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This paper proposes modeling forgetting in continual learning as interference between tasks and introduces Interference-Gated Functional Allocation (IGFA), a replay-free, Fisher-free method that shares or protects directions based on task geometry, achieving lossless retention when tasks are separable.
The paper identifies a blind spot in machine unlearning benchmarks: underrepresentation of causal (Why-type) knowledge, and proposes 5WBench, a balanced benchmark, and Maat, a three-phase unlearning framework on LoRA adapters that achieves high forgetting and retention on causal facts.
Introduces HPML, a method that projects the joint update field of multi-agent systems onto a metric-gradient component to stabilize and improve multi-agent reinforcement learning. It provides theoretical guarantees and shows improved stability and returns on CTDE benchmarks.
This paper introduces an interference-aware framework for multi-task machine unlearning, addressing task-level and instance-level interference through task-aware gradient projection and instance-level gradient orthogonalization, achieving effective unlearning on multi-task computer vision benchmarks.