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Research2026-09-16 · source-backed
BLINDSPOT evaluates complete user-agent-environment trajectories using 22 attack families and 35 scenarios across seven domains, producing 2,500+ trajectories averaging 14.7 turns. Each gets one of five outcomes: Safe Completion, Correct Refusal, Unsafe Completion, Over-Refusal, or Indeterminate. An agent that refuses everything doesn't score as safe. Across 13 proprietary and open-weight models the authors report substantial differences in safety-utility calibration and failures that only emerge after several initially safe steps.
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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...
The diagnosis in this paper is better than the fix, and the fix is very good. Recurrent memory agents fail at long context, but not for the reason most people assume. The bottleneck isn't capture. It's retention. Retention falls below 30% at 896K tokens because every consolida...
UK AI Safety Institute found model capability at 10M tokens jumped from 1.7 to 9.8 steps completed on a 32-step corporate network attack. Each 10x compute increase yields 59% more steps. No plateau found. Best run: 22/32 steps — 6 hours of a 14-hour human expert workload. Source
This one should change how you read leaderboards. A physics benchmark audit put faculty and graduate researchers through six widely used physics benchmarks, including ones feeding the Artificial Analysis Intelligence Index that half the industry quotes. They reviewed problem s...
Open issues on the AI SDK went from 1,022 to 844 in four weeks. That's the number I'd lead with if I were writing this post, and Vercel did. Vercel published the internals of the system it built to maintain the AI SDK, a repo with over 20 million npm downloads a week, 26,000+...
Each interaction creates a structured Note; the system retrieves related historical memories and generates contextual link descriptions; interconnected notes form a self-organizing graph. Unlike flat vector stores, explicit linking enables reasoning over relationships — not ju...
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