klotz: okf*

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  1. Anubhab Banerjee writes that three Qwen2.5-Coder agents of different sizes can share pre-computed token IDs instead of re-tokenizing the same text, using a Google Open Knowledge Format-inspired Markdown+YAML hand-off with an added token_pointer field to /dev/shm, cutting median TTFT by 28% on 3B and 37.8% on 1.5B while avoiding KV-cache sharing.

    - Uses /dev/shm/qwen_tokens/ with NumPy .npy int64 arrays for zero-copy hand-off
    - Agents run as separate OS processes to release VRAM automatically between models
    - Runtime guardrail verifies full ~151,936-entry get_vocab() dict equality across models
    - OKF files add non-standard token_pointer and tokenizer_model_id fields for provenance
    - Savings apply to input side only; each agent still tokenizes its own output
  2. Roger Montti writes that Google has released version 0.2 of the Open Knowledge Format, incorporating five trust signals to facilitate data verification for consumers. These updates cover provenance, generation and verification status, freshness via expiration dates, lifecycle stages, and computational attestation to address questions regarding a concept's origin, accuracy, and timeliness. By providing raw metadata rather than subjective credibility scores, Google enables users or agents to dynamically evaluate information based on objective signals such as authorship and usage counts.

    - Attested Computation is a new type used to confirm that values are calculated using sanctioned methods instead of improvised logic.
    - The "sources" field provides objective data points like author and last modified date rather than an inherent score.
    - Verification can be performed by humans or machine agents, allowing users to filter content based on verification level.

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