Papers Code Generation
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Agentic-J: An AI Agent for Biological Microscopy Image Analysis
Biological image analysis increasingly demands integration across heterogeneous tools, programming environments, and domain knowledge that few researchers can command simultaneously. We present Agentic-J, a containerised…
Code GenerationDFlare: Scaling Up Draft Capacity for Block Diffusion Speculative Decoding
Block diffusion speculative decoding accelerates LLM inference by predicting all tokens within a block simultaneously for the target model to verify in parallel. Predicting an entire block at once requires a sufficiently…
Mathematical ReasoningCode GenerationCodegenBench: Can LLMs Write Efficient Code Across Architectures?
While large language models (LLMs) have been extensively evaluated on code generation tasks for general-purpose programming and GPU-accelerated environments (e.g., PyTorch, CUDA), their capabilities in CPU-oriented high-…
Code GenerationA Local Perturbation Theory for Cross-Domain Interference and Recovery in Multi-Domain RL
Reinforcement learning (RL) post-training improves large language models (LLMs) on individual domains such as mathematical reasoning, code generation, question answering, and creative writing (CW), but training on one do…
Reinforcement LearningMathematical ReasoningQuestion AnsweringCode GenerationNot All Errors Are Equal: A Systematic Study of Error Propagation in Large Language Model Inference
Large language models (LLMs) are increasingly integrated into high-performance computing (HPC) workflows, accelerating scientific discovery through diverse perspectives such as code generation and domain-specific decisio…
Code GenerationKForge: LLM-Driven Cross-Platform Kernel Generation for AI Accelerators
Production inference increasingly targets a heterogeneous mix of accelerators. Agentic pipelines interleave reasoning, tool calls, and multi-agent coordination, each with distinct compute and memory profiles. For optimal…
Code GenerationAnyEdit++: Adaptive Long-Form Knowledge Editing via Bayesian Surprise
Editing complex, long-form knowledge in Large Language Models remains a significant challenge due to the difficulty of maintaining generation coherence. Existing autoregressive methods like AnyEdit alleviate length const…
Mathematical Reasoningknowledge editingCode GenerationTrust Region On-Policy Distillation
On-Policy Distillation (OPD) is a fundamental technique for efficient post-training of large language models (LLMs), with broad applications in agent learning, multi-task enhancement, and model compression. However, OPD …
Mathematical ReasoningModel CompressionCode GenerationMOSAIC: Modular Orchestration for Structured Agentic Intelligence and Composition
Automated data science is a structured model-selection problem. A solution must choose data transformations, feature representations, architecture, training procedure, evaluation protocol, and refinement strategy for a t…
Reinforcement LearningCode GenerationExpGraph: Model-Agnostic Experience Learning with Graph-Structured Memory for LLM Agents
Large language model (LLM) agents have shown strong capabilities in reasoning, tool use, and multi-step interaction, but they often solve tasks from scratch and fail to reuse successful strategies or failure lessons from…
Reinforcement LearningMathematical ReasoningQuestion AnsweringCode GenerationBenchmarking Multimodal LLMs on Code Generation for Complex Interactive Webpages
Recent advancements in multimodal large language models (MLLMs) have achieved remarkable progress in multimodal reasoning and code generation, catalyzing a new paradigm for front-end development. In particular, these mod…
Multimodal ReasoningCode GenerationMellum2 Technical Report
We present Mellum 2, an open-weight 12B-parameter Mixture-of-Experts (MoE) language model with 2.5B active parameters per token. Mellum 2 is a general-purpose language model specialized in software engineering, spanning …
Code GenerationKnowledge Boundary Probing and Demand-Guided Intervention for LLM-Based Power System Code Generation
Large language models (LLMs) are increasingly used to automate power-system analysis, but many utilities and energy-research labs require on-premise serving for confidentiality, regulatory, reproducibility, and cost reas…
Code GenerationHow Generation Architecture Shapes Code Complexity in Multi-Agent LLM Systems: A Paired Study on HumanEval
Large-language-model code generation has shifted from single-shot prompting to multi-agent orchestrations - analyst, coder, tester, and debugger pipelines - and is evaluated almost exclusively on functional correctness. …
Code GenerationReliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability
Large Language Models (LLMs) excel at understanding natural language but struggle with optimisation tasks involving multiple constraints and user-defined preferences, which commonly arise in domains such as robotics. We …
Code GenerationNaRA: Noise-Aware LoRA for Parameter-Efficient Fine-Tuning of Diffusion LLMs
Diffusion Large Language Models (dLLMs) have emerged as a promising non-autoregressive generative paradigm. Given the prohibitive computational cost of full fine-tuning, Parameter-Efficient Fine-Tuning (PEFT) has become …
parameter-efficient fine-tuningMathematical ReasoningCode GenerationHTAM: Hierarchical Transition-Attended Memory for Operator Optimization
High-performance GPU kernels are essential for efficient LLM deployment, yet optimizing them remains expertise-intensive. Recent LLM-based code generation makes automatic GPU operator generation promising, but operator o…
Code GenerationInferring Code Correctness from Specification
Large language models (LLMs) have become integral to modern software development, enabling automated code generation at scale. However, validating the correctness of LLM-generated code remains a critical and largely unso…
Code GenerationCodeGolf Bench: A Multi-Language Benchmark for Evaluating Concise Code Generation Capabilities of Large Language Models
This paper introduces Code Bench, a benchmark capable of evaluating Large Language Models (LLMs) concise code generation abilities in 60 programming languages. Based on code golf, a recreational programming competition f…
Code GenerationLearning to Choose: An Empowerment-Guided Multi-Agent System with semantic communication for Adaptive Method Selection
Automating scientific computing workflows requires more than generating executable code: autonomous systems must also select appropriate computational strategies, implement them faithfully, and ensure that the resulting …
Semantic CommunicationCode Generation