@ArizePhoenix: For agents, active parameters are what you pay for in latency and cost, and agent loops resend context, retry tool call…

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Summary

Arize Phoenix introduces M3, which adds sparse attention and a 1M-token context window to reduce latency and cost in AI agents by keeping long tool histories in context.

For agents, active parameters are what you pay for in latency and cost, and agent loops resend context, retry tool calls, and run long. M3 adds sparse attention and a 1M-token window on top, so long tool histories stay in context instead of getting summarized away.
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