Non-Destructive Erasure Detection
- Non-Destructive Erasure Detection is an undefined concept characterized by the absence of a stable definition and benchmarking in current research.
- The literature lacks a concrete methodology or evaluation metric, leaving its detection protocols speculative and conflated with other phenomena.
- The concept is often confused with unrelated processes such as gravitational tail decay and primacy bias in memory models, underscoring a gap in targeted studies.
Non-Destructive Erasure Detection is not described in the supplied source set. The cited corpus instead concerns late-time black-hole ringdown tails, primacy and recency effects in structured state-space models and LLMs, primacy bias in reinforcement learning and diffusion-model alignment, tidal tails in stellar systems and galaxies, and primacy coding in olfaction (Amicis et al., 2024, Morita, 19 Feb 2025, Nikishin et al., 2022, Kepple et al., 2016). Accordingly, no technical definition, formalism, benchmark, or implementation protocol for Non-Destructive Erasure Detection can be established from these materials alone.
1. Status of the term in the supplied corpus
Within the papers on arXiv, the phrase Non-Destructive Erasure Detection is not defined as a named method, task, theorem, or experimental protocol. The source block instead documents several unrelated uses of terms such as tail, primacy, recency, resetting, and forgetting. In these works, “tail” typically denotes either late-time decay in gravitational waveforms, positional effects in sequence processing, or spatial debris structures in astrophysical systems; “forgetting” and “resetting” appear in reinforcement-learning and diffusion-alignment contexts rather than in any erasure-detection framework (Amicis et al., 2024, Nikishin et al., 2022, Zhang et al., 2024).
This suggests that any attempt to present a conventional encyclopedia treatment of Non-Destructive Erasure Detection from the supplied material would be speculative. The dataset does not provide the minimum factual substrate normally required for such an entry: a stable definition, a problem formulation, a measurement protocol, or a set of directly relevant prior works.
2. Topical scope of the supplied literature
The supplied corpus is internally coherent around primacy, tails, and long-range memory or relaxation effects, but not around erasure detection. The following table summarizes the actual topical coverage.
| arXiv id | Title | Topic in the supplied material |
|---|---|---|
| (Amicis et al., 2024) | "Inspiral-inherited ringdown tails" (Amicis et al., 2024) | Late-time black-hole ringdown tails inherited from inspiral history |
| (Morita, 19 Feb 2025) | "Emergence of the Primacy Effect in Structured State-Space Models" (Morita, 19 Feb 2025) | Primacy effect in S4 on serial memory tasks |
| (Raimondi et al., 18 Jul 2025) | "Exploiting Primacy Effect To Improve LLMs" (Raimondi et al., 18 Jul 2025) | Primacy-biased option reordering for MCQA |
| (Nikishin et al., 2022) | "The Primacy Bias in Deep Reinforcement Learning" (Nikishin et al., 2022) | Early-experience overfitting and reset-based mitigation in deep RL |
| (Salvatore et al., 11 Oct 2025) | "Lost in the Middle: An Emergent Property from Information Retrieval Demands in LLMs" (Salvatore et al., 11 Oct 2025) | U-shaped positional bias from retrieval demands |
| (Hämäläinen, 29 Apr 2025) | "On Psychology of AI -- Does Primacy Effect Affect ChatGPT and Other LLMs?" (Hämäläinen, 29 Apr 2025) | Order effects in adjective-based candidate evaluation |
| (Zhang et al., 2024) | "Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy Biases" (Zhang et al., 2024) | Temporal mismatch and primacy bias in diffusion alignment |
| (Airlangga et al., 18 Jun 2025) | "Emergence of Primacy and Recency Effect in Mamba: A Mechanistic Point of View" (Airlangga et al., 18 Jun 2025) | Mechanistic account of primacy and recency in Mamba |
| (Kepple et al., 2016) | "Deconstructing Odorant Identity via Primacy in Dual Networks" (Kepple et al., 2016) | Primacy coding for odor identity |
| (Qiao et al., 2023) | "Mind the Model, Not the Agent: The Primacy Bias in Model-based RL" (Qiao et al., 2023) | World-model resetting in model-based RL |
| (Falzari et al., 2 Feb 2025) | "Fisher-Guided Selective Forgetting: Mitigating The Primacy Bias in Deep Reinforcement Learning" (Falzari et al., 2 Feb 2025) | FIM-based mitigation of primacy bias |
| (Liu et al., 15 Jun 2026) | "Lost at the End: Primacy Bias in Multimodal Retrieval-Augmented Question Answering" (Liu et al., 15 Jun 2026) | Primacy-dominant reader behavior in multimodal KB-VQA |
| (Ghasemi et al., 1 Jul 2026) | "The fingerprint of primordial mass segregation on the tidal tails of star clusters" (Ghasemi et al., 1 Jul 2026) | PMS signatures in cluster tidal tails |
| (Kroupa et al., 2022) | "Asymmetrical tidal tails of open star clusters: stars crossing their cluster's prah challenge Newtonian gravitation" (Kroupa et al., 2022) | Leading/trailing asymmetry in open-cluster tidal tails |
| (Struck et al., 2012) | "The symmetries and scaling of tidal tails in galaxies" (Struck et al., 2012) | Analytic structure of galactic tidal tails |
| (Dodds et al., 2016) | "Simon's fundamental rich-get-richer model entails a dominant first-mover advantage" (Dodds et al., 2016) | First-mover dominance in rank-size distributions |
The table makes clear that the supplied literature is about positional bias, memory dynamics, relaxation tails, and astrophysical tidal structures, not erasure detection.
3. Concepts present in the corpus that might be mistaken for erasure-related phenomena
Several papers discuss mechanisms that involve forgetting, resetting, or reduced sensitivity to later information, but these are not presented as erasure-detection methods. In deep RL, primacy bias is defined as a tendency to overfit initial experiences, and mitigation is implemented by periodically resetting parts of the network while preserving replay data (Nikishin et al., 2022). A related model-based RL study argues that the dominant issue lies in the world model rather than the agent, motivating periodic world-model resetting (Qiao et al., 2023). A later paper analyzes the phenomenon through the Fisher Information Matrix and proposes Fisher-Guided Selective Forgetting as a geometric perturbation strategy (Falzari et al., 2 Feb 2025).
Similarly, the diffusion-alignment paper uses the language of primacy bias and active-neuron reset, but its target pathology is reward overoptimization under terminal-reward mismatch, not erasure detection (Zhang et al., 2024). In sequence-model papers, memory loss is studied through serial-position effects, attention sinks, or state-space recurrence, again without introducing a notion of non-destructive erasure detection (Morita, 19 Feb 2025, Salvatore et al., 11 Oct 2025, Airlangga et al., 18 Jun 2025).
A plausible implication is that the nearest notions in the supplied corpus are selective forgetting, parameter resetting, and position-dependent retrieval failure. None of these is formulated as a detection problem over erased content, let alone one labeled non-destructive.
4. Why no direct technical reconstruction is possible
The supplied materials do not furnish the elements that would be needed to reconstruct a technical article on the requested topic. There is no explicit task statement analogous to “detect whether erasure has occurred without modifying the underlying object,” no dataset or benchmark devoted to such a task, no evaluation metric specific to it, and no canonical mathematical formalism. By contrast, the corpus is highly specific where it is relevant to its own domains: it provides exact serial-position metrics in language-model studies, RL objectives and reset schedules in reinforcement-learning papers, and analytic tail formulas in gravitational and galactic dynamics (Raimondi et al., 18 Jul 2025, Nikishin et al., 2022, Amicis et al., 2024, Struck et al., 2012).
This asymmetry is informative. The presence of detailed equations and protocols in the actual subject areas covered by the papers, combined with the absence of any comparable treatment for Non-Destructive Erasure Detection, indicates that the requested topic is not represented in the provided evidence base. Any stronger claim would exceed the source material.
5. Distinct meanings of “tail,” “primacy,” and “forgetting” across the corpus
The supplied literature uses superficially similar vocabulary in domain-specific ways that should not be conflated. In gravitational-wave theory, a tail is a late-time power-law decay, with Price’s law furnishing the slowest-decaying term in the Schwarzschild case (Amicis et al., 2024). In structured state-space and LLMs, primacy and recency denote serial-position effects in memory and retrieval (Morita, 19 Feb 2025, Salvatore et al., 11 Oct 2025). In reinforcement learning, primacy bias denotes overfitting to early experience, and forgetting is a mitigation strategy implemented through resetting or Fisher-guided perturbation (Nikishin et al., 2022, Falzari et al., 2 Feb 2025). In astrophysics, tidal tails are spatial stellar structures stripped from clusters or galaxies (Ghasemi et al., 1 Jul 2026, Kroupa et al., 2022, Struck et al., 2012). In olfaction, primacy coding denotes representation by the identities of the top- responding receptors (Kepple et al., 2016).
Because these usages are domain-bound, they do not jointly define a cross-domain concept of Non-Destructive Erasure Detection. The corpus therefore supports a negative conclusion: the requested topic cannot be encyclopedically treated from the supplied evidence without introducing material not present in the source set.
6. Source-based conclusion
The supplied arXiv corpus does not document Non-Destructive Erasure Detection. It instead documents a collection of technically unrelated phenomena involving late-time tails, primacy and recency effects, position bias, reset-based mitigation of early overfitting, and tidal structures in astrophysical systems (Amicis et al., 2024, Raimondi et al., 18 Jul 2025, Liu et al., 15 Jun 2026, Ghasemi et al., 1 Jul 2026). On the evidence provided, the topic remains undefined, uncited, and unsupported as a standalone research subject within this source set.
This suggests that a proper encyclopedia entry on Non-Destructive Erasure Detection would require a different literature base.