Papers Code Generation
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CoRe-Code: Collaborative Reinforcement Learning for Code Generation
Large language models (LLMs) have achieved strong performance in code generation, but most methods rely on autoregressive decoding without global planning, often leading to locally coherent yet globally suboptimal soluti…
Reinforcement LearningCode GenerationMeta-Agent: From Task Descriptions to Verified Multi-Agent Systems
AI agents are increasingly used to solve complex, multi-step tasks, but existing multi-agent frameworks remain brittle as workflows grow in scale and depth. Small errors at intermediate stages can propagate through agent…
Code GenerationFrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization
Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a harder capability: designing scalable a…
Computational EfficiencyCode GenerationEureka: Intelligent Feature Engineering for Enterprise AI Cloud Resource Demand Prediction
Effective features are crucial for predictive model performance, but creating them often requires domain expertise, limiting scalability across applications. We define feature engineering as an agentic code generation pr…
Reinforcement LearningFeature EngineeringCode GenerationThe Path Matters: Learning a Token-Commitment Policy for Diffusion Language Models
Diffusion large language models promise faster generation by refining many token positions in parallel, but this parallelism introduces a hidden control problem: which proposed tokens should be transferred into the parti…
Mathematical ReasoningQuestion AnsweringCode GenerationGRAIL: AI translation for scientists application workflow on satellite data
Domain scientists increasingly develop Python scripts to analyze satellite imagery but they lack scalability to large-scale data. This paper demonstrates GRAIL, an agentic translation system that converts Python geospati…
Code GenerationCoSPlay: Cooperative Self-Play at Test-Time with Self-Generated Code and Unit Test
Recently, Reinforcement Learning with Verifiable Rewards (RLVR) and Test-Time Scaling (TTS) have advanced LLM code generation through executable verification. Yet Ground-Truth Unit Tests (GT UTs) remain a bottleneck: SOT…
Reinforcement LearningCode GenerationAgentic Proving for Program Verification
Agentic systems have recently emerged as state-of-the-art approaches for automated theorem proving in formal mathematics. To assess how far these capabilities extend to program verification, we evaluate Claude Code in an…
Automated Theorem ProvingCode GenerationVISTA: An End-to-End Benchmark for Visual Spec-to-Web-App Coding Agents
We present VISTA (VIsual Spec-To-App Benchmark), a benchmark for evaluating the end-to-end web-app generation capabilities of LLM-based agents. Unlike prior code generation benchmarks that focus on algorithmic tasks, VIS…
Code GenerationAn Empirical Evaluation of LLM-Generated Code Security Across Prompting Methods
The growing use of Large Language Models (LLMs) for automated code generation has enhanced software development efficiency, but often at the cost of security. Generated code frequently overlooks critical concerns, leavin…
Prompt EngineeringCode GenerationEnhancing Reliability in LLM-Based Secure Code Generation
Large language models (LLMs) are widely used for code generation, but their security reliability remains inconsistent across languages and prompting strategies. Existing prompt engineering improves functional correctness…
Prompt EngineeringCode GenerationVeriScale: Adversarial Test-Suite Scaling for Verifiable Code Generation
As large language models (LLMs) are increasingly deployed for software engineering, constructing high-quality benchmarks is crucial for evaluating not just the functional correctness, but also the formal verifiability of…
Code GenerationSelf-Policy Distillation via Capability-Selective Subspace Projection
Self-distillation bootstraps large language models (LLMs) by training on their own generations. However, existing methods either rely on external signals to curate self-generated outputs (e.g., correctness filtering, exe…
Mathematical ReasoningDomain GeneralizationCode GenerationVector Policy Optimization: Training for Diversity Improves Test-Time Search
Language models must now generalize out of the box to novel environments and work inside inference-scaling search procedures, such as AlphaEvolve, that select rollouts with a variety of task-specific reward functions. Un…
Code GenerationRAS: Reflection-Augmented Scaling with In-Context Learning for Executable Cypher Query Generation
Inference-time scaling can reduce errors in structured query generation, but methods to allocate the compute for query code generation remains underexplored. We study Text2Cypher, where language models generate Cypher qu…
Code GenerationDomain-Adaptable Reinforcement Learning for Code Generation with Dense Rewards
Large language models show strong potential for automated code generation, but lack guarantees for correctness, quality, safety, and domain-specific constraints. For instance in robotics, where code generation is increas…
Reinforcement LearningProgram SynthesisCode GenerationDelTA: Discriminative Token Credit Assignment for Reinforcement Learning from Verifiable Rewards
Reinforcement learning from verifiable rewards (RLVR) has emerged as a central technique for improving the reasoning capabilities of large language models. Despite its effectiveness, how response-level rewards translate …
Reinforcement LearningCode GenerationManifold-Guided Attention Steering
Large language models frequently produce errors in reasoning tasks despite possessing the underlying knowledge required for correct reasoning. One possible approach to improve reasoning consistency is through activation …
Mathematical ReasoningCode GenerationPRISM: A Benchmark for Programmatic Spatial-Temporal Reasoning
Programmatic video generation through code offers geometric precision and temporal coherence beyond pixel-level diffusion models, yet rigorously evaluating whether language models can produce spatially correct animated o…
Spatial ReasoningVideo GenerationCode GenerationDEL: Digit Entropy Loss for Numerical Learning of Large Language Models
Number prediction stands as a fundamental capability of large language models (LLMs) in mathematical problem-solving and code generation. The widely adopted maximum likelihood estimation (MLE) for LLM training is not tai…
Mathematical ReasoningCode Generation