Papers Code Generation
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Security-by-Design for LLM-Based Code Generation: Leveraging Internal Representations for Concept-Driven Steering Mechanisms
Large Language Models (LLMs) show remarkable capabilities in understanding natural language and generating complex code. However, as practitioners adopt CodeLLMs for increasingly critical development tasks, research reve…
Code GenerationAgentic AI as a Network Control-Plane Intelligence Layer for Federated Learning over 6G
The shift toward user-customized on-device learning places new demands on wireless systems: models must be trained on diverse, distributed data while meeting strict latency, bandwidth, and reliability constraints. To add…
Federated LearningCode GenerationWrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL
Learning effective netlist representations is fundamentally constrained by the scarcity of labeled datasets, as real designs are protected by Intellectual Property (IP) and costly to annotate. Existing work therefore foc…
Component ClassificationRepresentation LearningData AugmentationCode GenerationMiniAppBench: Evaluating the Shift from Text to Interactive HTML Responses in LLM-Powered Assistants
With the rapid advancement of Large Language Models (LLMs) in code generation, human-AI interaction is evolving from static text responses to dynamic, interactive HTML-based applications, which we term MiniApps. These ap…
Code GenerationEsoLang-Bench: Evaluating Genuine Reasoning in Large Language Models via Esoteric Programming Languages
Large language models achieve near-ceiling performance on code generation benchmarks, yet most of the programming languages used by popular benchmarks such as SWE-bench and HumanEval (e.g. Python, JavaScript) are squarel…
Few-Shot LearningCode GenerationThinking to Recall: How Reasoning Unlocks Parametric Knowledge in LLMs
While reasoning in LLMs plays a natural role in math, code generation, and multi-hop factual questions, its effect on simple, single-hop factual questions remains unclear. Such questions do not require step-by-step logic…
Code GenerationCode-Space Response Oracles: Generating Interpretable Multi-Agent Policies with Large Language Models
Recent advances in multi-agent reinforcement learning, particularly Policy-Space Response Oracles (PSRO), have enabled the computation of approximate game-theoretic equilibria in increasingly complex domains. However, th…
Multi-agent Reinforcement LearningCode GenerationTowards a Neural Debugger for Python
Training large language models (LLMs) on Python execution traces grounds them in code execution and enables the line-by-line execution prediction of whole Python programs, effectively turning them into neural interpreter…
Code GenerationText to Automata Diagrams: Comparing TikZ Code Generation with Direct Image Synthesis
Diagrams are widely used in teaching computer science courses. They are useful in subjects such as automata and formal languages, data structures, etc. These diagrams, often drawn by students during exams or assignments,…
Code GenerationKCoEvo: A Knowledge Graph Augmented Framework for Evolutionary Code Generation
Code evolution is inevitable in modern software development. Changes to third-party APIs frequently break existing code and complicate maintenance, posing practical challenges for developers. While large language models …
Code GenerationBreaking Training Bottlenecks: Effective and Stable Reinforcement Learning for Coding Models
Modern code generation models exhibit longer outputs, accelerated capability growth, and changed training dynamics, rendering traditional training methodologies, algorithms, and datasets ineffective for improving their p…
Reinforcement LearningCode GenerationScaling Data Difficulty: Improving Coding Models via Reinforcement Learning on Fresh and Challenging Problems
Training next-generation code generation models requires high-quality datasets, yet existing datasets face difficulty imbalance, format inconsistency, and data quality problems. We address these challenges through system…
Reinforcement LearningCode GenerationDistillGuard: Evaluating Defenses Against LLM Knowledge Distillation
Knowledge distillation from proprietary LLM APIs poses a growing threat to model providers, yet defenses against this attack remain fragmented and unevaluated. We present DistillGuard, a framework for systematically eval…
Knowledge DistillationMathematical ReasoningCode GenerationReflexiCoder: Teaching Large Language Models to Self-Reflect on Generated Code and Self-Correct It via Reinforcement Learning
While Large Language Models (LLMs) have revolutionized code generation, standard ``System 1'' approaches that generate solutions in a single forward pass often hit a performance ceiling on complex algorithmic tasks. Exis…
Reinforcement LearningCode GenerationPolyBlocks: A Compiler Infrastructure for AI Chips and Programming Frameworks
We present the design and implementation of PolyBlocks, a modular and reusable MLIR-based compiler infrastructure for AI programming frameworks and AI chips. PolyBlocks is based on pass pipelines that compose transformat…
Code GenerationDiffusion Language Models Are Natively Length-Aware
Unlike autoregressive language models, which terminate variable-length generation upon predicting an End-of-Sequence (EoS) token, Diffusion Language Models (DLMs) operate over a fixed maximum-length context window for a …
Instruction FollowingQuestion AnsweringCode GenerationSAHOO: Safeguarded Alignment for High-Order Optimization Objectives in Recursive Self-Improvement
Recursive self-improvement is moving from theory to practice: modern systems can critique, revise, and evaluate their own outputs, yet iterative self-modification risks subtle alignment drift. We introduce SAHOO, a pract…
Mathematical ReasoningCode GenerationExact Is Easier: Credit Assignment for Cooperative LLM Agents
Removing an agent from a cooperative team to measure its contribution seems natural, yet in multi-agent LLM systems this evaluation distorts the result it claims to measure. This failure is not isolated: learned critics,…
Multi-agent Reinforcement LearningCode GenerationLearning From Design Procedure To Generate CAD Programs for Data Augmentation
Large Language Models (LLMs) have demonstrated impressive capabilities in a wide range of code generation tasks. However, generating code for certain domains remains challenging. One such domain is Computer-Aided Design …
Data AugmentationCode GenerationFVRuleLearner: Operator-Level Reasoning Tree (OP-Tree)-Based Rules Learning for Formal Verification
The remarkable reasoning and code generation capabilities of large language models (LLMs) have recently motivated increasing interest in automating formal verification (FV), a process that ensures hardware correctness th…
Code Generation