{"id":"16028b70-903c-451d-8ac2-97c0ac422867","arxiv_id":"2608.03123","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":14,"one_line_summary":"TFR-Net tracks channel-level risk trajectories across sequential unlearning requests to suppress persistent target-related channels and safely reactivate dormant channels, improving the forgetting-utility trade-off.","lead":"This paper proposes TFR-Net, a method for continually removing sensitive knowledge from a large language model by tracking which internal channels carry that knowledge over time and switching off only the persistent ones. It also reactivates low-risk dormant channels to preserve model capability, and tests show a better balance between forgetting and retained utility than six baselines.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Eq. (10) scores |∂L/∂m|, but suppressing a channel with positive forget-loss derivative improves forget accuracy; the magnitude-only score can select the wrong channels.","rationale":"The central claim is that TFR-Net improves the continual unlearning trade-off, and the mechanism is entirely driven by the channel scores in Eq. (10). Those scores enter the suppression priority, the historical risk memory, and the recovery candidate pool. The paper never considers the sign of ∂L_f/∂m, even though suppressing a channel with a positive derivative locally improves forget-set accuracy. This is not a subtle implementation detail: it means the method's stated selection criterion is not aligned with the unlearning objective. The retain guard only protects retained utility, so it cannot catch this mis-selection. I partially agree with the reader's weakest assumption about dormant channels: the score may be uninformative when m is near zero, but that is a special case of a more general issue that affects active channels as well. The concrete ablation I propose would settle whether the sign actually matters empirically. If the signed variant changes results, the paper's explanation of why TFR-Net works needs revision and the headline empirical claim may be dataset-dependent. If the signed variant gives similar results, then the magnitude-only score is empirically benign and the concern is resolved. I would keep the reader's conditional verdict because the issue is testable and does not by itself falsify the empirical tables; however, the condition should explicitly include the sign-direction ablation.","tokens_in":12010,"tokens_out":9687,"duration_ms":94762,"concrete_test":"Run an ablation on all three continual streams replacing Eq. (10)'s absolute value with the signed mask-gradient score for the forget split (e.g., score = max(0, -∂L_f/∂m(c))) and re-ranking suppression by that signed score, keeping all other hyperparameters fixed. If Mean Trade-off or F@20 changes materially, especially if BoolQ F Avg drops from 63.25, then the magnitude-only score is not a faithful relevance signal. A cheaper unit check: on one BoolQ request, suppress the top-128 channels with positive ∂L_f/∂m and separately the top-128 with negative ∂L_f/∂m; if the positive group leaves forget-set accuracy higher (or unchanged) while the negative group lowers it, the sign direction is decisive.","verdict_should_be":"UNCHANGED","load_bearing_attack":"Eq. (10) defines ψ_q_t(c) as |mean_i a_i ∂L_q/∂tilde_h_i|. Because tilde_h = m(c) a, this is |∂L_q/∂m(c)|. The suppression rule (Eq. 18) reduces m for channels with the largest |∂L_f/∂m|. To first order, suppressing channel c by δ changes forget loss by -δ ∂L_f/∂m(c). When ∂L_f/∂m(c) > 0, suppression decreases L_f and therefore improves forget-set accuracy, the opposite of unlearning. The contrast in Eq. (11) and the recovery filters in Eqs. (20)-(21) all consume this magnitude-only score, so they cannot distinguish target-carrying channels from channels that currently oppose target recall. The retain-loss guard in Eq. (24) does not fix this: it constrains retained utility, not the direction of the forget-loss gradient. The weak BoolQ forgetting (F Avg 63.25) is consistent with mis-selection, though not proof. The reader's dormant-channel concern is plausible, but the sign issue applies to active channels too and is more load-bearing.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper introduces TFR-Net, a mask-only method for continual LLM unlearning. It maintains persistent per-channel masks on selected projection channels, computes per-request channel-level risk scores from forget and retain batches, tracks temporal risk statistics across requests, suppresses channels with persistent target relevance, and reactivates dormant low-risk channels under a retain-loss guard. The claim is that this trajectory-guided suppression plus retain-guarded recovery improves the trade-off between unlearning effectiveness and retained utility compared with six baselines. Experiments are reported on LLaMA-7B over three continual unlearning streams (Arithmetic, OpenBookQA, BoolQ) plus a TOFU privacy evaluation, with the main metric being a harmonic-mean trade-off. The paper also includes an ablation study and a cross-request structural analysis of channel relevance and capacity loss.","tokens_in":12345,"tokens_out":5505,"duration_ms":53035,"significance":"Continual LLM unlearning is an important and timely problem, and the paper identifies two plausible structural phenomena: target relevance shifts across channels over requests, and repeated suppression depletes usable capacity. If the proposed mechanism worked as described, a mask-based, backbone-free unlearning scheme with a retain guard would be a useful contribution to the field. The ablation study gives some support for the separate roles of recovery, retain guidance, and historical risk. However, the central selection mechanism has a sign-error issue that can select channels whose suppression improves forget-set accuracy rather than degrading it, and the empirical evaluation lacks error bars, code, and a complete hyperparameter specification. For these reasons the main claim is not yet established.","major_comments":[{"comment":"The channel score in Eq. (10) is defined with an absolute value: psi_q_t(c) is the magnitude of the mean over tokens of a_t,c times the gradient of L_q with respect to the masked activation. Since the masked activation is tilde_h = m(c) a, the score is |partial L_q / partial m(c)|. The suppression rule in Eq. (18) decreases m(c) for channels in the selected set P_t. To first order, suppressing channel c by delta changes the forget loss by approximately -delta * m(c) * partial L_f / partial m(c). When partial L_f / partial m(c) > 0, suppression reduces L_f and therefore improves forget-set accuracy, which is the opposite of unlearning. Because Eq. (11), Eq. (17), and the recovery filters in Eqs. (20)-(21) all consume the magnitude-only score, they cannot distinguish channels whose suppression helps forgetting from channels whose suppression hurts it. The retain-loss guard in Eq. (24) only constrains retained utility and does not correct the direction of the forget-loss gradient. The method should use a signed score or an explicit sign-aware selection rule, and the authors should empirically verify that selected channels indeed increase L_f when suppressed.","section":"Methodology, Eq. (10)-(11) and Eq. (18)"},{"comment":"The manuscript asserts that the activation-gradient score 'remains informative for dormant channels even when their current masked contributions are small'. This assertion is load-bearing for the recovery candidate pool in Eq. (20) and the recovery score in Eq. (21), because dormant channels have mask values near zero. When m(c) is near zero, the gradient with respect to the masked activation can be large or arbitrary, and no argument is given that the resulting score reflects the channel's true relevance to forgetting or retention. This should be supported either by a theoretical derivation or by a direct experiment, for example comparing the proposed score-based recovery against random recovery and against an oracle that uses the true effect of unmasking on forget and retain losses.","section":"Methodology, Eq. (10) and Eq. (20)"},{"comment":"The main empirical claim is that TFR-Net 'consistently' achieves a better trade-off, but the reported numbers come from single runs with no error bars, no confidence intervals, and no significance tests. Evaluation uses only 100 examples per task at four checkpoints, which is a very small sample for a 7B-parameter model. In addition, no code is released and several hyperparameters are left unspecified, including the per-dataset pruning budget, the exact values of beta_mu, beta_sigma, beta_omega, lambda_omega, delta_t, tau_d, tau_f, tau_h, tau_g, delta_rec, and the coefficients in Eqs. (11) and (21). This prevents independent verification of the central trade-off claim and should be addressed in a revision.","section":"Experiments, Tables 1-3 and Implementation Details"}],"minor_comments":[{"comment":"The abstract and the text state that TFR-Net improves the mean trade-off over the best baseline by 8.34 percentage points, while Table 1 reports a Gain vs. Best of +8.35; the numbers should be reconciled.","section":"Abstract and Table 1"},{"comment":"Equation (3) is described as a 'conceptual cumulative objective', but the relation between this objective and the actual mask update rule is not formalized; the authors should clarify whether the procedure optimizes this objective or merely approximates it.","section":"Problem Definition, Eq. (3)"},{"comment":"The trade-off metric is defined only as the 'harmonic mean of forgetting effectiveness and retained utility' without an explicit formula; the exact definition should be given so readers can interpret Table 1 and Table 2.","section":"Evaluation Metrics"},{"comment":"The text says 'the maximum global pruning budget is set to 128 channels per request according to the dataset' and later mentions 'epsilon_r = 0.05', but the per-dataset budgets and the relation between epsilon_r and the two tolerances epsilon_rel and epsilon_abs in Eq. (24) are not specified.","section":"Implementation Details"},{"comment":"The ablation table would benefit from error bars or repeated-seed results, since several entries differ by only a few percentage points and the qualitative conclusions rely on those small differences.","section":"Table 2"}],"recommendation":"major_revision","confidential_remarks":"The sign-blindness of the score in Eq. (10) is the main technical blocker and should be resolved before publication. In addition, the manuscript cites several 2026 works (e.g., Cheng et al. 2026, Zade et al. 2026, Yan et al. 2026) that may not yet be publicly available; the authors should verify the citations. Given the lack of code and error bars, the empirical claims would be substantially strengthened by a reproducibility package."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nThe thing to know about arXiv:2608.03123 is that it proposes a genuinely new structural mechanism for continual LLM unlearning—persistent channel masks with trajectory-based risk tracking and a retain-guarded recovery step—and shows consistent trade-off improvements on three continual streams. But the central selection rule has a sign problem that the paper never addresses, and the empirical support is thinner than the tables suggest.\n\nWhat's new: the mask trajectory across requests, distinguishing persistent target-related channels from transient hotspots, and reactivating dormant channels under a retain-loss guard. That framing is a real step beyond O3, ALKN, ASU and LLM-Eraser. The structural analyses in Figures 3 and 4—target relevance redistribution and cumulative capacity loss—are useful and motivate the design. The ablation in Table 2, while lacking error bars, does show the components matter for stability at R20.\n\nWhere it gets soft. The risk score in Eq. (10) is the absolute value of the gradient of the loss with respect to the masked activation, which equals |∂L_f/∂m|. Suppression reduces m for the largest such scores. But to first order, reducing m changes L_f by -δ ∂L_f/∂m. If ∂L_f/∂m > 0, suppression lowers the forget loss, i.e., improves the model's accuracy on the forget set—the opposite of what unlearning should do. Using the magnitude discards the sign, so the top-priority channels can be exactly the wrong ones. The contrast in Eq. (11) and the recovery filters in Eqs. (20)-(21) all consume this magnitude-only score, so the issue propagates through the whole pipeline. The retain guard in Eq. (24) bounds retained-utility loss; it does not fix the direction problem. The weak BoolQ forgetting (F Avg 63.25) is consistent with mis-selection, though not proof. The reader's dormant-channel concern is plausible but secondary; the sign issue hits active channels too.\n\nAlso, no code, no released data, no error bars, and evaluation on 100 examples per task. The ablation and main results could be seed-dependent. Many hyperparameters are unstated.\n\nWho should read this: anyone working on continual unlearning or model editing will want to know the trajectory-recover idea. But I would not take the central claim at face value until the sign issue is addressed—either by deriving the correct selection rule or by adding an experiment that shows sign-aware scores make no difference (which would itself be surprising). A serious referee should ask for that, plus code and variance reporting.\n\nRecommendation: send to peer review, but conditional on the authors fixing or at least directly testing the sign problem. This is not a desk reject; the idea is worth engaging.","headline":"A genuinely new mask-trajectory/recovery mechanism for continual unlearning, but the core selection rule ignores the sign of the forget-loss gradient and needs fixing before the empirical claims can be trusted.","tokens_in":12859,"tokens_out":2649,"would_cite":false,"duration_ms":22880,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Channel-level risk trajectories keep LLM unlearning effective across a stream of forget requests.","keywords":["machine unlearning","continual unlearning","large language models","channel masking","trajectory-aware risk tracking","capacity recovery","retained utility","privacy unlearning"],"falsifier":"Restore each dormant channel one at a time in a final TFR-Net model, measure the actual change in forget-set accuracy, and compare it with the channel's trajectory and recovery scores; if high-scoring dormant channels do not disproportionately bring back forgotten knowledge, the central relevance assumption fails.","tokens_in":11827,"feed_emoji":"🔄","tokens_out":6391,"duration_ms":55651,"temperature":0.7,"pith_summary":"Continual unlearning for large language models faces two linked failures: an unlearning step can push the knowledge it was supposed to erase into other pathways, so it resurfaces later, and each suppression step consumes capacity needed for tasks the model should keep. This paper proposes TFR-Net, which tracks each channel's forget risk across requests, suppresses only channels that are persistently target-related rather than transient hotspots, and then reactivates dormant channels that are safe and useful. Each proposed update is accepted only if retained utility stays within a tolerance. On four datasets, including a privacy benchmark, the method achieves a better forgetting–utility balance than existing continual and one-shot unlearning baselines. If the mechanism holds, it suggests unlearning can be managed as an evolving structural mask rather than as repeated weight edits.","feed_headline":"Channel masks keep LLM unlearning strong across 20 requests","feed_subtitle":"TFR-Net separates persistent from transient channels and revives safe ones, improving the forget–retain trade-off.","key_machinery":"The trajectory-aware pathway priority $\\Gamma_t(c)$ is the central object: a per-channel score whose numerator rewards persistent risk and repeated high-risk hits, and whose denominator penalizes abrupt risk changes and uncertainty, so it separates persistent target-related channels from transient hotspots. It drives multiplicative suppression factors $\\alpha_t(c)$ and, together with retention and risk thresholds, selects the recovery candidate pool. The other load-bearing mechanism is the retain-guarded acceptance rule, which commits or atomically rolls back the entire tentative mask and trajectory memory depending on whether the retain loss meets a tolerance.","core_discovery":"TFR-Net establishes that the long-horizon unlearning problem can be cast as joint control of a channel mask trajectory. It maintains a mask over transformer projection channels with frozen backbone weights. For each request it computes a risk score $\\chi_t(c)$ by contrasting forget-set and retain-set activation-gradient signals, then maintains smoothed risk memory $\\mu_t(c)$, high-risk hit memory $\\omega_t(c)$, and temporal uncertainty $\\sigma^2_t(c)$ to form a trajectory priority $\\Gamma_t(c)$. Channels whose trajectory marks them as persistently target-related are suppressed; transient hotspots are spared. Dormant channels with low current and historical risk and positive retained-utility contribution are partially reactivated, and the whole structural state is committed only when the retain loss stays within tolerance. Across Arithmetic, OpenBookQA, BoolQ, and TOFU streams the method reports a more favorable forgetting–utility trade-off, with a mean trade-off gain of 8.34 percentage points over the best baseline.","pith_inferences":["My inference: the same trajectory-tracking idea could be adapted to sequential model editing or safety alignment updates, where the same self-repair phenomenon appears after targeted weight changes.","My inference: because the backbone stays frozen and only masks change, the method may translate to deployments where the weights are protected and updates arrive as inference-time masks, though the paper does not test this deployment mode.","My inference: the retain-loss guard is a generic commit-or-rollback condition that could be bolted onto other structural unlearning schemes; a direct test would be applying TFR-Net's recovery candidate rules to a different suppression method."],"forward_implications":["If TFR-Net is correct, an LLM can keep forgetting specific knowledge across many requests without finetuning the backbone weights; the entire unlearning state lives in per-channel masks.","Suppressing only trajectory-persistent target channels should reduce the re-emergence of forgotten knowledge, because alternative pathways that only transiently carry the target are left intact.","Reactivating low-risk dormant channels under a retain guard should slow the cumulative capacity loss that limits long horizons, so later requests keep more usable model capacity.","The reported TOFU results imply that structural unlearning can improve membership-inference privacy while preserving model utility, rather than trading one for the other.","Ablations without recovery or retain guidance show larger endpoint failures at Request 20, so the improvement is mainly long-horizon stability rather than single-request strength."],"supporting_citations":[{"why":"Supplies the LLM-Eraser selective-pruning baseline, the strongest localization-based comparison the method must beat.","marker":"(Zhang et al. 2025b)"},{"why":"Supplies the O3 continual-unlearning baseline based on orthogonal adapters, which stabilizes updates across requests.","marker":"(Gao et al. 2025)"},{"why":"Supplies the ASU attention-smoothed self-distillation baseline for continual unlearning.","marker":"(Zade et al. 2026)"},{"why":"Provides the TOFU benchmark and its official forget/retain and privacy evaluation protocol.","marker":"(Maini et al. 2024)"},{"why":"Supplies the RMU baseline and the representation-steering approach to unlearning.","marker":"(Li et al. 2024a)"},{"why":"Supplies the SimNPO baseline for reference-free negative preference optimization.","marker":"(Fan et al. 2025)"},{"why":"Supplies the gradient-ascent baseline and the knowledge-unlearning formulation.","marker":"(Jang et al. 2023)"},{"why":"Provides the BoolQ dataset used for a forget stream and retained-utility evaluation.","marker":"(Clark et al. 2019)"}],"fun_headline_variants":["Trajectory-guided masks beat baselines in continual unlearning","Channel-mask trajectory yields 8.3-pt trade-off gain","TFR-Net: persistent channel masks improve forget-retain balance","Mask trajectories improve continual LLM unlearning trade-off"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The method assumes a channel that is already suppressed still yields a trustworthy relevance score from the gradient of the loss through its masked activation; if that score is noise when masks are near zero, the trajectory priorities and the recovery candidate pool are built on unreliable signal.","fun_headline_variants_meta":{"raw":{"variants":["Trajectory-guided masks beat baselines in continual unlearning","Channel-mask trajectory yields 8.3-pt trade-off gain","TFR-Net: persistent channel masks improve forget-retain balance","Mask trajectories improve continual LLM unlearning trade-off"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.00164,"raw_usage":{"total_tokens":6506,"prompt_tokens":919,"completion_tokens":5587,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":535,"completion_tokens_details":{"reasoning_tokens":5515}},"tokens_in":535,"tokens_out":5587,"duration_ms":36247,"temperature":1.0,"reasoning_tokens":5515,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T14:51:23.677458+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Restore each dormant channel one at a time in a final TFR-Net model, measure the actual change in forget-set accuracy, and compare it with the channel's trajectory and recovery scores; if high-scoring dormant channels do not disproportionately bring back forgotten knowledge, the central relevance assumption fails.","supporting_citations":[],"review_version":1}