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NovaFabric: Tamper-Evident, Replayable Evidence for Autonomous AI Agent Runs
NovaFabric seals autonomous AI agent runs into tamper-evident, replayable Run Capsules enabling third-party audit under EU AI Act and ISO 42001.
NovaFabric records autonomous agent runs without modifying agent logic into portable Run Capsules (fifteen-entity schema) sealed with DSSE signatures, RFC 3161 timestamps, a Merkle log, and redaction attestations, supporting four-mode replay and third-party Evidence Bundle verification. Evaluation shows tampering rejected across three tested classes, 14/14 credential types redacted while preserving 9/9 decoys, and 140/140 mutations localized; blast-radius queries reach 45.5ms p99 over 10M edges. Limits include only 2/10 tool-using workloads completing replay due to missing tool-response substitution and ingest capped at 61.6 req/s. The contribution integrates OpenTelemetry, DSSE/in-toto, and W3C PROV rather than new cryptography.
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From Protocols to Evidence: Bounded Claims for AI in Service of the Common Good
Paper proposes a rupture test and RISE AI architecture for evidence-bounded responsible-AI claims, framed via EU AI Act and NIST AI RMF.
The paper argues AI deployment intervenes in pre-existing institutional failures of responsiveness, belonging, care, and accountability, and must therefore evaluate both the system and the institutional rupture it enters. It reviews how the EU AI Act, NIST AI RMF, and ISO/IEC 42001 translate principles into protocols, and draws on Pope Leo XIV's Magnifica Humanitas to develop a rupture test linking institutional baselines to system evaluation. It distinguishes evidence-bounded deployment from measurement-bounded governance and introduces RISE AI, an architecture for bounded claims about Responsibility, Inclusivity, Safety, and Empowerment.
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Anthropic and OpenAI want to embed safety evaluators. Will they really be independent?new
Anthropic and OpenAI propose embedding independent safety evaluators with deep access to training, but evaluators question whether true independence is achievable.
Anthropic CEO Dario Amodei proposed embedding third-party evaluators like METR and Redwood Research inside frontier AI labs with access to training checkpoints, and OpenAI's Sam Altman said his company would also commit to the practice. Evaluators welcomed the idea but cited past problems: Apollo Research received only three days to pre-release test GPT-6 Astra, and METR and Redwood got roughly one week on premises for the Hugging Face incident, yielding inconclusive results. Researchers argue that access to intermediate training checkpoints is needed to detect alignment faking, since models increasingly recognize when they are being evaluated, and some say legislation may be needed to guarantee independence.
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