuelong Xia, Fei Ma, Rizwan Qureshi
https://arxiv.org/abs/2506.06176

SatelliteFormula: Multi-Modal Symbolic Regression from Remote Sensing Imagery for Physics Discovery
We propose SatelliteFormula, a novel symbolic regression framework that derives physically interpretable expressions directly from multi-spectral remote sensing imagery. Unlike traditional empirical indices or black-box learning models, SatelliteFormula combines a Vision Transformer-based encoder for spatial-spectral feature extraction with physics-guided constraints to ensure consistency and interpretability. Existing symbolic regression methods struggle with the high-dimensional complexity of…

Bridging Compositional and Distributional Semantics: A Survey on Latent Semantic Geometry via AutoEncoder
Yingji Zhang, Danilo S. Carvalho, Andr\'e Freitas
https://arxiv.org/abs/2506.20083

Bridging Compositional and Distributional Semantics: A Survey on Latent Semantic Geometry via AutoEncoder
Integrating compositional and symbolic properties into current distributional semantic spaces can enhance the interpretability, controllability, compositionality, and generalisation capabilities of Transformer-based auto-regressive language models (LMs). In this survey, we offer a novel perspective on latent space geometry through the lens of compositional semantics, a direction we refer to as \textit{semantic representation learning}. This direction enables a bridge between symbolic and distri…

Ordinary and symbolic powers of matroids via polarization
Justin Lyle, Paolo Mantero
https://arxiv.org/abs/2505.17398 https://arxiv.o…

Ordinary and symbolic powers of matroids via polarization
In this paper, we propose a uniform approach to tackle problems about squarefree monomial ideals whose powers have good properties. We employ this approach to achieve a twofold goal: (i) recover and extend several well--known results in the literature, especially regarding Stanley--Reisner ideals of matroids, and (ii) provide short, elementary proofs for these results. Among them, we provide simple proofs of two celebrated results of Minh and Trung, Varbaro, and Terai and Trung elegantly charac…

From Generality to Mastery: Composer-Style Symbolic Music Generation via Large-Scale Pre-training
Mingyang Yao, Ke Chen
https://arxiv.org/abs/2506.17497 ht…

From Generality to Mastery: Composer-Style Symbolic Music Generation via Large-Scale Pre-training
Despite progress in controllable symbolic music generation, data scarcity remains a challenge for certain control modalities. Composer-style music generation is a prime example, as only a few pieces per composer are available, limiting the modeling of both styles and fundamental music elements (e.g., melody, chord, rhythm). In this paper, we investigate how general music knowledge learned from a broad corpus can enhance the mastery of specific composer styles, with a focus on piano piece genera…

(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…

This https://arxiv.org/abs/2505.00878 has been replaced.
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SynPAT: A System for Generating Synthetic Physical Theories with Data
Machine-assisted methods for discovering new physical laws of nature, starting from a given background theory and data, have recently emerged, and seem to hold the promise of someday advancing our understanding of the physical world. To address these needs, we have developed SynPAT, a system for generating synthetic physical theories comprising (i) a set of consistent axioms, (ii) a symbolic expression that is a consequence of the axioms and the challenge to be discovered, and (iii) noisy data …

Symbolic Reduction for Formal Synthesis of Global Lyapunov Functions
Jun Liu, Maxwell Fitzsimmons
https://arxiv.org/abs/2506.18171 https://

Symbolic Reduction for Formal Synthesis of Global Lyapunov Functions
We investigate the formal synthesis of global polynomial Lyapunov functions for polynomial vector fields. We establish that a sign-definite polynomial must satisfy specific algebraic constraints, which we leverage to develop a set of straightforward symbolic reduction rules. These rules can be recursively applied to symbolically simplify the Lyapunov candidate, enabling more efficient and robust discovery of Lyapunov functions via optimization or satisfiability modulo theories (SMT) solving. In…

Pretraining a Unified PDDL Domain from Real-World Demonstrations for Generalizable Robot Task Planning
Haoming Ye, Yunxiao Xiao, Cewu Lu, Panpan Cai
https://arxiv.org/abs/2507.21545

Pretraining a Unified PDDL Domain from Real-World Demonstrations for Generalizable Robot Task Planning
Robotic task planning in real-world environments requires reasoning over implicit constraints from language and vision. While LLMs and VLMs offer strong priors, they struggle with long-horizon structure and symbolic grounding. Existing methods that combine LLMs with symbolic planning often rely on handcrafted or narrow domains, limiting generalization. We propose UniDomain, a framework that pre-trains a PDDL domain from robot manipulation demonstrations and applies it for online robotic task pl…

ASP-Assisted Symbolic Regression: Uncovering Hidden Physics in Fluid Mechanics
Theofanis Aravanis, Grigorios Chrimatopoulos, Mohammad Ferdows, Michalis Xenos, Efstratios Em Tzirtzilakis
https://arxiv.org/abs/2507.17777

ASP-Assisted Symbolic Regression: Uncovering Hidden Physics in Fluid Mechanics
Unlike conventional Machine-Learning (ML) approaches, often criticized as "black boxes", Symbolic Regression (SR) stands out as a powerful tool for revealing interpretable mathematical relationships in complex physical systems, requiring no a priori assumptions about models' structures. Motivated by the recognition that, in fluid mechanics, an understanding of the underlying flow physics is as crucial as accurate prediction, this study applies SR to model a fundamental three-dimensional (3D) in…

Symbolic Generation and Modular Embedding of High-Quality abc-Triples
Michael A. Idowu
https://arxiv.org/abs/2506.10039 https://arxiv…

Symbolic Generation and Modular Embedding of High-Quality abc-Triples
We present a symbolic identity for generating integer triples $(a, b, c)$ satisfying $a + b = c$, inspired by structural features of the \emph{abc conjecture}. The construction uses powers of $2$ and $3$ in combination with modular inversion in $\mathbb{Z}/3^p\mathbb{Z}$, leading to a parametric identity with residue constraints that yield abc-triples exhibiting low radical values. Through affine transformations, these symbolic triples are embedded into a broader space of high-quality examples,…

Agentic Program Repair from Test Failures at Scale: A Neuro-symbolic approach with static analysis and test execution feedback
Chandra Maddila, Adam Tait, Claire Chang, Daniel Cheng, Nauman Ahmad, Vijayaraghavan Murali, Marshall Roch, Arnaud Avondet, Aaron Meltzer, Victor Montalvao, Michael Hopko, Chris Waterson, Parth Thakkar, Renuka Fernandez, Kristian Kristensen, Sivan Barzily, Sherry Chen, Rui Abreu, Nachiappan Nagappan, Payam Shodjai, Killian Murphy, James Everingham, Aparna Raman…

Agentic Program Repair from Test Failures at Scale: A Neuro-symbolic approach with static analysis and test execution feedback
Aim: With the advent of LLMs, sophisticated agentic program repair has become viable at large organizations with large codebases. In this work, we develop an Engineering Agent that fixes the source code based on test failures at scale across diverse software offerings internally. Method: Using Llama as the base, we employ the ReAct harness to develop an agent. We start with a test failure that was triaged by a rule-based test failure bot. We then set up an agentic harness and allow the agent …

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/4]:
- FGeo-HyperGNet: Geometric Problem Solving Integrating FormalGeo Symbolic System and Hypergraph Ne...
Xiaokai Zhang, Yang Li, Na Zhu, Cheng Qin, Zhenbing Zeng, Tuo Leng

Enhancing Symbolic Machine Learning by Subsymbolic Representations
Stephen Roth, Lennart Baur, Derian Boer, Stefan Kramer
https://arxiv.org/abs/2506.14569 …

Enhancing Symbolic Machine Learning by Subsymbolic Representations
The goal of neuro-symbolic AI is to integrate symbolic and subsymbolic AI approaches, to overcome the limitations of either. Prominent systems include Logic Tensor Networks (LTN) or DeepProbLog, which offer neural predicates and end-to-end learning. The versatility of systems like LTNs and DeepProbLog, however, makes them less efficient in simpler settings, for instance, for discriminative machine learning, in particular in domains with many constants. Therefore, we follow a different approach:…

[2025-07-02 Wed (UTC), no new articles found for cs.SC Symbolic Computation]
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The Hashed Fractal Key Recovery (HFKR) Problem: From Symbolic Path Inversion to Post-Quantum Cryptographic Keys
Mohamed Aly Bouke
https://arxiv.org/abs/2506.04383

The Hashed Fractal Key Recovery (HFKR) Problem: From Symbolic Path Inversion to Post-Quantum Cryptographic Keys
Classical cryptographic systems rely heavily on structured algebraic problems, such as factorization, discrete logarithms, or lattice-based assumptions, which are increasingly vulnerable to quantum attacks and structural cryptanalysis. In response, this work introduces the Hashed Fractal Key Recovery (HFKR) problem, a non-algebraic cryptographic construction grounded in symbolic dynamics and chaotic perturbations. HFKR builds on the Symbolic Path Inversion Problem (SPIP), leveraging symbolic tr…

Versatile Symbolic Music-for-Music Modeling via Function Alignment
Junyan Jiang, Daniel Chin, Liwei Lin, Xuanjie Liu, Gus Xia
https://arxiv.org/abs/2506.15548

Versatile Symbolic Music-for-Music Modeling via Function Alignment
Many music AI models learn a map between music content and human-defined labels. However, many annotations, such as chords, can be naturally expressed within the music modality itself, e.g., as sequences of symbolic notes. This observation enables both understanding tasks (e.g., chord recognition) and conditional generation tasks (e.g., chord-conditioned melody generation) to be unified under a music-for-music sequence modeling paradigm. In this work, we propose parameter-efficient solutions fo…

Worst-Case Symbolic Constraints Analysis and Generalisation with Large Language Models
Daniel Koh, Yannic Noller, Corina S. Pasareanu, Adrians Skapars, Youcheng Sun
https://arxiv.org/abs/2506.08171

Worst-Case Symbolic Constraints Analysis and Generalisation with Large Language Models
Large language models (LLMs) have been successfully applied to a variety of coding tasks, including code generation, completion, and repair. However, more complex symbolic reasoning tasks remain largely unexplored by LLMs. This paper investigates the capacity of LLMs to reason about worst-case executions in programs through symbolic constraints analysis, aiming to connect LLMs and symbolic reasoning approaches. Specifically, we define and address the problem of worst-case symbolic constraints a…

Refining Tc Prediction in Hydrides via Symbolic-Regression-Enhanced Electron-Localization-Function-Based Descriptors
Francesco Belli, Sean Torres, Julia Contreras-Garc\`ia, Eva Zurek
https://arxiv.org/abs/2506.17456

Refining Tc Prediction in Hydrides via Symbolic-Regression-Enhanced Electron-Localization-Function-Based Descriptors
Hydrogen-based materials are able to possess extremely high superconducting critical temperatures, \tc s, due to hydrogen's low atomic mass and strong electron-phonon interaction. Recently, a descriptor based on the Electron Localization Function (ELF) has enabled the rapid estimation of the \tc\ of hydrogen-containing compounds from electronic networking properties, but its applicability has been limited by the small size and homogeneity of the training dataset used. Herein, the model is re-ex…

[2025-06-02 Mon (UTC), no new articles found for cs.SC Symbolic Computation]
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Symbolic Foundation Regressor on Complex Networks
Weiting Liu, Jiaxu Cui, Jiao Hu, En Wang, Bo Yang
https://arxiv.org/abs/2505.21879 https://

Symbolic Foundation Regressor on Complex Networks
In science, we are interested not only in forecasting but also in understanding how predictions are made, specifically what the interpretable underlying model looks like. Data-driven machine learning technology can significantly streamline the complex and time-consuming traditional manual process of discovering scientific laws, helping us gain insights into fundamental issues in modern science. In this work, we introduce a pre-trained symbolic foundation regressor that can effectively compress …

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…

Automated Code Review Using Large Language Models with Symbolic Reasoning
Busra Icoz, Goksel Biricik
https://arxiv.org/abs/2507.18476 https://arxiv.org/pdf…

Automated Code Review Using Large Language Models with Symbolic Reasoning
Code review is one of the key processes in the software development lifecycle and is essential to maintain code quality. However, manual code review is subjective and time consuming. Given its rule-based nature, code review is well suited for automation. In recent years, significant efforts have been made to automate this process with the help of artificial intelligence. Recent developments in Large Language Models (LLMs) have also emerged as a promising tool in this area, but these models ofte…

Style-based Composer Identification and Attribution of Symbolic Music Scores: a Systematic Survey
Federico Simonetta
https://arxiv.org/abs/2506.12440 https…

Style-based Composer Identification and Attribution of Symbolic Music Scores: a Systematic Survey
This paper presents the first comprehensive systematic review of literature on style-based composer identification and authorship attribution in symbolic music scores. Addressing the critical need for improved reliability and reproducibility in this field, the review rigorously analyzes 58 peer-reviewed papers published across various historical periods, with the search adapted to evolving terminology. The analysis critically assesses prevailing repertoires, computational approaches, and evalua…

The Constitutional Controller: Doubt-Calibrated Steering of Compliant Agents
Simon Kohaut, Felix Divo, Navid Hamid, Benedict Flade, Julian Eggert, Devendra Singh Dhami, Kristian Kersting
https://arxiv.org/abs/2507.15478

The Constitutional Controller: Doubt-Calibrated Steering of Compliant Agents
Ensuring reliable and rule-compliant behavior of autonomous agents in uncertain environments remains a fundamental challenge in modern robotics. Our work shows how neuro-symbolic systems, which integrate probabilistic, symbolic white-box reasoning models with deep learning methods, offer a powerful solution to this challenge. This enables the simultaneous consideration of explicit rules and neural models trained on noisy data, combining the strength of structured reasoning with flexible represe…

Personalizable Long-Context Symbolic Music Infilling with MIDI-RWKV
Christian Zhou-Zheng, Philippe Pasquier
https://arxiv.org/abs/2506.13001 https://

Personalizable Long-Context Symbolic Music Infilling with MIDI-RWKV
Existing work in automatic music generation has primarily focused on end-to-end systems that produce complete compositions or continuations. However, because musical composition is typically an iterative process, such systems make it difficult to engage in the back-and-forth between human and machine that is essential to computer-assisted creativity. In this study, we address the task of personalizable, multi-track, long-context, and controllable symbolic music infilling to enhance the process …

Replaced article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- Differential equations satisfied by generating functions of 5-, 6-, and 7-regular labelled graphs...
Fr\'ed\'eric Chyzak, Marni Mishna

Think Like an Engineer: A Neuro-Symbolic Collaboration Agent for Generative Software Requirements Elicitation and Self-Review
Sai Zhang, Zhenchang Xing, Jieshan Chen, Dehai Zhao, Zizhong Zhu, Xiaowang Zhang, Zhiyong Feng, Xiaohong Li
https://arxiv.org/abs/2507.14969

Think Like an Engineer: A Neuro-Symbolic Collaboration Agent for Generative Software Requirements Elicitation and Self-Review
The vision of End-User Software Engineering (EUSE) is to empower non-professional users with full control over the software development lifecycle. It aims to enable users to drive generative software development using only natural language requirements. However, since end-users often lack knowledge of software engineering, their requirement descriptions are frequently ambiguous, raising significant challenges to generative software development. Although existing approaches utilize structured la…

Differential Privacy in Machine Learning: From Symbolic AI to LLMs
Francisco Aguilera-Mart\'inez, Fernando Berzal
https://arxiv.org/abs/2506.11687 http…

Differential Privacy in Machine Learning: From Symbolic AI to LLMs
Machine learning models should not reveal particular information that is not otherwise accessible. Differential privacy provides a formal framework to mitigate privacy risks by ensuring that the inclusion or exclusion of any single data point does not significantly alter the output of an algorithm, thus limiting the exposure of private information. This survey paper explores the foundational definitions of differential privacy, reviews its original formulations and tracing its evolution through…

[2025-07-01 Tue (UTC), no new articles found for cs.SC Symbolic Computation]
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This https://arxiv.org/abs/2501.00296 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

From Pixels to Predicates: Learning Symbolic World Models via Pretrained Vision-Language Models
Our aim is to learn to solve long-horizon decision-making problems in complex robotics domains given low-level skills and a handful of short-horizon demonstrations containing sequences of images. To this end, we focus on learning abstract symbolic world models that facilitate zero-shot generalization to novel goals via planning. A critical component of such models is the set of symbolic predicates that define properties of and relationships between objects. In this work, we leverage pretrained …

[2025-08-01 Fri (UTC), no new articles found for cs.SC Symbolic Computation]
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[2025-06-30 Mon (UTC), 1 new article found for cs.SC Symbolic Computation]
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[2025-07-31 Thu (UTC), no new articles found for cs.SC Symbolic Computation]
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[2025-07-30 Wed (UTC), no new articles found for cs.SC Symbolic Computation]
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[2025-05-30 Fri (UTC), no new articles found for cs.SC Symbolic Computation]
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Replaced article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- A non-commutative algorithm for multiplying 4x4 matrices using 48 non-complex multiplications
Jean-Guillaume Dumas (CASC), Cl\'ement Pernet (CASC), Alexandre Sedoglavic (CRIStAL)