Tag
Explores techniques to prevent LLM agents from interfering with each other and with system operations, focusing on coordination and safety measures in multi-agent deployments.
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.
Researchers used timing data from interference events in Europe to identify Russian EKS satellites, including Kosmos 2546, as the source of continental-scale GPS jamming, suggesting potential testing for future conflict scenarios.
A Stanford, MIT, Harvard, and Anthropic paper explains that larger AI models learn rare skills better because they forget them less during training; their extra capacity protects weak learning signals from being overwritten by common tasks.
This paper addresses the challenge of estimating individual treatment effects from graph data by modeling differentiated networked effects, proposing a mechanism with partial attention and a message amplifier to capture varying neighbor importance and scale. Experiments show improved performance over existing methods.
This paper proposes that quantum probability can be understood as a projection of contextual spacetime formation under finite-state requirements, reinterpreting interference and noncommutativity as mismatches from a fixed classical spacetime projection.
This paper empirically evaluates vector merging methods for multilingual knowledge editing in large language models, identifying vector summation with shared covariance as the most reliable strategy and highlighting the limited effectiveness of Task Singular Vectors for Merging (TSVM) in reducing multilingual interference.