{"id":"69169b87-0151-4ed1-baa5-bf7f248954a0","arxiv_id":"2508.05084","paper_version":2,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"In the delayed Vicsek model at high speed, delay widens the noise range of phase-separated bands, shifts the transition to disorder to higher noise, and speeds band formation by generating larger swirls.","lead":"The supplied full text is a numerical study of the delayed Vicsek model in active matter, not the pathology-AI paper named in the metadata. It reports that time delays widen the noise window for phase-separated traveling bands and speed their formation, which matters for understanding flocking and swarm control with sensing lags.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The η_o|s boundary, and hence the claimed non-monotonic delay dependence, rests on a coarse 13-bin C1 estimator with no error bars; it should be re-derived with finer binning and bootstrap before the broadening claim is accepted.","rationale":"The reader's weakest assumption—that the C1-based phase boundaries η_o|s and η_s|d lack uncertainty estimates and rely on a coarse 13-bin histogram and ten snapshots—is precisely the most load-bearing concern for the paper's central claim. The claim of a non-monotonic η_o|s(τ̄) and a widening coexistence window depends on accurately locating the lower boundary, and the C1 estimator is not validated against alternative order parameters or binning choices. The upper boundary η_s|d is better supported by the Binder cumulant and polarization data, so the fragility is concentrated on η_o|s. I agree with the reader's assessment. The metadata mismatch (AdaFusion vs. delayed Vicsek) is a serious packaging anomaly, but it does not change the scientific evaluation of the supplied full text. Since the reader already assigned CONDITIONAL, my concern supports that verdict without moving it; hence UNCHANGED is appropriate. The proposed concrete test—re-deriving η_o|s with finer binning, bootstrapped confidence intervals, and an independent stripe order parameter—would directly settle whether the nonmonotonic trend and window broadening are robust or artifacts of the C1 estimation procedure.","tokens_in":13143,"tokens_out":3936,"duration_ms":45916,"concrete_test":"Re-estimate η_o|s for all reported reduced delays (τ̄ = 0, 0.5, 1, 1.5, 2, 2.5) and both densities from the original simulation trajectories using: (i) finer bin counts for the marginal density in Appendix B (e.g., 25, 51, 101 bins instead of 13); (ii) bootstrap resampling over at least 100 snapshots per noise value to obtain confidence intervals on C1; and (iii) an independent stripe order parameter, such as the Fourier amplitude of the density along the mean-velocity direction or the Kürsten–Ihle stripe parameter (Ref. 40). Determine η_o|s as the noise where this order parameter jumps sharply. If the resulting η_o|s(τ̄) curve is not consistently non-monotonic across bin counts and order parameters, the broadening claim is not robust.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim that time delay broadens the coexistence window and acts as a control parameter rests on the two phase boundaries η_s|d and η_o|s. η_s|d is derived from Binder cumulant and polarization (Fig. 1), which are standard order parameters. However, η_o|s is derived solely from the height C1 of the first peak of the directional density autocorrelation (Appendix B, Eqs. B.3–B.6; Figs. 8–9). This estimator uses only 13 histogram bins for the marginal density along the polarization direction, and the plotted values are averaged over only ten snapshots. The location of the 'sharp increase' in C1 is therefore sensitive to bin width, to the normalization in Eq. (B.6) (dividing by the maximum absolute value, which itself fluctuates), and to sampling noise. The paper provides no confidence intervals on η_o|s. Since the nonmonotonic dependence of η_o|s on delay is a central novel result, a modest systematic shift in the C1 threshold (e.g., a delayed jump at small τ or an early jump at large τ) could change the nonmonotonic trend or even the sign of the window broadening. The visual stripe-counting used in Fig. 2f is explicitly subjective, and the cross-sea state ambiguity discussed in Sec. 5 further complicates the definition of the phase boundary. Thus the most load-bearing weakness is that the lower phase boundary, which drives the paper's headline conclusion, lacks a validated and statistically grounded measurement.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The manuscript presents a numerical study of the delayed Vicsek model at high speed (v0=0.5) and large system sizes (N=65,536 and 131,072), mapping the phase diagram in the (noise η, reduced delay τ̃) plane. Using polarization, its variance, the Binder cumulant, and a directional density autocorrelation peak height C1, the authors identify ordered, liquid-gas coexistence, and disordered phases. They report that the upper boundary η_{s|d} increases monotonically with delay, while the lower boundary η_{o|s} is non-monotonic, leading to a broadening of the coexistence window. They also report that the number of traveling bands increases and the band-formation time decreases with delay, attributed to the emergence of large swirls. The paper claims time delay acts as an effective control parameter for phase behavior.","tokens_in":13510,"tokens_out":6433,"duration_ms":60785,"significance":"The results are potentially significant for active matter physics, as they extend the delayed Vicsek model to a high-speed regime and propose delay as a control parameter. The study is based on large-scale simulations and uses multiple order parameters, including a Binder cumulant and susceptibility, which are standard tools. However, the key lower-phase boundary η_{o|s}, which underpins the non-monotonic claim and the broadening of the coexistence window, is determined from a coarse autocorrelation peak C1 with no error bars. The visual stripe counting is also subjective. These measurement weaknesses currently limit the strength of the conclusions.","major_comments":[{"comment":"The lower phase boundary η_{o|s}, central to the paper's main claims, is extracted from the height C1 of the first peak of the directional density autocorrelation, computed with only 13 histogram bins (Appendix B) and averaged over ten snapshots. No error bars or confidence intervals are given. The normalization in Eq. (B.6) divides by the maximum absolute value of the correlation, which itself fluctuates. This makes the location of the 'sharp increase' in C1 sensitive to bin width and sampling noise. A modest systematic shift in the C1 threshold could alter the reported non-monotonic η_{o|s} trend and even change the sign of the window broadening. The authors should re-derive η_{o|s} with finer binning, more snapshots, bootstrap estimates, and demonstrate robustness of the non-monotonic dependence.","section":"Sec. 5 and Appendix B (Eqs. B.3–B.6, Figs. 8–9)"},{"comment":"The claim that the maximum number of stripes increases with delay is based on visual inspection of snapshots. The paper itself acknowledges this is subjective and notes the ambiguity of 'cross-sea' states, where stripes move in different directions. A more objective measure (e.g., automated stripe detection or Fourier analysis) is needed to support this claim. Additionally, the stability of the cross-sea configurations is unresolved, which may affect the counting protocol. Without a reproducible criterion, this part of the phase characterization is not verifiable.","section":"Fig. 2f and Sec. 5"}],"minor_comments":[{"comment":"The abstract supplied at the top of the submission describes a pathology foundation model framework (AdaFusion) and does not correspond to the manuscript text, which concerns the delayed Vicsek model. This mismatch must be corrected before any further consideration.","section":"Abstract"},{"comment":"The relaxation time t_R is defined as the first time the polarization reaches half of its maximum value. For a bistable system, this first-passage quantity is not the standard relaxation time and may be dominated by a single fluctuation. The conclusion that 'delay has no significant effect' would be stronger with a more conventional autocorrelation-time estimate and statistical tests.","section":"Sec. 4, Fig. 4"}],"recommendation":"major_revision","confidential_remarks":"The manuscript's title and abstract in the submission system (AdaFusion, pathology foundation models) bear no relation to the full text (delayed Vicsek model). I suspect a submission error; the editor should verify the correct manuscript. Scientifically, the paper's main claims would be strengthened by addressing the C1 robustness issue. Despite the measurement concerns, the underlying simulation effort is substantial and the topic is suitable for New J. Phys."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Two things up front. First, the package is mismatched: the arXiv metadata and abstract describe AdaFusion, a pathology-foundation-model fusion method, but the full text is a numerical study of the delayed Vicsek model (looks like arXiv:2508.05086). Whatever happened in the pipeline, the AdaFusion paper cannot be reviewed from this file. Treating the full text as the work, here is the take.\n\nWhat is new: prior delayed-VM simulations ran at low speed (v0=0.05) and never saw the liquid-gas phase separation. This paper goes to v0=0.5, N=65536/131072, L=256, and maps the delay-noise phase diagram. The headline results—η_s|d increases and saturates with delay, the coexistence window broadens, band count grows and formation time shrinks, swirl radius grows—are plausible and, as far as I know, new. The paper is also honest: it flags the visual stripe counting as subjective, discusses the cross-sea ambiguity, and admits the C1-based boundaries are what they are.\n\nWhere it is soft. The lower boundary η_o|s, which drives the claimed non-monotonic delay dependence, comes from the height C1 of the first peak of a directional autocorrelation built from a 13-bin marginal density, averaged over ten snapshots, with no confidence intervals (Appendix B, Figs. 8–9). That is genuinely fragile: a shift in bin width or sampling noise could move the threshold and flatten or even invert the non-monotonic trend. The upper boundary η_s|d rests on Binder cumulant and polarization, which are standard, so the broadening claim is likely directionally right, but the 'non-monotonic control parameter' story leans on the weak estimator. The stripe counts in Fig. 2f are explicitly visual. And there is no released code or raw data, which makes all of this hard to check.\n\nThe paper's explanatory borrowings—effective parameter v0τ and the susceptibility interpretation—come from Ref. [29] by the same group; that is acceptable since it is their own published mechanism, but it means the novel part is largely empirical.\n\nBottom line: this is a serious numerical study of a real question, and I would send it to a referee if the submission matched the content. The C1-derived boundary needs to be re-derived with finer binning and bootstrap errors, and the data/code should be archived. As it stands, the AdaFusion submission is not reviewable. For the physics content: worth watching, not worth citing until the boundary is robust.","headline":"The file under review is actually a delayed-Vicsek simulation paper, not the AdaFusion abstract; the physics is interesting and mostly sound, but the main novel phase boundary lacks error bars and the package is mismatched.","tokens_in":14019,"tokens_out":2377,"would_cite":false,"duration_ms":26697,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"In the delayed Vicsek model at high agent speed, time delay acts as a control parameter that broadens the phase-separated noise window.","keywords":["Vicsek model","time delay","active matter","phase separation","order-disorder transition","collective motion","traveling bands","delay-induced control"],"falsifier":"Recompute the phase boundaries $\\eta_{o|s}$ and $\\eta_{s|d}$ using a finer directional density histogram or many more snapshots (or a different order parameter such as local density correlation) and check whether the non-monotonic dependence of $\\eta_{o|s}$ and the monotonic rise of $\\eta_{s|d}$ persist; alternatively, run simulations at larger system sizes to see whether the $C_1$ jumps sharpen or move.","tokens_in":12985,"feed_emoji":"🐦","tokens_out":5405,"duration_ms":55469,"temperature":0.7,"pith_summary":"This paper numerically studies the Vicsek model with delayed alignment interactions at a relatively high agent speed and a large fixed system size. It claims that delay, while preserving the three standard phases (ordered, liquid–gas coexistence, disordered), qualitatively changes their stability: the upper critical noise for the transition to the disordered state rises with delay, the lower critical noise for the ordered–coexistence transition responds non-monotonically, and as a result the noise window in which phase separation occurs widens. Delay also accelerates band formation, increases the number of bands, and generates swirling arcs whose radius grows with delay. The broader implication is that time delay can serve as a control parameter for tuning the dynamic phase behavior of active matter systems.","feed_headline":"Delay widens the phase-separated window in flocking swarms","feed_subtitle":"A numerical study shows longer delays stabilize the coexistence phase, yielding more bands faster","key_machinery":"The delayed Vicsek model itself, where each agent aligns to the average orientation of neighbors it perceived $\\tau$ time steps earlier, with random noise. The analysis uses standard order parameters (average polarization, polarization variance, Binder cumulant) plus the height $C_1$ of the first positive peak of the directional density autocorrelation function, computed from a coarse 13-bin histogram, to locate the phase boundaries. The key mechanism is the reduction of effective interaction time at long delays, which weakens collision-induced flocking and produces large-radius swirling arcs that seed quicker band formation.","core_discovery":"In the delayed Vicsek model with speed $v_0=0.5$, reduced delay times $\\bar\\tau$ from 0 to 2.5, and system sizes $N=65536$ and $131072$ at densities $\\rho=1$ and $2$, the authors find that the order–disorder transition remains discontinuous and bistable for all delays. The critical noise $\\eta_{o|s}$ for the ordered-to-coexistence transition first increases then decreases with delay, while $\\eta_{s|d}$ for the coexistence-to-disorder transition increases monotonically and appears to saturate, broadening the coexistence noise interval $\\eta_{s|d}-\\eta_{o|s}$. In this phase-separated state, the number of traveling bands increases and the stripe-formation time decreases with delay, an accelerat","pith_inferences":["If the non-monotonic $\\eta_{o|s}$ reflects the susceptibility trade-off proposed for slow agents, then at intermediate delays the system may be most responsive to external perturbations—a testable prediction for experiments with tunable feedback delay.","The observed saturation of order-parameter features with delay hints that the effective delay-controlled parameter might be $v_0 \\tau$; if so, different speed–delay combinations with the same product could produce similar phase behavior.","The observed bistability between parallel and cross-sea band configurations suggests the delayed VM may host multiple coexisting banded attractors; classifying their stability boundaries would settle whether these are transient or genuinely multistable states.","One could test the swirl-radius mechanism experimentally by measuring the typical curvature of transient density arcs in light-controlled bacterial or robotic swarms with imposed feedback delay."],"forward_implications":["If delay broadens the coexistence window, natural or synthetic swarms with longer perception–reaction lags may spend more time in phase-separated, banded configurations.","The saturating $\\eta_{s|d}$ suggests that beyond a moderate delay, further delay no longer stabilizes the ordered state, giving a finite operating window for delay-based control.","The delay-induced shortening of band formation time with growing swirl radius offers a mechanistic route to accelerate pattern formation in active matter.","The near-independence of polarization relaxation time from delay separates time scales: delay changes phase boundaries and band dynamics without slowing global ordering kinetics.","At high speeds, delay can reversibly switch the system between ordered and phase-separated states, offering a control knob complementary to noise."],"supporting_citations":[{"why":"Defines the original Vicsek model's alignment rule and the ordered/disordered phases that this paper extends.","marker":"[35]"},{"why":"Studied the delayed Vicsek model at low speeds, showing that short delays enhance order and long delays disrupt it; serves as the baseline for delay effects.","marker":"[18]"},{"why":"Analyzed the delayed VM at low speed, reporting susceptibility tails and saturating transition features; supplies the effective $v_0 \\tau$ parameter and the comparison for the high-speed regime.","marker":"[29]"},{"why":"Identified the order–disorder transition in the standard VM as a liquid–gas phase separation, the phenomenon this paper finds persists under delay.","marker":"[32]"},{"why":"Describes the discontinuous nature of the transition and band formation in finite-size systems, framing the bistability observed here.","marker":"[33]"},{"why":"Established the role of system size and speed in band formation, providing the scaling logic the authors apply to interpret delay effects.","marker":"[34]"},{"why":"Shows that the number of traveling bands increases with agent speed, directly supporting the delay–speed analogy for band count.","marker":"[36]"},{"why":"Offers an alternative order parameter for stripe phases and introduces cross-sea states, which the current paper observes and discusses.","marker":"[40]"}],"fun_headline_variants":["AdaFusion adaptively fuses pathology models for better inference","Prompt-guided fusion of pathology models lifts performance","AdaFusion: adaptive fusion outperforms individual path models","Combining pathology models with prompts improves predictions","Adaptive prompt-guided fusion boosts pathology AI"],"cache_read_input_tokens":2816,"weakest_assumption_plain":"The phase boundaries that support the claims are read off from jumps in the peak height $C_1$ of a one-dimensional density autocorrelation built from only thirteen bins and averaged over ten snapshots per noise value, so any shift in where the jump occurs would change the reported trends.","fun_headline_variants_meta":{"raw":{"variants":["AdaFusion adaptively fuses pathology models for better inference","Prompt-guided fusion of pathology models lifts performance","AdaFusion: adaptive fusion outperforms individual path models","Combining pathology models with prompts improves predictions","Adaptive prompt-guided fusion boosts pathology AI"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.001037,"raw_usage":{"total_tokens":4200,"prompt_tokens":741,"completion_tokens":3459,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":485,"completion_tokens_details":{"reasoning_tokens":3386}},"tokens_in":485,"tokens_out":3459,"duration_ms":29846,"temperature":1.0,"reasoning_tokens":3386,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-05T23:34:13.692398+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Recompute the phase boundaries $\\eta_{o|s}$ and $\\eta_{s|d}$ using a finer directional density histogram or many more snapshots (or a different order parameter such as local density correlation) and check whether the non-monotonic dependence of $\\eta_{o|s}$ and the monotonic rise of $\\eta_{s|d}$ persist; alternatively, run simulations at larger system sizes to see whether the $C_1$ jumps sharpen or move.","supporting_citations":[],"review_version":1}