{"id":"742e867e-fbdd-4260-8bb3-bd29e091e170","arxiv_id":"2605.26554","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":7.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"Proposes LDB-DF and NDB-DF algorithms for contextual dueling bandits with delayed feedback using an IPW estimator in the loss, with O(d sqrt(T)) regret for the linear case and sub-linear guarantees for the neural case.","lead":"The paper formalizes contextual dueling bandits with stochastic delayed feedback and introduces linear and neural algorithms that embed inverse probability weighting directly into the loss to correct for delays. A smart generalist might read it because delayed feedback is common in real preference learning tasks such as LLM prompt optimization and recommender systems.","discovery_kind":"unclear","skeptic_critique":{"model":"grok-4.3","headline":"Unbiasedness of IPW-integrated loss for non-closed-form dueling estimators is the load-bearing assumption","rationale":"The reader's weakest_assumption directly identifies the same point; the abstract-only review already flagged the technical gap, and the full-text placeholder does not supply an explicit derivation that would remove the concern.","tokens_in":1688,"tokens_out":229,"duration_ms":11089,"concrete_test":"Compute the expectation of the proposed loss (under the stochastic delay distribution) and verify whether it equals the expectation of the undelayed loss; if the equality fails for any delay probability >0, the unbiased-correction step does not hold.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The O(d sqrt(T)) regret and sublinear neural guarantees rest on the claim that embedding IPW directly into the loss yields an unbiased estimator for delayed dueling feedback. Because dueling estimators have no closed form, the weighting cannot be applied to an explicit solution; any implicit or approximate integration risks residual bias that would invalidate the subsequent concentration arguments used to derive the regret bounds.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper formalizes contextual dueling bandits with stochastic delayed feedback and introduces two algorithms, LDB-DF (linear) and NDB-DF (neural). It proposes a novel estimator that embeds an Inverse Probability Weighting (IPW) term directly into the loss function to produce unbiased estimates despite the absence of closed-form solutions for dueling estimators. The central theoretical claims are an O(d sqrt(T)) regret bound in the linear case and sub-linear regret guarantees in the neural case, accompanied by experiments on simulated and real-world datasets.","tokens_in":1771,"tokens_out":362,"duration_ms":23805,"significance":"If the IPW-integrated loss indeed yields unbiased estimators and the stated regret bounds hold, the work would meaningfully extend preference-based bandits to delayed-feedback regimes that arise in recommender systems and LLM alignment. The neural extension and the explicit handling of non-closed-form estimators would be the primary contributions.","major_comments":[{"comment":"The load-bearing claim (abstract) that integrating IPW directly into the loss produces an unbiased estimator for delayed dueling feedback must be shown explicitly. Because dueling estimators lack closed-form solutions, the weighting cannot be applied to an explicit solution; the manuscript must supply the precise derivation (or lemma) establishing that the resulting implicit estimator remains unbiased and that the subsequent concentration arguments for the O(d sqrt(T)) and sub-linear bounds remain valid.","section":"Abstract / theoretical analysis section"}],"minor_comments":[{"comment":"The final sentence of the abstract contains a grammatical error ('our propose' should read 'our proposal' or 'our proposed method').","section":"Abstract"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the careful review and constructive feedback on the theoretical claims. We address the single major comment below.","responses":[{"response":"We agree that an explicit derivation is required to substantiate the unbiasedness claim, given the implicit nature of dueling estimators. In the revised manuscript we will insert a new lemma (Lemma 3.2) immediately after the problem formulation. The lemma computes the expectation of the IPW-weighted loss over the stochastic delay distribution and shows that it equals the expected loss under immediate feedback; the proof relies only on the definition of the IPW weights and linearity of expectation, without requiring a closed-form solution. The same lemma is then invoked to justify the concentration inequalities used for both the linear O(d sqrt(T)) bound and the neural sub-linear bound, so the subsequent regret analysis remains valid.","revision_made":"yes","referee_comment":"[Abstract / theoretical analysis section] The load-bearing claim (abstract) that integrating IPW directly into the loss produces an unbiased estimator for delayed dueling feedback must be shown explicitly. Because dueling estimators lack closed-form solutions, the weighting cannot be applied to an explicit solution; the manuscript must supply the precise derivation (or lemma) establishing that the resulting implicit estimator remains unbiased and that the subsequent concentration arguments for the O(d sqrt(T)) and sub-linear bounds remain valid."}],"tokens_in":1318,"tokens_out":299,"duration_ms":32596,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main contribution here is spelling out the contextual dueling bandit problem when feedback arrives with stochastic delay and offering two algorithms, LDB-DF and NDB-DF, that try to handle it. They point out that standard weighting breaks because dueling estimators lack closed forms, then put IPW straight into the loss instead. That move is presented as new, and the abstract gives O(d sqrt(T)) regret for the linear case plus sublinear bounds for the neural case.\n\nThe work does a clean job naming a realistic gap—immediate feedback does not hold in prompt optimization or some recommender loops—and the experiments on simulated plus real data are at least mentioned. The formalization itself looks like a straightforward extension that people in preference-based online learning might use.\n\nThe soft spot is exactly the one the stress-test note flags: whether embedding IPW in the loss actually produces an unbiased estimator when there is no closed-form solution to weight. Any implicit approximation or residual bias would undermine the concentration steps that support the regret claims. Without the derivations in front of us it is impossible to tell how tight that step is.\n\nThis is for readers already working on dueling or preference bandits who care about delayed feedback. It is not a broad shift but could be a useful reference inside the subfield. The paper shows clear engagement with the setting and literature, so it deserves a serious referee to check the estimator and bounds rather than a desk reject.","headline":"The paper formalizes contextual dueling bandits with stochastic delays and claims an IPW-in-loss estimator fixes bias without closed forms, but that unbiasedness step is the part to check first.","tokens_in":2272,"tokens_out":374,"would_cite":false,"duration_ms":23544,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"A loss function embedding inverse probability weighting corrects bias from delayed feedback in contextual dueling bandits.","keywords":["contextual dueling bandits","delayed feedback","inverse probability weighting","regret bounds","linear bandits","neural bandits","preference learning","stochastic delay"],"falsifier":"A controlled experiment that measures cumulative regret of LDB-DF on linear dueling instances with known delay probabilities; if the observed regret grows faster than O(d sqrt(T)), the bound is falsified.","tokens_in":2599,"feed_emoji":"","tokens_out":613,"duration_ms":34415,"temperature":0.7,"pith_summary":"The paper studies contextual dueling bandits under stochastic delayed feedback, a setting that arises in recommender systems and large language model alignment. Because dueling estimators lack closed-form solutions, conventional delay-correction techniques produce biased updates. The authors embed an inverse probability weighting term directly inside the loss, which restores unbiasedness. This construction yields an O(d sqrt(T)) regret bound for linear dueling bandits and sub-linear regret for neural dueling bandits.","feed_headline":"IPW loss fixes bias from delayed feedback in dueling bandits","feed_subtitle":"Embedding inverse probability weighting in the loss restores unbiased updates and yields O(d sqrt(T)) regret for linear and sub-linear regre","key_machinery":"The IPW term embedded inside the loss function that produces unbiased gradient estimates for dueling comparisons under stochastic delay.","core_discovery":"We formalize Contextual Dueling Bandits with Stochastic Delayed Feedback and propose LDB-DF and NDB-DF algorithms. Their central device is a novel estimator that integrates an Inverse Probability Weighting mechanism directly into the loss function, ensuring unbiased correction for delayed or missing feedback even though dueling estimators lack closed-form solutions. The resulting linear algorithm attains an O(d sqrt(T)) regret bound while the neural algorithm attains sub-linear regret.","pith_inferences":["The same loss modification may let other preference-learning methods tolerate missing or late labels without new bias terms.","Deployment in production recommendation systems could become feasible when response latency is modeled rather than ignored.","Extending the approach to unknown or adversarial delay distributions would require a different weighting scheme."],"forward_implications":["Linear dueling bandits achieve the same O(d sqrt(T)) regret order as the immediate-feedback case once the IPW loss is used.","Neural dueling bandits obtain sub-linear regret under the same delay model.","The estimator applies directly to prompt optimization and recommender systems where user responses arrive after variable delays.","Experiments on both simulated and real-world datasets confirm that the corrected algorithms outperform naive adaptations."],"fun_headline_variants":["IPW in loss corrects bias from delayed dueling feedback","Delayed feedback IPW estimator for linear and neural bandits","New estimator uses IPW in loss for dueling bandit delays","Unbiased dueling updates from IPW loss despite delays","Linear neural dueling bandits bound regret via IPW"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The probability that any given feedback arrives is known or can be estimated without error so that the weighting term remains unbiased.","fun_headline_variants_meta":{"raw":{"variants":["IPW in loss corrects bias from delayed dueling feedback","Delayed feedback IPW estimator for linear and neural bandits","New estimator uses IPW in loss for dueling bandit delays","Unbiased dueling updates from IPW loss despite delays","Linear neural dueling bandits bound regret via IPW"]},"model":"grok-4.3","cost_usd":0.005921,"raw_usage":{"total_tokens":2809,"prompt_tokens":666,"num_sources_used":0,"completion_tokens":78,"cost_in_usd_ticks":59212000,"prompt_tokens_details":{"text_tokens":666,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2065,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":666,"tokens_out":78,"duration_ms":21767,"temperature":1.0,"reasoning_tokens":2065,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-29T19:59:50.242117+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A controlled experiment that measures cumulative regret of LDB-DF on linear dueling instances with known delay probabilities; if the observed regret grows faster than O(d sqrt(T)), the bound is falsified.","supporting_citations":[],"review_version":1}