VirtualME is a new infrastructure that continuously extracts and interprets in-IDE developer behaviors to build personalized personas, delivering 33.8% better performance on repository-level knowledge Q&A than generic baselines.
Search-based LLMs for code optimization
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
AdverMCTS frames code generation as a minimax game where an attacker evolves tests to expose flaws in solver-generated code, yielding more robust outputs than static-test baselines.
AutoTrainess exposes training operations via agent-computer interfaces and outperforms CLI-only baselines on PostTrainBench with scores of 26.94 vs 23.21 for GPT-5.4 and similar gains on other models.
citing papers explorer
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On the Road to Personalized Code Intelligence: Portraiting and Assisting Developers Based on Their In-IDE Behaviors
VirtualME is a new infrastructure that continuously extracts and interprets in-IDE developer behaviors to build personalized personas, delivering 33.8% better performance on repository-level knowledge Q&A than generic baselines.
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AdverMCTS: Combating Pseudo-Correctness in Code Generation via Adversarial Monte Carlo Tree Search
AdverMCTS frames code generation as a minimax game where an attacker evolves tests to expose flaws in solver-generated code, yielding more robust outputs than static-test baselines.
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AutoTrainess: Teaching Language Models to Improve Language Models Autonomously
AutoTrainess exposes training operations via agent-computer interfaces and outperforms CLI-only baselines on PostTrainBench with scores of 26.94 vs 23.21 for GPT-5.4 and similar gains on other models.