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ARXIV:2603.16729 · BENCHMARKING COMPLEX SYSTEMS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.16729BENCHMARKING COMPLEX SYSTEMSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
GeMA offers a novel framework for benchmarking complex systems using a latent manifold approach.
Opportunity summary
Pain GeMA offers a novel framework for benchmarking complex systems using a latent manifold approach.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
GeMA offers a novel framework for benchmarking complex systems using a latent manifold approach. Classical frontier methods, notably Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), estimate an efficient frontier in the observed…
Benchmarking the performance of complex systems such as rail networks, renewable generation assets and national economies is central to transport planning, regulation and macroeconomic analysis. Classical frontier methods, notably Data Envelopment Analysis (DEA) and…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Efficiency is evaluated with respect to the learned manifold, endogenous peer groups arise as clusters in latent technology space, a quotient construction supports scale-invariant…
Benchmarking Complex Systems moved forward this cycle; last verified April 2026. Public score 4.0/10.
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GeMA offers a novel framework for benchmarking complex systems using a latent manifold approach.
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10.48550/arXiv.2603.16729GeMA offers a novel framework for benchmarking complex systems using a latent manifold approach.
Abstract
Benchmarking the performance of complex systems such as rail networks, renewable generation assets and national economies is central to transport planning, regulation and macroeconomic analysis. Classical frontier methods, notably Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), estimate an efficient frontier in the observed input-output space and define efficiency as distance to this frontier, but rely on restrictive assumptions on the production set and only indirectly address heterogeneity and scale effects. We propose Geometric Manifold Analysis (GeMA), a latent manifold frontier framework implemented via a productivity-manifold variational autoencoder (ProMan-VAE). Instead of specifying a frontier function in the observed space, GeMA represents the production set as the boundary of a low-dimensional manifold embedded in the joint input-output space. A split-head encoder learns latent variables that capture technological structure and operational inefficiency. Efficiency is evaluated with respect to the learned manifold, endogenous peer groups arise as clusters in latent technology space, a quotient construction supports scale-invariant benchmarking, and a local certification radius, derived from the decoder Jacobian and a Lipschitz bound, quantifies the geometric robustness of efficiency scores. We validate GeMA on synthetic data with non-convex frontiers, heterogeneous technologies and scale bias, and on four real-world case studies: global urban rail systems (COMET), British rail operators (ORR), national economies (Penn World Table) and a high-frequency wind-farm dataset. Across these domains GeMA behaves comparably to established methods when classical assumptions hold, and provides additional insight in settings with pronounced heterogeneity, non-convexity or size-related bias.
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Dimensions overall score 4.0
PROBLEM
GeMA offers a novel framework for benchmarking complex systems using a latent manifold approach. Classical frontier methods, notably Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), estimate an efficient frontier in the observed input-output space and defi...
METHOD
Benchmarking the performance of complex systems such as rail networks, renewable generation assets and national economies is central to transport planning, regulation and macroeconomic analysis. Classical frontier methods, notably Data Envelopment Analysis (DEA) and Stochastic F...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Efficiency is evaluated with respect to the learned manifold, endogenous peer groups arise as clusters in latent technology space, a quotient construction supports scale-invariant benchmarking, and a loca...
WHY NOW
Benchmarking Complex Systems moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
GeMA offers a novel framework for benchmarking complex systems using a latent manifold approach. Classical frontier methods, notably Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), estimate an efficient frontier in the observed input-output space and define efficiency as distance to this frontier, but rely on restrictive assumptions on the production set and only indirectly address heterogeneity and scale effects.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Benchmarking the performance of complex systems such as rail networks, renewable generation assets and national economies is central to transport planning, regulation and macroeconomic analysis. Classical frontier methods, notably Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), estimate an efficient frontier in the observed input-output space and define efficiency as distance to this frontier, but rely on restrictive assumptions on the production set and only indirectly address heterogeneity and scale effects.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Efficiency is evaluated with respect to the learned manifold, endogenous peer groups arise as clusters in latent technology space, a quotient construction supports scale-invariant benchmarking, and a local certification radius, derived from the decoder Jacobian and a Lipschitz bound, quantifies the geometric robustness of efficiency scores.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Benchmarking Complex Systems moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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GeMA offers a novel framework for benchmarking complex systems using a latent manifold approach.
Segment
Benchmarking Complex Systems
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