Information Self-Locking Mechanisms

• Information self-locking is a family of mechanisms where a system’s internal dynamics constrain, preserve, or selectively reveal information without external control.
• It manifests in diverse fields such as photonics, reinforcement learning, metasurfaces, quantum theory, and neural-network security, each with unique stabilization or failure modes.
• Practical insights include enhancing laser stabilization, mitigating RL training pitfalls, and designing materials that inherently lock electromagnetic or quantum information.

Searching arXiv for papers on “information self-locking” and related usages. Information self-locking denotes a family of mechanisms in which information-bearing states become constrained, preserved, or rendered selectively accessible by the internal dynamics of a system rather than by an external supervisory channel. Across the literature, the phrase is used in several technically distinct ways: in photonics, it refers to self-injection locking in microresonator–laser systems, where a resonator’s spectral information is passively impressed onto a semiconductor laser through coherent optical feedback (Shitikov et al., 2020); in reinforcement learning for active-reasoning LLM agents, it names a failure mode in which deficient information-seeking and deficient belief updating reinforce one another and trap training in a low-information regime (Zou et al., 12 Mar 2026); in coding metasurfaces, it refers to passive retention of discrete electromagnetic states by bistable mechanical bits (Zhang et al., 27 Jan 2026); in quantum information theory, it is closely related to information locking, where a large amount of classical correlation becomes inaccessible without a small key subsystem (Dupuis et al., 2010); and in neural-network access control, it appears as a model-level locking mechanism that conditions utility on the presence of a certificate-like input pattern (Ren et al., 2021). The common motif is not a single mathematical formalism but a recurrent structural pattern: information becomes latched, suppressed, or unlocked through endogenous coupling between state, feedback, and observability.

1. Conceptual scope and terminological variants

The term has no single universal definition across disciplines. In microresonator photonics, “information self-locking” refers to self-injection locking (SIL) of a semiconductor laser by resonant optical feedback from a high-$Q$ whispering-gallery-mode (WGM) microresonator, in which the resonator’s eigenfrequency is imposed on the laser through coherent back-reflection (Shitikov et al., 2020). In that setting, the “information” is spectral: the resonance line shape, phase, and decay parameters of the WGM are transferred to the laser via passive optical feedback.

In reinforcement learning, by contrast, information self-locking is defined as a structural failure mode of outcome-based RL for active reasoning. An agent ceases to ask informative questions and struggles to internalize already-obtained information, producing a feedback loop between deficient Action Selection (AS) and deficient Belief Tracking (BT) that locks training into a low-information regime (Zou et al., 12 Mar 2026). Here the “lock” is epistemic and dynamical rather than physical.

In origami-based coding metasurfaces, the term denotes physical preservation of discrete electromagnetic coding states without continuous power. Bistable Kresling origami mechanical bits discretize continuous deformation into two locked geometric configurations mapped to binary electromagnetic phase states; intrinsic energy barriers prevent spontaneous switching under perturbation (Zhang et al., 27 Jan 2026). In this use, the lock is mechanical and non-volatile.

Quantum information theory uses the shorter term “information locking” for a stronger and older phenomenon: after removing a logarithmic-sized quantum subsystem, even optimal measurements on the remaining system may yield outcomes essentially independent of a classically correlated message (Dupuis et al., 2010). The key system acts as a small unlock resource. Although this literature does not use the exact phrase “self-locking” in the same sense, it supplies an important formal archetype for selective accessibility of information.

A related but operationally different use appears in neural-network security. Model-Lock trains a single model to perform well on “certified” inputs and poorly on “suspect” inputs, creating local dynamic access control through training-time conditioning on certificate motifs (Ren et al., 2021). This suggests a broader interpretation in which self-locking design couples usable information to an internally recognized pattern rather than to external access infrastructure.

2. Optical self-locking in microresonator–laser systems

In photonic systems, information self-locking is realized by self-injection locking of a semiconductor laser to a high-$Q$ WGM microresonator. Rayleigh backscattering inside the resonator couples clockwise and counterclockwise propagating WGMs, producing a coherent reflected field at the pump frequency. This reflected field re-enters the laser diode cavity and suppresses phase noise by forcing the laser to operate near the resonator’s eigenfrequency (Shitikov et al., 2020).

The fundamental back-reflection mechanism is described through the coupling rate $\gamma$ between the forward and backward WGM components. In the simplest coupled-mode picture, the CW/CCW doublet is split by $2\gamma$, and the resonant reflection coefficient seen by the laser is

$$\Gamma_m \;=\; \frac{4\,\gamma\,\kappa_{mc}}{\kappa_m^2},$$

where $\kappa_{mc}$ is the coupling loss and $\kappa_m = \kappa_{mc} + \kappa_{mi}$ is the total decay rate (Shitikov et al., 2020). The same scattering process therefore both splits the resonance and provides the coherent feedback that stabilizes the laser.

The quality factor is defined in the usual way,

$$Q \;=\; \frac{\omega}{\kappa} \;=\; \frac{f}{\Delta f},$$

with the loaded quality factor obeying

$$\frac{1}{Q_m} = \frac{1}{Q_{\mathrm{int}}} + \frac{1}{Q_{\mathrm{coupling}}}.$$

Because direct linewidth measurements can be difficult in the mid-infrared, the 2020 study developed an SIL-based method that infers the intrinsic $Q$ and the vertical mode index $Q$0 from locking observables rather than from direct transmission scans or ringdown (Shitikov et al., 2020).

A central observable is the sum of forward and backward locking ranges measured while scanning the laser frequency up and down,

$Q$1

with $Q$2 negligible for high-$Q$3 or under/critically coupled resonators (Shitikov et al., 2020). The coupling loss depends exponentially on the coupler gap $Q$4: $Q$5 Measuring $Q$6 versus $Q$7 reveals an extremum at critical coupling, from which the intrinsic decay rate and thus $Q$8 can be extracted. The same dependence at zero gap is used to identify the vertical index $Q$9.

The work demonstrated stabilization of a $\gamma$0 distributed-feedback laser diode by a high-$\gamma$1 crystalline silicon WGM microresonator and determined the microresonator quality factor to be $\gamma$2 (Shitikov et al., 2020). In the mid-IR demonstration, $\gamma$3, the vertical index was identified as $\gamma$4, and the total locking range exceeded $\gamma$5 (Shitikov et al., 2020).

Theoretical treatment is commonly based on the Lang–Kobayashi model for delayed optical feedback,

$\gamma$6

$\gamma$7

where the linewidth-enhancement factor enters through the effective feedback gain (Shitikov et al., 2020). In the WGM-SIL model, the apparent soft-resonance width is

$\gamma$8

while the dominant high-$\gamma$9 contribution to the locking bandwidth scales as

$2\gamma$0

The stabilization factor is

$2\gamma$1

In the silicon mid-IR experiment, $2\gamma$2 was inferred, corresponding to multi-thousand-fold instantaneous linewidth reduction relative to the free-running diode (Shitikov et al., 2020).

Subsequent work optimized SIL with respect to coupling and locking phase. In MgF$2\gamma$3 WGM systems at $2\gamma$4, precise control of the locking phase $2\gamma$5 allowed fine tuning of the generated frequency, and the stabilization coefficient followed an approximate $2\gamma$6 dependence near critical coupling (Shitikov et al., 2022). Measured values included $2\gamma$7, $2\gamma$8, and $2\gamma$9; the maximal $$\Gamma_m \;=\; \frac{4\,\gamma\,\kappa_{mc}}{\kappa_m^2},$$0 was approximately $$\Gamma_m \;=\; \frac{4\,\gamma\,\kappa_{mc}}{\kappa_m^2},$$1, implying a locked linewidth near $$\Gamma_m \;=\; \frac{4\,\gamma\,\kappa_{mc}}{\kappa_m^2},$$2 for a free-running linewidth of about $$\Gamma_m \;=\; \frac{4\,\gamma\,\kappa_{mc}}{\kappa_m^2},$$3 (Shitikov et al., 2022). The same study reported instantaneous linewidths approaching $$\Gamma_m \;=\; \frac{4\,\gamma\,\kappa_{mc}}{\kappa_m^2},$$4, fast linear chirping inside the locking regime, and demonstrations relevant to FMCW LIDAR and Doppler velocimetry (Shitikov et al., 2022).

The SIL paradigm has also been extended to gain-switched lasers. Self-injection locking of a gain-switched $$\Gamma_m \;=\; \frac{4\,\gamma\,\kappa_{mc}}{\kappa_m^2},$$5 DFB laser to a high-$$\Gamma_m \;=\; \frac{4\,\gamma\,\kappa_{mc}}{\kappa_m^2},$$6 MgF$$\Gamma_m \;=\; \frac{4\,\gamma\,\kappa_{mc}}{\kappa_m^2},$$7 microresonator reduced comb-teeth linewidths to sub-kHz Lorentzian scale while preserving wide electrical tunability of line spacing from $$\Gamma_m \;=\; \frac{4\,\gamma\,\kappa_{mc}}{\kappa_m^2},$$8 up to $$\Gamma_m \;=\; \frac{4\,\gamma\,\kappa_{mc}}{\kappa_m^2},$$9 (Shitikov et al., 2021). This suggests that the resonator does not merely stabilize a carrier but can transfer its phase purity to a comb structure generated by modulation.

A further extension is nonlinear self-injection locking (N-SIL), where feedback is taken not from a passive cavity resonance but from the gain-narrowed Stokes mode of a fiber Brillouin oscillator. By blue-shifting the Stokes field back to the pump frequency with an electro-optic modulator and re-injecting it, recursive linewidth reduction is obtained (Bishop et al., 2023). A commercial DFB laser reached an integrated linewidth of $\kappa_{mc}$0 and a fundamental linewidth of $\kappa_{mc}$1, while the Brillouin oscillator itself exhibited a measured phonon-limited linewidth of $\kappa_{mc}$2 at $\kappa_{mc}$3 pump current (Bishop et al., 2023). This suggests that optical information self-locking can be generalized from passive resonant reflection to nonlinear reference generation.

3. Information self-locking as an RL failure mode

In the 2026 active-reasoning literature, information self-locking is not a stabilization mechanism but a pathology of training. LLM agents trained with outcome-based RL on multi-turn active-reasoning tasks can become trapped in a low-information regime: they stop asking informative questions and fail to absorb the evidence they already obtained (Zou et al., 12 Mar 2026).

The paper formalizes active reasoning as a POMDP $\kappa_{mc}$4 with fixed latent state $\kappa_{mc}$5 over an episode of horizon $\kappa_{mc}$6. At turn $\kappa_{mc}$7, the agent has a belief $\kappa_{mc}$8, selects a query $\kappa_{mc}$9, observes $\kappa_m = \kappa_{mc} + \kappa_{mi}$0, and updates its belief. The two core capabilities are Action Selection (AS), implemented by a belief-conditioned query policy $\kappa_m = \kappa_{mc} + \kappa_{mi}$1, and Belief Tracking (BT), implemented by an update operator $\kappa_m = \kappa_{mc} + \kappa_{mi}$2 (Zou et al., 12 Mar 2026).

For theoretical analysis, the paper defines an oracle Bayesian update under deterministic observations,

$\kappa_m = \kappa_{mc} + \kappa_{mi}$3

and a value function $\kappa_m = \kappa_{mc} + \kappa_{mi}$4 (Zou et al., 12 Mar 2026). AS informativeness is then measured through oracle-belief progress,

$\kappa_m = \kappa_{mc} + \kappa_{mi}$5

while BT capability is indexed by

$\kappa_m = \kappa_{mc} + \kappa_{mi}$6

The first quantity isolates information supplied by the query policy under oracle updating; the second measures how much of the supplied information is actually absorbed by the agent’s own update mechanism (Zou et al., 12 Mar 2026).

The self-locking mechanism arises from negative coupling between these quantities. Weak BT masks the reward value of informative queries, so AS receives little gradient signal to improve. Conservative AS, in turn, starves BT of informative evidence. The result is a feedback loop in which exploration remains weak, outcome reward may improve only marginally or through non-interactive shortcuts, and genuine information acquisition stagnates (Zou et al., 12 Mar 2026).

This interaction is formalized by one-sided projected drift bounds inside a low-information region

$\kappa_m = \kappa_{mc} + \kappa_{mi}$7

for which the paper proves

$\kappa_m = \kappa_{mc} + \kappa_{mi}$8

$\kappa_m = \kappa_{mc} + \kappa_{mi}$9

with constants $$Q \;=\; \frac{\omega}{\kappa} \;=\; \frac{f}{\Delta f},$$0, $$Q \;=\; \frac{\omega}{\kappa} \;=\; \frac{f}{\Delta f},$$1, and $$Q \;=\; \frac{\omega}{\kappa} \;=\; \frac{f}{\Delta f},$$2 (Zou et al., 12 Mar 2026). The interpretation given is that AS improvement is BT-limited, while BT improvement is jointly bounded by evidence supply and current BT quality. An escape-time lower bound then implies that outcome-only RL can remain stuck unless extra signal is introduced.

The proposed mitigation, AREW, injects directional critiques and reweights per-step advantages. For a trajectory $$Q \;=\; \frac{\omega}{\kappa} \;=\; \frac{f}{\Delta f},$$3, with positively and negatively critiqued step sets $$Q \;=\; \frac{\omega}{\kappa} \;=\; \frac{f}{\Delta f},$$4 and $$Q \;=\; \frac{\omega}{\kappa} \;=\; \frac{f}{\Delta f},$$5, the auxiliary objective is

$$Q \;=\; \frac{\omega}{\kappa} \;=\; \frac{f}{\Delta f},$$6

yielding an augmented surrogate

$$Q \;=\; \frac{\omega}{\kappa} \;=\; \frac{f}{\Delta f},$$7

This preserves the original outcome reward and RL optimizer while redistributing gradient mass toward informative actions and belief-improving updates (Zou et al., 12 Mar 2026).

Across 7 tasks in preference estimation, medical diagnosis, and troubleshooting, AREW outperformed vanilla RL in 27 of 28 settings and produced improvements up to about 60 points, consistent with the abstract’s “up to 60% improvements” (Zou et al., 12 Mar 2026). Representative results for Qwen-2.5-7B included PE-G $$Q \;=\; \frac{\omega}{\kappa} \;=\; \frac{f}{\Delta f},$$8 improving from $$Q \;=\; \frac{\omega}{\kappa} \;=\; \frac{f}{\Delta f},$$9 under vanilla PPO to $$\frac{1}{Q_m} = \frac{1}{Q_{\mathrm{int}}} + \frac{1}{Q_{\mathrm{coupling}}}.$$0 with AS+BT critiques, MediQ improving from $$\frac{1}{Q_m} = \frac{1}{Q_{\mathrm{int}}} + \frac{1}{Q_{\mathrm{coupling}}}.$$1 to $$\frac{1}{Q_m} = \frac{1}{Q_{\mathrm{int}}} + \frac{1}{Q_{\mathrm{coupling}}}.$$2, and FloDial-Hard improving from $$\frac{1}{Q_m} = \frac{1}{Q_{\mathrm{int}}} + \frac{1}{Q_{\mathrm{coupling}}}.$$3 to $$\frac{1}{Q_m} = \frac{1}{Q_{\mathrm{int}}} + \frac{1}{Q_{\mathrm{coupling}}}.$$4 (Zou et al., 12 Mar 2026). A plausible implication is that “self-locking” in this context identifies a generic credit-assignment pathology in interactive partially observable RL, rather than an LLM-specific defect.

4. Mechanical and electromagnetic self-locking in metasurfaces

In coding metasurfaces, information self-locking is realized physically rather than algorithmically. The system uses Kresling origami mechanical bits embedded into individual meta-atoms. Each bit has two bistable configurations—expanded (“0”) and folded (“1”)—and these are mapped onto binary electromagnetic phase states. Because switching requires crossing an intrinsic energy barrier, the encoded EM pattern is retained passively and without holding power (Zhang et al., 27 Jan 2026).

The mechanical bistability is set by geometry and materials. Under the “critical design” relation

$$\frac{1}{Q_m} = \frac{1}{Q_{\mathrm{int}}} + \frac{1}{Q_{\mathrm{coupling}}}.$$5

folding yields an approximately $$\frac{1}{Q_m} = \frac{1}{Q_{\mathrm{int}}} + \frac{1}{Q_{\mathrm{coupling}}}.$$6 reduction in height and a near $$\frac{1}{Q_m} = \frac{1}{Q_{\mathrm{int}}} + \frac{1}{Q_{\mathrm{coupling}}}.$$7 relative rotation of the basal surfaces; experimentally, the paper reports approximately $$\frac{1}{Q_m} = \frac{1}{Q_{\mathrm{int}}} + \frac{1}{Q_{\mathrm{coupling}}}.$$8 rotation and a $$\frac{1}{Q_m} = \frac{1}{Q_{\mathrm{int}}} + \frac{1}{Q_{\mathrm{coupling}}}.$$9 height change for $Q$0 and $Q$1 (Zhang et al., 27 Jan 2026). The strain-energy model is

$Q$2

with equilibria satisfying

$Q$3

and resisting force

$Q$4

These relations generate the characteristic force–displacement hysteresis of snap-through (Zhang et al., 27 Jan 2026).

Measured quasi-static tests at $Q$5 showed an elastic regime up to $Q$6, with a peak force of approximately $Q$7 at $Q$8, snap-through over $Q$9–$Q$00, and densification beyond $Q$01 (Zhang et al., 27 Jan 2026). Simulated energy barriers were of order $Q$02–$Q$03 per unit, and critical buckling forces varied in the approximately $Q$04–$Q$05 range (Zhang et al., 27 Jan 2026). Since room-temperature thermal energy is $Q$06, the condition

$Q$07

holds by roughly $Q$08, and the practical retention criterion is

$Q$09

Each $Q$10 unit supports over $Q$11 times its own weight in the expanded state, and durability tests reported robust performance after at least $Q$12 compression–torsion cycles (Zhang et al., 27 Jan 2026).

Electromagnetic encoding is implemented in two architectures. In the transmission-type metasurface, a grating–split-ring resonator–grating sandwich produces cross-polarized transmission with complex coefficient

$Q$13

Across $Q$14–$Q$15, the unit shows $Q$16 in both states and a stable phase difference of $Q$17–$Q$18 (Zhang et al., 27 Jan 2026). In the reflection-type metasurface, a Jerusalem Cross above a metallic ground plane yields

$Q$19

with $Q$20 over $Q$21–$Q$22 and a similarly stable phase difference of $Q$23–$Q$24 (Zhang et al., 27 Jan 2026).

These binary phase states support beam steering and holography. For a phase gradient $Q$25, generalized Snell’s law is

$Q$26

For a $Q$27-bit “0101…” pattern under normal incidence at $Q$28 with period $Q$29, the predicted steering angle is approximately $Q$30, consistent with measured dual-beam lobes near $Q$31 (Zhang et al., 27 Jan 2026). The array factor is

$Q$32

while near-field holography uses a Rayleigh–Sommerfeld propagation model and weighted MSE optimization (Zhang et al., 27 Jan 2026).

The significance of the self-locking aspect is that state retention is intrinsic to the meta-atom. The EM code is not preserved by continuous electrical bias or control electronics but by the mechanical energy landscape itself. This suggests a broader class of physically self-locking information-processing materials in which geometric multistability serves as a storage primitive.

5. Quantum information locking and selective accessibility

Quantum information theory provides the most formal treatment of locking. “Locking classical information” studies bipartite quantum states for which the maximum classical mutual information obtainable by measurement can drastically underestimate the quantum mutual information (Dupuis et al., 2010). The central phenomenon is that removing a logarithmic-sized key subsystem from one half of a correlated state can render the remaining information essentially inaccessible to any measurement.

For a bipartite state $Q$33, the accessible information is

$Q$34

where the supremum is over measurement superoperators (Dupuis et al., 2010). The paper adopts a stronger locking criterion than earlier work: for every measurement superoperator $Q$35,

$Q$36

Thus every measurement outcome distribution on the available subsystem is $Q$37-close to one that is independent of the message. By an Alicki–Fannes bound, this implies small accessible information: $Q$38 with $Q$39 (Dupuis et al., 2010).

The main theorem states that for Haar-random unitary encodings, locking occurs with high probability when the key size $Q$40 satisfies

$Q$41

with an additional technical constraint on $Q$42 (Dupuis et al., 2010). In the no-entanglement case this simplifies to

$Q$43

For a uniform message with no shared entanglement, the result yields $Q$44: only logarithmically many qubits are required to lock an $Q$45-bit message.

The corresponding decodability threshold is

$Q$46

and the gap between locked and decodable regimes is only logarithmic plus entropy-spread terms (Dupuis et al., 2010). The paper therefore concludes that classical information remains strongly locked almost until it can be completely decoded.

This literature clarifies an important point that reappears in later uses of the phrase: locking need not destroy information. Rather, it can make information operationally unavailable under a restricted access pattern. A plausible implication is that later “self-locking” usages in engineering and machine learning inherit this distinction between stored information and accessible information, even when the underlying mathematics is different.

6. Controlled utility and access in neural models

Model-Lock applies a related intuition to neural networks by making model utility conditional on a certificate motif embedded in the input (Ren et al., 2021). The architecture is unchanged; the lock is induced entirely by data construction and supervision. During training, authorized inputs are transformed by adding a certificate and retain their ground-truth labels, whereas unauthorized clean inputs are relabeled by one of three interference rules: single target interference (STI), rule-based target interference (RTI), or random target interference (RDI) (Ren et al., 2021).

With $Q$47 indicating authorized samples, the loss is

$Q$48

At inference, inputs containing the certificate produce near-baseline performance, while uncertified inputs yield systematically degraded predictions (Ren et al., 2021).

The empirical divergence between certified and suspect performance can be large. Under RTI, suspect accuracy on MNIST was reduced to $Q$49 or $Q$50 depending on motif, while certified performance remained near the baseline of $Q$51 (Ren et al., 2021). Similar patterns were reported on FashionMNIST, CIFAR10, CIFAR100, SVHN, and GTSRB, with RTI often producing the strongest lock (Ren et al., 2021). The paper does not provide formal guarantees, and it does not evaluate robustness to adaptive white-box removal, but it establishes a model-internal mechanism of access-conditioned utility.

This use differs from both photonic and RL self-locking. It is neither a passive physical stabilization nor a training pathology. Instead, it is an intentional access-control design in which the network learns a conditional input channel that enables or suppresses useful inference. The connection to “information self-locking” lies in internal gating of usable information rather than in explicit runtime control.

7. Cross-domain structure, misconceptions, and significance

Despite the diversity of domains, several structural regularities recur.

First, locking is usually generated by feedback or bistability internal to the system. In microresonator SIL, the feedback loop is optical and phase-coherent (Shitikov et al., 2020). In RL self-locking, it is a detrimental credit-assignment loop between AS and BT (Zou et al., 12 Mar 2026). In metasurfaces, it is a mechanical energy landscape with two stable minima (Zhang et al., 27 Jan 2026). In quantum locking, it is the geometry of high-dimensional random unitary encodings and subsystem access (Dupuis et al., 2010). In Model-Lock, it is a learned conditional mapping between certificate-bearing inputs and correct labels (Ren et al., 2021).

Second, locking need not mean immobility or information loss. In photonics, SIL broadens the apparent tuning curve while suppressing frequency excursions inside the locking band (Shitikov et al., 2020). In quantum information, the message remains present but inaccessible without the key (Dupuis et al., 2010). In metasurfaces, the binary state is retained indefinitely under sub-threshold perturbations but can still be switched by intentional actuation (Zhang et al., 27 Jan 2026). In RL, the lock is not a hard constraint but a region of weak positive drift from which escape is slow without auxiliary signal (Zou et al., 12 Mar 2026).

Third, the role of a “small control resource” is recurrent. A logarithmic quantum key can unlock extensive classical information (Dupuis et al., 2010). Small feedback-phase adjustments $Q$52 strongly affect stabilization and tuning in SIL (Shitikov et al., 2022). Directional critiques in AREW are intentionally lightweight, yet sufficient to reallocate learning signal and mitigate self-locking (Zou et al., 12 Mar 2026). Small origami units with millijoule-scale switching barriers can store macroscopic EM coding patterns (Zhang et al., 27 Jan 2026).

Several misconceptions follow from conflating these uses. One is that information self-locking always denotes improved robustness. In RL it denotes a failure mode, not an enhancement (Zou et al., 12 Mar 2026). Another is that “locking” implies cryptographic secrecy. Quantum information locking shows a strong distinction between accessible information and total correlation, but the paper explicitly notes that accessible information is an unsafe secrecy metric for cryptographic security under leakage (Dupuis et al., 2010). A third misconception is that passive locking eliminates design trade-offs. SIL requires careful management of coupling, locking phase, thermal effects, and $Q$53-factor sensitivity (Shitikov et al., 2020, Shitikov et al., 2022). Origami metasurfaces trade tuning speed for non-volatility and currently provide 1-bit phase quantization (Zhang et al., 27 Jan 2026). Model-Lock lacks formal robustness against adaptive attacks (Ren et al., 2021).

Taken together, the literature suggests that “information self-locking” is best understood as a cross-disciplinary motif rather than a unitary theory. It describes systems in which information becomes stabilized, concealed, latched, or trapped because the system’s own structure couples state evolution to access conditions. In one branch, that coupling is exploited to create ultranarrow-linewidth lasers and robust wavefront-control hardware (Shitikov et al., 2020, Zhang et al., 27 Jan 2026, Bishop et al., 2023). In another, it identifies pathologies of learning dynamics and motivates corrective credit-assignment methods (Zou et al., 12 Mar 2026). In the quantum branch, it formalizes the surprising separability of stored correlation from measurable correlation (Dupuis et al., 2010). This suggests that the enduring significance of the concept lies less in any one implementation than in its recurring demonstration that information flow is governed not only by what is present in a system, but by the endogenous mechanisms that determine when, how, and to whom that information becomes accessible.