Plan2Cleanse frames RL backdoor detection as a Monte Carlo planning problem to achieve over 61 percentage point gains in trigger detection and improved win rates in competitive environments.
Advsim: Generating safety-critical scenarios for self-driving vehicles
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Pref-CTRL trains a multi-objective value function on preferences to guide representation editing for LLM alignment, outperforming RE-Control on benchmarks with better out-of-domain generalization.
citing papers explorer
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Plan2Cleanse: Test-Time Backdoor Defense via Monte-Carlo Planning in Deep Reinforcement Learning
Plan2Cleanse frames RL backdoor detection as a Monte Carlo planning problem to achieve over 61 percentage point gains in trigger detection and improved win rates in competitive environments.
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Pref-CTRL: Preference Driven LLM Alignment using Representation Editing
Pref-CTRL trains a multi-objective value function on preferences to guide representation editing for LLM alignment, outperforming RE-Control on benchmarks with better out-of-domain generalization.