Optimal Watermark Localization in Mixed-Source Large Language Model Texts

arXiv:2608.14906 · stat.ME, cs.CL, cs.LG, stat.ML · Submitted 2026-08-14 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "Optimal Watermark Localization in Mixed-Source Large Language Model Texts".

Jane: As a fastidious researcher,

Tom: First, who's behind it and why it matters.

Title and authors: Tom: So we're looking at this paper, "Optimal Watermark Localization in Mixed-Source Large Language Model Texts," and the authors are trying to figure out how to find hidden watermarks when the text has been messed with—rewritten, inserted, deleted.

Jane: It sounds like a big problem because in reality, when you edit an LLM's output, you don't always know where the watermark evidence survives; sometimes it only appears at a few spots.

Lu: The paper frames this as a token-level multiple testing problem using pivotal statistics, which is important because it lets us look at the statistical limits rather than just guessing where to look.

Meng: So, what's their main takeaway? Are they saying we can always find the watermark if we use enough tests?

Tom: They do have a boundary for global detection. They say that if the maximum of p plus q, or two p plus q minus alpha is less than one, then the null and alternative hypotheses separate asymptotically.

Jane: That’s a specific mathematical condition—p, q, and alpha are parameters that describe how sparse the signal is in the text and how much the vocabulary grows as we go along.

Lu: Yes, those parameters index the asymptotic regime of signal sparsity, next-token concentration, and effective-vocabulary growth. It sets a baseline for what's mathematically possible under their coordinatewise pivot-based localization rules.

Meng: If they can detect it globally, does that mean we can always find it? I'm thinking about real production text here.

Tom: Not necessarily. The paper shows that discovery is strictly harder than detection because the condition for discovery requires p to be less than alpha and p plus q to be less than one, up to some boundary cases.

Jane: That suggests that just being able to detect it across the whole text isn't enough; you need a much stricter condition for finding those specific spots where the watermark is actually preserved.

Lu: And they prove that consistent classification is impossible within this class of rules because you can't simultaneously ensure all missed discoveries vanish, which means the false discovery rate won't go to zero.

Meng: That’s a big limitation then; if you try to build a system that classifies edits perfectly, it won't work with these specific localization rules.

Tom: Exactly. The paper also introduces something new called SPOT, which is an adaptive thresholding method designed to achieve discovery even when the parameters aren't known beforehand.

Jane: It sounds like they are moving away from fixed rules toward a more flexible search strategy that adapts to the data it sees.

Title and authors: Lu: SPOT searches for positions whose pivotal statistics fall in an extreme upper tail relative to the null law, and it uses a data-driven estimate of the surviving watermark fraction to dynamically choose a threshold.

Meng: If we deploy this, does that mean I don't need to pre-calculate all those complex exponents p, q, and alpha for every new text generation?

Tom: No. The theoretical backing for SPOT shows that under assumptions four point one through four point three and with the right scaling of the number of tests—roughly n log n—it is guaranteed to achieve discovery if those same conditions on p and q hold.

Jane: So, this means we can actually locate a watermark adaptively in mixed-source text without needing deep prior knowledge about how the source mixing process works.

Lu: That adaptability is what makes it interesting because the paper shows that SPOT is boundary-optimal and near-optimal in terms of discovery power compared to standard homogeneous pivot rules.

Meng: How sure are we about this near-optimality? I'm worried that in a real, messy deployment, we might run into edge cases where it doesn't perform as well as the theory suggests.

Tom: The empirical validation shows that transition bands observed in simulations line up perfectly with the theoretically predicted discovery boundary.

Jane: And experiments comparing SPOT with other methods like AOL and SPOT-plugin show that SPOT generally performs better, especially when the text generation is at higher temperatures.

Lu: It confirms thatSPOT has a robust mechanism for handling those varying conditions in mixed-source data, which is what they were aiming for when they asked if localization could be achieved adaptively.

Meng: So to sum up the core idea: we have a way to find watermarks in edited text, and SPOT is the method that makes it flexible enough to work without needing all those complex statistical inputs upfront.

Tom: Precisely. The paper's main contribution is proving that discovery is possible under specific conditions, providing a rigorous framework for when and how you can locate these signals.

Jane: And the biggest practical implication for us right now is that we have a method to perform token-level localization even in texts where the edit mechanism is unknown.

Lu: This work really helps fill that gap they identified earlier—the one about whether localization is possible adaptively without knowing the source-mixing process.

Meng: From an engineering standpoint, it means we can build systems that are more resilient to whatever kind of human or automated tampering we encounter in our models.

Tom: It moves us from just detecting a signal existing somewhere to actually pinpointing its location with statistical rigor.

Title and authors: Jane: And as for the overall picture, this paper gives us a very concrete map showing the exact statistical conditions under which these localization tasks are possible or impossible.

Lu: It sets clear, measurable boundaries for what's achievable in terms of detection and discovery within this specific mathematical class of rules.

Meng: It’s helpful to know where the limits are so we don't waste time trying to find a watermark in a text that just doesn't have one surviving at all.

Tom: So, as we wrap up this discussion on "Optimal Watermark Localization in Mixed-Source Large Language Model Texts," we see that detection is achievable under certain constraints, but discovery requires those stricter conditions defined by the parameters p, q, and alpha.

Jane: The authors prove that discovery is strictly harder than detection because of these constraints, and they also show that consistent classification across all positions is impossible within their framework.

Lu: This impossibility stems from the fundamental incompatibility between needing to ensure missed discoveries vanish and controlling the false discovery rate at the same time.

Meng: So, we have a clear picture of what we can expect from this method when we apply it to real-world text reconstruction problems.

Tom: And as for future work, they’ve set up a path using SPOT to achieve that discovery without needing prior knowledge of those exponents, which is the core improvement they focus on.

Jane: They are focused on making the localization process adaptive so it can handle the heterogeneity of mixed-source text without needing explicit knowledge of the mixing process.

Lu: The next step seems to be scaling this SPOT method beyond these specific asymptotic regimes and testing its performance across a wider range of textual corruption types.

Meng: I’m curious if this will translate well when we move from simulation results to actual production traffic where the noise is much messier than what they modeled.

Tom: The empirical validation suggests it holds up well, especially at higher generation temperatures, which is relevant because those are often where LLMs get more ambiguous.

Jane: So, in summary for this paper on "Optimal Watermark Localization in Mixed-Source Large Language Model Texts," we've learned that discovery is harder than detection and classification isn't possible under the current rules.

Lu: It provides a robust multiple-testing framework that helps characterize these statistical limits for localization problems.

Meng: It gives us a practical, adaptive search method called SPOT to try and push past those discovery boundaries in real-world scenarios.

Tom: This research is useful because it moves the discussion from just *if* we can find a watermark to *how* statistically certain we are about its location.

The paper's summary: Tom: So, to recap, this paper is about finding watermarks in LLM text that’s been messed with—like edited or rewritten—and they’re using some big statistical tools called pivotal statistics to figure out where that evidence survives across different positions.

Jane: Right. The main thing is they're setting up a test for every single token in the text to see if the watermark dependence stays at that spot after someone changes it, which is kind of a massive multiple testing problem.

Lu: They’re indexing this with three parameters: p, q, and alpha—those describe how sparse the signal is in the text, how much the vocabulary grows, and how concentrated those next-token predictions are.

Meng: So what’s their big finding? Are they saying we can just scan every spot to find it eventually?

Tom: Not really. They establish a hard boundary for global detection—if the maximum of p plus q or two p plus q minus alpha is less than one, then the null and alternative hypotheses separate asymptotically. That’s detection.

Jane: Detection means you can tell if *any* watermark dependence exists somewhere in the whole text, but that’s not finding it specifically, is it?

Lu: Exactly. They prove a boundary for discovery—to actually find the watermark itself—it requires p to be less than alpha and p plus q to be less than one. Discovery is strictly harder than detection because of those conditions.

Tom: That's a key distinction there. The authors also prove that consistent classification is impossible within their specific set of rules, meaning you can’t build a system that reliably classifies all the edits perfectly using these pivot rules.

Jane: It sounds like they’ve drawn some pretty firm walls around what this approach can and cannot do mathematically.

Lu: They then propose SPOT, which stands for Scanning Pivots Over Thresholds. It's an adaptive method that doesn't need you to know p, q, or alpha beforehand to start searching.

Meng: Adaptive search sounds good on paper, but what does it actually cost in terms of computation when we scale this up?

Tom: The theory guarantees that with the right scaling of tests—n log n for example—SPOT is guaranteed to find the watermark if those p and q conditions hold. And they show it's near-optimal compared to fixed rules.

Jane: So, the practical implication is that we can try to locate a watermark in mixed-source text without needing deep prior knowledge about how the source mixing process works during training.

Lu: That adaptability is what makes it useful because real-world text corruption isn't always neatly categorized like this setup suggests.

Tom: The empirical validation backs this up. their transition bands in simulations line up exactly with the discovery boundary they predicted mathematically.

Jane: And experiments with methods like AOL show that SPOT often performs better than those more traditional homogeneous pivot rules, especially when the text generation temperature is high.

Lu: So, while classification is out of reach, they’ve given us a strong tool for discovery that adapts to the statistical properties of the text itself.

Meng: It sounds like they’ve solved a specific version of this problem within their mathematical class, but how does that translate when the noise in real production traffic gets much messier?

Tom: That's exactly what we need to test next. The paper sets up a path using SPOT to achieve that discovery without needing prior knowledge of those exponents.

The paper's improvements: Tom: So, looking at the improvements they suggest for their method, SPOT, they’re basically trying to make it work even when you don't know those tricky parameters p, q, and alpha in advance.

Jane: That makes sense. They want a system that can just scan for positions whose pivotal statistics are in an extreme upper tail relative to the null law without needing all that upfront knowledge of the asymptotic regime.

Lu: That’s where they focus their work on making it adaptive localization via SPOT, which allows it to find those optimal discovery boundaries without needing prior knowledge of those exponents p, q, and alpha.

Meng: So if you deploy that in production on a text that we haven't fully characterized yet—like a brand new type of mixed-source data—does the method still hold up?

Tom: The theoretical backing for this adaptive localization is solid under assumptions four point one through four point three, provided you scale the number of tests by n log n. And they show it’s near-optimal compared to standard homogeneous pivot rules.

Jane: So, beyond just finding the boundary, they also focus on robustness against human edits. The method uses a data-driven estimate of the surviving watermark fraction to dynamically choose its threshold.

Lu: That’s how they achieve robustness against human edits. It lets the system reliably identify which parts of a mixed-source text still preserve evidence, which leads to near-optimal discovery power relative to those fixed rules.

Tom: The paper also tackles failure mode diagnosis using phase transition analysis in the parameter space of p, q, and alpha. This lets you diagnose whether localization is possible or just detectable or discoverable.

Jane: It’s like they give you a diagnostic tool for the statistical regime itself—showing that discovery is strictly harder than detection and classification is impossible within this specific rule class.

Lu: That impossibility of consistent classification stems from needing to ensure missed discoveries vanish, which inherently leads to a non-vanishing false discovery rate in their framework.

Meng: If we look at what this means for deployment, it means we can build systems that are more resilient because the localization process adapts to the text structure rather than requiring a perfect upfront model of the mixing process.

Tom: Precisely. It moves us from just detecting a signal existing somewhere to actually pinpointing its location with statistical rigor, even when things get messy.

Jane: So, while they can't classify perfectly, they’ve built a very strong adaptive mechanism for discovery that seems ready to handle the heterogeneity of mixed-source text without needing explicit knowledge of the mixing process.

Lu: The next step for this work is scaling SPOT beyond these specific asymptotic regimes and testing its performance across a wider range of textual corruption types.

Conclusion: Tom: So, to wrap up, we’ve looked at how this paper on "Optimal Watermark Localization in Mixed-Source Large Language Model Texts" establishes the hard statistical limits for finding watermarks in edited LLM text.

Jane: Basically, they proved that while global detection is possible under certain conditions—when p plus q is less than one—discovery requires much stricter constraints on those parameters.

Lu: And they showed that consistent classification across all positions is impossible because you can't simultaneously ensure all missed discoveries vanish and control the false discovery rate.

Meng: So for a production system, this means we can’t rely on a fixed rule set to perfectly classify every edit; there will always be uncertainty left over.

Tom: But they’ve given us SPOT, which is an adaptive method that uses data-driven estimates of the surviving watermark fraction to dynamically choose its threshold without needing prior knowledge of p, q, and alpha.

Jane: That adaptability is key; it means the system can find the watermark even when we don't know exactly how messy a particular mixed-source text is.

Lu: They’ve moved toward making localization adaptive so it can handle that heterogeneity without needing explicit knowledge of the mixing process during training.

Meng: That sounds promising for shipping, because we don't have time to run exhaustive pre-characterization on every single data stream we encounter.

Tom: The empirical validation confirms this; simulations show SPOT performs better than standard pivot rules and aligns with the predicted discovery boundaries.

Jane: It’s a lot of work proving these statistical boundaries, but it gives us a concrete map showing exactly when localization is possible or impossible within this mathematical framework.

Lu: The main contribution here is providing that rigorous multiple testing framework to characterize these statistical limits for localization problems in LLM text generation.

Meng: I’m just wondering how much computational overhead that SPOT adaptive thresholding adds during real-time inference on high-throughput systems.

Tom: That’s a fair concern, but the theoretical guarantees suggest it scales with n log n, which is manageable for large texts.

Jane: So, we have a very concrete understanding of the boundaries for detection and discovery in LLM text localization now.

Lu: This research sets clear statistical boundaries for what's achievable in terms of detection versus discovery within this specific mathematical class of rules.

Meng: It’s helpful to know where the limits are so we don't waste time trying to find a watermark in a text that just doesn't have one surviving at all.

Tom: So, "Optimal Watermark Localization in Mixed-Source Large Language Model Texts" gives us a robust method that’s adaptive and theoretically sound for finding those signals.

Jane: It really moves the discussion from just *if* we can find a watermark to *how* statistically certain we are about its location.

Lu: The future work is scaling this SPOT method beyond these specific asymptotic regimes and testing its performance across a wider range of textual corruption types.

Stanford University · University of Pennsylvania

stat.ME, cs.CL, cs.LG, stat.ML

Submitted: 2026-08-14

Updated: 2026-10-15

Comments: 73 pages, 13 figures. Updated experiments and supplementary material

Code: https://github.com/SHL20/WatermarkLocalization

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 92/100

The gist: As a fastidious researcher, I have meticulously analyzed both provided texts (A and B) concerning the paper "Optimal Watermark Localization in Mixed-Source Large Language Model Texts." The synthesis

Key concepts

Token-level Multiple Testing Problem: This frames the localization task as testing many positions simultaneously to find a signal. Instead of checking one spot at a time, the method tests many spots at once using statistical tools. The goal is to determine which specific token positions are likely carrying the hidden watermark signal after text manipulation.
Pivotal Statistics: These are mathematical statistics used to make decisions about whether a specific position holds evidence of the watermark. They allow researchers to compare the probability of observing data at a certain position against what would be expected under normal conditions, helping pinpoint where the signal is strongest.
Discovery vs. Detection: Detection means finding *if* a watermark exists anywhere (global detection). Discovery means finding *exactly which* token positions contain the watermark. The paper proves that discovery requires much stricter conditions than detection, making it significantly more difficult to perform reliably.

Terminology

Core — 46× in this paper · explained in 2 episodes
Verifier — 20× in this paper · explained in 2 episodes
Phase Transition — 17× in this paper · explained in 2 episodes
Calibration — 16× in this paper · explained in 7 episodes
Phase Transitions — 12× in this paper · explained in 2 episodes
Decoder — 11× in this paper · explained in 2 episodes
Accuracy — 10× in this paper · explained in 1 episode
Sparsity — 6× in this paper · explained in 4 episodes
Theoretical Guarantees — 6× in this paper · explained in 2 episodes
Benchmark — 5× in this paper · explained in 2 episodes
Next-Token Prediction — 5× in this paper
Robustness — 5× in this paper · explained in 5 episodes
Verification — 5× in this paper · explained in 2 episodes
Adam — 4× in this paper · explained in 2 episodes
Scanning Pivots Over Thresholds — 4× in this paper
Adaptability — 3× in this paper · explained in 1 episode
Goodness-of-Fit Tests — 3× in this paper · explained in 2 episodes
Large Language Models (LLMs) — 3× in this paper · explained in 8 episodes
Papers — 3× in this paper · explained in 3 episodes
Transformer — 3× in this paper · explained in 2 episodes
Accountability — 2× in this paper · explained in 2 episodes
Adaptivity — 2× in this paper · explained in 1 episode
Computational overhead — 2× in this paper · explained in 2 episodes
Decode — 2× in this paper · explained in 2 episodes
Editor — 2× in this paper · explained in 2 episodes
Adaptive Mechanism — 1× in this paper · explained in 1 episode
Adaptive Oracle Localization — 1× in this paper
Credit assignment — 1× in this paper · explained in 3 episodes
Transfer Learning — 1× in this paper · explained in 12 episodes
Transformers — 1× in this paper · explained in 2 episodes
Vector Representations — 1× in this paper · explained in 2 episodes

Summary

As a fastidious researcher, I have meticulously analyzed both provided texts (A and B) concerning the paper Optimal Watermark Localization in Mixed-Source Large Language Model Texts. The synthesis below integrates the theoretical framework, key contributions, formal proofs, and empirical validations into a comprehensive and detailed summary suitable for high-level technical review.

Comprehensive Research Summary: Optimal Watermark Localization in Mixed-Source LLM Texts

This paper addresses the critical problem of authenticating text generated by Large Language Models (LLMs) by formulating watermark localization as a token-level multiple testing problem based on pivotal statistics. The core challenge lies in determining whether watermark dependence survives at specific token positions after text manipulation (rewriting, insertion, deletion, or paraphrasing), where evidence may only survive at a subset of positions.

Theoretical Framework and Core Formulation

The authors establish the foundational mathematical structure by formulating the localization task as a token-level multiple testing problem using pivotal statistics. They introduce a latent indicator to record whether watermark dependence survives at each position, allowing for rigorous statistical analysis. The theoretical analysis is indexed by parameters (p, q, alpha), which characterize the asymptotic regime of signal sparsity (related to p), next-token concentration (related to q), and effective-vocabulary growth (related to alpha).

The central theoretical results establish sharp boundaries for three distinct localization goals: global detection, discovery, and classification.

Global Detection Boundary: The paper derives an explicit boundary for global detection: "If p + q, 2p + q - alpha < 1, then H 0 and H 1 separate asymptotically." This result is significant as it generalizes the fixed-vocabulary detection boundary established in prior work (Li et al. [25]) to the more complex growing-vocabulary regime.
Discovery Boundary: The authors prove a fundamental constraint on locating the watermark: "For the main localization goal, we prove that discovery is possible if and only if p < alpha and p + q < 1, up to boundary cases." This result rigorously demonstrates that discovery is strictly harder than global detection, as it necessitates finding a watermark-preserving position with a vanishing false discovery rate.
Impossibility of Classification: A crucial theoretical finding is the proof that consistent classification is impossible within the class of coordinatewise pivot-based localization rules: We further prove that consistent classification is impossible throughout the (p, q, alpha) regime within this class. This impossibility stems from a fundamental incompatibility between the requirements for classification: to ensure missed discoveries vanish, one must select almost all watermark-preserving positions, which inherently leads to a non-vanishing false discovery rate.

Proposed Adaptive Solution: SPOT Method

To address the difficulty of discovery and overcome the limitations of fixed rules, the authors develop an adaptive thresholding method called SPOT (Scanning Pivots Over Thresholds). SPOT is designed to achieve discovery throughout the statistically discoverable region without requiring prior knowledge of problem-dependent quantities such as p, q, alpha, or time-varying Next-Token Prediction (NTP) distributions.

Mechanism and Optimality:

SPOT operates by searching for positions whose pivotal statistics fall in an extreme upper tail relative to the null law. Crucially, it employs a data-driven estimate of the surviving watermark fraction to dynamically choose a threshold that balances controlling false discoveries against maximizing discovery power. The method is formally shown to be boundary-optimal and near-optimal in discovery power compared to homogeneous pivot-based rules.

Theoretical Guarantees for SPOT:

The theoretical backing for SPOT’s success is robust:

Theorem 4.5 (Adaptive Optimality for Discovery): Under specific assumptions (Assumptions 4.1–4.3) and with appropriate scaling of the number of tests (M n about (n) a), SPOT is guaranteed to achieve discovery if p < alpha and p + q < 1.
Theorem 4.6 (Near-optimal Number of Discoveries): This theorem confirms the efficiency of SPOT, showing that the empirical false discovery rate (mFDR delta SPOT) is bounded by a term proportional to lambda n (where lambda n = C 1/ n), and its expected true positive rate (ETP) scales near-optimally relative to homogeneous rules.

Supporting Proof Details

The theoretical rigor is supported by detailed auxiliary proofs concerning the underlying statistical components:

Lemma B.6 and B.7: These proofs analyze the tail probabilities of the Next-Token Prediction (NTP) distribution by decomposing it into dominant, light, and core parts, deriving bounds on signal tails based on parameter regimes (e.g., 0 < u < alpha + q vs u at least alpha + q). Lemma B.7 specifically relates the optimal threshold choice to right-tail events in the probability of labeling a coordinate as true.
Proof of Theorem 4.3 (Impossibility of Classification): The proof demonstrates that assuming classification leads to a contradiction with necessary conditions derived from Lemma B.11, showing that even when q=0, consistent classification is impossible for all valid parameter ranges (p, q, alpha).
Proof of Theorem 4.4 (Global Detection Optimality): This proof utilizes concentration inequalities (Lemma B.13) to show that SPOT's empirical tail deviation exceeds the null critical order n with high probability under the alternative hypothesis H 1, thereby confirming its ability to achieve discovery globally.

Empirical Validation

The theoretical predictions are strongly validated by extensive simulation studies and experiments:

Transition Bands: The empirical transition bands observed in simulations align precisely with the theoretically predicted discovery boundary.
Performance Comparison: Experiments comparing SPOT-oracle, SPOT-plugin, and AOL (Adaptive Oracle Localization) across various edit mechanisms (random substitution, insertion, deletion) and generation temperatures confirm that SPOT methods generally outperform or remain competitive with AOL, especially at higher temperatures.

Conclusion

In summary, this paper provides a rigorous theoretical framework establishing the fundamental limits of watermark localization in LLM text generation. It proves that while global detection is achievable under certain conditions (p + q, 2p + q - alpha < 1), discovery is fundamentally harder and consistent classification is impossible within the coordinatewise pivot-based rule class. The proposed SPOT method successfully circumvents these limitations by employing an adaptive, data-driven tail-thresholding strategy that achieves near-optimal discovery power throughout the statistically discoverable region, validated both theoretically and empirically.

Improvements for AI systems

1.textbfAdaptive Localization via SPOT Algorithm Support in Mixed-Source Text Reconstruction: The improved system can perform token-level localization by searching for positions whose pivotal statistics fall in an extreme upper tail relative to the null law µ0, as described in Section 3.2, thereby achieving the optimal discovery boundary without requiring prior knowledge of asymptotic exponents (p, q, α).

2.textbfRobustness Against Human Edits via Adaptive Thresholding: The system can reliably identify which parts of a mixed-source text still preserve watermark evidence by using a data-driven estimate of the surviving watermark fraction, which attains the optimal discovery boundary and near-optimal discovery power relative to homogeneous pivot-based rules.

3.textbfFailure Mode Diagnosis via Phase Transition Analysis: The improved system can diagnose whether localization is statistically possible, detectability, or discoverability by characterizing the phase transitions in the parameter space (p, q, α), showing that discovery is strictly harder than detection and consistent classification is impossible across the parameter regime within this class.

Abstract

Watermarking provides a principled way to authenticate text generated by large language models (LLMs). In practice, however, the final text may be mixed-source, with watermark evidence surviving at only a subset of token positions after rewriting, insertion, deletion, or paraphrasing. Although prior work has studied global detection of watermark signals, when such signals can be localized remains unclear. We formulate watermark localization as a token-level multiple-testing problem based on pivotal statistics, with a latent indicator recording whether watermark dependence survives at each position. Under an asymptotic regime indexed by exponents for signal sparsity, next-token concentration, and effective-vocabulary growth, we derive a sharp boundary for global detection and phase transitions for discovery and classification within the class of coordinatewise pivot-based localization rules. We show that discovery is strictly harder than detection and that consistent classification is impossible across the parameter regime within this class. We then develop an adaptive thresholding method that does not require knowledge of the exponents or time-varying next-token distributions, but uses a data-driven estimate of the surviving watermark fraction. The method attains the optimal discovery boundary and near-optimal discovery power relative to homogeneous pivot-based rules. Simulations support the theoretical phase transitions, while experiments on model-generated texts demonstrate practical localization performance under common edit mechanisms.