PPU-Bench is a real-world benchmark exposing forget-retain trade-offs in MLLM unlearning and motivating Boundary-Aware Optimization to enforce intra-subject factual boundaries.
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SCPRM adds prefix conditioning and schema distance to process reward models so that Monte Carlo Tree Search can explore knowledge-graph reasoning paths with both cumulative and future guidance, yielding a 1.18% average Hits@k gain on medical, legal, and CWQ tasks.
EMS reduces the average number of agents invoked for majority voting by 32% via reliability-aware prioritization and early stopping on six benchmarks.
citing papers explorer
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PPU-Bench:Real World Benchmark for Personalized Partial Unlearning in Vision Language Models
PPU-Bench is a real-world benchmark exposing forget-retain trade-offs in MLLM unlearning and motivating Boundary-Aware Optimization to enforce intra-subject factual boundaries.
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SCPRM: A Schema-aware Cumulative Process Reward Model for Knowledge Graph Question Answering
SCPRM adds prefix conditioning and schema distance to process reward models so that Monte Carlo Tree Search can explore knowledge-graph reasoning paths with both cumulative and future guidance, yielding a 1.18% average Hits@k gain on medical, legal, and CWQ tasks.
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EMS: Multi-Agent Voting via Efficient Majority-then-Stopping
EMS reduces the average number of agents invoked for majority voting by 32% via reliability-aware prioritization and early stopping on six benchmarks.