evidence
Research
Researching Hidden Computational Instability.
Convia combines systems research, reproducible validation, runtime metrics, and mathematical programs to study how execution-level variability, redundant computation, and structural instability emerge — and how computational order can be restored.
Research areas
Scientific, experimental, reproducible.
Structural Runtime Research
- Structural Instability
- Execution Variability
- Recomputation Analysis
Coordination & Stabilization
- Distributed Coordination
- Execution Control Architecture
- Computational Order Restoration
Validation
The research page shows actual validation surfaces.
The public Argus validation protocol records concrete runtime metrics and execution context rather than relying on philosophical claims alone.
Benchmark protocol
seed, steps, repeat count, warmup steps, workload nodes, requests per step, scenario config
Argus / execution-instability-bench
Schema-based benchmark output
instability_metrics.json, scenario_info, raw_metrics, derived_metrics, artifacts, notes
execution-instability-bench
Runtime environment checks
cpu_load_percent, memory_usage_percent, gpu_temperature, gpu_utilization
Argus reliability checks
Observation record
metrics.json, report.md, run_meta.json, and optional sanitized execution exports
Argus output package
Validation evidence
Observing execution variance across repeated runs.
By tracking identical workloads across multiple runs, Argus reveals the underlying environmental noise, migration propagation, and structural instability that raw performance metrics obscure.
repeated-run variance plotmigration propagation heatmap
run 01
run 02
run 03
run 04
evidence
Migration propagation
evidence
Runtime fluctuation
Metrics
Benchmark dimensions are explicit.
Execution VarianceExecution Path VariabilityReuse FailureRetry AmplificationRecompute RatioObserved WorkReference WorkWork InflationStructural Stability ScoreMigration PropagationP95 latencyP99 latencyThroughputMigration countRetry countInvalidation countError rateOOM countDeadlock countCrash countPeak memoryAverage memory
argus_config.yaml
seed: 42
steps: 500
repeat_count: 5
warmup_steps: 20
ricci: [off, on]
workload:
nodes: 8
requests_per_step: 128Papers
Preprints and mathematical program records.
Convia research combines empirical execution observation with mathematical approaches to structure, instability, regularization, and computational order.
Execution Instability Empirical Study
An Empirical Study on Execution-Level Variability and Redundant Computation in Computer Systems
Preprint introducing a schema-based measurement framework for execution path variability, reuse failure, work inflation, and recompute ratio across repeated executions.
Record: 10.5281/zenodo.20262763
Convia Mathematical Program I
Ricci–Schrödinger Correspondence (RSC) and the Hodge Problem
Programmatic research exploring flow-based structural regularization and instability reduction through coupled geometric systems.
Record: 10.5281/zenodo.17575221
Convia Mathematical Program II
High-Probability Region Restriction for Cost Reduction
Research exploring probabilistic restriction methods for reducing structural computational overhead under uncertainty.
Record: zenodo.org/records/17932569
Source
Public research artifacts are available.
The execution-instability research line links the paper record to reproducible observation and benchmark repositories.
Observation framework
Argus validation protocol is public.
Argus records runtime metrics and execution context for hidden instability observation.
Explore Argus Repository →Benchmark framework
execution-instability-bench reproduces controlled workload scenarios.
The benchmark provides CPU/GPU-labeled workloads, scenario configs, schema validation, and summary utilities centered on instability_metrics.json.
Explore Benchmark Repository →