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--> [Submitted on 8 Jul 2026] Title:Pattern-Derived Visual Swarm Games: Multi-Scale Drone-Vision States for Interception and Sustainability Audits View a PDF of the paper titled Pattern-Derived Visual Swarm Games: Multi-Scale Drone-Vision States for Interception and Sustainability Audits, by Faruk Alpay and 1 other authors View PDF HTML (experimental) Abstract:We convert drone-vision annotation streams into virtual swarm-game states without controlling physical drones. VisDrone and UAVSwarm metadata are compressed into a Bloom representation; deterministic probes produce bounded capability vectors, image-space formations, finite zero-sum payoffs, and human-readable visual overlays. The audit scales from $6\times 6$ to $32\times 32$ finite games and adds a repeated Markov layer with stock, fatigue, adaptation, exposure, stress, budget, data-growth, model-improvement, and entropy-budget state variables. Local screen tuning raises robust screen security from $0.526$ to $0.593$, and the $32\times 32$ tuned screen reaches value $0.616$. A field readout audit shows that fixed-pixel rasters do not improve monotonically: $128\times 128$ accuracy is $67.2\%$ and hotspot error is $0.136$. The diagnosed error is shrinking image-plane bandwidth. A finite empirical-risk encoder over scale-normalized Gaussian bandwidths selects a scale-normalized encoder with $\lambda=1.50$, reaching $77.6\%$ accuracy at $128\times 128$ and reducing joint loss by $0.185$. A server-side audit checks $16{,}777{,}216$ target-localization states, and a 32-round repeated-game audit over $16{,}777{,}216$ trajectories selects a budget-adaptive policy with value $0.461$. Comments: 20 pages, 8 figures. Ancillary files include reproducibility code, dataset manifests, numerical tables, remote audit outputs, and Lean/Coq formal checks Subjects: Robotics (cs.RO); Computer Science and Game Theory (cs.GT); Image and Video Processing (eess.IV) MSC classes: 68T45, 68U10, 91A10, 91A15, 91A20, 94A17 ACM classes: I.2.10; I.4.8; I.5.4; I.2.11; G.3; F.2.2 Cite as: arXiv:2608.23575 [cs.RO] (or arXiv:2608.23575v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2608.23575 arXiv-issued DOI via DataCite Submission history From: Levent Sarioglu [view email] [v1] Wed, 8 Jul 2026 02:30:15 UTC (7,763 KB) Full-text links: Access Paper: View a PDF of the paper titled Pattern-Derived Visual Swarm Games: Multi-Scale Drone-Vision States for Interception and Sustainability Audits, by Faruk Alpay and 1 other authors View PDF HTML (experimental) TeX Source view license Ancillary-file links: Ancillary files (details): ANCILLARY_README.md Dockerfile Makefile README.md code/markov_game.py code/render_tactical.py code/sketch.py code/swarm_game.py data/manifests/local_downloads.json data/manifests/source_manifest.json dockerignore.txt docs/dataset_sources.md formal/README.md formal/coq/SwarmRisk.v formal/lean/SwarmRisk.lean requirements.txt results/figures/fig_bloom_game_summary.pdf results/figures/fig_bloom_game_summary.png results/figures/fig_field_resolution_audit.pdf results/figures/fig_field_resolution_audit.png results/figures/fig_field_resolution_optimization.pdf results/figures/fig_field_resolution_optimization.png results/figures/fig_large_field_overlay.pdf results/figures/fig_large_field_overlay.png results/figures/fig_markov_entropy_budget.pdf results/figures/fig_markov_entropy_budget.png results/figures/fig_multiscale_summary.pdf results/figures/fig_multiscale_summary.png results/figures/fig_tactical_before_after.pdf results/figures/fig_tactical_before_after.png results/figures/fig_tactical_overlay.pdf results/figures/fig_tactical_overlay.png results/remote_gpu/dataset_strategy_audit.csv results/remote_gpu/dataset_strategy_audit.json results/remote_gpu/field_resolution_audit.json results/remote_gpu/formal_checks.json results/remote_gpu/gpu_game_theory_sweep.json results/remote_gpu/gpu_live_nvidia_smi_sample.txt results/remote_gpu/markov_entropy_audit.csv results/remote_gpu/markov_entropy_audit.json results/remote_gpu/server_download_manifest.txt results/summaries/experiment_summary.json results/tables/cv_regime_benchmark.csv results/tables/ethics_stress_audit.csv results/tables/field_bandwidth_candidates.csv results/tables/field_numeric_certificates.csv results/tables/field_resolution_audit.csv results/tables/field_resolution_optimization.csv results/tables/field_resolution_samples.csv results/tables/formation_payoff_matrix_seed17.csv results/tables/literature_language_comparison.csv results/tables/markov_policy_summary.csv results/tables/multiscale_swarm_summary.csv results/tables/reference_evidence_audit.csv results/tables/seed17_tactical_audit.csv results/tables/seed17_tactical_summary.csv results/tables/source_ablation_seed17.csv results/tables/swarm_game_summary.csv results/tables/visual_regime_samples.csv scripts/materialize_experiments.py server/bootstrap.sh server/dataset_strategy_audit.py server/download_datasets.sh server/field_resolution_audit.py server/gpu_game_theory_sweep.py server/markov_entropy_audit.py server/run_dataset_strategy_audit.sh server/run_field_resolution_audit.sh server/run_formal_checks.sh server/run_gpu_game_theory_sweep.sh server/run_markov_entropy_audit.sh (66 additional files not shown) Current browse context: cs.RO new | recent | 2026-08 Change to browse by: cs cs.GT eess eess.IV References & Citations NASA ADS Google Scholar Semantic Scholar Loading... 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