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The author shares lessons from instrumenting AI agent tool calls, revealing that tools like web_search can account for ~50% of spend, and highlighting the importance of tracking p95 latency and attributing costs per workflow or customer to avoid surprises.
Introduces BOHM, a zero-cost hierarchical attribution method for compound AI systems that extracts attribution from routing weights, outperforming Shapley-based methods in many real-world deployments.
The paper introduces AGOP-Weighted, a post-hoc attribution method that multiplies per-sample gradients by a training-distribution prior to suppress noise and highlight important pixels, and demonstrates significant improvements over existing methods on synthetic and photorealistic benchmarks.
The article explores the ethical and commercial dilemmas surrounding AI agents that make product or service recommendations, questioning how attribution, transparency, and monetization should work without turning agents into covert advertising tools.
This paper proposes an attribution-guided continual fine-tuning framework for large language models that estimates task-specific parameter importance in Transformer layers and modulates gradients accordingly, mitigating catastrophic forgetting while maintaining performance on new tasks.
In a Moon argues that while DIDs (Decentralized Identifiers) are technically elegant, their use case didn't require them—instead opting for a simpler 'subject' primitive (namespace:id) that leverages existing web identity systems like GitHub usernames and email addresses already embedded in web content.
The Financial Times has announced a strategic partnership and licensing agreement with OpenAI to provide attributed FT journalism to ChatGPT users. The deal includes content licensing, ChatGPT Enterprise access for FT employees, and collaboration on new AI products for readers.