Papers Program Synthesis
“Program Synthesis” 태그가 달린 논문 538편 · 필터 해제
Certified Program Synthesis with a Multi-Modal Verifier
Certified program synthesis (aka vericoding) is the process of automatically generating a program, its formal specification, and a machine-checkable proof of their alignment from a natural-language description. Two chall…
Program SynthesisSignalClaw: LLM-Guided Evolutionary Synthesis of Interpretable Traffic Signal Control Skills
Traffic signal control TSC requires strategies that are both effective and interpretable for deployment, yet reinforcement learning produces opaque neural policies while program synthesis depends on restrictive domain-sp…
Reinforcement LearningProgram SynthesisScientific Graphics Program Synthesis via Dual Self-Consistency Reinforcement Learning
Graphics Program Synthesis is pivotal for interpreting and editing visual data, effectively facilitating the reverse-engineering of static visuals into editable TikZ code. While TikZ is the de facto standard for scientif…
Reinforcement LearningProgram SynthesisHierarchical SVG Tokenization: Learning Compact Visual Programs for Scalable Vector Graphics Modeling
Recent large language models have shifted SVG generation from differentiable rendering optimization to autoregressive program synthesis. However, existing approaches still rely on generic byte-level tokenization inherite…
Program SynthesisGIFT: Bootstrapping Image-to-CAD Program Synthesis via Geometric Feedback
Generating executable CAD programs from images requires alignment between visual geometry and symbolic program representations, a capability that current methods fail to learn reliably as design complexity increases. Exi…
Data AugmentationProgram SynthesisAutomated near-term quantum algorithm discovery for molecular ground states
Designing quantum algorithms is a complex and counterintuitive task, making it an ideal candidate for AI-driven algorithm discovery. To this end, we employ the Hive, an AI platform for program synthesis, which utilises l…
Program SynthesisLikelihood Hacking in Probabilistic Program Synthesis
When language models are trained by reinforcement learning (RL) to write probabilistic programs, they can artificially inflate their marginal-likelihood reward by producing programs whose data distribution fails to norma…
Reinforcement LearningProgram SynthesisCan we automatize scientific discovery in the cognitive sciences?
The cognitive sciences aim to understand intelligence by formalizing underlying operations as computational models. Traditionally, this follows a cycle of discovery where researchers develop paradigms, collect data, and …
Program SynthesisVisual Set Program Synthesizer
A user pointing their phone at a supermarket shelf and asking "Which soda has the least sugar?" poses a difficult challenge for current visual Al assistants. Such queries require not only object recognition, but explicit…
Object RecognitionProgram SynthesisVisual ReasoningLLMs for Text-Based Exploration and Navigation Under Partial Observability
Exploration and goal-directed navigation in unknown layouts are central to inspection, logistics, and search-and-rescue. We ask whether large language models (LLMs) can function as \emph{text-only} controllers under part…
Program SynthesisThe ARC of Progress towards AGI: A Living Survey of Abstraction and Reasoning
The Abstraction and Reasoning Corpus (ARC-AGI) has become a key benchmark for fluid intelligence in AI. This survey presents the first cross-generation analysis of 82 approaches across three benchmark versions and the AR…
Test-time AdaptationProgram SynthesisTwitch: Learning Abstractions for Equational Theorem Proving
Several successful strategies in automated reasoning rely on human-supplied guidance about which term or clause shapes are interesting. In this paper we aim to discover interesting term shapes automatically. Specifically…
Program SynthesisSymmetry-Constrained Language-Guided Program Synthesis for Discovering Governing Equations from Noisy and Partial Observations
Discovering compact governing equations from experimental observations is one of the defining objectives of quantitative science, yet practical discovery pipelines routinely fail when measurements are noisy, relevant sta…
Program SynthesisNeuroProlog: Multi-Task Fine-Tuning for Neurosymbolic Mathematical Reasoning via the Cocktail Effect
Large Language Models (LLMs) achieve strong performance on natural language tasks but remain unreliable in mathematical reasoning, frequently generating fluent yet logically inconsistent solutions. We present \textbf{Neu…
Mathematical ReasoningProgram SynthesisProgram RepairFoundation World Models for Agents that Learn, Verify, and Adapt Reliably Beyond Static Environments
The next generation of autonomous agents must not only learn efficiently but also act reliably and adapt their behavior in open worlds. Standard approaches typically assume fixed tasks and environments with little or no …
Reinforcement LearningProgram SynthesisSafe and Interpretable Multimodal Path Planning for Multi-Agent Cooperation
Successful cooperation among decentralized agents requires each agent to quickly adapt its plan to the behavior of other agents. In scenarios where agents cannot confidently predict one another's intentions and plans, la…
Autonomous DrivingProgram SynthesisNeurosymbolic Language Reasoning as Satisfiability Modulo Theory
Natural language understanding requires interleaving textual and logical reasoning, yet large language models often fail to perform such reasoning reliably. Existing neurosymbolic systems combine LLMs with solvers but re…
Natural Language UnderstandingLogical ReasoningProgram SynthesisPLLM: Pseudo-Labeling Large Language Models for CAD Program Synthesis
Recovering Computer-Aided Design (CAD) programs from 3D geometries is a widely studied problem. Recent advances in large language models (LLMs) have enabled progress in CAD program synthesis, but existing methods rely on…
Program SynthesisOptiML: An End-to-End Framework for Program Synthesis and CUDA Kernel Optimization
Generating high-performance CUDA kernels remains challenging due to the need to navigate a combinatorial space of low-level transformations under noisy and expensive hardware feedback. Although large language models can …
Program SynthesisDiscovering High Level Patterns from Simulation Traces
Large Language Models (LLMs) are unable to reliably reason about specific physical systems. Attempts to imbue LLMs with knowledge of the necessary physics concepts have shown great promise, but explainability and validat…
Program Synthesis