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Former Datadog engineers launch Niteshift, an AI coding cloud that routes between models to reduce lock-in, raising $7M seed round led by Greylock.
ExpGraph is a model-agnostic framework that enables LLM agents to reuse past experiences via a self-evolving graph of skills and failures, improving task performance by 12–21% without retraining the executor.
This paper proposes CR4T, a model-agnostic safeguarding framework that rewrites unsafe or refusal-style LLM outputs into developmentally appropriate, guidance-oriented responses for adolescents, offering a more human-centered alternative to traditional refusal-centric guardrails.
INSIGHTS is a model-agnostic approach for providing global explanations of time-series models by generating diverse, informative sample summaries that capture domain-specific behaviors, outperforming local attribution methods in user studies.
This paper introduces COSMOS, a model-agnostic personalized federated learning framework that uses clustered server models and pseudo-label-only communication. It provides theoretical analysis showing exponential personalization risk contraction and demonstrates superior performance over existing baselines in heterogeneous environments.