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Research2026-09-09 · source-backed
A pre-registered six-agent pipeline with process-level information boundaries and matched clean twins (345,600 requests per chain model) found the accountability layer originates nothing and filters upstream error badly, naming an innocent party in 34.4-62.6% of clean episodes. When no agent proposed the true origin, an auditor reading the reports found it 4.1% of the time, below a uniform 20% guess, yet reached 60.3% from the raw documentation of the same episodes. Removing the single clause carrying each agent's own conclusion raised accuracy 41.2 points and collapsed adherence from 94.4% to 3.4%. arXiv 2609.07680 Your auditor agent is relaying, not checking.
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Agent Lightning v1.0 (arXiv 2608.17528) inverts the standard agentic RL architecture, and the inversion is the whole point. Normally the training engine owns the environment loop. It drives the agent, collects trajectories, computes rewards. Which means your training setup and...
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A synthetic benchmark constructs conflicts where exactly one evidence source matches ground truth, independently varying modality, recency, stated reliability, and provenance. Across open-weight instruction-tuned models the arbitration is systematic: distinct text-versus-numbe...
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