Search: “root”

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DomainAll Cyber AIWindow24h 3d 7d 30d AllIn the wild Important only Show duplicates

CategoryAll Vulnerability 30 Exploit / PoC 20 Advisory 12 Research 8 Threat actor 4 AI safety & security 4 Data breach 3 AI research 3 Malware 3 Tools 2 Industry 2 AI industry 1 AI tools & infra 1 Model release 1

SeverityAll critical 18 high 43 medium 17 low 2 info 17

Fast-varying Natural Frequencies and Damping Ratio Identification for Linear Time-Varying System

Physics-enhanced LSTM-EKF method identifies fast-varying natural frequencies and damping ratios in offshore wind turbines with 0.0012 Hz RMSE.

The work combines a long short-term memory network with an Extended Kalman Filter for physics-enhanced system identification of linear time-varying systems. It targets fast-varying natural frequencies and damping ratios in a 2-blade offshore wind turbine under wind and wave loading, validated on synthetic data from a finite element model. The identified FA-1 fore-aft mode frequency achieves a maximum RMSE of 0.0012 Hz, and damping ratio estimation via grid search improves on covariance-driven stochastic subspace identification.

arXiv cs.AI / cs.LG / cs.CLSep 17, 12:29 UTC · 1d agoAI research

Decomposition Buys Integrity, Not Yield

Study of 600 production deep-research traces finds agent-tree decomposition loses findings at rate N^(1-δ); flat architectures maximize yield.

The paper models multi-agent decomposition as a tree where an agent holding b items retains each with probability r(b); with r(b)=1/b every tree delivers exactly one finding regardless of shape. Analysis of 600 production deep-research traces estimates delta=0.34 retention decay, and 1,012 annotated traces show one brief in sixteen goes off-target per tier, giving an alignment penalty of 0.536. Depth still cuts root context exposure from N to N^(1/k) and is cheaper at scale, with a hazard model over 743,819 production tool calls showing delegation is an opening move rather than a response to filling context.

arXiv cs.AI / cs.LG / cs.CLSep 15, 17:04 UTC · 2d agoAI research

A Scalable Trust Discovery Architecture for the Internet of Agents

Researchers propose a three-layer trust discovery architecture for the Internet of Agents with registry-anchored identities, sustaining 19,000 registrations and 29,000 discoveries per second.

Researchers propose a three-layer trust discovery architecture for the Internet of Agents: Agent Root for trusted registry governance, Agent Registry for agent registration and metadata publication, and Agent Resolver for distributed trust-aware capability discovery. It binds native agent identifiers to trusted registry suffixes for globally discoverable composite identities and uses dual certificates with multi-level authentication. A prototype sustains over 19,000 registration requests and 29,000 discovery requests per second with average latencies of 58ms and 25ms respectively.

arXiv cs.AI / cs.LG / cs.CLSep 17, 11:55 UTC · 1d agoAI research