Papers Code Generation
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Agentic AI for Scalable and Robust Optical Systems Control
We present AgentOptics, an agentic AI framework for high-fidelity, autonomous optical system control built on the Model Context Protocol (MCP). AgentOptics interprets natural language tasks and executes protocol-complian…
Code GenerationLearning to Solve Complex Problems via Dataset Decomposition
Curriculum learning is a class of training strategies that organizes the data being exposed to a model by difficulty, gradually from simpler to more complex examples. This research explores a reverse curriculum generatio…
Code GenerationVectorGym: A Multitask Benchmark for SVG Code Generation, Sketching, and Editing
We introduce VectorGym, a comprehensive benchmark suite for Scalable Vector Graphics (SVG) that spans generation from text and sketches, complex editing, and visual understanding. VectorGym addresses the lack of realisti…
Reinforcement LearningCode GenerationAutomated Generation of Microfluidic Netlists using Large Language Models
Microfluidic devices have emerged as powerful tools in various laboratory applications, but the complexity of their design limits accessibility for many practitioners. While progress has been made in microfluidic design …
Code GenerationOperational Robustness of LLMs on Code Generation
It is now common practice in software development for large language models (LLMs) to be used to generate program code. It is desirable to evaluate the robustness of LLMs for this usage. This paper is concerned in partic…
Code GenerationEvo: Autoregressive-Diffusion Large Language Models with Evolving Balance
We introduce \textbf{Evo}, a duality latent trajectory model that bridges autoregressive (AR) and diffusion-based language generation within a continuous evolutionary generative framework. Rather than treating AR decodin…
Code GenerationText GenerationAnalyzing and Improving Chain-of-Thought Monitorability Through Information Theory
Chain-of-thought (CoT) monitors are LLM-based systems that analyze reasoning traces to detect when outputs may exhibit attributes of interest, such as test-hacking behavior during code generation. In this paper, we use i…
Code Generation1D-Bench: A Benchmark for Iterative UI Code Generation with Visual Feedback in Real-World
Design-to-code translates high-fidelity UI designs into executable front-end implementations, but progress remains hard to compare due to inconsistent datasets, toolchains, and evaluation protocols. We introduce 1D-Bench…
Reinforcement LearningCode GenerationNon-Interfering Weight Fields: Treating Model Parameters as a Continuously Extensible Function
Large language models store all learned knowledge in a single, fixed weight vector. Teaching a model new capabilities requires modifying those same weights, inevitably degrading previously acquired knowledge. This fundam…
Code GenerationGraphSkill: Documentation-Guided Hierarchical Retrieval-Augmented Coding for Complex Graph Reasoning
The growing demand for automated graph algorithm reasoning has attracted increasing attention in the large language model (LLM) community. Recent LLM-based graph reasoning methods typically decouple task descriptions fro…
Code GenerationVisual Model Checking: Graph-Based Inference of Visual Routines for Image Retrieval
Information retrieval lies at the foundation of the modern digital industry. While natural language search has seen dramatic progress in recent years largely driven by embedding-based models and large-scale pretraining, …
Natural Language QueriesInformation RetrievalCode GenerationImage RetrievalCan Adversarial Code Comments Fool AI Security Reviewers -- Large-Scale Empirical Study of Comment-Based Attacks and Defenses Against LLM Code Analysis
AI-assisted code review is widely used to detect vulnerabilities before production release. Prior work shows that adversarial prompt manipulation can degrade large language model (LLM) performance in code generation. We …
Vulnerability DetectionCode GenerationTeam of Thoughts: Efficient Test-time Scaling of Agentic Systems through Orchestrated Tool Calling
Existing Multi-Agent Systems (MAS) typically rely on homogeneous model configurations, failing to exploit the diverse expertise inherent in different post-trained architectures. We propose Team-of-Thoughts, a heterogeneo…
Mathematical ReasoningCode GenerationTAROT: Test-driven and Capability-adaptive Curriculum Reinforcement Fine-tuning for Code Generation with Large Language Models
Large Language Models (LLMs) are changing the coding paradigm, known as vibe coding, yet synthesizing algorithmically sophisticated and robust code still remains a critical challenge. Incentivizing the deep reasoning cap…
Code GenerationPrecedent-Informed Reasoning: Mitigating Overthinking in Large Reasoning Models via Test-Time Precedent Learning
Reasoning in Large Language Models (LLMs) often suffers from inefficient long chain-of-thought traces with redundant self-exploration and validation, which inflate computational costs and even degrade performance. Inspir…
Mathematical ReasoningSemantic SimilarityCode GenerationGenAI for Systems: Recurring Challenges and Design Principles from Software to Silicon
Generative AI is reshaping how computing systems are designed, optimized, and built, yet research remains fragmented across software, architecture, and chip design communities. This paper takes a cross-stack perspective,…
Code GenerationWhy Code, Why Now: An Information-Theoretic Perspective on the Limits of Machine Learning
This paper offers a new perspective on the limits of machine learning: the ceiling on progress is set not by model size or algorithm choice but by the information structure of the task itself. Code generation has progres…
Reinforcement LearningCode GenerationiML: Executable, Problem-Grounded, and Broadly Exploratory Code-Driven AutoML
Automated Machine Learning (AutoML) has improved access to machine learning, yet existing techniques often remain limited in flexibility, transparency, and execution reliability. Code-driven AutoML offers a promising dir…
Code GenerationLogitsCoder: Towards Efficient Chain-of-Thought Path Search via Logits Preference Decoding for Code Generation
Code generation remains a challenging task that requires precise and structured reasoning. Existing Test Time Scaling (TTS) methods, including structured tree search, have made progress in exploring reasoning paths but s…
Code GenerationFrom What to How: Bridging User Requirements with Software Development Using Large Language Models
Recently, large language models (LLMs) are extensively utilized to enhance development efficiency, leading to numerous benchmarks for evaluating their performance. However, these benchmarks predominantly focus on impleme…
Code Generation