dense-armor 1.1.22

Runtime shield for AI signals and robotic commands. No retraining. No magic — just adaptive damping and control verified with real tests.

📖 Full documentation, API reference, quick guide →

$ what it does

A sensor that sends lost readings (NaN) or spits out an absurd value (1e6 instead of 1.2) silently breaks any downstream pipeline. Dense-Armor steps in between the raw data and the model that consumes it:

  corrupted data ──► [ INPUT SHIELD ] ──► AI model ──► [ OUTPUT SHIELD ] ──► clean output
                     purifies vs             │             purifies vs
                     reference               │             response-to-reference
                     (or robust blind        │             (or self-consistency)
                     estimate)

• Input: cleans the corrupted data toward a clean reference, if you have one — or toward a robust estimate derived from the data itself, if you do not.
• Output: checks that the model's response is not itself corrupted, comparing it with the response the model would give to the clean reference.
• Error margin: for each corrected value, returns how much it was shifted to clean it. Small correction → trust it. Large correction → treat with caution.

It leaves the weights untouched. It runs at runtime. It works from 1D up to 11D, tested (see test/test_orca2.py).

Same discipline, a second domain: from $ rate_limiter onward the package also covers command and trajectory safety for real robots — velocity/acceleration limits, spatial control barrier functions, minimum-jerk trajectory generation, rigid dynamics from real URDF (including .xacro, including <mimic> joints), passivity+CBF controllers up to 6-DoF. JAX backend shared with Armatura/Orca, same verification discipline on real data/robots — a different application domain, not a different package.

$ install

pip install dense-armor                 # core: numpy + jax
pip install "dense-armor[quantum]"      # + Dense-Evolution (NISQ simulator)
pip install "dense-armor[audio,data]"   # + WAV, HDF5, NetCDF

# mandatory before every import — required by 64-bit gating
import jax
jax.config.update("jax_enable_x64", True)

# equivalent from PowerShell, before launch
$env:JAX_ENABLE_X64="True"

To run the test suite locally (by cloning the repo; not needed if you only installed from pip):

pip install -e ".[dev]"
pytest test/ -v

$ quickstart

python -m dense_armor --json 1.2 1.3 9999 1.25 nan 1.3

> anomaly @ index 2 (peak 9999)
> anomaly @ index 4 (NaN)
> everything else: intact

Connected to a real model:

from dense_armor.protect.orca import Orca

orca = Orca()
protected_output = orca.protect_and_forward(
    my_model,                                 # callable: x -> output (JAX/NumPy)
    corrupted_data,                           # tensor from the sensor/pipeline
    x_reference=clean_reference_data,         # optional but recommended
)

orca.margine_ingresso_medio, orca.margine_uscita_medio   # how much to trust

1D series (training loss, metrics, token stream):

from dense_armor import Armatura

a = Armatura(livello_ia=0.0)   # 0 = actively filters · 1 = only flags
clean, K, anomalies = a.analizza(series)

$ internals

Two distinct engines, depending on where you enter:

Armatura (1D series: loss, metrics, sensors)   Orca (input+output shield for an entire AI model)
─────────────────────────────────────────      ─────────────────────────────────────────────────────
core/hybrid_engine.py                           STAGE 1  core/engine.py
binary-trigger engine (phi_ab/dynamic           adaptive stabilizer, dynamic threshold on
vector), verified in Dense-Evolution and        recent volatility
adapted to scalar signals at free scale         STAGE 2  utility/collatz.py
                                                  Collatz-based gating, decides HOW MUCH
                                                  to damp toward the clean reference

Armatura.analizza() decides point by point, with no intermediate degrees: a value is either a genuine change (it passes) or isolated noise/spike (replaced with the local baseline — the recent window if you have no reference, your explicit reference if you pass one).

Orca.protect_and_forward() (complete shield for a model) still uses the two original stages. Without a clean reference (blind mode), before falling back to the local estimate Orca searches its own memory of past clean references (by shape, via apply_fast_resonance — the same resonance described later in the toolkit) for a reference similar to the current corrupted input; if it does not find one, rejection of severe outliers via local median, then Stage 1 in causal version — it uses the entire history of the series, not only immediate neighbors, to estimate what that point "should" be. Every time a clean reference is passed explicitly, it stays in memory for subsequent blind calls (reference_memory_size, default 32 per shape).

Before every heavy chunk, Orca also checks available RAM/VRAM via UniversalMemoryGuard (see toolkit below): below the critical threshold it raises MemoryPressureError instead of risking a silent OOM a few chunks later. The chunk size itself (chunk_threshold) is auto-derived from AIHardwareProfiler — it scales with the host's RAM/backend instead of a fixed value equal for every machine — unless you pass an explicit one to the constructor.

Technical note: AdaptiveSignalStabilizer.filter_batch_scenarios (used e.g. by the adversarial test suite) accepts only 2D/3D/4D. Orca's input shield is a different path, without that limit.

$ orca --use_arbiter

Orca.protect_and_forward(..., use_arbiter=True) — optional, default False — routes each point to the right corrector instead of forcing a single one over the whole row:

orca = Orca()
protected_output = orca.protect_and_forward(my_model, corrupted_data, use_arbiter=True)

orca.etichette_arbitro       # array 'clean'/'spike'/'regime', one per point
orca.incertezza_arbitro_media   # 0..1: how ambiguous the classification itself is
orca.tipi_corruzione_visti(corrupted_data.shape)   # Counter, populated when x_reference is known

spike (isolated impulse) → hard rejection toward the median of a wide reference window; regime (sustained level change, recognized by looking at length and internal consistency of the sequence of anomalous points) → raw value, full trust; clean → whatever the standard 4-phase shield has already produced, not the raw value — a continuous non-anomalous signal still needs the soft damping of AdaptiveSignalStabilizer.

Verified on the 7 scenarios of test/testKalman.py (test/test_arbiter_orca_integration.py): never worse than the default, better on 5/7 (isolated impulses, below-threshold corruption, heavy-tailed noise, level break, NaN data gap), equal on one case, identical on the remaining one by correct design (continuous structured signal → falls back to the standard shield). Details, chronology of a real bug found and fixed (symmetric → causal reference window) in the changelog and in the docstring of utility/arbiter.py.

$ streaming --realtime

A real robot runs at 30-100Hz and cannot wait for an already-recorded array. StreamingDeviationDetector (dense_armor.anomaly.streaming) brings to zero latency only the causal half of classify_segments — the per-point deviation flag, not the final spike/regime label, which requires looking ahead in the sequence and remains a batch question by design:

from dense_armor.anomaly.streaming import StreamingDeviationDetector

det = StreamingDeviationDetector(radius=10, ref_mult=3, n_sigmas=3.0)
for x in sensor_stream:
    is_deviant = det.update(x)

MultiChannelStreamingDeviationDetector and classify_segments_multichannel apply the same already-validated logic to multiple independent channels (the joints of a robotic arm, the axes of an IMU) without requiring a manual loop — each channel keeps its own reference window. Promoted by Dense-Evolution-Discovery after validation on two independent real physical domains (SO-101 robotic arm, real human IMU) — the same discipline already used for stable_frame_filter.py and velocity_gated_stable_mask. Full documentation (auto-generated from the real docstrings) on the site.

$ cusum --detectability

A drift too slow to exceed, point by point, the instantaneous threshold of classify_segments escapes Arbiter by design. cusum_detector (dense_armor.drift.cusum) accumulates small deviations over time instead of judging each point in isolation:

from dense_armor.drift.cusum import cusum_detector, detectability_report

flagged, cusum = cusum_detector(x, radius=10, ref_mult=3, k=0.5, h=20.0)

report = detectability_report(local_noise_scale=local_mad, k=0.5, h=5.0, candidate_shift=10.0)
# {'false_alarm_arl': ..., 'detection_arl': ..., 'shift_in_sigma': ...}

detectability_report estimates before running a benchmark how many samples are needed to detect a given shift given the detector's real local noise -- Reynolds (1975)/Siegmund (1985) theory, promoted by Dense-Evolution-Discovery after validation on two independent real physical domains (lidar, accelerometer): on the lidar the real latency always beats the theoretical estimate; on the accelerometer the result is genuinely mixed -- documented as is, not forced to coincide. Full documentation (auto-generated from the real docstrings) on the site.

$ calibration --platt

OnlinePlattScaling (dense_armor.learn.calibration, pip install dense-armor[river]) wraps any river classifier and recalibrates its probabilities one sample at a time, following Algorithm 1 of Gupta and Ramdas (ICML 2023, arXiv:2305.00070):

from dense_armor.learn.calibration import OnlinePlattScaling

model = OnlinePlattScaling(tree.HoeffdingTreeClassifier())

On Phishing the log-loss of a Hoeffding tree drops from 0.4535 to 0.3502. Full documentation on the site.

$ metric_learning --knn

dense_armor.learn.metric_learning (pip install dense-armor[river]) learns a k-NN distance online with OASIS, LEGO or POLA, ported from their papers, and plugs it into river through MetricKNNClassifier:

from dense_armor.learn.metric_learning import MetricKNNClassifier, POLA

model = MetricKNNClassifier(POLA(), n_neighbors=5)

Full documentation on the site.

$ rate_limiter --damping

A robotic arm cannot execute an unlimited instantaneous jump without risk -- rate_limited_follower (dense_armor.control.rate_limiter) limits how fast an applied command can physically change (velocity + acceleration), instead of trying to classify whether a deviation is real:

from dense_armor.control.rate_limiter import rate_limited_follower

applied = rate_limited_follower(raw_command, max_vel=2.0, max_accel=1.0)

Based on Berscheid & Kroger (2021), "Jerk-limited Real-time Trajectory Generation" (RSS 2021, arXiv:2105.04830) — causal by construction, verified directly. Promoted by Dense-Evolution-Discovery after validation on two independent real physical domains (SO-101, 14-DOF bimanual ALOHA): it always wins (400/400 real trials) on the real safety metric (maximum instantaneous jump), but it is not a signal cleaner — on average fidelity (RMSE) the picture is genuinely mixed between the two domains, not hidden. Full documentation (auto-generated from the real docstrings) on the site.

$ cbf_filter --spatial

A command that moves at a perfectly safe speed but straight toward an obstacle remains dangerous — rate_limiter limits HOW FAST, cbf_filter limits WHERE:

from dense_armor.control.cbf_filter import cbf_filtered_trajectory

applied = cbf_filtered_trajectory(raw_command, obstacle=5.0, safe_dist=2.0, alpha_gain=2.0)

Based on Ames et al. (2019), "Control Barrier Functions: Theory and Applications" (2019 ECC, arXiv:1903.11199) — same theory as SAFER-Splat, applied to a known geometric obstacle instead of GPU Gaussian-Splatting perception (not available on every machine). A real numerical problem found and solved along the way: the CBF guarantee is continuous in time, sub-steps are needed (20/sample, default) to hold in discrete time on real commands that can jump a lot between one sample and the next. Promoted by Dense-Evolution-Discovery after validation on two independent real physical domains (SO-101, ALOHA): 100% invariance from safe starts on both, minimal invasiveness practically exact (99.9%+ on SO-101, perfectly exact on ALOHA). cbf_safety_filter_live is the same mathematics for a real control loop that reacts to one sensor tick at a time (a real dt, not a pre-recorded array) — promoted after a real ROS2/Ignition live loop needed it and had to rebuild it by hand. Full documentation (auto-generated from the real docstrings) on the site.

$ trajectory --quintic

rate_limiter limits HOW FAST, cbf_filter limits WHERE — but neither generates a reference to follow. quintic_trajectory covers exactly this: a smooth, minimum-jerk path between two points, for any number of joints in a single call:

from dense_armor.control.trajectory import quintic_trajectory

t, q, v, a = quintic_trajectory(q0=[0.0], qf=[10.0], T=2.0)

Deliberately reduced compared to two real papers that propose much larger optimizers (full dynamics, URDF, torques) — Lozer, Scalera, Boscariol & Gasparetto (Robotics and Autonomous Systems) and Fried & Paternain (arXiv:2412.07859), both read in full before writing code — to the simplest and most universal piece: no URDF, no dynamics, no connection to the robot. Promoted by Dense-Evolution-Discovery after validation on two independent real physical domains (SO-101, ALOHA, 20 real joint excursions): the quintic's peak velocity is always lower than the real one recorded for the same start/end/duration — expected, not a bug, since it is the smoothest possible path. Full documentation (auto-generated from the real docstrings) on the site.

$ kinematic_controller --tracking

trajectory generates a smooth reference, but something must turn it into a real command — kinematic_tracking_controller does this, at the same single-integrator scale as rate_limiter/cbf_filter:

from dense_armor.control.kinematic_controller import kinematic_tracking_controller

u_des = kinematic_tracking_controller(q=[0.2], q_ref=[0.5], qd_ref=[1.0], kp=5.0)

u = qd_ref + kp*(q_ref - q) — for the system qdot = u this makes the tracking error exactly edot = -kp*e: exponential convergence in closed form, for any reference trajectory, verified numerically. It is not "passivity-based" in the sense of the papers that motivated this research (Wu & Tan 2025, the real target, behind a paywall with no open copy found; Scruggs, real but requires convex optimization in infinite dimension; Califano et al., real but requires Hamiltonian mechanics) — honest about this, it is simpler. Promoted by Dense-Evolution-Discovery after validation on two real physical domains (SO-101, ALOHA), chained with quintic_trajectory: every real excursion recovers from a declared real initial error and converges. Full documentation (auto-generated from the real docstrings) on the site.

$ rigid_body --urdf

rate_limiter/cbf_filter/trajectory/kinematic_controller all work at the single-integrator level: you give a joint velocity, you receive a safe joint velocity. RigidBodyModel (dense_armor.dynamics.urdf_dynamics) is different — it requires a real physical description of the robot (a real URDF, including .xacro) and returns true torque-level dynamics:

from dense_armor.dynamics.urdf_dynamics import RigidBodyModel
import jax.numpy as jnp

model = RigidBodyModel("panda.urdf")   # or a .xacro path, expanded automatically
q = jnp.zeros(model.n)
M = model.mass_matrix(q)
qdd = model.forward_dynamics(q, jnp.zeros(model.n), jnp.zeros(model.n))

model.n is the number of real, independent degrees of freedom (a joint with <mimic> does not count separately). mass_matrix, gravity_forces, bias_forces and forward_dynamics (solves M(q)*qdd + C(q,qd)*qd + g(q) = tau) use the standard Lagrangian construction via autodiff (jax.grad/jax.jvp), not hand-written Christoffel symbols. link_position/link_jacobian/link_pose/link_spatial_jacobian work for any link named in the URDF, not only the end effector.

Promoted by Dense-Evolution-Discovery (Experiment 62) after validation on three independent real robots (Kinova Gen3 7-DoF, Kinova Gen3 6-DoF, Franka Emika Panda — different manufacturer, prismatic joints): symmetric/positive-definite mass matrix and energy conservation with correct RK4 convergence on all three. Full documentation on the site.

$ passivity_cbf --controller

RigidBodyModel gives M(q), gravity and forward dynamics for any robot — solve_control_qp (dense_armor.dynamics.passivity_cbf_controller) uses them to drive that robot toward an operational-space target safely, guaranteeing passivity of the tracking error and distance from kinematic singularities, both as constraints in a small QP:

from dense_armor.dynamics.urdf_dynamics import RigidBodyModel
from dense_armor.dynamics.passivity_cbf_controller import solve_control_qp

model = RigidBodyModel("panda.urdf")
qdd, tau, mu, h = solve_control_qp(model, "panda_hand", q, qd, p_des, pd_des, pdd_des, eps=0.03)

eps is the minimum manipulability index that the controller maintains — mu (returned) never drops much below it, even when the commanded target would otherwise push the robot straight into a singularity. The real position/velocity limits of each joint (from the URDF <limit> tag) are a third constraint, added only where the robot actually declares them.

Based on Kurtz, Wensing & Lin (2021, arXiv:2109.13349). Promoted by Dense-Evolution-Discovery (Experiment 61→63) after validation on the same three robots as RigidBodyModel, each pushed toward its own real singularity: manipulability maintained within 0.1-1.8% of the declared threshold in every case. A real OSQP bug found and solved along the way (infeasibility of the passivity+CBF QP, solved by falling back to CBF alone). Full documentation on the site.

$ six_dof_cbf --pose

passivity_cbf_controller tracks only the position of a link. six_dof_pbc_cbf_controller.solve_control_qp extends the same QP to the full pose — position and orientation together — using the link's 6xN spatial Jacobian instead of only the translation Jacobian:

from dense_armor.dynamics.six_dof_pbc_cbf_controller import solve_control_qp

qdd, tau, mu, h = solve_control_qp(model, "panda_hand", q, qd, p_des, pd_des, pdd_des,
                                    r_des, w_des, wd_des, eps=0.03)

r_des is the desired orientation (rotation matrix), w_des/wd_des the desired angular velocity/acceleration in world frame. The orientation error uses the SO(3) formula of Lee, Leok & McClamroch (2010) — smooth everywhere, without the real gimbal lock of a roll-pitch-yaw formulation.

Promoted by Dense-Evolution-Discovery (Experiment 65). Validated with exact gravity compensation at zero error (machine precision) and real closed-loop convergence (initial offset 10cm/30°, RK4 over 1000 ticks, final error 1e-6 m / 1e-4 rad) — then on the same three robots as passivity_cbf_controller, where a second real OSQP infeasibility emerged (6-DoF manipulability can be well below the 3-DoF one at the same configuration) solved with a third fallback level (CBF alone, without box). Full documentation on the site.

$ xacro --macros

Manufacturers publish robot descriptions as .xacro macros (parameterized blocks, mathematical expressions, xacro:include), not as flat URDF. RigidBodyModel now accepts .xacro directly:

model = RigidBodyModel("panda_arm_hand.urdf.xacro")
model.n   # 8 -- 7 arm joints + 1 independent gripper coordinate

The real xacro package (the same expander from the ROS ecosystem, no ROS installation required) does the expansion — nothing about macros/math/conditionals is reimplemented here.

Promoted by Dense-Evolution-Discovery (Experiment 66), which found and solved a real inconsistency in the published Franka Panda macros (clvrai/furniture): a hand attachment link commented out in the arm macro but required by the hand one. New dependency: xacro. Full documentation on the site.

$ mimic --joints

The two fingers of a gripper move together: closing one also closes the other. URDF expresses this with a <mimic joint="..." multiplier="..." offset="..."/> tag — the slave joint angle is always multiplier * master_angle + offset, never a free variable. RigidBodyModel now respects this instead of giving the slave joint its own independent coordinate:

model.n                                    # 8, not 9 -- the two fingers share a single real DOF
model.mimic_map["panda_finger_joint2"]     # (master_dof_idx, multiplier, offset)

Forward kinematics substitutes q[master] * multiplier + offset for the mimic joint angle; the hand-built geometric Jacobian scales the mimic joint's local column by multiplier and sums it into its master's column, instead of giving it its own column.

Promoted by Dense-Evolution-Discovery (Experiment 67), verified against a real central finite difference of link_pose (not just plausibility): moving the master by 0.02 moves both fingertips by exactly 0.02 in opposite directions, and the hand-written Jacobian matches the numerical derivative within 1e-5. Full documentation on the site.

$ mcp --server

An MCP server (dense_armor.mcp_server) exposes 8 tools — dense_armor_health, dense_armor_clean_signal (full Orca, with use_arbiter), dense_armor_detect_anomalies (classification only), dense_armor_robust_filter, dense_armor_heal_series, plus dense_armor_stream_start/_update/_end (stateful session for a real-time sensor stream, one multiple channel at a time) — so an agent (Claude Code, Claude Desktop, or any MCP client) can clean a series, or follow a live sensor stream, without writing Python. Direct and in-process (no separate HTTP kernel, unlike the Dense-Evolution adapter — Dense-Armor has no web UI to share):

pip install -e ".[mcp]"
claude mcp add dense_armor -- dense-armor-mcp

It intentionally lives under dense_armor.mcp_server, not a simple mcp_server — with Dense-Evolution installed in the same environment (its adapter is named exactly mcp_server), a non-nested name is a real, verified collision, not a hypothesis. See dense_armor/mcp_server/README.md for the complete tool list and test/test_mcp_server.py for the end-to-end verification of each one.

$ robust_filters --standalone

Four classic anomaly detectors (dense_armor.anomaly.robust_filters), independent of Armatura/Orca — no dynamic model, no state, only arithmetic on a centered local window (intended for offline/batch cleaning, not the real-time causal loop; also suitable for a future embedded port, where one will not be able to rely on numpy):

from dense_armor.anomaly.robust_filters import pressure_valve

clean, anomalies, pressure, effective_threshold = pressure_valve(series)

pressure_valve combines Chauvenet's criterion (1863), Tukey's fences/IQR, the Hampel filter and iterative sigma-clipping — not with a vote (how many of the 4 flag a point), but with the classic minimum-variance combination (BLUE estimator): each method produces a local (center, scale) pair, and the weights are derived with a Lagrange multiplier (minimizes the variance of the weighted combination, constraint Σw=1 → w_k ∝ 1/scale_k²) — a method whose uncertainty inflates (e.g. Chauvenet when the window already contains an outlier, its mean/std are not robust) is automatically weighted less, without discarding it by hand. Final decision always binary (flagged/replaced with the local median, or intact).

The threshold itself is not fixed: the local window is compared with a wider one via Jensen-Shannon divergence, and it widens when the two distributions diverge (a real regime transition, not noise) — never the opposite, effective_threshold >= pressure_threshold always.

The four individual methods also remain callable one by one (chauvenet_criterion, tukey_fences, hampel_filter, sigma_clip) if a single criterion is needed instead of the combination.

$ toolkit --standalone

Under core//utility/ there is also a second part of the package, largely independent of Armatura/Orca — most of these modules do not participate in the anomaly shield, they are tools in their own right that only share the JAX/NumPy backend. Three exceptions: UniversalMemoryGuard, apply_fast_resonance and AIHardwareProfiler, also called by Orca (see $ internals above) — they remain usable standalone anyway. Full documentation (auto-generated from the real docstrings) on the site; here is the summary.

Pipeline and chunking (dense_armor.core)

• DynamicAICodegen — compiles a list of operation names (relu, sigmoid, tanh, scale, dropout, clip, l2_normalize, identity) into a JAX JIT-compiled pipeline via lax.switch, with block execution for long lists and gradient via autodiff (compute_gradients).
• ImageChunker (dense_armor.core.chunk) — splits/recomposes a large batch into fixed-size blocks, both for data arrays and for compiled instruction lists.
• UniversalMemoryGuard — checks RAM (and VRAM, if there is an NVIDIA GPU) before a heavy allocation, computes the number of blocks needed to fit; raises MemoryPressureError below threshold. Also used by Orca before every heavy chunk.

Hardware and profiling (dense_armor.core)

• AIHardwareProfiler — detects available CPU/RAM/backend and computes a safe maximum tensor size for the current host. Also used by Orca to auto-derive chunk_threshold (scales proportionally to the host instead of a fixed value equal for every machine; passing an explicit value overrides it without even instantiating the profiler).
• StochasticAdversarialNoise — injects synthetic noise (bitflip, dropout, Gaussian blur) into a tensor, preserving its norm; useful for generating attack data when one wants to test a detector.
• PipelineProfiler — measures JIT latency in microseconds (compilation warm-up separated from steady-state time) of a DynamicAICodegen pipeline or of AdaptiveSignalStabilizer.

Tensors and configurations (dense_armor.core)

• TensorVault — library of static (invert, identity, edge_detector, blend) and parametric (scale_project, amplify, bias_shift) transformation matrices, auto-detected backend/precision.
• ParametricScenarioSimulator — parallel Monte Carlo simulations (jax.vmap) on a scalar state over time, plus a stochastic decision collapse conditioned by the distribution.
• BitwisePermutationEngine — permutes the elements of a combinatorial vector (2^n space) via target/control bit masks.
• SIGNAL_STABILIZER_PRESETS (dense_armor.core.preset) — 4 calibrated configurations (balanced_v2, cifar10_best_v1, pure_1d_time_v1, cifar10_hardened_lyapunov) for the parameters of AdaptiveSignalStabilizer.

Logging and provenance (dense_armor.core)

• MinimalConsoleFormatter / CompactJsonFormatter (dense_armor.core.logger) — two logging.Formatters: one readable at the console, one compact JSON for files.
• AIEngineVisualizer — exports a SHA-256 signed provenance archive (parameters, execution environment, integrity hashes) and textual reports of raw/filtered variance.

Audio and data I/O (dense_armor.utility)

• anwav(fpath) — analyzes a WAV file: peak, RMS, estimated loudness (LUFS), crest factor, with a compliance verdict.
• diag(iorig, ifilt) — differential comparison between two audio signals (file paths or NumPy arrays): structural fidelity, removed energy, distortion peak.
• lodat(fpath, dname) (dense_armor.misc.iodat) — reads a tensor from an HDF5 or NetCDF file.
• apply_fast_resonance(matrix, query) (dense_armor.anomaly.resonance_search) — cosine similarity score between a query and the rows of a matrix, modulated by apply_damping_blend (the same operator used by Orca). Also used by Orca in blind mode to recall a similar clean reference already seen in the past (see $ internals above).

Each tested individually (test/test_chunk.py, test_compiler.py, test_memory.py, test_preset.py, test_tensor.py, test_noise.py, test_vector.py, test_profiler.py, test_visualizer.py, test_logger.py, test_anwav.py, test_diagnostic.py, test_iodat.py, test_resonance_search.py). Requires pip install "dense-armor[audio,data]" for anwav/diagnostic (scipy) and iodat (h5py/netCDF4).

from dense_armor.core import DynamicAICodegen, UniversalMemoryGuard, TensorVault, AIHardwareProfiler
from dense_armor.core.chunk import ImageChunker
from dense_armor.core.preset import SIGNAL_STABILIZER_PRESETS
from dense_armor.misc.anwav import anwav
from dense_armor.misc.iodat import lodat

$ error margin

orca.margine_ingresso      orca.margine_ingresso_medio      orca.margine_ingresso_max
orca.margine_uscita        orca.margine_uscita_medio        orca.margine_uscita_max

|received value − corrected value| — how much the shield had to shift a datum to clean it. It is not a covariance calibrated in the strict statistical sense, but it correlates well in tests: low when the correction is reliable, high when the shield is guessing blindly.

Complementary signal, not redundant, with use_arbiter=True: orca.incertezza_arbitro/incertezza_arbitro_media tells how ambiguous the classification itself is (near the deviant/clean or spike/regime boundary), not how large the correction was — a small correction with high uncertainty is an ambiguous case that went well by chance, not a truly safe one. See $ orca --use_arbiter above.

$ vs kalman-filter --honest

It does not replace a Kalman filter — they solve different problems, period.

Random walk, 15% missing data, 3% huge spikes:

+ against an unprotected Kalman (the most common scenario in practice): wins clearly
+ a single huge spike throws off the Kalman gain and drags it along behind itself
+ zero setup: no process model to know, estimate or calibrate (no Q/R)
+ also works where Kalman does not apply at all: images, embeddings, generic tensors

The advantage is freedom, not specialization. A well-designed Kalman filter, calibrated on a known dynamic process, remains more precise on that single use case — but it requires knowing the system model in advance and recalibrating it for every new type of data. Dense-Armor is a general filter: no customization, no a priori knowledge required, it applies as is to any tensor (temporal or not). The price of this freedom is a bit less precision in the specific case where a known and calibrated dynamic model already exists — a small loss (~0.23 vs ~0.12 MSE in our test) compared to the advantage of never having to configure anything.

$ adversarial robustness --tested

9 shared engine tests run all the way through, no crash, no NaN escaped. No defense ever below 64%. Real code, not just reported numbers: test/test_boundA.py–test_boundE.py — the same attacks (PGD/BIM/MI-FGSM, affine/elastic, Carlini-Wagner, DeepFool, Fourier) run against dense_armor.core.engine.AdaptiveSignalStabilizer, not a separate engine for the benchmark.

Honest: C&W in L∞ norm is the attack that breaks through the most among those tested. It is not a failure — it remains real protection — but it is the crack closest to a failure among all the tests done, and it must be known before relying on it against that specific scenario.

$ limits --known

1. semantics       distinguishes geometric deviations, not meanings
2. blind mode without reference: good for avoiding collapses/NaNs, not for
                    accurately reconstructing data that is truly lost
3. generality      zero calibration required, applies to any tensor --
                    at the cost of a pinch of precision where there already is a
                    known and calibrated dynamic model (e.g. Kalman on pure series)
4. slow drift      invisible point by point, requires reference=historical_baseline
5. C&W L-inf norm  the weakest defense measured so far (64%, against 79-83%
                    of the other attack variants tested) -- see table above
6. adaptive        attacks built specifically to mimic the coherence of the
   adversarial      clean signal (in addition to PGD/BIM/MI-FGSM/C&W/DeepFool/Fourier
                    already tested) not yet covered by the suite

Real cause of point 5, not just the number: investigated in depth, not yet solved. C&W in L-inf norm constructs a spatially smooth perturbation over the entire grid in one shot (global gradient optimization). No purely local coherence check (comparing a point with its immediate neighbors, what this engine uses) can distinguish a genuinely smooth spatial structure from one built specifically to look like it -- it is the exact same statistical signal. Three targeted interventions were tried and empirically verified (rate-limit on volatility, long-term coherence anchor, rigid leash on maximum drift): none moved the number, one even made it worse. It is not a tuning limit -- an external reference is needed (not only local spatial context) to truly solve it.

$ license

Business Source License 1.1 — free non-commercial use, converts to Apache 2.0 on 2029-06-01. See LICENSE.md.

© 2026 Salvatore Pennacchio <jtatopenn@libero.it>

Twin project of Dense-Evolution (NISQ quantum circuit simulator).