Alien Adaptation Training: XR & LLM Security

• Alien Adaptation Training is a framework that adapts both human and machine agents to operate in non-terrestrial and obfuscated digital environments using XR simulations and input transformations.
• In astronautical training, it employs XR technologies, parabolic flights, and precise sensor fusion to simulate partial-gravity conditions like lunar or Martian surfaces for enhanced operational performance.
• For language models, it uses a bijective alienization of token vocabularies to ensure privacy and security, achieving over 81% recovery relative to plaintext baselines.

Alien Adaptation Training (AAT) encompasses systematic procedures and methodologies for adapting agents—human or artificial—to operate in environments exhibiting "alien" characteristics, such as novel gravitational regimes for astronauts or transformed linguistic representations for privacy-preserving LLMs. Its primary instantiation lies in two distinct but conceptually unified domains: (1) extraterrestrial physical training, especially for partial-gravity adaptation using eXtended Reality (XR) technologies and parabolic flight, and (2) privacy- and security-driven adaptation of LLMs to synthetic, losslessly permuted "alien" input spaces. Each domain employs AAT as a framework to bridge real-world operational demands and high-fidelity simulation or transformation, ensuring agent competence under conditions that diverge significantly from terrestrial or plaintext baselines (Saling et al., 2024, Kim et al., 30 Jan 2026).

1. XR-Based Alien Adaptation Training for Astronauts

AAT for astronaut training aims to immerse crewmembers in controlled, high-fidelity simulations of extraterrestrial environments—primarily the lunar surface—using XR technologies synchronized with partial-gravity profiles in parabolic flight. This methodology facilitates the transfer and certification of perceptual, locomotor, and operational skills tailored to non-terrestrial settings (Saling et al., 2024).

System Architecture:

The training system integrates enterprise-class 6DoF head-mounted displays (HMDs, e.g., Varjo XR-3, HTC VIVE Focus 3), custom "Spacetacles" prototypes with optical ground-truth, stereo Azure Kinect units for body kinematics, and low-latency computational racks equipped with mobile GPUs. Tracking infrastructure involves customized IMU control (e.g., disabling onboard IMUs under partial-g) and a rigidly anchored controller for inertial referencing. Unreal Engine 5/4 provides real-time rendering, with drift-correction and sensor fusion modules compensating for partial-g artifacts.

Flight Profile Integration:

AAT leverages consecutive partial-gravity parabolas executed aboard an Airbus A310, targeting specific alien gravity levels (e.g., lunar $g_{\text{lunar}}\approx1.62~\mathrm{m/s}^2$, or $0.16g$). Aircraft acceleration is precisely commanded to maintain $$g_{\mathrm{eff}}(t)=g-\ddot{z}_{\mathrm{plane}}(t)=g_{\mathrm{lunar}}$$, and all system time stamps are synchronized via a visual master trigger.

Calibration Protocols:

Prior to each session and after hyper-g maneuvers, the visual–vestibular congruence is validated through static IMU bias recording (stationary at 1g), dynamic in-flight recentering (2 s IMU logging pre-parabola), and thresholds for recentering (pose error >2 cm or $>1^\circ$ drift over 10 s). Optical alignment is enforced via virtual-to-physical reticle matching (lateral error $<$5 cm). Empirical residual drift post-correction is $<0.03$ m; rotational jitter remains $<0.5^\circ$ RMS (Saling et al., 2024).

2. Task Protocols and Performance Metrics in Partial-Gravity AAT

Within 20 s lunar-g windows, participants execute standardized sequences approximating Extra-Vehicular Activities (EVAs):

• Straight-line bounding: Three forward hops ($\ell$, $t_c$, $\Delta z$ measured).
• Lateral sidestepping: Five 1 m side-shuffles (step width/frequency, COM sway).
• Object transfer: Transport a 5 kg payload over 2 m (RMSE $0.16g$010 cm, handling time, trajectory smoothness).
• Simulated tool use: Virtual torque wrench task; metrics include time, overshoot count, and RMSE.

Metabolic cost is quantified with indirect calorimetry, specifically comparing hop efficiency (J/kg/hop) against terrestrial baselines (Saling et al., 2024).

Summary of Human-Subject Findings:

Locomotor Variable	Change in 0.16g AAT
Bounding stride length	+60% ($0.16g$1 m vs $0.16g$2 m)
Contact time	–20%
Peak COM vertical excursion	+30%
Box-transfer accuracy (RMSE)	$0.16g$3 cm
Handling time (object transfer)	+15%
Tool-handling overshoot count	–25%

Subjects reported lowered Borg RPE by $0.16g$4 unit, but a 10% increase in metabolic cost per unit distance. Head-tracking jitter and scene flicker had negligible operational impact ($0.16g$55% failures).

3. Practical Guidelines for XR-Based AAT Implementation

Hardware Recommendations:

• HMDs: HTC VIVE Focus 3 (Simulator VR mode, ISS-certified), with rigid anchoring and clear IR visibility.
• Tracking: Tape over IR-interfering LED strips; disable base-station IMUs as necessary.
• Latency and Safety: Custom optical-tracking HMDs require $0.16g$650 ms motion-to-photon latency and compliance with aerospace safety (battery, outgassing, EMI).

Software & Parameters:

• Unreal Engine with dynamic drift correction.
• Frame rates $0.16g$7 Hz; asynchronous time warping for dropped-frames.
• Enforce recentering for pose errors $0.16g$82 cm or angular drift %%%%19$0.16g$20%%%% over 10 s.

Operational Constraints:

• Secure cables and hardware; use safety harnesses compatible with training hops.
• Limit to $$g_{\mathrm{eff}}(t)=g-\ddot{z}_{\mathrm{plane}}(t)=g_{\mathrm{lunar}}$$18 parabolas/day to minimize motion sickness and cognitive fatigue.

Curricular Design:

• Begin with 1g rehearsals, then Martian (0.38g) to lunar (0.16g) sequences.
• Progress task complexity with participant proficiency.
• Interleave objective and subjective metrics (stride, error rates, presence, motion sickness, RPE).
• Finalize with scenario-based EVA drills reflecting lunar surface variability (Saling et al., 2024).

4. Alien Adaptation Training for LLMs via Alienized Vocabularies

In LLM security domains, AAT denotes a black-box fine-tuning procedure that equips LLMs to directly process inputs and outputs alienized by a vocabulary-scale bijection ("alienization"). This obfuscates content at the API boundary, enabling privacy-preserving deployment without model access (Kim et al., 30 Jan 2026).

Alienization Bijection Construction:

• Vocabulary: $$g_{\mathrm{eff}}(t)=g-\ddot{z}_{\mathrm{plane}}(t)=g_{\mathrm{lunar}}$$2, where $$g_{\mathrm{eff}}(t)=g-\ddot{z}_{\mathrm{plane}}(t)=g_{\mathrm{lunar}}$$3 are token IDs, excluding the special token set $$g_{\mathrm{eff}}(t)=g-\ddot{z}_{\mathrm{plane}}(t)=g_{\mathrm{lunar}}$$4.
• Define permutation $$g_{\mathrm{eff}}(t)=g-\ddot{z}_{\mathrm{plane}}(t)=g_{\mathrm{lunar}}$$5 such that $$g_{\mathrm{eff}}(t)=g-\ddot{z}_{\mathrm{plane}}(t)=g_{\mathrm{lunar}}$$6 is bijective, invertible, and identity for special tokens.
• Alienization/De-alienization:
  • $$g_{\mathrm{eff}}(t)=g-\ddot{z}_{\mathrm{plane}}(t)=g_{\mathrm{lunar}}$$7
  • $$g_{\mathrm{eff}}(t)=g-\ddot{z}_{\mathrm{plane}}(t)=g_{\mathrm{lunar}}$$8
• $$g_{\mathrm{eff}}(t)=g-\ddot{z}_{\mathrm{plane}}(t)=g_{\mathrm{lunar}}$$9 maximizes the normalized edit distance (surface unreadability) minus $>1^\circ$0 times cosine embedding similarity (learnability).

Computation is optimized (128K vocabulary: top-100 neighbor search; $>1^\circ$1), completing in $>1^\circ$220 min for standard models.

5. AAT Training Pipeline and Empirical Evaluations for LLMs

Pipeline:

• Data: 300K instruction + 150K reasoning instances (Magpie); prompts/responses alienized offline.
• Platform: 4$>1^\circ$3NVIDIA A100 80GB; batch size 32; AdamW (paged, 8-bit); bf16; $>1^\circ$4 LR; runs $>1^\circ$58.3K steps ($>1^\circ$610M tokens, $>1^\circ$712h, \$>1^\circ$8150).
• Objective:

$>1^\circ$9

Model is trained to predict on $<$0-permuted token IDs while API tokenizer remains unchanged.

Performance:

Model	Oracle Avg. (%)	AlienLM Avg. (%) [RR]
LLaMA 3 8B	64.77	52.92 (81.7%)
Qwen 2.5 7B	65.60	57.33 (87.4%)
Qwen 2.5 14B	73.49	62.05 (84.4%)
Gemma 2 9B	69.20	56.63 (81.8%)
Average	—	64.23 (83.3%)

AAT achieves $<$181% recovery relative to plaintext-oracle accuracy across standard LLMs and benchmarks.

6. Privacy Guarantees and Limitations of AAT for LLMs

Adversary scenarios include passive frequency analysis, LLM few-shot decoding, known-plaintext attacks, and embedding mapping with model weight access:

Attack Method	Scenario	Success Rate
Frequency analysis	O1 (passive)	$<$20.01% token recovery
LLM- / MT- based decoding	O2 (few-shot)	BLEU $<$312
Known-plaintext (1K pairs)	O2 (leakage)	$<$40.22% tokens recovered
Embedding mapping, model weights	O3 (weights)	$<$50.11% top-1 accuracy

Even with 20 parallel samples or adapted weights, inverting $<$6 achieves negligible recovery. However, there is no formal DP/cryptographic protection, and metadata leakage can occur. Fine-tuning can compromise model safety/alignment on jailbreak metrics (Kim et al., 30 Jan 2026).

Practical reproduction requires public tokenizer, a proxy for embedding similarity, and API-based fine-tuning. The key ($<$7) must remain strictly secret; leakage degrades privacy. Multi-tenant deployments are best supported via separate AAT checkpoints per key, as naive mixing introduces interference.

7. Synthesis and Domain Significance

AAT, in both astronautical and deep learning contexts, establishes a discipline for systematically adapting agents to non-native or obfuscated operational domains. In astronautics, AAT delivers validated protocols for sensorimotor coherence, performance evaluation, and curricular design, facilitating safe and efficient human activity on lunar or Martian surfaces (Saling et al., 2024). In LLM privacy, AAT operationalizes high-dimensional, lossless obfuscation while retaining utility and quantifying adversarial recovery resistance, offering a scalable and practical API-layer privacy mechanism (Kim et al., 30 Jan 2026).

A plausible implication is that expansion of AAT frameworks will further enable robust adaptation of both human and machine agents to unforeseen or adversarial environments, with applications in space operations, secure AI deployment, and beyond.