sampling bias

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topic2 events

papersTODAY 04:00 UTC

Sampled Constraints Bias Equilibria in Augmented Primal-Dual Dynamics

A new study examines how augmented primal-dual methods behave when constraint values are estimated from samples rather than known exactly. The authors show that unbiased constraint estimates can still yield a biased augmented multiplier signal, which moves the equilibrium point of the mean dynamics. The paper analyzes stability and convergence properties under this sampling-induced shift.

optimization/primal-dual/convergence/theory

papersSEP 12 04:00 UTC

Study Finds MCP Server Sampling Skews Toward Servers That Work

An arXiv paper argues that research into the Model Context Protocol server ecosystem relies on sampling methods that implicitly favor functioning servers, such as reference sets, popularity rankings, hand-picked collections, or pipelines that fix servers until they run. The authors examine what a random draw from the MCP registry actually returns and contrast it with the servers used in tool-use benchmarks. The work highlights how these sampling choices can distort findings about the ecosystem and about agent tool-use performance.

mcp/benchmarks/agents/evaluation