Papers Code Generation
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Convergence Theory for Iterative LLM-Based Neural Architecture Search: A Parametric Cross-Entropy Framework with Closed-Form Proxy Reliability
Large language models (LLMs) are increasingly used as generators in iterative neural architecture search (NAS), yet no formal convergence theory exists for this class of algorithms. We model iterative LLM-NAS as a parame…
Neural Architecture SearchCode GenerationSEAL: Can Saturated Benchmarks Be Revived by LLM-as-a-Meta-Judge?
Widely used language-model benchmarks are increasingly saturated, with frontier systems often receiving near-tied scores that standard metrics cannot resolve. Rather than constructing harder alternatives, we ask whether …
Mathematical ReasoningQuestion AnsweringCode GenerationImproving Small Language Models for Code Generation with Reinforcement Learning from Verification Feedback
Reinforcement learning with verifiable rewards (RLVR) trains language models using programmatically checkable signals such as unit-test outcomes, enabling direct optimization for functional correctness in code generation…
Reinforcement LearningCode GenerationFPMoE: A Sparse Mixture-of-Experts Approach to Functional Code Generation
Despite rapid progress in LLM-based code generation, existing models are predominantly trained on imperative languages, leaving functional programming languages (FPLs) such as Haskell, OCaml, and Scala chronically undere…
Code GenerationDo Agents Think Deeper? A Mechanistic Investigation of Layer-Wise Dynamics in Sequential Planning
Recent mechanistic studies suggest that large language models (LLMs) may utilize their depth inefficiently in standard single-turn tasks. Whether this still holds in autonomous agent settings, where models must perform m…
Code GenerationBeyond pass@k: Redundancy-Aware RLVR for Multi-Sample Code Generation
LLMs for code generation are commonly evaluated in repeated-sampling settings using Pass@k, where multiple candidate programs are executed against unit tests under a finite sampling budget. While recent verifier-based re…
Reinforcement LearningCode GenerationRobo-Blocks: Generative Scaffolding in End-User Design and Programming of Social Robots
Programming social robots is challenging for novice robot programmers due to required expertise in planning, interaction design, and programming. While large language models (LLMs) hold significant promise through code g…
Code GenerationGUI Agents for Continual Game Generation
Generating a game is not the same as making one that can be played. Despite advances in code generation, existing approaches treat game generation as one-shot translation from prompt to artifact, leaving interaction-leve…
Code GenerationEfficient Post-training of LLMs for Code Generation With Offline Reinforcement Learning
Post-training using online reinforcement learning (RL) is an important training step for LLMs, including code-generating models. However, online RL for code generation involves LLM inference and verification of the gener…
Reinforcement LearningCode GenerationOffline RLFunctional Entropy: Predicting Functional Correctness in LLM-Generated Code with Uncertainty Quantification
Large language models have shown impressive capabilities in code generation, yet they often produce functionally incorrect code. Uncertainty quantification (UQ) methods have emerged as a promising approach for detecting …
Natural Language InferenceCode GenerationDo LLMs Favor Their Providers? Measuring Vertical Integration Bias in Code Generation
Large Language Models (LLMs) have become an integral part of software development, especially with the advent of agentic capabilities. Yet, many frontier LLMs are affiliated with specific providers. This raises the quest…
Code GenerationSafeRx-Agent: A Knowledge-Grounded Multi-Agent Framework for Safe and Explainable Medication Recommendation
Medication recommendation predicts medications for patient visits, but existing methods still face two key challenges. At the model level, traditional drug recommendation methods only predict structured drug codes with l…
Clinical KnowledgeCode GenerationCast a Wider Net: Coordinated Pass@K Policy Optimization for Code Reasoning
Repeated sampling with a verifier is the standard way to allocate test-time compute for code generation, with pass@$K$ as the canonical metric. Yet the standard policy class draws $K$ independent samples from a single an…
Code GenerationUnityMAS-O: A General RL Optimization Framework for LLM-Based Multi-Agent Systems
LLM-based multi-agent systems decompose complex tasks into interacting roles, but most remain manually orchestrated by prompts, tools, and control rules, while agents are rarely optimized through a unified reinforcement …
Reinforcement LearningNatural QuestionsCode GenerationMONA: Muon Optimizer with Nesterov Acceleration for Scalable Language Model Training
The Muon optimizer has recently offered a promising alternative to AdamW for large language model training, leveraging matrix orthogonalization to produce geometry-aware updates. However, like all first-order methods, Mu…
Mathematical ReasoningCode GenerationAutomating Formal Verification with Agent-Guided Tree Search
Formal verification offers a path to provably correct software, but writing verified code remains expensive enough that the technique is rarely used in production. Recent large language models can accelerate this work, a…
Code GenerationCausalFlow: Causal Attribution and Counterfactual Repair for LLM Agent Failures
Large language model (LLM) agents frequently fail on multi-step tasks involving reasoning, tool use, and environment interaction. While such failures are typically logged or retried heuristically, they contain structured…
Mathematical ReasoningQuestion AnsweringCode GenerationGeoMathCode: Understanding Interleaved Math-Code Reasoning for Geometry Problem Solving
Mathematical reasoning is a hallmark of human intelligence, requiring logical deduction, symbolic manipulation, and abstract thinking. Recent multimodal large language models (MLLMs) have demonstrated strong performance …
Mathematical ReasoningCode GenerationGeoSVG-RL: Geometry-Aware Reinforcement Learning for Layout-Constrained Text-to-SVG Diagram Generation
Generating structured, editable diagrams remains a significant challenge for contemporary large language models, despite their proficiency in general-purpose vector code generation. The primary difficulty lies in the str…
Reinforcement LearningCode GenerationPennySynth: RAG-Driven Data Synthesis for Automated Quantum Code Generation
The growing complexity of quantum programming frameworks has exposed a critical limitation in existing large language model (LLM)-based code assistants: general-purpose models hallucinate PennyLane-specific gate names, m…
Code Generation