Papers Code Generation
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TDD-Agent: Test-Driven Reasoning for Code Generation
Large Language Models (LLMs) have achieved remarkable progress in code generation, yet ensuring correctness in complex, repository-level tasks remains challenging. Existing approaches often use generated tests as static …
Code GenerationRemote-Sensing City Layout Extraction with MLLM
Remote-sensing systems usually describe urban content with detection boxes, semantic masks, or vector boundaries. Such outputs locate classes and support image-plane scoring, yet they do not by themselves constitute an e…
Code GenerationCompeting at Every Price Point with Agentic Evolution over a Menu of LLMs
Consider a firm that surveys its competition for a particular agentic task and seeks to offer superior accuracy at every competitor price point. A firm that Pareto-dominated its competitors would leave no rational custom…
Code GenerationSecuring AI-Generated Code: A Just-in-Time Vulnerability Detection and Remediation Pipeline
AI-assisted development tools generate vulnerable code at significant rates, yet few automated mechanisms exist to detect, enrich, fix, and verify security issues at development velocity, particularly ones that ground re…
Vulnerability DetectionCode GenerationDetecting Contaminated Code-Generation Prompt Batches via Influence Functions
Large language models (LLMs) are increasingly used for code generation, yet they remain vulnerable to prompts that elicit insecure implementations. Existing defenses typically rely on predefined threat models or known vu…
Code GenerationAgentic Transaction: Towards ACID-Compliant Agent Systems
Large language model (LLM) agents are evolving from conversational assistants into autonomous systems that execute long-horizon tasks through reasoning, tool use, code generation, and workspace manipulation. As agents in…
Code GenerationVero: Can AI Agents Build Formally Verified Software Repositories?
AI agents are increasingly used for programming, but do not provide any guarantee on the correctness of generated code. Verified code generation, in which an agent produces both an implementation and a machine-checked pr…
Code GenerationEdit2TikZ: A Comprehensive and Challenging Benchmark for Scientific Figure Editing with TikZ
Although multimodal large language models (MLLMs) have shown substantial potential in visual understanding and graphic code generation, editing scientific figures through code presents a greater challenge: a model must j…
Instruction FollowingVisual LocalizationCode GenerationStateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems
Large language model based multi-agent systems usually communicate in text, i.e., using discrete tokens. However, text introduces a discrete bottleneck. Converting the sender's continuous hidden states into discrete toke…
Question AnsweringCode GenerationLatent On-Policy Self-Distillation
Enabling agents to learn from experience and internalize it into their policy has become a central problem in self-evolving AI. On-policy self-distillation (OPSD) offers an effective pathway by using a privileged self-te…
Code GenerationDoes ISO-Grounded NFR Specification Improve LLM Code Generation? A Comparison of Rich and Structured Interventions against a Natural-Language Baseline
In LLM-based code generation, Non-Functional Requirements (NFRs) are often specified as terse one-line phrases. We ask whether grounding those specifications in ISO/IEC 25010 Quality Model, either as rich natural-languag…
Code GenerationINSIDE the Student's Mind: Jointly Modeling Latent Reasoning and Action in LLM Student Simulators
Large Language Model (LLM)-based simulators often reproduce observable actions but fail to capture the underlying reasoning behind them. In education, where student simulation is increasingly used for various application…
Code GenerationParameter Exploration for RLVR via Variational Learning
Exploration has been a focus of reinforcement learning research for a long time. Recently, there has been growing evidence that it is also an important ingredient in LLM reinforcement learning recipes that can significan…
Mathematical ReasoningReinforcement LearningCode GenerationSeqFeed: Improving Agentic RTL Code Generation with Sequential Behavior Feedback
RTL code generation is a critical stage in hardware design, and the emergence of agentic systems offers new opportunities to automate this process. To generate correct RTL code, agents must understand sequential behavior…
Code GenerationRepoProbe: Benchmarking Architecture-Aware Repository Comprehension with Checklists
The integration of Large Language Models (LLMs) into software engineering has shifted the focus from function-level generation to repository-scale assistance. However, existing benchmarks largely rely on bug reports from…
Code GenerationPattern over Pixels: Measuring Pattern Completion Bias in Multimodal Code Generation
Multimodal large language models (MLLMs) are increasingly used to translate webpage screenshots into front-end code, but repeated UI patterns may sway them toward visually incorrect yet pattern-consistent outputs. In thi…
Code GenerationGenOS: Compositional Certificates for Semantic Robustness in AI Code Generation
AI coding agents are stochastic workflows: prompts are interpreted, artifacts are sampled, validators produce observations, and orchestrators commit or repair. Small prompt or specification changes can therefore alter pr…
Code GenerationRoute-Align-Verify for Functional Correctness in Code Generation
Large language models (LLMs) have substantially improved code generation, yet achieving strong functional correctness remains difficult, especially for heterogeneous programming tasks where a single prompting strategy an…
Code GenerationAgentSLABench: Evaluating and Benchmarking Agentic Systems Under Resource Constraints
We present AgentSLABench, a resource-aware evaluation framework for autonomous AI agents that measures correctness alongside latency, cost, compute, memory, and network usage under declared resource budgets. Unlike stand…
Code GenerationTracing the Cascade: A Topology-Aware Evaluation Framework for Scientific Agent Hallucinations
Large language model (LLM) agents are increasingly deployed in scientific research, where reliability is critical and the underlying knowledge is densely interconnected. In such settings, hallucinations are particularly …
Code Generation