{"id":"cb4fece1-431a-433e-85ad-c4dd76035d15","arxiv_id":"2506.19843","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":5,"one_line_summary":"Temporal-IRL learns a reward function for berth scheduling from AIS data, predicting vessel actions, port congestion, and departure times with reported accuracies of 82.64%, 74.06%, and 71.51% respectively.","lead":"A team applies inverse reinforcement learning to learn how the Port of New York/New Jersey schedules ships at berths, then uses that learned policy to forecast port congestion and when ships leave. The method combines LSTM-based temporal features with maximum entropy IRL, and reports accuracies up to 82.64% for predicting berth actions on Maher Terminal data.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Action accuracy may be inflated by empty-slot 'Nothing' and majority 'Stay' actions; a trivial baseline is needed before 82.64% can be read as learned scheduling.","rationale":"The reader's weakest_assumption is feature insufficiency (the gap between Section 3.1.1 and Section 3.2.1). That is a real limitation, but it is not the most load-bearing threat to the empirical claim: even with only three features, a model could predict actions accurately if the included features correlate with omitted drivers. The more immediate threat is the evaluation metric itself. Section 3.2.2 reveals extreme action-class imbalance, and Eq. 7 counts one-hot entries element-wise, so empty-slot and stay base rates can dominate the score. The absence of a null baseline makes the 82.64% figure uninterpretable. The reader's rationale did mention missing trivial baselines and error bars, but the formal weakest_assumption was feature omission, so I mark partial agreement. This concern does not overturn the leave-accuracy result, which is event-level, but it does mean the central scheduling-prediction claim needs a decisive control before the paper can be accepted. The existing CONDITIONAL verdict is therefore the right one; no adjustment is needed.","tokens_in":12874,"tokens_out":5652,"duration_ms":60746,"concrete_test":"On the same test split, compute Eq. 7 for three null models: (a) always output all-zero 'Nothing' for every slot; (b) for occupied slots, output 'Stay' as long as the vessel remains in the same slot, otherwise 'Leave system'; (c) the per-slot modal action from the training set. Also report accuracy restricted to non-empty slots only. If any null baseline exceeds roughly 75% or comes within a few points of 82.64%, the action-accuracy claim does not demonstrate scheduling skill. Additionally, inspect the confusion matrix by action type to identify which actions drive the 82.64%.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The headline result is the 82.64% action accuracy (Table 4, Eq. 7). That number is computed as element-wise matches over a 190-dimensional one-hot action vector (19 slots x 10 actions). Empty slots are encoded as an all-zero 'Nothing' vector, and Section 3.2.2 states that 'Nothing' is by far the most frequent action, which is why its loss weight is reduced to 0.1; 'Stay' and 'Leave system' are also downweighted to 0.3. Consequently, a model that outputs 'Nothing' for empty slots and 'Stay' for occupied slots can accumulate many correct zero entries without making any scheduling decision. The evaluation uses raw element-wise accuracy, not per-slot or per-vessel accuracy, and no trivial majority-class or persistence baseline is reported. If such a baseline also reaches roughly 80%, the claimed superiority of Temporal-IRL for action prediction is not evidence of learning berth scheduling. The leave-accuracy result (71.51%) is less affected by this artifact, but the central action-accuracy claim is not yet supported.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes Temporal-IRL, a pipeline that combines LSTM-AE feature extraction with maximum-entropy inverse reinforcement learning to model berth scheduling at the Maher Terminal in the Port of New York/New Jersey. The port is discretized into 19 slots (6 berths, 7 waiting-area positions, 6 incoming vessels), and scheduling decisions are represented as per-slot one-hot actions over 10 action types. Using AIS-based data from January 2015 to September 2023, the authors train the model on historical state-action trajectories, infer a reward function, and use it to predict next-step actions, congestion states, and vessel departure time windows. They report action accuracy of 82.64%, congestion accuracy of 74.06%, and leave accuracy of 71.51%, comparing against LSTM, FNN, FNN-IRL, and XGBoost baselines on the same data.","tokens_in":13047,"tokens_out":3317,"duration_ms":33334,"significance":"If the reported results are robust, the paper would demonstrate a useful application of IRL to a practical logistics problem, with the temporal feature-extraction component as a plausible contribution. The temporal train/test split is a reasonable evaluation choice, and the inclusion of several baselines is commendable. However, the central quantitative claims need additional support: the action-accuracy metric is vulnerable to class-imbalance artifacts, no uncertainty quantification is provided, and the reported state features omit several factors the paper itself identifies as influential. The work is a promising case study, but the evidence as presented is insufficient to establish that Temporal-IRL learns port scheduling priorities better than trivial baselines.","major_comments":[{"comment":"The action-accuracy metric is computed element-wise over a 190-dimensional one-hot vector (19 slots times 10 actions), with empty slots encoded as an all-zero 'Nothing' vector. Section 3.2.2 and Figure 3 state that 'Nothing' and 'Stay' are by far the most frequent actions. Under this metric, a model that always predicts 'Nothing' for empty slots and 'Stay' for occupied slots could accumulate many correct entries without making any substantive scheduling decision. No majority-class or persistence baseline is reported. Please report per-slot and per-vessel accuracy, class-balanced metrics (e.g., macro-F1), and baselines such as always-Nothing, always-Stay, and previous-state persistence, before claiming that 82.64% reflects learned scheduling behavior.","section":"Section 4.2.2, Eq. (7), Table 4"},{"comment":"All reported accuracies appear to come from a single run or split, with no error bars, confidence intervals, or repeated-seed experiments. Consequently, the small gaps between methods (e.g., Temporal-IRL 74.06% vs. XGBoost 75.89% for congestion, and 71.51% vs. 66.01% for leave accuracy) cannot be assessed for statistical significance. Please provide multiple seeds or resampling-based intervals and perform a significance test, or at minimum report the variance across runs.","section":"Tables 4 and 5"},{"comment":"Section 3.1.1 lists vessel origin, customs information, berth size, channel depth, and contractual agreements as factors that influence berth allocation, but Section 3.2.1 states that the implemented state features are only size, carrier, and stay time. The paper later claims that the learned reward function captures the port's underlying priorities and decision logic. This claim is not supported by the feature set. Please either temper the interpretation to describe the reward as conditional on the three modeled features, or add an ablation/sensitivity analysis that examines the effect of omitting the listed factors.","section":"Sections 3.1.1 and 3.2.1"},{"comment":"The MaxEnt IRL description is generic, presenting Eqs. (2)-(6) without the implementation details needed for reproducibility. The paper does not specify the parameterization of the reward function R(s,a), how the partition function is computed or estimated over the 19-slot/10-action space, the transition model used in the Bellman equation, or the optimization hyperparameters (learning rate, number of iterations, discount factor). It is also unclear how the LSTM-AE features are combined with the one-hot state and action encodings before being passed to IRL. These details are essential; please provide a complete algorithmic description, pseudocode, or released code.","section":"Section 3.3"}],"minor_comments":[{"comment":"The abstract contains several missing spaces, e.g., 'Improvedshipment planning, reducedelays' and 'optimizeinventoryanddistributionstrategies'; please proofread.","section":"Abstract"},{"comment":"Table 2's caption says the action space is 'a vector of length 19', but the text and Section 3.1.2 define the action vector as length 190. Please correct the inconsistency.","section":"Table 2 and Section 3.1.2"},{"comment":"The caption lists 'Go to berth 4' twice and omits one of the berth actions in the enumeration; the action distribution itself also appears to show 'Go to berth 4' twice. Please fix the caption and verify the figure labels.","section":"Figure 3"},{"comment":"The loss is written as weighted binary cross-entropy over all action entries, but the action space is described as one-hot over 10 mutually exclusive actions per slot. Please clarify whether the model outputs a per-slot softmax and whether the loss is summed over slots, and specify the weights assigned to the non-downweighted actions.","section":"Eq. (1), Section 3.2.2"},{"comment":"The congestion threshold (three or more vessels in the waiting area) is defined from observations in the same dataset (Figure 6). Please report sensitivity to this threshold or at least acknowledge the choice as dataset-derived, since the congestion accuracy metric depends directly on it.","section":"Section 4.2.3"},{"comment":"The temporal split is mentioned only as 'according to its temporal sequence'; please report the exact split dates, number of time windows in training and test sets, and the number of vessels/actions used in evaluation.","section":"Section 4.1"},{"comment":"The phrase 'state-of-the-art accuracy of 71.51%' refers to best among the five evaluated methods on one dataset; please qualify this as state-of-the-art among the compared baselines to avoid overclaiming.","section":"Section 4.2.2"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Short version: this is a legitimate first application of IRL to berth scheduling, built on a real multi-year AIS dataset from Maher Terminal, with a clean temporal train/test split. The methodological novelty is modest—MaxEnt IRL plus LSTM-AE feature extraction—but the integration is sensible and the paper is honest about its limitations. The main weakness is the evaluation: the headline 82.64% action accuracy is likely inflated by the dominant 'Nothing' and 'Stay' actions, and no trivial baseline is reported. That needs to be fixed before the empirical claims are credible.\n\nI checked the stress-test concern against the paper, and it holds up. The action space is 19 slots x 10 one-hot actions; empty slots are encoded as all-zero 'Nothing' vectors, and the paper says 'Nothing' is by far the most frequent action, so they downweight it to 0.1 in the loss. Yet the evaluation is raw element-wise accuracy over the full 190-dimensional vector. A model that mostly predicts 'Nothing' for empty slots and 'Stay' for occupied slots could collect a lot of free correct entries. Without a majority-class baseline, 82.64% is not yet evidence of learning scheduling. The leave-accuracy result (71.51%) is less affected by this artifact, but even that needs confidence intervals.\n\nEqually, the congestion threshold of three or more waiting vessels is derived from the same data used to evaluate the model, and the 74.06% congestion accuracy is below XGBoost's 75.89%, so the 'demonstrably excellent results' language in the abstract overstates things. The reader's weakest assumption is also on point: Section 3.1.1 lists origin, customs, berth size, channel depth, and contractual agreements as factors, but the states only use size, carrier, and stay time. That gap means the learned reward function may not represent the port's actual decision logic.\n\nWhat the paper does well: real data from 2015 to 2023, careful data processing, multiple baselines (LSTM, FNN, FNN-IRL, XGBoost), a temporal split that avoids leakage, and an explicit limitations section. The leave accuracy beating all baselines is a genuinely interesting result, and the SHAP analysis shows the features are non-trivially informative. The citation pattern looks fine, and the authors are appropriately careful about claiming novelty.\n\nBottom line: this is a solid application paper that deserves refereeing, but the experimental evidence needs a round of hardening: trivial baselines, per-slot/per-vessel accuracy, confidence intervals, and ideally a second terminal. I would not desk-reject it; I would send it back for major revision.","headline":"First IRL application to berth scheduling on real port data, but the headline accuracy is not yet credible without trivial baselines and error bars.","tokens_in":13642,"tokens_out":2064,"would_cite":false,"duration_ms":21810,"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":"Temporal-IRL learns a port's tacit berth-scheduling rules from AIS ship-tracking data and uses the learned reward to predict vessel actions, congestion, and departure windows.","keywords":["inverse reinforcement learning","berth scheduling","port congestion prediction","LSTM autoencoder","temporal feature extraction","AIS data","maximum entropy IRL","vessel dwell time"],"falsifier":"Compare a variant of Temporal-IRL that adds origin, customs status, berth size, channel depth, and a contract-priority index as reward features on the same Maher test split; if leave accuracy rises materially above 71.51% or the learned reward assigns substantial weight to these added features, the three-feature model has not captured the port's true scheduling logic.","tokens_in":12621,"feed_emoji":"⚓","tokens_out":9222,"duration_ms":86203,"temperature":0.7,"pith_summary":"This paper proposes Temporal-IRL, a method that treats a container terminal's berth scheduling as an inverse problem: instead of predicting congestion directly, it first recovers the reward function that explains the port's historical assignment decisions and then uses that reward to predict what happens next. The model rebuilds eight years of vessel movements at Maher Terminal into expert trajectories over 8-hour windows, represents each ship by size, carrier, and time already in port, and augments these features with temporal encoding from an LSTM autoencoder. Under maximum entropy inverse reinforcement learning, the inferred reward yields the next scheduling action for every vessel, a congestion flag (three or more vessels waiting), and the window in which a vessel leaves. On held-out data the paper reports 82.64% action accuracy, 74.06% congestion accuracy, and a claimed best-in-class 71.51% departure-window accuracy, beating LSTM, FNN, FNN-IRL, and XGBoost on most metrics. A reader should care because ports keep their scheduling logic tacit, and a learned reward makes that logic usable for supply-chain planning.","feed_headline":"IRL decodes port berth rules from AIS data at 82.64%","feed_subtitle":"Infers the port's hidden scheduling reward from ship tracks, then forecasts congestion and vessel departures.","key_machinery":"The load-bearing mechanism is the maximum entropy inverse reinforcement learning loop, which models the port's scheduling policy as $\\pi^*(a|s)=\\exp(Q(s,a))/Z(s)$ with $Q$ defined by the Bellman expectation equation and $Z(s)$ the normalizing partition function; the trainable object is the reward $R(s,a)$. Before IRL sees a state, an LSTM autoencoder turns the 19-slot state vector — each slot an IMO expanded to (size, carrier, stay time) — into compressed temporal features, which the paper finds is the decisive component: replacing it with a feedforward network drops action accuracy from 82.64% to 70.01%. The IRL loop then uses the log-likelihood of observed expert state-action pairs to fit the reward, and the resulting probability distribution over actions is what yields the congestion and departure-window predictions.","core_discovery":"On its own terms, the paper's discovery is that the port's berth-allocation behavior can be reconstructed from AIS positions alone and then explained by a reward function learned through maximum entropy IRL, without any direct communication from port operators. The terminal is modeled as 19 ordered slots — six berths, seven waiting positions, six incoming vessels — and each vessel is described by three attributes: size, carrier, and elapsed stay time. A long short-term memory autoencoder compresses the temporal sequence of these attributes, and the compressed features feed a MaxEnt IRL loop whose Boltzmann policy assigns probabilities to ten possible actions per slot: do nothing, stay, move to waiting, move to one of six berths, or leave the system. From that policy the model predicts the next scheduling action, declares congestion when three or more vessels are waiting, and outputs the departure time window. Tested on Maher Terminal data from January 2015 to September 2023, the authors report 82.64% action accuracy, 74.06% congestion accuracy, and 71.51% departure-window accuracy, the last of which they state is the best among the compared models (LSTM, FNN, FNN-IRL, and XGBoost).","pith_inferences":["The learned reward itself is a research artifact the paper does not fully exploit: reading which slot-action pairs carry high reward could reveal which vessel attributes the port actually prioritizes, turning the model into a measurement tool.","Adding the factors listed in Section 3.1.1 but omitted from the features (origin, customs, berth size, channel depth, contracts) is a natural extension; if accuracy improves, the current three-feature reward is a deliberate simplification, and if it does not, the omitted factors are not decision-relevant at this terminal.","The method is presented as terminal-agnostic, but only one terminal is tested; a multi-terminal or post-2023 evaluation would reveal whether the reward function captures general scheduling logic or just Maher-specific patterns.","The binary congestion definition (three or more vessels waiting) is an editorial choice grounded in the data; a graded congestion scale might be better for practical use, and the same IRL reward could be evaluated against it."],"forward_implications":["Port scheduling logic can be extracted from ship-tracking data alone, so terminals that do not publish their berth rules can still be modeled for forecasting.","Congestion warnings can be issued one 8-hour step ahead with roughly 74% accuracy (XGBoost is slightly better at 75.89%), which supports logistics planning even when absolute precision is lacking.","Departure-window forecasts at 71.51%, the best among the compared models, translate directly into berth-release and inventory-timing estimates for downstream supply chain decisions.","The large gap between Temporal-IRL's 82.64% and FNN-IRL's 70.01% action accuracy shows that temporal feature extraction is responsible for most of the gain over non-temporal IRL.","Because the model emits a full scheduling action for every slot, it can be used for simulation: replaying the learned policy under hypothetical arrivals produces synthetic congestion scenarios."],"supporting_citations":[{"why":"Defines inverse reinforcement learning as recovering a reward function from expert behavior; the paper's foundational method.","marker":"Ng et al., 2000"},{"why":"Introduces the maximum entropy IRL formulation whose Boltzmann policy and partition function the model uses.","marker":"Ziebart et al., 2008"},{"why":"Provides the LSTM architecture used to extract temporal features from the state sequence.","marker":"Hochreiter and Schmidhuber, 1997"},{"why":"Supplies the autoencoder component of the LSTM-AE feature extractor.","marker":"Bank et al., 2023"},{"why":"Defines the feedforward neural network used as a non-temporal baseline.","marker":"Rumelhart et al., 1986"},{"why":"LSTM-based port congestion prediction approach the paper compares against and cites to motivate temporal modeling.","marker":"Peng et al., 2023"},{"why":"Artificial neural network and XGBoost baselines for berth congestion risk that the experiments emulate.","marker":"LAMII et al., 2022"},{"why":"Establishes waiting-vessel counts and berth service times as congestion indicators, informing the paper's congestion definition.","marker":"Yeo et al., 2007"}],"fun_headline_variants":["IRL learns port's hidden berth rules from ship tracks","AIS tracks teach IRL to predict port congestion","Port congestion forecast from AIS and IRL at 82.64%","Hidden berth rules inferred from AIS alone"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The argument collapses if the three features the model keeps — vessel size, carrier, and time already spent in port — do not carry the information that actually drives berth assignments, because the paper itself lists origin, customs status, berth size, channel depth, and contractual agreements as scheduling factors that the model never sees.","fun_headline_variants_meta":{"raw":{"variants":["IRL learns port's hidden berth rules from ship tracks","AIS tracks teach IRL to predict port congestion","Port congestion forecast from AIS and IRL at 82.64%","Hidden berth rules inferred from AIS alone"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000501,"raw_usage":{"total_tokens":2490,"prompt_tokens":1028,"completion_tokens":1462,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":644,"completion_tokens_details":{"reasoning_tokens":1393}},"tokens_in":644,"tokens_out":1462,"duration_ms":10838,"temperature":1.0,"reasoning_tokens":1393,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T18:23:56.669880+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Compare a variant of Temporal-IRL that adds origin, customs status, berth size, channel depth, and a contract-priority index as reward features on the same Maher test split; if leave accuracy rises materially above 71.51% or the learned reward assigns substantial weight to these added features, the three-feature model has not captured the port's true scheduling logic.","supporting_citations":[{"cited_title":"Autoencoders","cited_arxiv_id":null,"evidence_quote":"Supplies the autoencoder component of the LSTM-AE feature extractor."},{"cited_title":"A deep learning approach for port congestion estimation and prediction","cited_arxiv_id":null,"evidence_quote":"LSTM-based port congestion prediction approach the paper compares against and cites to motivate temporal modeling."},{"cited_title":"Using artificial neural network model for berth congestion risk prediction","cited_arxiv_id":null,"evidence_quote":"Artificial neural network and XGBoost baselines for berth congestion risk that the experiments emulate."},{"cited_title":"Evaluation of the marine traffic congestion of north harbor in busan port","cited_arxiv_id":null,"evidence_quote":"Establishes waiting-vessel counts and berth service times as congestion indicators, informing the paper's congestion definition."}],"review_version":2}