Applied & Human-Centered · Computer Science & AI
Symbolic Computation
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38 papers, from 10 September 2026 to 5 November 2026: 38 arXiv preprints.
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Newest papers
Using Transformers for Teaching and Learning via Symbolic Distillation of Neural A-life Agents
Neural networks controlling behavior of A-life agents can evolve over multiple generations.Finding Integer Roots of Polynomials in Nearly Optimal Time
Let $f\in\mathbb Z[x]$ be a polynomial of degree $n$ with integer coefficients of absolute value less than $2^b$.ALBI: LLM-Guided Inductive Logic Programming via Iterative Generate-Test-Constrain Refineme
We introduce ALBI (Agent for Learning By Induction), a neuro-symbolic ILP framework that replaces the ASP-based hypothesis generator in the Learning from Failures (LFF) paradigm with a Large…Combining Relevant Expert Knowledge Using Mixtures of Feedback Fuzzy Cognitive Maps
A neuro-symbolic system should combine only relevant answers from expert agents or other knowledge sources.Ramanujan Challenge Results: AI-Assisted Evaluation Pipeline for Multi-Format Proofs
As AI's mathematical capabilities rapidly improve, meaningful benchmarks are shifting from university-level problems and math olympiads to research-level mathematics.NeSySpec: Neuro-Symbolic Technical Intelligence for Auditing Engineering Specifications with an Application to Aircraft
Engineers who audit a declared specification of an engineered system need to know which physical constraints it respects, which it violates, which field was altered and how to correct it.Near-Quadratic Time Multivariate Polynomial Interpolation
Let $\F$ be a finite field.A 55-Addition Rank-23 Scheme for $3\times3$ Matrix Multiplication via Exact Two-Sided Minimization
We give a scheme that multiplies two $3\times3$ matrices with 23 multiplications and 55 additions over any ring, one addition below the previous record of 56 (Sun, 2026) and the first below 56 for…Racer: Hunting GPU Kernel Bugs with Symbolic Equivalence Checking
GPU kernels are critical to the performance of today's AI services, yet the same operators are rewritten repeatedly: for each GPU generation, numeric format, serving regime, and kernel language.OptScientist: Automated Discovery of Optimization Laws
Many scientific phenomena are best understood as outcomes of underlying optimization problems, yet automated scientific discovery has largely focused on recovering equations that describe observed…Auditing Safety Preference Supervision with Recorded Derivations
Preference labels align model training, but the judgments behind them are difficult to inspect or verify.Diverse Reasoning Paths Matter: Symbolic Equivalence Augmentation for Enhancing RL Exploration
Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for improving the reasoning capabilities of LLMs.Sparse Autoencoders Are a Shared Coordinate System for Symbolic Matching
Large language models (LLMs) hold broad knowledge in one set of weights, but lack interpretability and trustworthy reasoning, especially in high-stakes use cases.MakSafe: A Spatiotemporal Semantic Framework for Formal Specification and Compliance Checking of Road Vehicle Safety Rules
Scenario-based validation is essential for automated road vehicles but cannot cover the full variability of openworld traffic.HelpMeOut: A Neuropsychology-Grounded Benchmark for Error-Aware Monitoring of Household Task Execution
We introduce HelpMeOut, an embodied AI benchmark for error-aware monitoring of household task execution.Compiling Expert Procedures into Verified Programs: When a Small Model Is Enough
In procedural tool-use domains, how much of the gap between base models is procedure rather than capability?Generative Flow on Symbolic Abstractions for Embodiment Capability Discovery
Achieving complex, long-horizon robot behavior remains challenging because successful execution depends on skills grounded in a robot's physical affordances.Have You Been Convinced? A Neuro-symbolic Argumentation Reasoning Approach to Detecting Persuasiveness in ChangeMyView Discussions
Argument structures represent how individual arguments interact within a discourse.SAT-RL: Training Language Models for Logical Reasoning with Symbolic Solvers
Large language models (LLMs) have achieved strong performance in mathematical and logical reasoning, yet they still suffer from the curse of complexity and struggle as problem difficulty increases…RISR: Residual-Informed Scientific Equation Discovery with Large Language Models
Symbolic regression combines structural search with numerical fitting, but aggregate fit scores do not describe how the remaining error varies across inputs.