#program synthesis

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9 papers match

cs.AI2026

Tycho: Active Abstraction with Programmatic World Models for ARC-AGI-3

Jens Lehmann, Andrei Aioanei, Sahar Vahdati

The paper presents Tycho, a coding-agent that actively builds and uses programmatic world models to infer game rules and improve action efficiency in the ARC-AGI-3 benchmark, achie…

#active abstraction#world modeling#game AI#program synthesis
cs.SE2026

SpecFirst: Behavioral Specification Elicitation as a First-Class Step in Agent-Based Program Synthesis from Scratch

Yihao Chen, Shi Chang, Feng Lin +4

The paper introduces SpecFirst, a two-stage framework that first elicits a behavioral specification from an execute-only binary and documentation before synthesizing code, improvin…

#program synthesis#behavioral specification#requirements engineering#llm agents
cs.LG2026

DHRCL:Training Code LLMs with Dense Hierarchical Rewards and Curriculum Learning

Shuhang Wang, Ziming Li, Hui Cheng

The paper introduces DHRCL, a reinforcement‑learning framework for code‑focused large language models that uses a hierarchy of dense rewards (syntax, execution, unit‑test pass, and…

#code generation#reinforcement learning#curriculum learning#dense rewards
cs.LG2026

RLPF: Reinforcement Learning from Performance Feedback for Code Generation

Huihao Jing, Haozhe Cui, Wenbin Hu +9

The paper introduces RLPF, a reinforcement‑learning approach that uses staged performance feedback to train code‑generation models to produce not only correct programs but also fas…

#reinforcement learning#code generation#performance optimization#execution feedback
cs.LG2026

FunL2O: LLM-Guided Feature Function Design for Learning to Optimize

Bingheng Li, Junyang Cai, Yupeng Zhang +3

The paper presents FunL2O, a framework that uses large language models to automatically generate feature functions for learning-to-optimize systems, showing improved performance ov…

#learning to optimize#feature engineering#large language models#program synthesis
cs.MA2026

ARCHER: Agentic Rule and Compliance Harness for Executable Regulations

Chiraag Singh Anand, Xue Wen Tan, Lionel Teo +1

The paper presents ARCHER, a multi‑agent program‑synthesis system that automatically generates auditable verification code from building regulations to enable scalable, transparent…

#building information modeling#compliance checking#multi-agent systems#program synthesis
cs.AI2026

Compile, Then Page: Executable SOP Programs and a Capability-Gated Runtime for Procedural LLM Agents

Chenglin Yu, Li Yin, Ying Yu +4

The paper proposes compiling standard operating procedures into executable pseudo‑code and running them with a program‑guided stack machine that pages the active frame while a larg…

#large language models#program synthesis#runtime systems#procedural agents
cs.AI2026

OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration for ARC-AGI-3

David Courtis, Wenhao Li, Scott Sanner

OPINE-World is an LLM-driven agent that learns object‑centric programmatic world models online by alternating hypothesis generation and testing, using a Bayesian ontology‑error mea…

#program synthesis#world modeling#object-centric learning#interactive exploration
cs.DB2026

From Interpretation to Compilation: Compilation-Based Execution of Semantic Operators [Vision]

Wenkai Dong, Yifan Wang

The paper proposes generating deterministic executable code from LLMs once during a compilation step, enabling faster execution of semantic filter, map, and join operators without…

#semantic operators#llm compilation#query processing#program synthesis

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