Tootfinder

Hierarchical Bayesian Operator-induced Symbolic Regression Trees for Structural Learning of Scientific Expressions
Somjit Roy, Pritam Dey, Debdeep Pati, Bani K. Mallick
https://arxiv.org/abs/2509.19710

Hierarchical Bayesian Operator-induced Symbolic Regression Trees for Structural Learning of Scientific Expressions
The advent of Scientific Machine Learning has heralded a transformative era in scientific discovery, driving progress across diverse domains. Central to this progress is uncovering scientific laws from experimental data through symbolic regression. However, existing approaches are dominated by heuristic algorithms or data-hungry black-box methods, which often demand low-noise settings and lack principled uncertainty quantification. Motivated by interpretable Statistical Artificial Intelligence,…

Scalable and Approximation-free Symbolic Control for Unknown Euler-Lagrange Systems
Ratnangshu Das, Shubham Sawarkar, Pushpak Jagtap
https://arxiv.org/abs/2509.19859 https://

Scalable and Approximation-free Symbolic Control for Unknown Euler-Lagrange Systems
We propose a novel symbolic control framework for enforcing temporal logic specifications in Euler-Lagrange systems that addresses the key limitations of traditional abstraction-based approaches. Unlike existing methods that require exact system models and provide guarantees only at discrete sampling instants, our approach relies only on bounds on system parameters and input constraints, and ensures correctness for the full continuous-time trajectory. The framework combines scalable abstraction…

Trump frequently campaigned with coal miners during his 2016 presidential run,
and has since continued to participate in photo-ops using miners as a symbolic backdrop for his energy policies while campaigning and serving in office.
However, despite the president courting the CEOs of the mining industry heavily during his campaign,
miners themselves feel neglected and left behind.
Days after the executive order, on April 11,
just before coal mines were expected t…

Coal Miners Were Poised to Win Key Safety Protections. Then Trump Showed Up.
After courting coal miners during his campaign, Trump has left a trail of broken promises.

EngravingGNN: A Hybrid Graph Neural Network for End-to-End Piano Score Engraving
Emmanouil Karystinaios, Francesco Foscarin, Gerhard Widmer
https://arxiv.org/abs/2509.19412 http…

EngravingGNN: A Hybrid Graph Neural Network for End-to-End Piano Score Engraving
This paper focuses on automatic music engraving, i.e., the creation of a humanly-readable musical score from musical content. This step is fundamental for all applications that include a human player, but it remains a mostly unexplored topic in symbolic music processing. In this work, we formalize the problem as a collection of interdependent subtasks, and propose a unified graph neural network (GNN) framework that targets the case of piano music and quantized symbolic input. Our method employs…

Trump's pardon of Rudi Guilliani
could wind up having a huge effect on election conspirators like Matthew Alan Laiss,
who is accused of voting in both Pennsylvania and Florida in the 2020 election.
According to a federal indictment handed down in September,
Laiss moved from Pennsylvania to Florida in August of 2020 and voted first with a mail-in ballot in Pennsylvania and then in person in Florida on election day.
Both votes were for Trump, Laiss’ lawyers …

Crosslisted article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- PySymmetry: A Sage/Python Framework for the Symmetry Reduction of Linear G-Equivariant Systems
Leon D. da Silva, Marcelo P. Santos

Replaced article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new
[1/1]:
- Unifying Symbolic Music Arrangement: Track-Aware Reconstruction and Structured Tokenization
Longshen Ou, Jingwei Zhao, Ziyu Wang, Gus Xia, Qihao Liang, Torin Hopkins Ye Wang

So symbolic, Trump destroying the White House and the USA.
https://www.nytimes.com/live/2025/10/22/us/trump-news?unlocked_article_code=1.vU8.nfNL.lMQE_N2QL03K&smid=nytcore-android-share

Trump Will Demolish Entire White House East Wing for Ballroom: Live Updates

Proof Strategy Extraction from LLMs for Enhancing Symbolic Provers
Jian Fang, Yican Sun, Yingfei Xiong
https://arxiv.org/abs/2510.10131 https://arxiv.org/p…

Proof Strategy Extraction from LLMs for Enhancing Symbolic Provers
One important approach to software verification is interactive theorem proving. However, writing formal proofs often requires substantial human effort, making proof automation highly important. Traditionally, proof automation has relied on symbolic provers. Recently, large language models (LLMs) have demonstrated strong capabilities in theorem proving, complementing symbolic provers. Nonetheless, prompting LLMs can be expensive and may pose security risks for confidential codebases. As a result…

One Life to Learn: Inferring Symbolic World Models for Stochastic Environments from Unguided Exploration
Zaid Khan, Archiki Prasad, Elias Stengel-Eskin, Jaemin Cho, Mohit Bansal
https://arxiv.org/abs/2510.12088

One Life to Learn: Inferring Symbolic World Models for Stochastic Environments from Unguided Exploration
Symbolic world modeling requires inferring and representing an environment's transitional dynamics as an executable program. Prior work has focused on largely deterministic environments with abundant interaction data, simple mechanics, and human guidance. We address a more realistic and challenging setting, learning in a complex, stochastic environment where the agent has only "one life" to explore a hostile environment without human guidance. We introduce OneLife, a framework that models world…

[2025-09-25 Thu (UTC), no new articles found for cs.SC Symbolic Computation]
toXiv_bot_toot

The last Volkswagen ID.3 has been built at the 'Gläserne Manufaktur' (Transparent Factory) in Dresden.
A symbolic end to a symbolic factory, itself an icon of early 21st-century Germany, when nothing but progress seemed possible?
https://www.instagram.com/vwmanufaktur_de/

Gläserne Manufaktur Dresden on Instagram: "Zu Beginn nächster Woche endet ein Kapitel und öffnet gleichzeitig ein Neues … 🫶🏻 #Volkswagen #HomeOfID #GläserneManufaktur #WayToZero #elektromobilität #vwlove #vwlife #vwid3 #emobility #elektroauto #carsofinstagram #vwid #ID3 #carmanufacturing"
1,543 likes, 363 comments - vwmanufaktur_de on December 12, 2025: "Zu Beginn nächster Woche endet ein Kapitel und öffnet gleichzeitig ein Neues … 🫶🏻 #Volkswagen #HomeOfID #GläserneManufaktur #WayToZero #elektromobilität #vwlove #vwlife #vwid3 #emobility #elektroauto #carsofinstagram #vwid #ID3 #carmanufacturing".

Adaptive LLM-Symbolic Reasoning via Dynamic Logical Solver Composition
Lei Xu, Pierre Beckmann, Marco Valentino, Andr\'e Freitas
https://arxiv.org/abs/2510.06774 https://

Adaptive LLM-Symbolic Reasoning via Dynamic Logical Solver Composition
Neuro-symbolic NLP methods aim to leverage the complementary strengths of large language models and formal logical solvers. However, current approaches are mostly static in nature, i.e., the integration of a target solver is predetermined at design time, hindering the ability to employ diverse formal inference strategies. To address this, we introduce an adaptive, multi-paradigm, neuro-symbolic inference framework that: (1) automatically identifies formal reasoning strategies from problems expr…

I say it again: they need to keep the dementia patient busy, so they give him something familiar to play with
https://mastodon.sdf.org/@AmandaMarcotte/115746731024779684

Moody Urbanity - TWO II 2️⃣
情绪化城市 - 贰 II 2️⃣
📷 Nikon FE
🎞️ Fujifilm NEOPAN SS, expired 1993
#filmphotography #Photography #blackandwhite

RE: https://mastodon.social/@davidzipper/115746520818673258
I would call it asshole-mode, rather than Max Max, and note that it is really symbolic of techbro-ism. Taking advantage because we can get away with breaking the rules.

😳 New research identifies link between endorsing easily disproven claims and prioritizing symbolic strength
https://phys.org/news/2025-10-link-endorsing-easily-disproven-prioritizing.html

New research identifies link between endorsing easily disproven claims and prioritizing symbolic strength
Why do some people endorse claims that can easily be disproved? It's one thing to believe false information, but another to actively stick with something that's obviously wrong.

Augmented Intelligence, which is building neuro-symbolic AI models, raised $20M in a bridge SAFE round at a $750M valuation, bringing its total funding to ~$60M (Carl Franzen/VentureBeat)
https://venturebeat.com/ai/the-beginning-of-the-end…

Ontolearn-A Framework for Large-scale OWL Class Expression Learning in Python
Caglar Demir, Alkid Baci, N'Dah Jean Kouagou, Leonie Nora Sieger, Stefan Heindorf, Simon Bin, Lukas Bl\"ubaum, Alexander Bigerl, Axel-Cyrille Ngonga Ngomo
https://arxiv.org/abs/2510.11561

Ontolearn-A Framework for Large-scale OWL Class Expression Learning in Python
In this paper, we present Ontolearn-a framework for learning OWL class expressions over large knowledge graphs. Ontolearn contains efficient implementations of recent stateof-the-art symbolic and neuro-symbolic class expression learners including EvoLearner and DRILL. A learned OWL class expression can be used to classify instances in the knowledge graph. Furthermore, Ontolearn integrates a verbalization module based on an LLM to translate complex OWL class expressions into natural language sen…

Efficient Symbolic Computation vis Hash Consing
Bowen Zhu, Aayush Sabharwal, Songchen Tan, Yingbo Ma, Alan Edelman, Christopher Rackauckas
https://arxiv.org/abs/2509.20534 https…

Efficient Symbolic Computation vis Hash Consing
Symbolic computation systems suffer from memory inefficiencies due to redundant storage of structurally identical subexpressions, commonly known as expression swell, which degrades performance in both classical computer algebra and emerging AI-driven mathematical reasoning tools. In this paper, we present the first integration of hash consing into JuliaSymbolics, a high-performance symbolic toolkit in Julia, by employing a global weak-reference hash table that canonicalizes expressions and elim…

Analog and Symbolic Computation through the Koopman Framework
Francesco Caravelli, Jean-Charles Delvenne
https://arxiv.org/abs/2510.05863 https://arxiv.org…

Analog and Symbolic Computation through the Koopman Framework
We develop a Koopman operator framework for studying the {computational properties} of dynamical systems. Specifically, we show that the resolvent of the Koopman operator provides a natural abstraction of halting, yielding a ``Koopman halting problem that is recursively enumerable in general. For symbolic systems, such as those defined on Cantor space, this operator formulation captures the reachability between clopen sets, while for equicontinuous systems we prove that the Koopman halting prob…

Equality of ordinary and symbolic powers and the Conforti-Cornu\'ejols conjecture for $(n-2)$-uniform clutters
Amit Roy, Kamalesh Saha
https://arxiv.org/abs/2510.15864 https…

RustAssure: Differential Symbolic Testing for LLM-Transpiled C-to-Rust Code
Yubo Bai, Tapti Palit
https://arxiv.org/abs/2510.07604 https://arxiv.org/pdf/25…

RustAssure: Differential Symbolic Testing for LLM-Transpiled C-to-Rust Code
Rust is a memory-safe programming language that significantly improves software security. Existing codebases written in unsafe memory languages, such as C, must first be transpiled to Rust to take advantage of Rust's improved safety guarantees. RustAssure presents a system that uses Large Language Models (LLMs) to automatically transpile existing C codebases to Rust. RustAssure uses prompt engineering techniques to maximize the chances of the LLM generating idiomatic and safe Rust code. Moreove…

Final ICFP keynote on Proof-Carrying Neuro-Symbolic Code by Ekaterina Komendantskaya
#icfpsplash25
https://share.google/G4C1iRyA0qE5m5Mi2

ConstraintLLM: A Neuro-Symbolic Framework for Industrial-Level Constraint Programming
Weichun Shi, Minghao Liu, Wanting Zhang, Langchen Shi, Fuqi Jia, Feifei Ma, Jian Zhang
https://arxiv.org/abs/2510.05774

ConstraintLLM: A Neuro-Symbolic Framework for Industrial-Level Constraint Programming
Constraint programming (CP) is a crucial technology for solving real-world constraint optimization problems (COPs), with the advantages of rich modeling semantics and high solving efficiency. Using large language models (LLMs) to generate formal modeling automatically for COPs is becoming a promising approach, which aims to build trustworthy neuro-symbolic AI with the help of symbolic solvers. However, CP has received less attention compared to works based on operations research (OR) models. We…

Symbolic Reduction of Multi-loop Feynman Integrals via Generating Functions
Bo Feng, Xiang Li, Yuanche Liu, Yan-Qing Ma, Yang Zhang
https://arxiv.org/abs/2509.21769 https://

Symbolic Reduction of Multi-loop Feynman Integrals via Generating Functions
We introduce a novel, systematic method for the complete symbolic reduction of multi-loop Feynman integrals, leveraging the power of generating functions. The differential equations governing these generating functions naturally yield symbolic recurrence relations. We develop an efficient algorithm that utilizes these recurrences to reduce integrals to a minimal set of master integrals. This approach circumvents the exponential growth of traditional Integration-by-Parts relations, enabling the …

Reading this below on the 36th anniversary of the fall of the Berlin Wall and by extension the symbolic end of the GDR, the place I grew up in... with neighbors and parents of classmates belonging to the Stasi (east German secret police), the fear my own parents might be disappeared — years later seeing the thickness of the files of surveillance materials the state/stasi had collected on my family... remembering early autumn 1989, joining friends protesting in the streets every Monday after …

Iterated Agent for Symbolic Regression
Zhuo-Yang Song, Zeyu Cai, Shutao Zhang, Jiashen Wei, Jichen Pan, Shi Qiu, Qing-Hong Cao, Tie-Jiun Hou, Xiaohui Liu, Ming-xing Luo, Hua Xing Zhu
https://arxiv.org/abs/2510.08317

Iterated Agent for Symbolic Regression
Symbolic regression (SR), the automated discovery of mathematical expressions from data, is a cornerstone of scientific inquiry. However, it is often hindered by the combinatorial explosion of the search space and a tendency to overfit. Popular methods, rooted in genetic programming, explore this space syntactically, often yielding overly complex, uninterpretable models. This paper introduces IdeaSearchFitter, a framework that employs Large Language Models (LLMs) as semantic operators within an…

Agent P: Guiding UI Agents via Symbolic Planning
Shang Ma, Xusheng Xiao, Yanfang Ye
https://arxiv.org/abs/2510.06042 https://arxiv.org/pdf/2510.06042

Agent+P: Guiding UI Agents via Symbolic Planning
Large Language Model (LLM)-based UI agents show great promise for UI automation but often hallucinate in long-horizon tasks due to their lack of understanding of the global UI transition structure. To address this, we introduce AGENT+P, a novel framework that leverages symbolic planning to guide LLM-based UI agents. Specifically, we model an app's UI transition structure as a UI Transition Graph (UTG), which allows us to reformulate the UI automation task as a pathfinding problem on the UTG. Th…

Discovering interpretable piecewise nonlinear model predictive control laws via symbolic decision trees
Ilias Mitrai
https://arxiv.org/abs/2510.10411 https://

Discovering interpretable piecewise nonlinear model predictive control laws via symbolic decision trees
In this paper, we propose symbolic decision trees as surrogate models for approximating model predictive control laws. The proposed approach learns simultaneously the partition of the input domain (splitting logic) as well as local nonlinear expressions for predicting the control action leading to interpretable piecewise nonlinear control laws. The local nonlinear expressions are determined by the learning problem and are modeled using a set of basis functions. The learning task is posed as a m…

NL2GenSym: Natural Language to Generative Symbolic Rules for SOAR Cognitive Architecture via Large Language Models
Fang Yuan, Junjie Zeng, Yue Hu, Zhengqiu Zhu, Quanjun Yin, Yuxiang Xie
https://arxiv.org/abs/2510.09355

NL2GenSym: Natural Language to Generative Symbolic Rules for SOAR Cognitive Architecture via Large Language Models
SOAR, a classic symbol-based cognitive architecture, has been fostering the development of general, human-like intelligent agents. Nevertheless, its practical adoption is hindered by the laborious manual rule coding. Emerging Large Language Models (LLMs) present the immense potential for efficient rules generation. However, there is a critical gap that current research predominantly focuses on conceptual frameworks and lacks robust experimental validation. To bridge this gap, we propose \textit…

jewish doctor: fine i'll circumcise ur child just before shabbat
non-denominational evangelical mother: finally the blood moon alignment can reveal the black stone prophecy to the chain breaker league (womens division) as foretold by my amazon cart. long have i hunger for Israeli-grade ontology, flag blue of the sea, white of my skin, one twilight world rendered a landing pad for the physical and symbolic phalluses of Jesus and the AH-64
nonbinary rabbi: oy they

EvoCAD: Evolutionary CAD Code Generation with Vision Language Models
Tobias Preintner, Weixuan Yuan, Adrian K\"onig, Thomas B\"ack, Elena Raponi, Niki van Stein
https://arxiv.org/abs/2510.11631

EvoCAD: Evolutionary CAD Code Generation with Vision Language Models
Combining large language models with evolutionary computation algorithms represents a promising research direction leveraging the remarkable generative and in-context learning capabilities of LLMs with the strengths of evolutionary algorithms. In this work, we present EvoCAD, a method for generating computer-aided design (CAD) objects through their symbolic representations using vision language models and evolutionary optimization. Our method samples multiple CAD objects, which are then optimiz…

SymBoltz.jl: a symbolic-numeric, approximation-free and differentiable linear Einstein-Boltzmann solver
Herman Sletmoen
https://arxiv.org/abs/2509.24740 https://

SymBoltz.jl: a symbolic-numeric, approximation-free and differentiable linear Einstein-Boltzmann solver
SymBoltz is a new Julia package that solves the linear Einstein-Boltzmann equations. It features a symbolic-numeric interface for specifying equations, is free of approximation switching schemes and is compatible with automatic differentiation. Cosmological models are built from replaceable physical components in a way that scales well in model space. The modeler should simply write down their equations, and SymBoltz solves them and eliminates much of the friction in the process. SymBoltz enabl…

Hybrid Diffusion for Simultaneous Symbolic and Continuous Planning
Sigmund Hennum H{\o}eg, Aksel Vaaler, Chaoqi Liu, Olav Egeland, Yilun Du
https://arxiv.org/abs/2509.21983 http…

Hybrid Diffusion for Simultaneous Symbolic and Continuous Planning
Constructing robots to accomplish long-horizon tasks is a long-standing challenge within artificial intelligence. Approaches using generative methods, particularly Diffusion Models, have gained attention due to their ability to model continuous robotic trajectories for planning and control. However, we show that these models struggle with long-horizon tasks that involve complex decision-making and, in general, are prone to confusing different modes of behavior, leading to failure. To remedy thi…

⛐ Bridging Vision, Language, and Mathematics: Pictographic Character Reconstruction with Bézier Curves
#cs

Bridging Vision, Language, and Mathematics: Pictographic Character Reconstruction with Bézier Curves
While Vision-language Models (VLMs) have demonstrated strong semantic capabilities, their ability to interpret the underlying geometric structure of visual information is less explored. Pictographic characters, which combine visual form with symbolic structure, provide an ideal test case for this capability. We formulate this visual recognition challenge in the mathematical domain, where each character is represented by an executable program of geometric primitives. This is framed as a program …

BACHI: Boundary-Aware Symbolic Chord Recognition Through Masked Iterative Decoding on Pop and Classical Music
Mingyang Yao, Ke Chen, Shlomo Dubnov, Taylor Berg-Kirkpatrick
https://arxiv.org/abs/2510.06528

BACHI: Boundary-Aware Symbolic Chord Recognition Through Masked Iterative Decoding on Pop and Classical Music
Automatic chord recognition (ACR) via deep learning models has gradually achieved promising recognition accuracy, yet two key challenges remain. First, prior work has primarily focused on audio-domain ACR, while symbolic music (e.g., score) ACR has received limited attention due to data scarcity. Second, existing methods still overlook strategies that are aligned with human music analytical practices. To address these challenges, we make two contributions: (1) we introduce POP909-CL, an enhance…

On the cohomology of homshifts
Nishant Chandgotia, Silv\`ere Gangloff, Benjamin Hellouin de Menibus, Piotr Oprocha
https://arxiv.org/abs/2510.11969 https://

On the cohomology of homshifts
We study the cohomology of symbolic dynamical systems called homshifts: they are the nearest-neighbour $\mathbb{Z}^d$ shifts of finite type whose adjacency rules are the same in every direction. Building on the work of Klaus Schmidt (Pacific J. Math. 170 (1995), no.1, 237-269) we give a necessary and sufficient condition for their cohomological triviality. This condition is expressed in terms of the topology of a natural simplicial complex arising from the shift space which can be analyzed in m…

Symbolic and High-Accuracy Solutions to Differential and Integral Problems via a Novel Recursive Inverse Laplace Method
Mohamed Mostafa
https://arxiv.org/abs/2510.00719 https://…

Symbolic and High-Accuracy Solutions to Differential and Integral Problems via a Novel Recursive Inverse Laplace Method
In this paper, we introduce a novel semi-analytical method for solving a broad class of initial value problems involving differential, integro-differential, and delay equations, including those with fractional and variable-order derivatives. The proposed approach is based on the inverse Laplace transform, applied initially - unlike traditional Laplace-based techniques which begin with a forward transformation. By assuming the unknown solution is the Laplace transform of an auxiliary function, t…

Admissible set and squarefree-power-like function with applications to squarefree symbolic powers
Trung Chau, Kanoy Kumar Das, Amit Roy, Kamalesh Saha
https://arxiv.org/abs/2510.01366

Admissible set and squarefree-power-like function with applications to squarefree symbolic powers
We introduce the abstract notion of squarefree-power-like functions, which unify the sequences of squarefree ordinary and symbolic powers of squarefree monomial ideals. By employing the Tor-vanishing criteria for mixed sums of ideals, we establish sharp lower bounds for their Castelnuovo-Mumford regularity in terms of what we call the admissible set of the associated hypergraph. As an application, we derive the first general combinatorial lower bound for the regularity of squarefree symbolic po…

#Syriza's capitulation to the #Troika, 10 years on.
<<The limits of left populism when it gains power: without sufficient institutional and economic support, symbolic actions can rebound on those who promote them.>>
"los límites del populismo de izquierdas cuando este alcanza…

El largo final del verano de la izquierda griega
Diez años después del referendo y la capitulación ante la troika, la izquierda griega intenta resurgir de sus cenizas pero sin un proyecto clave ni ningún liderazgo que la guíe.

Honestly, #emoji and icons in #Unicode are a true horror.
Yeah, sure. It's great that you don't have to use <img/> anymore and you can just paste a random Unicode character. You can get graphics into fields where only text was originally intended (like bug summaries). Even better, you can now easily get cool colorful icons on terminal with almost no effort.
However, it is an #accessibility nightmare. People are now encoding *information* in random graphical symbols. Symbols that require huge fonts to render, or huge character tables to describe.
Yeah, a bare <img/> carrying information sucks. However, you can add a *meaningful* alt-text to the image, and accessibility tools can use that text to provide meaningful context. Like "bug fix".
However, emojis and icons are symbolic. The best you can get is some description like "hammer and wrench", so people can kinda figure out that it's probably a "bug fix". Or maybe it was a "maintenance task"? Or you'll get a "unknown character 0x1F6E0". And I'm sure people will surely enjoy cross-referencing a "legend" of such "unknown characters".

Domain-Informed Genetic Superposition Programming: A Case Study on SFRC Beams
Mohammad Sadegh Khorshidi, Navid Yazdanjue, Hassan Gharoun, Mohammad Reza Nikoo, Fang Chen, Amir H. Gandomi
https://arxiv.org/abs/2509.21355

Domain-Informed Genetic Superposition Programming: A Case Study on SFRC Beams
This study presents domain-informed genetic superposition programming (DIGSP), a symbolic regression framework tailored for engineering systems governed by separable physical mechanisms. DIGSP partitions the input space into domain-specific feature subsets and evolves independent genetic programming (GP) populations to model material-specific effects. Early evolution occurs in isolation, while ensemble fitness promotes inter-population cooperation. To enable symbolic superposition, an adaptive …

From The Conversation
Canada’s long history with public service media offers a useful model for thinking about how AI could serve the public.
A publicly funded AI system could draw on public-domain materials, government datasets and openly licensed cultural content. It could be offered as an open-source system, making it widely available to researchers, developers and everyday users alike.

Segment-Factorized Full-Song Generation on Symbolic Piano Music
Ping-Yi Chen, Chih-Pin Tan, Yi-Hsuan Yang
https://arxiv.org/abs/2510.05881 https://arxiv.or…

Segment-Factorized Full-Song Generation on Symbolic Piano Music
We propose the Segmented Full-Song Model (SFS) for symbolic full-song generation. The model accepts a user-provided song structure and an optional short seed segment that anchors the main idea around which the song is developed. By factorizing a song into segments and generating each one through selective attention to related segments, the model achieves higher quality and efficiency compared to prior work. To demonstrate its suitability for human-AI interaction, we further wrap SFS into a web …

GeoSketch: A Neural-Symbolic Approach to Geometric Multimodal Reasoning with Auxiliary Line Construction and Affine Transformation
Shichao Weng, Zhiqiang Wang, Yuhua Zhou, Rui Lu, Ting Liu, Zhiyang Teng, Xiaozhang Liu, Hanmeng Liu
https://arxiv.org/abs/2509.22460

GeoSketch: A Neural-Symbolic Approach to Geometric Multimodal Reasoning with Auxiliary Line Construction and Affine Transformation
Geometric Problem Solving (GPS) poses a unique challenge for Multimodal Large Language Models (MLLMs), requiring not only the joint interpretation of text and diagrams but also iterative visuospatial reasoning. While existing approaches process diagrams as static images, they lack the capacity for dynamic manipulation - a core aspect of human geometric reasoning involving auxiliary line construction and affine transformations. We present GeoSketch, a neural-symbolic framework that recasts geome…

Metropolitana IV 🏯
城 IV 🏯
📷 Pentax MX
🎞️ Ilford Pan 100
#filmphotography #Photography #blackandwhite

Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic Architectures
Marco Bronzini, Carlo Nicolini, Bruno Lepri, Jacopo Staiano, Andrea Passerini
https://arxiv.org/abs/2509.25045

Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic Architectures
Despite their capabilities, Large Language Models (LLMs) remain opaque with limited understanding of their internal representations. Current interpretability methods, such as direct logit attribution (DLA) and sparse autoencoders (SAEs), provide restricted insight due to limitations such as the model's output vocabulary or unclear feature names. This work introduces Hyperdimensional Probe, a novel paradigm for decoding information from the LLM vector space. It combines ideas from symbolic repre…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[5/5]:
- Object-centric proto-symbolic behavioural reasoning from pixels
Ruben van Bergen, Justus H\"ubotter, Alma Lago, Pablo Lanillos

Deducing Closed-Form Expressions for Bright-Solitons in Strongly Magnetized Plasmas with Physics Informed Symbolic Regression (PISR)
Edward Finkelstein
https://arxiv.org/abs/2510.02551

Deducing Closed-Form Expressions for Bright-Solitons in Strongly Magnetized Plasmas with Physics Informed Symbolic Regression (PISR)
This paper presents a novel approach to finding analytical approximations for bright-soliton solutions in strongly magnetized plasmas. We leverage Physics-Informed Symbolic Regression (PISR) to discover closed-form expressions for the vector potential and number density profiles, governed by a reduced-order model derived from Maxwell-fluid equations. The PISR framework combines symbolic regression with physics-based constraints, boundary conditions, and available simulation data to guide the se…

In the Netherlands, two new government coalitions appear possible. One that focuses on (European) climate policy, or a right-wing coalition with a party that does not want to pursue climate policy, but instead focuses on “higher dykes, rather than sky-high costs and symbolic politics”. (sic)

Grid-forming Control of Converter Infinite Bus System: Modeling by Data-driven Methods
Amir Bahador Javadi, Philip Pong
https://arxiv.org/abs/2510.09411 https://

Grid-forming Control of Converter Infinite Bus System: Modeling by Data-driven Methods
This study explores data-driven modeling techniques to capture the dynamics of a grid-forming converter-based infinite bus system, critical for renewable-integrated power grids. Using sparse identification of nonlinear dynamics and deep symbolic regression, models were generated from synthetic data simulating key disturbances in active power, reactive power, and voltage references. Deep symbolic regression demonstrated more accuracy in capturing complex system dynamics, though it required subst…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[8/8]:
- FireGNN: Neuro-Symbolic Graph Neural Networks with Trainable Fuzzy Rules for Interpretable Medica...
Prajit Sengupta, Islem Rekik

The Feng Shui of Visualization: Design the Path to SUCCESS and GOOD FORTUNE
Chang Han, Andrew Mcnutt
https://arxiv.org/abs/2510.00344 https://arxiv.org/pdf…

The Feng Shui of Visualization: Design the Path to SUCCESS and GOOD FORTUNE
Superstition and religious belief system have historically shaped human behavior, offering powerful psychological motivations and persuasive frameworks to guide actions. Inspired by Feng Shui -- an ancient Chinese superstition -- this paper proposes a pseudo-theoretical framework that integrates superstition-like heuristics into visualization design. Rather than seeking empirical truth, this framework leverages culturally resonant (superstitious) narratives and symbolic metaphors as persuasive …

Replaced article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new
[1/1]:
- Divide, Conquer and Verify: Improving Symbolic Execution Performance
Christopher Scherb, Luc Bryan Heitz, Hermann Grieder, Olivier Mattmann

Asymptotic invariants of symbolic powers of binomial edge ideals
Dennis Belotserkovskiy, Mariana Land\'in, Charlie Ruppe, Lizzy Teryoshin
https://arxiv.org/abs/2510.14272 ht…

Crosslisted article(s) found for cs.LO. https://arxiv.org/list/cs.LO/new
[1/1]:
- Analog and Symbolic Computation through the Koopman Framework
Francesco Caravelli, Jean-Charles Delvenne

On Sets of Periodic Orbit Lengths in Finitely Presented Dynamical Systems
Huub de Jong
https://arxiv.org/abs/2510.10848 https://arxiv.org/pdf/2510.10848

On Sets of Periodic Orbit Lengths in Finitely Presented Dynamical Systems
We classify the sets of natural numbers $n$ for which certain dynamical systems $(X,f)$ on a compact metric space $X$ have a periodic point of (least) period $n$. Interest in this question dates back to Sharkovskii's theorem for continuous maps on intervals of the real line, but it also ties to checkable conditions for Krieger's embedding theorem for symbolic dynamical systems. Given a system for which the logarithmic derivative of the Artin-Mazur zeta function is rational, we use the Skolem-Ma…

SAGE-Music: Low-Latency Symbolic Music Generation via Attribute-Specialized Key-Value Head Sharing
Jiaye Tan, Haonan Luo, Linfeng Song, Shuaiqi Chen, Yishan Lyu, Zian Zhong, Roujia Wang, Daniel Jiang, Haoran Zhang, Jiaming Bai, Haoran Cheng, Q. Vera Liao, Hao-Wen Dong
https://arxiv.org/abs/2510.00395…

SAGE-Music: Low-Latency Symbolic Music Generation via Attribute-Specialized Key-Value Head Sharing
Low-latency symbolic music generation is essential for real-time improvisation and human-AI co-creation. Existing transformer-based models, however, face a trade-off between inference speed and musical quality. Traditional acceleration techniques such as embedding pooling significantly degrade quality, while recently proposed Byte Pair Encoding (BPE) methods - though effective on single-track piano data - suffer large performance drops in multi-track settings, as revealed by our analysis. We pr…

O-Forge: An LLM Computer Algebra Framework for Asymptotic Analysis
Ayush Khaitan, Vijay Ganesh
https://arxiv.org/abs/2510.12350 https://arxiv.org/pdf/251…

O-Forge: An LLM + Computer Algebra Framework for Asymptotic Analysis
Large language models have recently demonstrated advanced capabilities in solving IMO and Putnam problems; yet their role in research mathematics has remained fairly limited. The key difficulty is verification: suggested proofs may look plausible, but cannot be trusted without rigorous checking. We present a framework, called LLM+CAS, and an associated tool, O-Forge, that couples frontier LLMs with a computer algebra systems (CAS) in an In-Context Symbolic Feedback loop to produce proofs that a…

Replaced article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- LogicMP: A Neuro-symbolic Approach for Encoding First-order Logic Constraints
Xu, Wang, Xie, He, Zhou, Wang, Wan, Chen, Qu, Chu

The Senate voted Wednesday to end the national emergency that Donald Trump declared to impose tariffs on imports from Canada.
Four Republicans joined with all Senate Democrats to approve the measure, which passed 50 to 46.
It was the second bipartisan vote against Trump’s tariffs in as many days — the Senate also voted Tuesday to block tariffs on Brazilian goods,
— though neither move will take effect, because they would still need to be approved by the House
— whi…

Senate votes to block tariffs on Canada
While symbolic, it is the Senate’s second rebuke of Trump’s trade policies in as many days.

Synthetic Series-Symbol Data Generation for Time Series Foundation Models
Wenxuan Wang, Kai Wu, Yujian Betterest Li, Dan Wang, Xiaoyu Zhang
https://arxiv.org/abs/2510.08445 http…

Synthetic Series-Symbol Data Generation for Time Series Foundation Models
Foundation models for time series analysis (TSA) have attracted significant attention. However, challenges such as training data scarcity and imbalance continue to hinder their development. Inspired by complex dynamic system theories, we design a series-symbol data generation mechanism, enabling the unrestricted creation of high-quality time series data paired with corresponding symbolic expressions. To leverage series-symbol data pairs with strong correlations, we develop \texttt{SymTime}, a p…

MUSE-Explainer: Counterfactual Explanations for Symbolic Music Graph Classification Models
Baptiste Hilaire, Emmanouil Karystinaios, Gerhard Widmer
https://arxiv.org/abs/2509.26521

MUSE-Explainer: Counterfactual Explanations for Symbolic Music Graph Classification Models
Interpretability is essential for deploying deep learning models in symbolic music analysis, yet most research emphasizes model performance over explanation. To address this, we introduce MUSE-Explainer, a new method that helps reveal how music Graph Neural Network models make decisions by providing clear, human-friendly explanations. Our approach generates counterfactual explanations by making small, meaningful changes to musical score graphs that alter a model's prediction while ensuring the …

Object-Centric Case-Based Reasoning via Argumentation
Gabriel de Olim Gaul, Adam Gould, Avinash Kori, Francesca Toni
https://arxiv.org/abs/2510.00185 https://

Object-Centric Case-Based Reasoning via Argumentation
We introduce Slot Attention Argumentation for Case-Based Reasoning (SAA-CBR), a novel neuro-symbolic pipeline for image classification that integrates object-centric learning via a neural Slot Attention (SA) component with symbolic reasoning conducted by Abstract Argumentation for Case-Based Reasoning (AA-CBR). We explore novel integrations of AA-CBR with the neural component, including feature combination strategies, casebase reduction via representative samples, novel count-based partial orde…

Hamster: A Large-Scale Study and Characterization of Developer-Written Tests
Rangeet Pan, Tyler Stennett, Raju Pavuluri, Nate Levin, Alessandro Orso, Saurabh Sinha
https://arxiv.org/abs/2509.26204

Hamster: A Large-Scale Study and Characterization of Developer-Written Tests
Automated test generation (ATG), which aims to reduce the cost of manual test suite development, has been investigated for decades and has produced countless techniques based on a variety of approaches: symbolic analysis, search-based, random and adaptive-random, learning-based, and, most recently, large-language-model-based approaches. However, despite this large body of research, there is still a gap in our understanding of the characteristics of developer-written tests and, consequently, in …

Compiling by Proving: Language-Agnostic Automatic Optimization from Formal Semantics
Jianhong Zhao, Everett Hildenbrandt, Juan Conejero, Yongwang Zhao
https://arxiv.org/abs/2509.21793

Compiling by Proving: Language-Agnostic Automatic Optimization from Formal Semantics
Verification proofs encode complete program behavior, yet we discard them after checking correctness. We present compiling by proving, a paradigm that transforms these proofs into optimized execution rules. By constructing All-Path Reachability Proofs through symbolic execution and compiling their graph structure, we consolidate many semantic rewrites into single rules while preserving correctness by construction. We implement this as a language-agnostic extension to the K framework. Evaluation…

Foundation models for equation discovery in high energy physics
Manuel Morales-Alvarado
https://arxiv.org/abs/2510.03397 https://arxiv.org/pdf/2510.03397…

Foundation models for equation discovery in high energy physics
Foundation models, large machine learning models trained on broad, multimodal datasets, have been gaining increasing attention in scientific applications due to their strong performance on diverse downstream tasks. Large Language Models (LLMs), a prominent instance of foundation models, have achieved remarkable success in tasks such as text and image generation. In this work, we investigate their potential for equation discovery in high energy physics, focusing on symbolic regression. We apply …

Replaced article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- DRIFT: Decompose, Retrieve, Illustrate, then Formalize Theorems
Meiru Zhang, Philipp Borchert, Milan Gritta, Gerasimos Lampouras

ViReSkill: Vision-Grounded Replanning with Skill Memory for LLM-Based Planning in Lifelong Robot Learning
Tomoyuki Kagaya, Subramanian Lakshmi, Anbang Ye, Thong Jing Yuan, Jayashree Karlekar, Sugiri Pranata, Natsuki Murakami, Akira Kinose, Yang You
https://arxiv.org/abs/2509.24219

ViReSkill: Vision-Grounded Replanning with Skill Memory for LLM-Based Planning in Lifelong Robot Learning
Robots trained via Reinforcement Learning (RL) or Imitation Learning (IL) often adapt slowly to new tasks, whereas recent Large Language Models (LLMs) and Vision-Language Models (VLMs) promise knowledge-rich planning from minimal data. Deploying LLMs/VLMs for motion planning, however, faces two key obstacles: (i) symbolic plans are rarely grounded in scene geometry and object physics, and (ii) model outputs can vary for identical prompts, undermining execution reliability. We propose ViReSkill,…

GraphMERT: Efficient and Scalable Distillation of Reliable Knowledge Graphs from Unstructured Data
Margarita Belova, Jiaxin Xiao, Shikhar Tuli, Niraj K. Jha
https://arxiv.org/abs/2510.09580

GraphMERT: Efficient and Scalable Distillation of Reliable Knowledge Graphs from Unstructured Data
Researchers have pursued neurosymbolic artificial intelligence (AI) applications for nearly three decades because symbolic components provide abstraction while neural components provide generalization. Thus, a marriage of the two components can lead to rapid advancements in AI. Yet, the field has not realized this promise since most neurosymbolic AI frameworks fail to scale. In addition, the implicit representations and approximate reasoning of neural approaches limit interpretability and trust…

Limits of equilibrium states for coupled weakly interacting systems. Application to the measure of maximal entropy
Renaud Leplaideur (LMBA, ISEA)
https://arxiv.org/abs/2510.07846

Limits of equilibrium states for coupled weakly interacting systems. Application to the measure of maximal entropy
We study metastability for symbolic dynamic. We prove that for a global system given by two independent sub-systems linked by a hole, and for a Lipschitz continuous potential, the global equilibrium state converges, as the hole shrinks, to a convex combination of the two independent equilibria in each component. Two kinds of convergence occur, depending on the assumptions on how long an orbit has to stay in each well. As a by-product, we show that this can be applied to a geometrical system in…

Crosslisted article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- HYPERDOA: Robust and Efficient DoA Estimation using Hyperdimensional Computing
Rajat Bhattacharjya, Woohyeok Park, Arnab Sarkar, Hyunwoo Oh, Mohsen Imani, Nikil Dutt

Repairing Regex Vulnerabilities via Localization-Guided Instructions
Sicheol Sung, Joonghyuk Hahn, Yo-Sub Han
https://arxiv.org/abs/2510.09037 https://arxi…

Repairing Regex Vulnerabilities via Localization-Guided Instructions
Regular expressions (regexes) are foundational to modern computing for critical tasks like input validation and data parsing, yet their ubiquity exposes systems to regular expression denial of service (ReDoS), a vulnerability requiring automated repair methods. Current approaches, however, are hampered by a trade-off. Symbolic, rule-based system are precise but fails to repair unseen or complex vulnerability patterns. Conversely, large language models (LLMs) possess the necessary generalizabili…

ProGress: Structured Music Generation via Graph Diffusion and Hierarchical Music Analysis
Stephen Ni-Hahn, Chao P\'eter Yang, Mingchen Ma, Cynthia Rudin, Simon Mak, Yue Jiang
https://arxiv.org/abs/2510.10249

ProGress: Structured Music Generation via Graph Diffusion and Hierarchical Music Analysis
Artificial Intelligence (AI) for music generation is undergoing rapid developments, with recent symbolic models leveraging sophisticated deep learning and diffusion model algorithms. One drawback with existing models is that they lack structural cohesion, particularly on harmonic-melodic structure. Furthermore, such existing models are largely "black-box" in nature and are not musically interpretable. This paper addresses these limitations via a novel generative music framework that incorporate…

Chimera: Diagnosing Shortcut Learning in Visual-Language Understanding
Ziheng Chi, Yifan Hou, Chenxi Pang, Shaobo Cui, Mubashara Akhtar, Mrinmaya Sachan
https://arxiv.org/abs/2509.22437

Chimera: Diagnosing Shortcut Learning in Visual-Language Understanding
Diagrams convey symbolic information in a visual format rather than a linear stream of words, making them especially challenging for AI models to process. While recent evaluations suggest that vision-language models (VLMs) perform well on diagram-related benchmarks, their reliance on knowledge, reasoning, or modality shortcuts raises concerns about whether they genuinely understand and reason over diagrams. To address this gap, we introduce Chimera, a comprehensive test suite comprising 7,500 h…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[11/11]:
- Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic Architectures
Marco Bronzini, Carlo Nicolini, Bruno Lepri, Jacopo Staiano, Andrea Passerini

Replaced article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- Computing with D-Algebraic Sequences
Bertrand Teguia Tabuguia
https://

GenesisGeo: Technical Report
Minfeng Zhu, Zi Wang, Sizhe Ji, Zhengtong Du, Junming Ke, Xiao Deng, Zanlang Yin, Xiuqi Huang, Heyu Wang, Wei Chen
https://arxiv.org/abs/2509.21896 …

GenesisGeo: Technical Report
We present GenesisGeo, an automated theorem prover in Euclidean geometry. We have open-sourced a large-scale geometry dataset of 21.8 million geometric problems, over 3 million of which contain auxiliary constructions. Specially, we significantly accelerate the symbolic deduction engine DDARN by 120x through theorem matching, combined with a C++ implementation of its core components. Furthermore, we build our neuro-symbolic prover, GenesisGeo, upon Qwen3-0.6B-Base, which solves 24 of 30 problem…

[2025-10-15 Wed (UTC), no new articles found for cs.SC Symbolic Computation]
toXiv_bot_toot

An Algorithmic Information-Theoretic Perspective on the Symbol Grounding Problem
Zhangchi Liu
https://arxiv.org/abs/2510.05153 https://arxiv.org/pdf/2510.0…

An Algorithmic Information-Theoretic Perspective on the Symbol Grounding Problem
This paper provides a definitive, unifying framework for the Symbol Grounding Problem (SGP) by reformulating it within Algorithmic Information Theory (AIT). We demonstrate that the grounding of meaning is a process fundamentally constrained by information-theoretic limits, thereby unifying the Gödelian (self-reference) and No Free Lunch (statistical) perspectives. We model a symbolic system as a universal Turing machine and define grounding as an act of information compression. The argument pr…

[2025-10-14 Tue (UTC), no new articles found for cs.SC Symbolic Computation]
toXiv_bot_toot

Crosslisted article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- Computing moment polytopes - with a focus on tensors, entanglement and matrix multiplication
van den Berg, Christandl, Lysikov, Nieuwboer, Walter, Zuiddam

Replaced article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- Divide, Conquer and Verify: Improving Symbolic Execution Performance
Christopher Scherb, Luc Bryan Heitz, Hermann Grieder, Olivier Mattmann

ICL Optimized Fragility
Serena Gomez Wannaz
https://arxiv.org/abs/2510.00300 https://arxiv.org/pdf/2510.00300

ICL Optimized Fragility
ICL guides are known to improve task-specific performance, but their impact on cross-domain cognitive abilities remains unexplored. This study examines how ICL guides affect reasoning across different knowledge domains using six variants of the GPT-OSS:20b model: one baseline model and five ICL configurations (simple, chain-of-thought, random, appended text, and symbolic language). The models were subjected to 840 tests spanning general knowledge questions, logic riddles, and a mathematical oly…

[2025-10-13 Mon (UTC), no new articles found for cs.SC Symbolic Computation]
toXiv_bot_toot

Crosslisted article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- Comprehensive Restriction Algorithm for Hypergeometric Systems
Hiromasa Nakayama, Nobuki Takayama

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/17]:
- Comprehension Without Competence: Architectural Limits of LLMs in Symbolic Computation and Reasoning
Zheng Zhang

Crosslisted article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- Efficient Symbolic Computation vis Hash Consing
Bowen Zhu, Aayush Sabharwal, Songchen Tan, Yingbo Ma, Alan Edelman, Christopher Rackauckas

ExoPredicator: Learning Abstract Models of Dynamic Worlds for Robot Planning
Yichao Liang, Dat Nguyen, Cambridge Yang, Tianyang Li, Joshua B. Tenenbaum, Carl Edward Rasmussen, Adrian Weller, Zenna Tavares, Tom Silver, Kevin Ellis
https://arxiv.org/abs/2509.26255

ExoPredicator: Learning Abstract Models of Dynamic Worlds for Robot Planning
Long-horizon embodied planning is challenging because the world does not only change through an agent's actions: exogenous processes (e.g., water heating, dominoes cascading) unfold concurrently with the agent's actions. We propose a framework for abstract world models that jointly learns (i) symbolic state representations and (ii) causal processes for both endogenous actions and exogenous mechanisms. Each causal process models the time course of a stochastic causal-effect relation. We learn th…

Replaced article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- Extensions of the Cylindrical Algebraic Covering Method for Quantifiers
Jasper Nalbach, Gereon Kremer

Replaced article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- A Novel Algorithm for Representing Positive Semi-Definite Polynomials as Sums of Squares with Rat...
Zhenbing Zeng, Yong Huang, Lu Yang, Yongsheng Rao

Crosslisted article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- Simulation to Rules: A Dual-VLM Framework for Formal Visual Planning
Yilun Hao, Yongchao Chen, Chuchu Fan, Yang Zhang

Crosslisted article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- An Effective Version of the $p$-Curvature Conjecture for Order One Differential Equations
Florian F\"urnsinn, Lucas Pannier

[2025-10-08 Wed (UTC), 1 new article found for cs.SC Symbolic Computation]
toXiv_bot_toot

Replaced article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- R1-Code-Interpreter: LLMs Reason with Code via Supervised and Multi-stage Reinforcement Learning
Yongchao Chen, Yueying Liu, Junwei Zhou, Yilun Hao, Jingquan Wang, Yang Zhang, Na Li, Chuchu Fan

Crosslisted article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- On Explaining Proxy Discrimination and Unfairness in Individual Decisions Made by AI Systems
Belona Sonna, Alban Grastien

[2025-10-07 Tue (UTC), no new articles found for cs.SC Symbolic Computation]
toXiv_bot_toot

Replaced article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- Improving NLSAT for Nonlinear Real Arithmetic
Zhonghan Wang
https://

Crosslisted article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- AI Noether -- Bridging the Gap Between Scientific Laws Derived by AI Systems and Canonical Knowle...
Karan Srivastava, Sanjeeb Dash, Ryan Cory-Wright, Barry Trager, Lior Horesh