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.
HumanEval on Latest GPT Models–2024
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A-ProS uses a hybrid multi-model feedback framework with stateful refinement to improve success rates on competitive programming problems, achieving over 2x gains compared to baseline agent loops.
The GPT family has shifted from scaled text predictors to aligned multimodal tool-oriented systems, with persistent limitations like hallucination and prompt sensitivity remaining unchanged.
citing papers explorer
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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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A-ProS: Towards Reliable Autonomous Programming Through Multi-Model Feedback
A-ProS uses a hybrid multi-model feedback framework with stateful refinement to improve success rates on competitive programming problems, achieving over 2x gains compared to baseline agent loops.
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From GPT-3 to GPT-5: Mapping their capabilities, scope, limitations, and consequences
The GPT family has shifted from scaled text predictors to aligned multimodal tool-oriented systems, with persistent limitations like hallucination and prompt sensitivity remaining unchanged.