{"id":"bdcfd441-b3f2-467c-bfca-ae4fb66ec35e","arxiv_id":"2608.04416","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":8,"one_line_summary":"An evidence-driven method that records user feedback in temporal and activity-semantic bins outperforms LLM self-improvement methods for proactive interaction timing on a simulated assistant benchmark.","lead":"This paper proposes EOPA, a method that helps AI assistants learn when to proactively speak up by tracking each user's interaction and silence patterns across time and activity contexts. In a simulated benchmark, it substantially improves interaction-timing accuracy over LLM-based baselines while cutting adaptation cost and latency.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"EOPA's 19.80-point F1 gain requires full-information timing labels on silence steps; real feedback is missing exactly on those steps, and Table 3's random-drop test does not model that missingness.","rationale":"The reader's verdict is CONDITIONAL, and the reader's weakest_assumption identifies the full-information timing-feedback protocol. My independent reading of Sections 3, 4.1, 4.2, and 5.1 leads to the same point, sharpened as missing-not-at-random: after a SIL decision the user has no interaction to react to, so no feedback is generated; after an erroneous SIL, the user cannot communicate the missed opportunity. Equations (8) and (15) specifically require y_t=SIL to build silence evidence, and Equations (5)/(12), together with the variance scaling in (6)/(13), convert those counts into the evidence used for the decision in (17). Table 3's random retention is missing-completely-at-random and therefore cannot validate the protocol against the realistic missingness pattern. The benchmark result itself is internally coherent since all methods are compared on identical labels, so I do not see an internal inconsistency or a reason to reject the paper outright. But the central claim about personalized proactive assistance in deployed settings is unsupported without an interaction-conditioned feedback experiment. This is exactly the condition the reader attached, so the CONDITIONAL verdict should be kept rather than changed to ACCEPT or REJECT.","tokens_in":14942,"tokens_out":7065,"duration_ms":70304,"concrete_test":"Rerun the main comparison with a feedback protocol in which timing labels are emitted only on steps where the assistant chose INT (or where the user explicitly rejects an interaction), so y_t=SIL is never observed; freeze N_SIL and H_SIL updates in Eqs. (8)/(15) and let silence steps contribute no evidence. If EOPA's F1 falls toward the Reflexion baseline, or precision/recall become degenerate, the full-information timing-feedback assumption is load-bearing. As a secondary check, compare this structured-missingness result against Table 3's 50% random-drop condition to confirm that the missingness mechanism, not merely the retention rate, determines the outcome.","verdict_should_be":"UNCHANGED","load_bearing_attack":"EOPA's central empirical claim depends on a training signal that deployed proactive assistants do not have. Section 5.1 states that at each step the assistant 'receives binary timing feedback,' and Section 3 derives a timing label y_t after every decision. EOPA then increments silence counts in Eqs. (8) and (15) using y_t=SIL. In real use, a user who is not interrupted gives no explicit feedback, and missed opportunities (silence steps that should have been interactions) generate no feedback at all. The missingness is therefore not random: it is concentrated exactly on SIL-labeled steps and on false-negative errors. Table 3's robustness test drops labels uniformly at random, which does not reproduce this structure. If N_SIL and H_SIL never grow, or are inferred from 'no complaint means silence was fine,' then p_temp and p_act in Eqs. (5)/(12) are biased, and the evidence scores in Eqs. (7)/(14) no longer separate appropriate from inappropriate moments. The adaptive threshold calibration in Section 4.3 would be fit to distorted labels. The claimed 19.80-point F1 gain is thus a property of a full-information protocol, not of feedback available to a deployed assistant. This does not make the benchmark result internally incorrect, but it leaves the headline generalization to personalized proactive assistants unsupported without a structured-missingness evaluation.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes EOPA, an evidence-driven online adaptation method for deciding when a proactive AI assistant should initiate an interaction. EOPA maintains two evidence carriers, temporal preference anchors and evidence-bearing activity prototypes, which accumulate interaction-appropriate and silence-preferred counts from user feedback. At each polling step, it estimates temporal and activity evidence via user-prior-smoothed Beta-Binomial posteriors with uncertainty-guided scaling, fuses them with an adaptive weight, and compares the sum to an online-calibrated threshold. Response generation is decoupled from timing decisions and only invoked when an interaction is selected. On a ProPerSim-based benchmark with 32 simulated user personas over 14 days, EOPA achieves 30.46 interaction-timing F1, improves over the strongest LLM-based baseline by 19.80 F1 points, reduces silence-step latency from 688 ms to 1.96 ms, and lowers daily adaptation time from 11.41 s to 0.39 s. Incremental ablations, partial-feedback robustness, evidence-discriminability analysis, and similar-state decision accuracy are provided.","tokens_in":15280,"tokens_out":5650,"duration_ms":52708,"significance":"The contribution is timely and practically motivated. A decisive strength is that timing decisions are made by deterministic evidence statistics rather than by an LLM, giving large latency and adaptation-time reductions while keeping response generation decoupled. The incremental ablations (Table 2), the evidence-discriminability curves (Table 4), and the similar-state decision accuracy analysis (Table 5) are well-designed and internally consistent. If the feedback-availability assumption is repaired, the method offers a credible lightweight alternative to LLM self-reflection for proactive assistants. The main limitation on significance is external validity: the benchmark protocol supplies timing labels at every step, and the paper does not yet show that the strong F1 gain survives realistic missingness of silence feedback. The skeptical concern about structured label missingness is therefore load-bearing and should be addressed before the headline gain is interpreted as a deployable-policy result.","major_comments":[{"comment":"The online protocol assumes that after each decision the assistant receives user feedback from which a timing label y_t in {INT, SIL} is derived. In a deployed proactive assistant, a user who was not interrupted gives no explicit feedback, and missed opportunities (steps whose correct label is INT but where EOPA chose SIL) also produce no label. The counters N^SIL and H^SIL, and therefore the estimates p_temp and p_act in Eqs. (5) and (12), would not grow as modeled under realistic feedback. Table 3 randomly drops a fraction of all labels, which does not reproduce missingness concentrated on SIL-labeled and false-negative steps. This is load-bearing because the claimed 19.80-point F1 gain is obtained under the full-information protocol. Please add a structured-missingness experiment in which (i) SIL steps receive no label, or (ii) labels are available only for steps where the assistant interacted, and report the resulting F1 and adaptation curves. If the method relies on imputed or synthetic SIL labels, state that assumption explicitly as a limitation of the deployment claim.","section":"Section 5.1; Eqs. (8) and (15)"},{"comment":"The adaptive fusion weight lambda_t and decision threshold delta_t are calibrated by replaying decisions under candidate parameter pairs and optimizing F1 on recent historical data. The same accumulated stream is subsequently scored for the reported per-user metrics, so the evaluation does not cleanly separate the calibration signal from the test signal. Please state explicitly that each day's lambda_t and delta_t are computed only from steps strictly before the evaluation window, and ideally report a walk-forward variant in which the parameters are frozen before the target day. Without this clarification, the 30.46 F1 number could partly reflect tuning to the evaluated stream rather than a deployable online policy.","section":"Section 4.3; Section 5.2"},{"comment":"The results depend on several hand-set hyperparameters (kappa=2.5, K=12, gamma=0.1, rho=0.96, number of time intervals B, and progress-index cap R). The manuscript states that hyperparameter analysis is in Appendix I, but no appendix is included in the provided text. Because EOPA's advantage over baselines is large and concentrated in the timing branch, the absence of the promised sensitivity curves leaves open the possibility that performance is peaked at the reported settings. Please include the appendix or add a sensitivity table in the main text, reporting the F1 range across reasonable values of kappa, K, gamma, rho, and B.","section":"Section 5.1; Appendix I"},{"comment":"The comparison set contains only an LLM parameter-learning method and LLM-based self-improvement baselines. A simple non-LLM baseline, such as always-silence, interaction at the most frequent time intervals for each persona, or a threshold on the global user prior, would clarify whether the 30.46 F1 reflects effective use of feedback or simply the difficulty of the class-imbalanced task. This is inexpensive to add and would strengthen the claim that the evidence carriers, rather than the mere absence of an LLM for timing decisions, drive the observed gain.","section":"Section 5.2"}],"minor_comments":[{"comment":"Parts of the figure captions and labels render as uninterpretable glyph sequences (for example, '/uni00000014/uni00000015/...'); please replace these with readable textual labels in the final version.","section":"Figures 3 and 4"},{"comment":"The row headers use dashes to denote disabled components without a legend; adding check/cross marks or explicit component names would make the incremental ablation unambiguous.","section":"Table 2"},{"comment":"The definitions of F1@1 and F1@2 count predictions within one and two polling steps of an annotated interaction moment; please clarify how duplicate matches are handled when one prediction is near two annotated moments, and whether precision and recall are computed per moment or per prediction.","section":"Section 5.1"},{"comment":"Equation (16) introduces the fusion weight lambda_t but the main text does not state its initialization, positivity constraint, or allowed range; these details are presumably in the missing appendix and should be stated in the main text.","section":"Equation (16)"}],"recommendation":"major_revision","confidential_remarks":"I am not recommending rejection: the technical core is coherent, the ablations are responsive, and the efficiency gains are genuinely impressive. My recommendation hinges on the authors adding a structured-missingness evaluation for silence feedback and clarifying the calibration timeline; without those, the headline gain should not be read as a deployable-policy result. Please also ensure that the promised appendices are actually included in the revision, since several claims in the main text depend on material that is currently referenced but not present."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Here's the short version: the 19.80-point F1 gain is real on the paper's own simulated benchmark, but that benchmark gives the assistant a feedback signal no deployed assistant gets. If you treat the result as a demonstration of the method's internal mechanics rather than as evidence about real proactive assistants, the paper is solid; if you take the headline at face value, it overreaches.\n\nWhat's genuinely new: EOPA packages count-based temporal anchors and semantic activity prototypes with Bayesian smoothing, and adaptively fuses them with an online-calibrated threshold. Each component is known, but the combination—especially the uncertainty-scaled evidence scores and the lightweight updates—is not a direct restatement of prior work. The ablations are clean: adding temporal anchors, evidence scaling, the activity branch, and adaptive fusion each improve F1 in the expected direction. The math checks out, and the tables showing evidence separation and similar-state discrimination support the internal story.\n\nThe soft spots, in order of importance. First, the feedback assumption. Section 5.1 says that at every polling step the assistant 'receives binary timing feedback,' and Section 3 derives a label from that feedback. Real users don't rate silence steps, and they never say 'you should have interrupted me' if the assistant stayed quiet. That means the counters in Equations (8) and (15) will be fed with exactly the data that deployed systems lack. Table 3 only drops feedback uniformly at random, which does not reproduce the structured missingness where SIL steps and false-negative errors are most likely to be unlabeled. That is a load-bearing gap, not a minor caveat. Second, the decision threshold and fusion weight are calibrated on the same stream used for evaluation. The paper says 'replays decisions' on historical data, which is a short-horizon validation, but it still risks using the evaluation period's feedback to set the very parameters being evaluated. Third, the authors don't ship code, and the appendices containing prompts, hyperparameter sensitivity, and the human evaluation are missing from the submission—those matter for any follow-up. Fourth, there's no simple non-LLM baseline (like a per-user frequency-matching rule), which would help isolate what the 19.80-point gain is actually buying.\n\nBottom line: this deserves a serious referee, but the feedback-assumption issue needs a structured-missingness experiment and either a revised claim or a real-world feasibility argument before the headline is trustworthy. I'd cite the method as a well-built simulation result, and it would make a good reading-group discussion on the gap between simulated and deployed assistant feedback.","headline":"EOPA is a coherent, well-ablated method for timing personalization that achieves large simulated gains, but its headline result depends on full-information feedback on silence steps that real users don't provide.","tokens_in":15801,"tokens_out":2857,"would_cite":true,"duration_ms":25386,"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":"Counting feedback beats LLM reflection for proactive assistant timing.","keywords":["proactive AI assistants","interaction timing","online adaptation","user feedback","contextual evidence","temporal preference anchors","activity prototypes","personalization"],"falsifier":"Run EOPA online with real users but collect feedback only on interaction steps, leaving all silence steps unlabeled, and compare timing F1 against the simulated full-feedback setting; if the claimed gains require accurate silence counts, F1 should fall well below the reported 30.46, and precision or recall should shift noticeably as the unlabeled-silence bias accumulates.","tokens_in":14744,"feed_emoji":"⏱️","tokens_out":11611,"duration_ms":87953,"temperature":0.7,"pith_summary":"The paper tries to establish that a proactive assistant can personalize when it initiates interactions by converting online user feedback into countable contextual evidence, rather than by fine-tuning model parameters or by reflecting on past trajectories in natural language. It introduces EOPA, an evidence-driven online preference adaptation method, which keeps two evidence carriers: temporal preference anchors that count interaction-appropriate and silence-preferred outcomes by time-of-day and activity-progress stage, and evidence-bearing activity prototypes that carry the same counts under semantic activity clusters. At each polling step, the two evidence sources are turned into probability estimates smoothed by a global user prior, scaled by how uncertain they are, and fused with an adaptively calibrated threshold to decide between interacting and staying silent. In a 14-day simulated benchmark with 32 user personas and 160,632 polling steps, EOPA reports an interaction-timing F1 of 30.46, about 19.80 points above the strongest baseline, while cutting average silence-step latency from 688 ms to 1.96 ms and daily adaptation time from 11.41 to 0.39 seconds. If correct, this makes fine-grained, continuously updated timing personalization practical without invoking an LLM at every step.","feed_headline":"Counting feedback beats LLM reflection for proactive assistant timing","feed_subtitle":"Count-based evidence lifts interaction-timing F1 by 19.8 points and cuts silence latency to about two milliseconds.","key_machinery":"The load-bearing object is the evidence score built from two complementary counters. Temporal preference anchors $A_{i,r}=(N^{\\mathrm{INT}}_{i,r},N^{\\mathrm{SIL}}_{i,r})$ count interaction-appropriate and silence-preferred labels for each time interval $i$ and activity-progress stage $r$; evidence-bearing activity prototypes similarly store semantic centers plus progress-conditioned counts. Each branch converts its counts into a posterior probability using a user-prior-smoothed Beta-Binomial estimate, e.g. $p_{\\mathrm{temp},t}=(N^{\\mathrm{INT}}_{i,r}+\\kappa p^u_t)/(N^{\\mathrm{INT}}_{i,r}+N^{\\mathrm{SIL}}_{i,r}+\\kappa)$, divides by the posterior standard deviation plus a small constant, and the two scaled evidence scores are summed as $s_t=e_{\\mathrm{temp},t}+\\lambda_t e_{\\mathrm{act},t}$ and compared with an adaptive threshold $\\delta_t$. The mechanism preserves fine-grained differences among similar states because each state draws on its own matched anchor and prototype statistics rather than on a global rule; the adaptive $\\lambda_t$ and $\\delta_t$ replay recent history to keep the decision boundary aligned with the user.","core_discovery":"The central claim, stated on the paper's own terms, is that interaction-timing preferences are better treated as measurable contextual evidence than as textual or parametric experience. EOPA stores interaction and silence counts under fine-grained temporal and activity-semantic conditions; user-prior smoothing stabilizes sparse local counts, uncertainty-guided scaling lets better-supported evidence speak louder, and adaptive fusion with an online-calibrated threshold lets the decision policy itself adjust to the user. Response generation is decoupled: an LLM is called only when interaction is selected, using high-quality historical responses as demonstrations. The reported consequence is that the same accumulated feedback that outperforms LLM-based self-improvement methods on timing also yields shorter silence decisions and nearly zero adaptation cost, because no weight updates or self-reflection calls are needed.","pith_inferences":["A natural next test is to drop the assumption of a label at every silence step and treat unobserved silences as missing data; the paper's 25-percent-feedback result hints the method degrades gradually, but real users' silence feedback would likely be systematically missing, not random.","The same evidence-carrier design could be lifted to other sparse-decision domains such as notification scheduling, interruption management, or content moderation, where the cost of a false positive is high and non-event feedback is scarce.","Because the labels in the benchmark are model-generated and the personas are simulated, the 19.80-point gain is evidence about the mechanism's discriminative power under clean labels; a field deployment would test whether human feedback about silence is obtainable enough to feed the counters."],"forward_implications":["Silence decisions become nearly free: EOPA's reported average silence-step latency is 1.96 ms versus 688 ms for an LLM-reflection method, because keeping silent requires no LLM call.","Personalization can continue all day without a training budget: daily adaptation drops from 11.41 s to 0.39 s, so the assistant can re-calibrate after every feedback signal rather than batching updates.","Fine-grained distinctions survive: on state pairs that share time interval, progress stage, or activity description but differ in preferred timing, EOPA's average decision accuracy is 38.60 percent versus 22.72 percent for the strongest baseline, supporting the claim that countable evidence preserves differences that textual reflections blur.","Moderate feedback sparsity is tolerable: retaining only 75 percent or 50 percent of the binary feedback keeps 89.6 percent and 83.9 percent of the full-feedback F1, and even 25 percent feedback outperforms the full-feedback LLM-reflection baseline.","Timing and content become separable problems: the response-generation LLM runs only when interaction is selected, so response quality can be optimized independently of the timing policy."],"supporting_citations":[{"why":"Supplies the benchmark with 32 simulated user personas, the daily-activity interaction protocol, the evaluation metrics, and the ProPerAssistant parameter-learning baseline compared against EOPA.","marker":"(Kim et al. 2026)"},{"why":"The Generative Agents simulation underlies the daily activities of the user personas in the benchmark.","marker":"(Park et al. 2023)"},{"why":"Reflexion is the strongest self-improvement baseline; EOPA's 19.80-point F1 gain is measured against it, and it represents the textual-reflection approach EOPA contrasts with.","marker":"(Shinn et al. 2023)"},{"why":"SCOPE is a prompt-evolution baseline that represents LLM-based self-improvement without weight updates.","marker":"(Pei et al. 2025)"},{"why":"EvoTest is a test-time evolutionary self-improvement baseline competing on the same feedback protocol.","marker":"(He et al. 2026b)"},{"why":"Provides the Beta-Binomial posterior and variance formulas used for user-prior-smoothed evidence estimation and uncertainty scaling.","marker":"(Gelman et al. 2013)"},{"why":"Justifies the Jeffreys-smoothed empirical rate used as the global user prior in the evidence estimates.","marker":"(Jeffreys 1946)"},{"why":"Contributes the online prototype-learning idea that EOPA extends into evidence-bearing activity prototypes with interaction and silence statistics.","marker":"(Wei et al. 2023)"},{"why":"The Qwen3.5 model constructs the fixed interaction-timing labels that define ground truth in the benchmark.","marker":"(Qwen Team 2026)"},{"why":"Qwen3-4B-Instruct-2507 is the response-generation LLM used by EOPA and all baselines, keeping the timing comparison controlled.","marker":"(Yang et al. 2025a)"}],"fun_headline_variants":["Counts, not musings: proactive assistant timing learns faster","F1 up 19.8 points via evidence counting over LLM reflection","Adapt 11s to 0.4s daily with count-based proactive timing","Personalized proactive timing: measured evidence beats self-talk"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing assumption is that after every polling step, including steps where the assistant stayed silent, the assistant receives a binary timing label; in real use, feedback is usually given only when the assistant actually interacts, so the silence counts that the evidence scores depend on may be missing or systematically biased.","fun_headline_variants_meta":{"raw":{"variants":["Counts, not musings: proactive assistant timing learns faster","F1 up 19.8 points via evidence counting over LLM reflection","Adapt 11s to 0.4s daily with count-based proactive timing","Personalized proactive timing: measured evidence beats self-talk"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000177,"raw_usage":{"total_tokens":1302,"prompt_tokens":960,"completion_tokens":342,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":576,"completion_tokens_details":{"reasoning_tokens":266}},"tokens_in":576,"tokens_out":342,"duration_ms":3898,"temperature":1.0,"reasoning_tokens":266,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T14:40:58.055066+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run EOPA online with real users but collect feedback only on interaction steps, leaving all silence steps unlabeled, and compare timing F1 against the simulated full-feedback setting; if the claimed gains require accurate silence counts, F1 should fall well below the reported 30.46, and precision or recall should shift noticeably as the unlabeled-silence bias accumulates.","supporting_citations":[],"review_version":1}