Papers for
system integrators
Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.
Photonic matrix multipliers compared for robustness under noise and drift
Comparative Robustness of Mach-Zehender and Microring Photonic Matrix Multipliers Under Laser RIN and Thermal Drift
Abstract: Photonic matrix multipliers can accelerate neural-network inference, but their practical utility depends on how optical weight elements respond to source noise, thermal variation, and calibration error. This paper compares Mach-Zehnder modulator (MZM) and microring-resonator (MRR) implementations under laser relative-intensity noise (RIN), column-wise thermal/phase drift, and their joint action. Two CIFAR-10 classifiers - a compact four-layer CNN and ResNet-8 - are mapped to matched photonic multiply-accumulate chains using common source-distribution, detection, numerical, and calibration assumptions. Both architectures reproduce the same zero-perturbation baseline for each workload: 79.5% classification accuracy for the Small CNN and 70.0% for ResNet-8. Robustness is then evaluated using separate RIN-only and drift-only sweeps and a full two-dimensional RIN-drift grid, with dimensionless RMS perturbation levels extending from 0.5% to 20%. The analysis reports classification accuracy, class normalized confusion matrices, rolling-accuracy traces, one-dimensional robustness curves, and multidimensional response surfaces. Device equations and parameter definitions follow the models implemented in the QPI Labs Photonic Inference Simulator, with shared laser fluctuations propagated as correlated multiplicative noise and slow drift represented as a per-output-column perturbation of calibrated weights. All numerical results are generated from recorded experiment configurations and random seeds. The study provides a controlled system-level framework for comparing interferometric and resonant photonic weight elements while limiting conclusions to the implemented device models, perturbation ranges, calibration policy, and neural-network workloads.
Name collisions cause wrong routing in multi-agent systems
Agent Name Collision Attacks in Multi-Agent Systems
Abstract: Multi-agent hosts turn remote Agent Cards into local agents, tools, workflow targets, and broker routes. A2A defines the card's name as human-readable metadata, not as a stable identity, and specifies no collision semantics. The security failure begins when a host nevertheless uses that remote name as a local routing identifier. We traced registration through dispatch and ran isolated regression tests at seven pinned open-source revisions. Six client-style integrations selected an attacker-controlled peer's client or loopback endpoint for a request addressed to a trusted peer's name. A seventh, brokered implementation collapsed both peers onto one name-derived route; queue and access-control state determine whether the result is interception or denial. The common result is wrong-peer dispatch, not universal privilege inheritance. Synthetic credential and tool tests found no A-specific credential transfer in the tested client bindings and no direct transfer of A-owned tools. The broker path forwards a caller-configuration object; delegated identity or tokens reach B only if present and B can consume the route. Two other paths expose a later, model-mediated decision rather than direct execution authority. The necessary conditions assign different responsibilities to the protocol, implementations, and deployments. Hosts should route by an origin-bound stable identity, keep names presentational, and reject ambiguous aliases. The evidence establishes a recurring implementation vulnerability class, not a universal A2A protocol exploit or a count of vulnerable deployments.