FusionCore: Multi-Context Fusion Approaches

• FusionCore is a shared name for three distinct systems: a prototype operator-fusion method for state-space models, a 23-state UKF-based ROS 2 sensor fusion package, and a laser-driven nuclear fusion micro-reactor concept.
• In operator fusion, FusionCore reduces DRAM round-trips and enhances on-chip reuse by fusing tensor operations via entropy-driven scheduling and Hadamard reparameterization.
• In robotics and nuclear applications, FusionCore improves state estimation with adaptive bias correction in a 23-state UKF and proposes novel nano-rod designs for efficient non-thermal fusion.

FusionCore is a shared name used in recent literature for three technically unrelated systems: a prototype operator-fusion methodology for selective state space models, presented as COREY; an open-source ROS 2 sensor-fusion package centered on a 23-state Unscented Kalman Filter; and a laser-driven mixed-fuel nuclear fusion micro-reactor concept based on nanostructured fuel targets (Ma et al., 12 Apr 2026, Kharwar, 24 May 2026, Ruhl et al., 2022). The common label therefore denotes neither a single framework nor a unified research program. Instead, it identifies distinct proposals in sequence-model systems, robotic state estimation, and pulsed fusion engineering.

1. Disambiguation and nomenclature

In the sequence-modeling literature, the term appears in the detailed description of COREY as the “FusionCore operator-fusion methodology (COREY),” a prototype framework for selective state space models (SSMs) of the Mamba family. Its central concern is memory-bandwidth-limited inference caused by fragmented kernels and repeated intermediate tensor materialization (Ma et al., 12 Apr 2026).

In robotics, FusionCore denotes an open-source ROS 2 package that fuses IMU, wheel encoder odometry, GPS, and Visual SLAM pose into a single 100 Hz odometry stream using a 23-state UKF. The package includes explicit gyroscope, accelerometer, and wheel-encoder yaw-rate bias estimation, native GPS handling, outlier gating, and adaptive covariance estimation (Kharwar, 24 May 2026).

In nuclear-fusion research, FusionCore denotes a laser-driven micro-reactor concept introduced by Ruhl and Korn. In that proposal, the fuel itself forms both the fusion “catcher” and an embedded nano-accelerator, with nanoscopic rods driven by ultra-short UV–VUV laser pulses to generate non-thermal ion distributions at near-solid density (Ruhl et al., 2022).

A common misconception would be to treat these as variants of one platform. The cited works instead use the same name for separate domain-specific constructions.

2. FusionCore in selective state space models: operator fusion, entropy, and Hadamard reparameterization

For selective SSMs, the baseline recurrence is written per time step $t$ as

$h_t = A_t\,h_{t-1} + B_t\,x_t,$

$y_t = C_t\,h_t + D_t\,x_t.$

These updates are linear-time in sequence length $L$, i.e. $O(L)$, which is attractive for long-context modeling. The practical bottleneck described in COREY is not asymptotic complexity but kernel fragmentation: pointwise multiply, scan, and reduction often become separate kernels, forcing repeated reads and writes of intermediate tensors to global memory. The stated motivation for FusionCore is therefore to fuse chains of operators into single kernels, reduce DRAM round-trips, increase on-chip reuse, and shift the critical path back toward compute (Ma et al., 12 Apr 2026).

The runtime scheduling signal is activation entropy. Given an activation tensor $Z$ with $N$ values, a fixed-width $K$-bin histogram yields counts $\mathrm{count}_k$ and empirical probabilities $p_k=\mathrm{count}_k/N$. The Shannon entropy estimate is

$h_t = A_t\,h_{t-1} + B_t\,x_t,$0

and the prototype scheduler normalizes it as

$h_t = A_t\,h_{t-1} + B_t\,x_t,$1

Prototype defaults are $h_t = A_t\,h_{t-1} + B_t\,x_t,$2, $h_t = A_t\,h_{t-1} + B_t\,x_t,$3, and EMA smoothing

$h_t = A_t\,h_{t-1} + B_t\,x_t,$4

with $h_t = A_t\,h_{t-1} + B_t\,x_t,$5 (Ma et al., 12 Apr 2026).

Candidate fusion regions $h_t = A_t\,h_{t-1} + B_t\,x_t,$6 are scored by combining normalized entropy, arithmetic intensity, and estimated memory traffic:

$h_t = A_t\,h_{t-1} + B_t\,x_t,$7

Regions with $h_t = A_t\,h_{t-1} + B_t\,x_t,$8 and satisfying resource constraints are fused. The reported scheduler weights are $h_t = A_t\,h_{t-1} + B_t\,x_t,$9, with entropy thresholds $y_t = C_t\,h_t + D_t\,x_t.$0 and default $y_t = C_t\,h_t + D_t\,x_t.$1. After boundary selection, tile sizes are chosen by mapping normalized entropy to the range $y_t = C_t\,h_t + D_t\,x_t.$2 and rounding to the nearest $y_t = C_t\,h_t + D_t\,x_t.$3:

$y_t = C_t\,h_t + D_t\,x_t.$4

Higher-entropy regions therefore receive larger tiles, while lower-entropy regions receive smaller tiles to reduce register pressure (Ma et al., 12 Apr 2026).

The second mechanism is Hadamard reparameterization. Let $y_t = C_t\,h_t + D_t\,x_t.$5 be a normalized Hadamard matrix with $y_t = C_t\,h_t + D_t\,x_t.$6. For an original projection $y_t = C_t\,h_t + D_t\,x_t.$7, COREY inserts an $y_t = C_t\,h_t + D_t\,x_t.$8 pair:

$y_t = C_t\,h_t + D_t\,x_t.$9

Defining $L$0 and $L$1 preserves exact function while mixing coordinates. The description states that this fused reparameterization increases histogram entropy, flattens peaks, and reduces coordinate $L$2 outliers by up to a $L$3 factor through the bound $L$4 (Ma et al., 12 Apr 2026).

3. Fused-kernel design and prototype results for COREY

The fused-kernel design is specified as a single memory-pass pipeline: load a tile of input activations into on-chip shared or SRAM memory; apply the absorbed Hadamard rotation via $L$5; execute linear projections, elementwise updates, and selective scan contributions; update and materialize the selective SSM state $L$6; and write the final output back to global memory. Fusion groups are admitted only if per-group resource constraints satisfy $L$7, $L$8, and occupancy $L$9 (Ma et al., 12 Apr 2026).

Empirical results are reported from a Python-level prototype with synthetic heavy-tailed activations, not from real GPU kernels. In FP16 prototype measurements over five runs, the “Short” bucket reports latency $O(L)$0 ms, throughput $O(L)$1 k tok/s, and DRAM $O(L)$2 B/token for No Fusion; $O(L)$3 ms, $O(L)$4 k tok/s, and $O(L)$5 B/token for Static Fusion; and $O(L)$6 ms, $O(L)$7 k tok/s, and $O(L)$8 B/token for Ours. In the “Medium” bucket, the corresponding values are $O(L)$9 ms, $Z$0 k tok/s, and $Z$1 B/token; $Z$2 ms, $Z$3 k tok/s, and $Z$4 B/token; and $Z$5 ms, $Z$6 k tok/s, and $Z$7 B/token. In the “Long” bucket, they are $Z$8 ms, $Z$9 k tok/s, and $N$0 B/token; $N$1 ms, $N$2 k tok/s, and $N$3 B/token; and $N$4 ms, $N$5 k tok/s, and $N$6 B/token. In the “Ultra-long” bucket, they are $N$7 ms, $N$8 k tok/s, and $N$9 B/token; $K$0 ms, $K$1 k tok/s, and $K$2 B/token; and $K$3 ms, $K$4 k tok/s, and $K$5 B/token, respectively (Ma et al., 12 Apr 2026).

The summary interpretation in the source is that FusionCore reduces DRAM bytes per token by approximately $K$6–$K$7 relative to static fusion, with corresponding latency and throughput gains that increase with sequence length. Low-bit experiments are reported only as a diagnostic stability proxy. In the ultra-long bucket under W4A8, the internal proxy is $K$8 for No Fusion, $K$9 for Static Fusion, and $\mathrm{count}_k$0 for Ours; the source describes this as a reduction of about $\mathrm{count}_k$1 versus static fusion at matched latency, while explicitly stating that checkpoint-level validation remains to be done (Ma et al., 12 Apr 2026).

4. FusionCore in ROS 2: the 23-state UKF architecture

The robotics FusionCore is built around a single, continuous-time twelve-parameter kinematic or dynamic model augmented with eleven bias states, for a total of $\mathrm{count}_k$2 states. The state vector is

$\mathrm{count}_k$3

The components represent ENU position $\mathrm{count}_k$4, a body-to-ENU orientation quaternion $\mathrm{count}_k$5, body-frame linear velocity $\mathrm{count}_k$6, body-frame angular velocity $\mathrm{count}_k$7, body-frame linear acceleration $\mathrm{count}_k$8, gyroscope biases $\mathrm{count}_k$9, accelerometer biases $p_k=\mathrm{count}_k/N$0, and a wheel-encoder yaw-rate bias $p_k=\mathrm{count}_k/N$1 (Kharwar, 24 May 2026).

The continuous-time dynamics use IMU inputs $p_k=\mathrm{count}_k/N$2 at approximately $p_k=\mathrm{count}_k/N$3 Hz:

$p_k=\mathrm{count}_k/N$4

$p_k=\mathrm{count}_k/N$5

with

$p_k=\mathrm{count}_k/N$6

The model is discretized at each IMU step with $p_k=\mathrm{count}_k/N$7 s, using quaternion-exponential integration in

$p_k=\mathrm{count}_k/N$8

This formulation places the inertial biases and the encoder yaw-rate bias inside the filter state rather than in external correction logic (Kharwar, 24 May 2026).

Measurement models are defined for multiple sensor paths. IMU updates are split into raw $p_k=\mathrm{count}_k/N$9-DOF bias-and-gravity updates with

$h_t = A_t\,h_{t-1} + B_t\,x_t,$00

where $h_t = A_t\,h_{t-1} + B_t\,x_t,$01 in ENU, plus optional roll–pitch or roll–pitch–yaw updates from a magnetometer. Wheel encoders provide a $h_t = A_t\,h_{t-1} + B_t\,x_t,$02-DOF body-frame velocity measurement

$h_t = A_t\,h_{t-1} + B_t\,x_t,$03

along with zero-vertical pseudo-measurements $h_t = A_t\,h_{t-1} + B_t\,x_t,$04 and $h_t = A_t\,h_{t-1} + B_t\,x_t,$05. GPS position is handled through a loose-coupling model with $h_t = A_t\,h_{t-1} + B_t\,x_t,$06 and covariance

$h_t = A_t\,h_{t-1} + B_t\,x_t,$07

or direct $h_t = A_t\,h_{t-1} + B_t\,x_t,$08 for gps_msgs/GPSFix. GPS Doppler contributes a $h_t = A_t\,h_{t-1} + B_t\,x_t,$09-DOF ENU velocity update $h_t = A_t\,h_{t-1} + B_t\,x_t,$10. VSLAM pose is fused as a $h_t = A_t\,h_{t-1} + B_t\,x_t,$11-DOF update

$h_t = A_t\,h_{t-1} + B_t\,x_t,$12

with covariance floors $h_t = A_t\,h_{t-1} + B_t\,x_t,$13 m and $h_t = A_t\,h_{t-1} + B_t\,x_t,$14 rad (Kharwar, 24 May 2026).

5. Bias estimation, gating, adaptation, and benchmark results in robotic odometry

A distinctive component is the online estimation of the wheel-encoder yaw-rate bias $h_t = A_t\,h_{t-1} + B_t\,x_t,$15, which is initialized to zero with large prior variance and modeled as a random walk. When GPS heading is available, the cross-covariance between encoder $h_t = A_t\,h_{t-1} + B_t\,x_t,$16 error and GPS-derived bearing innovation drives a Kalman update of $h_t = A_t\,h_{t-1} + B_t\,x_t,$17. During GPS blackouts, the most recent estimate is subtracted from $h_t = A_t\,h_{t-1} + B_t\,x_t,$18 before propagation to mitigate heading drift in coast mode (Kharwar, 24 May 2026).

Outlier rejection uses Mahalanobis $h_t = A_t\,h_{t-1} + B_t\,x_t,$19 gating. For each innovation $h_t = A_t\,h_{t-1} + B_t\,x_t,$20 with predicted innovation covariance $h_t = A_t\,h_{t-1} + B_t\,x_t,$21,

$h_t = A_t\,h_{t-1} + B_t\,x_t,$22

Default thresholds are given as $h_t = A_t\,h_{t-1} + B_t\,x_t,$23, or $h_t = A_t\,h_{t-1} + B_t\,x_t,$24 for fast paths. The explicit values listed are $h_t = A_t\,h_{t-1} + B_t\,x_t,$25 for GPS position with $h_t = A_t\,h_{t-1} + B_t\,x_t,$26, $h_t = A_t\,h_{t-1} + B_t\,x_t,$27 for VSLAM pose with $h_t = A_t\,h_{t-1} + B_t\,x_t,$28, $h_t = A_t\,h_{t-1} + B_t\,x_t,$29 for heading-only with $h_t = A_t\,h_{t-1} + B_t\,x_t,$30, $h_t = A_t\,h_{t-1} + B_t\,x_t,$31 for encoder velocity with $h_t = A_t\,h_{t-1} + B_t\,x_t,$32 and $h_t = A_t\,h_{t-1} + B_t\,x_t,$33, and $h_t = A_t\,h_{t-1} + B_t\,x_t,$34 for IMU with $h_t = A_t\,h_{t-1} + B_t\,x_t,$35 and approximately $h_t = A_t\,h_{t-1} + B_t\,x_t,$36. Sensor noise covariance is adapted from a sliding innovation window of $h_t = A_t\,h_{t-1} + B_t\,x_t,$37 using

$h_t = A_t\,h_{t-1} + B_t\,x_t,$38

with per-diagonal floors $h_t = A_t\,h_{t-1} + B_t\,x_t,$39 to prevent collapse below configured minima (Kharwar, 24 May 2026).

GPS is handled natively by converting WGS84 latitude, longitude, and altitude to ENU about the first fix through PROJ, without an external navsat_transform or UTM zones. Lever-arm corrections use

$h_t = A_t\,h_{t-1} + B_t\,x_t,$40

VSLAM integration accepts nav_msgs/Odometry inputs from any visual SLAM system. Large pose jumps caused by map reinitialization are gated by $h_t = A_t\,h_{t-1} + B_t\,x_t,$41, and after vslam.reinit_n consecutive rejections, with default $h_t = A_t\,h_{t-1} + B_t\,x_t,$42, the map-to-odom offset is re-anchored to the current filter state (Kharwar, 24 May 2026).

Evaluation on twelve full-length NCLT sequences of $h_t = A_t\,h_{t-1} + B_t\,x_t,$43–$h_t = A_t\,h_{t-1} + B_t\,x_t,$44 minutes uses Absolute Trajectory Error RMSE computed with EVO and SE3 alignment to RTK ground truth. FusionCore is compared against robot_localization EKF and UKF with matched gates. The reported sequence-wise results for FusionCore versus robot_localization EKF are: 2012-01-08, $h_t = A_t\,h_{t-1} + B_t\,x_t,$45 m versus $h_t = A_t\,h_{t-1} + B_t\,x_t,$46 m; 2012-02-04, $h_t = A_t\,h_{t-1} + B_t\,x_t,$47 m versus $h_t = A_t\,h_{t-1} + B_t\,x_t,$48 m; 2012-03-31, $h_t = A_t\,h_{t-1} + B_t\,x_t,$49 m versus $h_t = A_t\,h_{t-1} + B_t\,x_t,$50 m; 2012-05-11, $h_t = A_t\,h_{t-1} + B_t\,x_t,$51 m versus $h_t = A_t\,h_{t-1} + B_t\,x_t,$52 m; 2012-06-15, $h_t = A_t\,h_{t-1} + B_t\,x_t,$53 m versus $h_t = A_t\,h_{t-1} + B_t\,x_t,$54 m; 2012-08-20, $h_t = A_t\,h_{t-1} + B_t\,x_t,$55 m versus $h_t = A_t\,h_{t-1} + B_t\,x_t,$56 m; 2012-09-28, $h_t = A_t\,h_{t-1} + B_t\,x_t,$57 m versus $h_t = A_t\,h_{t-1} + B_t\,x_t,$58 m; 2012-10-28, $h_t = A_t\,h_{t-1} + B_t\,x_t,$59 m versus $h_t = A_t\,h_{t-1} + B_t\,x_t,$60 m; 2012-11-04, $h_t = A_t\,h_{t-1} + B_t\,x_t,$61 m versus $h_t = A_t\,h_{t-1} + B_t\,x_t,$62 m; 2012-12-01, $h_t = A_t\,h_{t-1} + B_t\,x_t,$63 m versus $h_t = A_t\,h_{t-1} + B_t\,x_t,$64 m; 2013-02-23, $h_t = A_t\,h_{t-1} + B_t\,x_t,$65 m versus $h_t = A_t\,h_{t-1} + B_t\,x_t,$66 m; and 2013-04-05, $h_t = A_t\,h_{t-1} + B_t\,x_t,$67 m versus $h_t = A_t\,h_{t-1} + B_t\,x_t,$68 m. FusionCore is stated to achieve lower ATE on ten of twelve sequences, with winning-sequence improvements ranging from $h_t = A_t\,h_{t-1} + B_t\,x_t,$69 to $h_t = A_t\,h_{t-1} + B_t\,x_t,$70, while the robot_localization UKF diverges numerically on all twelve runs (Kharwar, 24 May 2026).

The two losses are specifically attributed to a rare $h_t = A_t\,h_{t-1} + B_t\,x_t,$71 minute GPS blackout beyond the $h_t = A_t\,h_{t-1} + B_t\,x_t,$72 correction range and an adversarial cluster of gross GPS outliers at a blackout boundary. The source characterizes both as cases with “clear architectural fixes,” which suggests that the reported failure modes are localized rather than random (Kharwar, 24 May 2026).

6. FusionCore as a laser-driven mixed-fuel nuclear fusion micro-reactor

In the nuclear-fusion usage, FusionCore is a fully integrated micro-reactor in which nuclear fuel itself forms both the fusion catcher and an embedded nano-accelerator. The central structure is an array of cylindrical nano-rods of radius $h_t = A_t\,h_{t-1} + B_t\,x_t,$73 nm and axial length $h_t = A_t\,h_{t-1} + B_t\,x_t,$74–$h_t = A_t\,h_{t-1} + B_t\,x_t,$75m, made of a high-$h_t = A_t\,h_{t-1} + B_t\,x_t,$76 fuel matrix such as boron or lithium and loaded with lighter fuel ions such as protons, deuterons, tritium, or $h_t = A_t\,h_{t-1} + B_t\,x_t,$77. The rods are separated by a distance $h_t = A_t\,h_{t-1} + B_t\,x_t,$78 chosen for stable propagation of a circularly polarized UV/VUV laser pulse with wavelength $h_t = A_t\,h_{t-1} + B_t\,x_t,$79–$h_t = A_t\,h_{t-1} + B_t\,x_t,$80 nm, pulse duration $h_t = A_t\,h_{t-1} + B_t\,x_t,$81–$h_t = A_t\,h_{t-1} + B_t\,x_t,$82 fs, and intensity $h_t = A_t\,h_{t-1} + B_t\,x_t,$83 (Ruhl et al., 2022).

The interaction model begins with rapid ionization of the rods, electron overheating and expulsion, and the formation of a positively charged cylinder whose radial Coulomb field accelerates the light ions. For species $h_t = A_t\,h_{t-1} + B_t\,x_t,$84, the density is written as $h_t = A_t\,h_{t-1} + B_t\,x_t,$85 with $h_t = A_t\,h_{t-1} + B_t\,x_t,$86–$h_t = A_t\,h_{t-1} + B_t\,x_t,$87, while the host-ion density is given as $h_t = A_t\,h_{t-1} + B_t\,x_t,$88–$h_t = A_t\,h_{t-1} + B_t\,x_t,$89. The laser field satisfies

$h_t = A_t\,h_{t-1} + B_t\,x_t,$90

and must exceed the ionization threshold

$h_t = A_t\,h_{t-1} + B_t\,x_t,$91

Inside a rod, the radial accelerating field is

$h_t = A_t\,h_{t-1} + B_t\,x_t,$92

and the inter-rod gap must satisfy

$h_t = A_t\,h_{t-1} + B_t\,x_t,$93

for stable propagation under the stated critical-density condition (Ruhl et al., 2022).

The non-thermal fuel distribution is modeled during the collisionless Coulomb-explosion phase by a light-ion Vlasov equation,

$h_t = A_t\,h_{t-1} + B_t\,x_t,$94

with characteristics

$h_t = A_t\,h_{t-1} + B_t\,x_t,$95

Shell decomposition of the initial rod produces a phase-space “ring” structure,

$h_t = A_t\,h_{t-1} + B_t\,x_t,$96

and after the shell exits the rod, the ions stream ballistically with sharply peaked velocities. The source describes the resulting distribution as near-homogeneous in space and multi-peaked and non-Maxwellian in velocity, with peaks engineered to coincide with Gamow resonances of the selected fuel mixture (Ruhl et al., 2022).

The binary fusion rate between species $h_t = A_t\,h_{t-1} + B_t\,x_t,$97 and $h_t = A_t\,h_{t-1} + B_t\,x_t,$98 is

$h_t = A_t\,h_{t-1} + B_t\,x_t,$99

In the delta-shell model,

$y_t = C_t\,h_t + D_t\,x_t.$00

For many fuels, the cross section is approximated as constant over a velocity window, while the usual $y_t = C_t\,h_t + D_t\,x_t.$01-factor parameterization is given by

$y_t = C_t\,h_t + D_t\,x_t.$02

with

$y_t = C_t\,h_t + D_t\,x_t.$03

The details list two example cycles: $y_t = C_t\,h_t + D_t\,x_t.$04 with $y_t = C_t\,h_t + D_t\,x_t.$05 and $y_t = C_t\,h_t + D_t\,x_t.$06, and $y_t = C_t\,h_t + D_t\,x_t.$07 with $y_t = C_t\,h_t + D_t\,x_t.$08 and $y_t = C_t\,h_t + D_t\,x_t.$09 (Ruhl et al., 2022).

For non-thermal operation, the Lawson form $y_t = C_t\,h_t + D_t\,x_t.$10 is replaced by an effective path-length condition involving $y_t = C_t\,h_t + D_t\,x_t.$11, with $y_t = C_t\,h_t + D_t\,x_t.$12. The single-pulse burn fraction is then

$y_t = C_t\,h_t + D_t\,x_t.$13

and the per-pulse yield and gain are

$y_t = C_t\,h_t + D_t\,x_t.$14

with $y_t = C_t\,h_t + D_t\,x_t.$15. The stated scaling argument is that $y_t = C_t\,h_t + D_t\,x_t.$16 and $y_t = C_t\,h_t + D_t\,x_t.$17, so the burn fraction and gain become largely independent of density at fixed rod geometry. The concluding summary claims a high burn fraction $y_t = C_t\,h_t + D_t\,x_t.$18–$y_t = C_t\,h_t + D_t\,x_t.$19 per pulse at near-solid densities and $y_t = C_t\,h_t + D_t\,x_t.$20 in aneutronic regimes (Ruhl et al., 2022).

7. Limitations, engineering constraints, and comparative interpretation

Each FusionCore instance is framed in its source as a prototype or concept with explicit constraints. For COREY, the evaluation is a Python-level prototype over synthetic heavy-tailed activations, and low-bit results are reported only through a hand-crafted stability proxy rather than checkpoint-level quality measurements. This limits direct extrapolation to deployed Triton kernels, although the source explicitly notes future Triton integration as a single-pass kernel with shared-memory load, fused Hadamard or projection or elementwise or update stages, and global write-back (Ma et al., 12 Apr 2026).

For the ROS 2 FusionCore, numerical robustness is a central claim because the comparison robot_localization UKF diverges on all twelve NCLT runs. At the same time, the source isolates two cases where FusionCore loses to the EKF baseline and attributes them to specific operating conditions: a $y_t = C_t\,h_t + D_t\,x_t.$21 minute GPS blackout and a cluster of gross GPS outliers at a blackout boundary. This suggests that the package is designed for fault-tolerant multimodal odometry, but not that it is immune to prolonged observability loss or adversarial measurement sequences (Kharwar, 24 May 2026).

For the micro-reactor concept, the source foregrounds engineering challenges rather than operational demonstration. Heat removal at average power densities of $y_t = C_t\,h_t + D_t\,x_t.$22–$y_t = C_t\,h_t + D_t\,x_t.$23 is assigned to micro-channel cooling in or behind the nano-rod substrate; neutron shielding for neutronic cycles is assigned to Li–Pb blankets or polymer or boron-carbide layers; and the destruction of individual nano-rods on each shot is addressed by tape-drive or rotating-disc targets that continuously present fresh nanostructured areas to the laser focus. These are design proposals, not experimental validations, and the distinction is essential for interpreting the concept’s maturity (Ruhl et al., 2022).

Taken together, the three usages show that “FusionCore” functions as a high-level label for fusion in very different senses: operator fusion for SSM inference, probabilistic fusion of heterogeneous sensors, and physical fusion of nuclear fuel. The shared nomenclature is semantically suggestive, but the underlying mathematical objects, performance criteria, and validation regimes are entirely domain-specific.