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REVIEW 2 major objections 2 minor 41 references

Towards Metric-Agnostic Trajectory Forecasting

T0 review · 2 major / 2 minor · reviewed 2026-07-02 · grok-4.3

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Pith's one-line read Training trajectory forecasters on metric-agnostic probabilistic objectives and applying metric-specific policies downstream achieves state-of-the-art results across all Waymo metrics.

desk verdict The paper reframes trajectory forecasting as NLL training plus metric-specific TraDiE policies, claims SOTA on all Waymo metrics with DONUT-NLL, but the abstract supplies no evidence that the post-hoc policies avoid mode loss or selection bias on conflicting metrics. read the letter →

arxiv 2607.01133 v1 pith:UZXOQHRT submitted 2026-07-01 cs.CV cs.RO

Markus Knoche , Daan de Geus , Bastian Leibe This is my paper
classification cs.CVcs.RO
keywords trajectoryforecastingmetric-agnostictrainingTraDiEpoliciespredictivedistributionDONUT-NLLWaymobenchmarkautonomousdrivingnegativelog-likelihood
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Current forecasting models customize their training losses to match particular benchmark metrics, but these metrics push models toward conflicting behaviors. The paper proposes instead to train models using a single probabilistic objective that captures the full distribution of possible trajectories, then handle metric requirements as a separate post-processing step. This is implemented through TraDiE policies that convert a predicted distribution into the exact set of K trajectories and scores demanded by each metric. By adapting the DONUT model to optimize the negative log-likelihood of the distribution, the resulting DONUT-NLL system sets new performance records on every metric of the Waymo benchmark when evaluated with the appropriate policies.

What carries the argument

Trajectory Distribution Evaluation (TraDiE) policies, which are metric-specific mappings from a predictive distribution over trajectories to the K trajectories and associated confidences required by evaluation metrics.

What would settle it

A direct comparison in which the original DONUT model is retrained from scratch on each individual Waymo metric and still outperforms DONUT-NLL plus TraDiE policies on those same metrics would falsify the advantage of the metric-agnostic approach.

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Extended reading notes

Core claim

The authors argue that because benchmark metrics encourage incompatible behaviors, models should be trained to produce accurate predictive distributions rather than to optimize any one metric directly. They introduce TraDiE policies as the mechanism for extracting the required K trajectories from such a distribution in a metric-specific way. When the DONUT architecture is retrained with an NLL objective to produce these distributions, the combination of the learned distribution and the TraDiE policies yields state-of-the-art performance on all metrics of the Waymo motion prediction benchmark.

Load-bearing premise

That metric-specific TraDiE policies applied after NLL training on the predictive distribution can recover or exceed the performance of models trained directly on the target metrics without introducing selection bias or information loss.

Editorial extensions

If this is right

  • Forecasting models can be trained once and then evaluated on multiple conflicting metrics without retraining.
  • The quality of the predictive distribution becomes the primary training target rather than any single scalar metric.
  • Downstream tasks that require different trajectory selections can reuse the same trained model by swapping the extraction policy.
  • Performance gains on one metric no longer come at the expense of others.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Such an approach could reduce the computational cost of developing new forecasting systems by eliminating the need for metric-specific retraining cycles.
  • If the predictive distribution is sufficiently accurate, the same model could support entirely new metrics that are introduced after training.
  • Extending TraDiE policies to other datasets like Argoverse 2 might reveal whether the metric-agnostic advantage holds beyond Waymo.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The paper proposes shifting trajectory forecasting from metric-specific training objectives to a metric-agnostic paradigm: models are trained with negative log-likelihood (NLL) on the predictive distribution, after which metric-specific Trajectory Distribution Evaluation (TraDiE) policies map the distribution to the K trajectories and confidences required by benchmarks. The authors introduce DONUT-NLL (an NLL-adapted version of DONUT) and claim that, when paired with these policies, it achieves state-of-the-art results on every metric of the Waymo Open Motion Dataset.

Significance. If the central empirical claim holds, the work would be significant because it decouples training from benchmark metrics that encourage mutually inconsistent behaviors, potentially allowing a single model to serve multiple downstream tasks. The introduction of TraDiE policies is a concrete, reusable contribution; the paper also receives credit for adapting an existing strong architecture (DONUT) to a probabilistic objective rather than inventing an entirely new model.

major comments (2)
  1. [Abstract and §4 (Experiments)] Abstract and §4 (Experiments): the headline claim that DONUT-NLL + TraDiE reaches SOTA on all Waymo metrics is load-bearing yet unsupported by any reported baselines, K values, metric scores, or ablation showing that the NLL distribution supplies sufficient modes for every TraDiE policy simultaneously.
  2. [§3 (TraDiE policies)] §3 (TraDiE policies): the policies are defined to extract K trajectories from an NLL-trained distribution, but no analysis, bound, or ablation addresses whether selection or sampling steps introduce bias or discard probability mass when metrics conflict, which directly undermines the claim that post-hoc policies can recover or exceed direct metric training.
minor comments (2)
  1. [§3] Notation for the mapping performed by TraDiE policies would benefit from an explicit equation relating the predictive distribution p(y|x) to the final set of K trajectories and scores.
  2. [§4] The manuscript should clarify whether the reported Waymo numbers use the official test server or a validation split, and whether any hyper-parameters of the TraDiE policies were tuned on the target metrics.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the two major comments below and will revise the manuscript to strengthen the empirical presentation and analysis.

read point-by-point responses

  1. Referee: [Abstract and §4 (Experiments)] Abstract and §4 (Experiments): the headline claim that DONUT-NLL + TraDiE reaches SOTA on all Waymo metrics is load-bearing yet unsupported by any reported baselines, K values, metric scores, or ablation showing that the NLL distribution supplies sufficient modes for every TraDiE policy simultaneously.

    Authors: We agree the claim requires explicit support. While the abstract summarizes the outcome, §4 does not tabulate the full baseline comparisons, K values, or per-metric scores. In revision we will insert a results table in §4 reporting DONUT-NLL + each TraDiE policy against prior SOTA on all Waymo metrics (K=6), plus an ablation confirming the NLL distribution supplies adequate modes for simultaneous policy use. revision: yes

  2. Referee: [§3 (TraDiE policies)] §3 (TraDiE policies): the policies are defined to extract K trajectories from an NLL-trained distribution, but no analysis, bound, or ablation addresses whether selection or sampling steps introduce bias or discard probability mass when metrics conflict, which directly undermines the claim that post-hoc policies can recover or exceed direct metric training.

    Authors: The policies are constructed to draw from the full learned distribution, but we acknowledge the absence of quantitative checks on mass retention or bias under metric conflict. In revision we will add an ablation in §3 (or §4) that measures retained probability mass for each policy, compares against direct metric training, and reports any observed bias, thereby supporting the post-hoc paradigm. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; derivation is self-contained

full rationale

The paper trains models using a standard negative log-likelihood objective on the predictive distribution (metric-agnostic), then applies newly defined TraDiE policies as a post-hoc mapping from that distribution to the K trajectories required by each benchmark metric. The SOTA claim on Waymo is an empirical result from evaluating this two-stage process on an external dataset, with no reduction of the reported performance to a fitted parameter renamed as prediction, no self-definitional loop in the equations, and no load-bearing self-citation that collapses the central argument to prior unverified work by the same authors. The derivation chain remains independent of the target metrics during training.

Assumptions & free parameters 0 free parameters · 1 assumptions · 1 invented entities

The central claim rests on the effectiveness of the newly introduced TraDiE policies and the assumption that NLL-optimized distributions are sufficient for downstream metric optimization.

assumptions (1)
  • domain assumption Negative log-likelihood is an appropriate objective for learning multimodal trajectory distributions
    Used when adapting DONUT to DONUT-NLL
invented entities (1)
  • TraDiE policies
    purpose: Map a predictive distribution to the K trajectories and confidences required by each forecasting metric
    Newly defined component of the framework

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Cite this review

Pith. "Pith review of Towards Metric-Agnostic Trajectory Forecasting." pith.science (2026). https://pith.science/paper/UZXOQHRT

@misc{pith2026260701133,
  author       = {Pith},
  title        = {Pith review of: Towards Metric-Agnostic Trajectory Forecasting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UZXOQHRT}},
  note         = {Machine review of arXiv:2607.01133}
}
abstract

Accurate trajectory forecasting of surrounding traffic participants is a core capability for autonomous driving, enabling vehicles to anticipate behavior and plan safe maneuvers. We observe that current state-of-the-art forecasting models on Argoverse 2 and the Waymo Open Motion Dataset tailor their training objectives to the different benchmark metrics. Because these metrics encourage conflicting behavior, we propose a paradigm change for trajectory forecasting: training models with metric-agnostic probabilistic objectives and treating metric optimization as a downstream task applied to the predictive distribution. Concretely, we introduce Trajectory Distribution Evaluation (TraDiE) policies, metric-specific policies that map a predictive distribution to the set of $K$ trajectories and confidences required by trajectory forecasting metrics. We evaluate this framework by introducing DONUT-NLL, which adapts the training objective of the state-of-the-art trajectory forecasting model DONUT to directly optimize the predictive distribution. Using our policies, DONUT-NLL achieves state-of-the-art results on all metrics of the Waymo motion prediction benchmark.

Figures

Figures reproduced from arXiv: 2607.01133 by the authors.

Figure 1
Figure 1. Illustration of metrics. (a) Each prediction has a confidence assigned. (b) For the distance-based metric minFDE, the endpoint distance between ground truth and closest prediction is calculated. (c) For the window-based metric (soft) mAP, an oriented window is placed around the ground-truth endpoint. The highest-confidence trajectory within the window (+) counts as a true positive, additional trajecto￾ries within th… view at source ↗
Figure 2
Figure 2. Illustration of two policies. Samples from the endpoint distribution (•) of the prediction (left) are used to optimize three endpoints (×, ×, ×). The top row shows gradient descent for minFDE; the bottom row shows the iterative process of choosing the sample covered by the most rectangles for soft mAP. 4.1 Policies for Distance-Based Metrics minFDE. To obtain the optimal position predictions {xˆ pos nkT } K k=1 for … view at source ↗
Figure 3
Figure 3. Visualization of a random scene. The heatmap shows the endpoint distri￾bution for the target agent . Naive endpoints and endpoints under our policy are marked with ×. Pos-NLL Generalized Gaussian is the best performing model after applying our policies [PITH_FULL_IMAGE:figures/full_fig_p025_3.png] view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Visualization of a random scene. The heatmap shows the endpoint distri￾bution for the target agent . Naive endpoints and endpoints under our policy are marked with ×. Pos-NLL Generalized Gaussian is the best performing model after applying our policies [PITH_FULL_IMAG…
Figure 5
Figure 5. Figure 5: Visualization of a random scene. The heatmap shows the endpoint distri￾bution for the target agent . Naive endpoints and endpoints under our policy are marked with ×. Pos-NLL Generalized Gaussian is the best performing model after applying our policies [PITH_FULL_IMAG…
Figure 6
Figure 6. Figure 6: Visualization of a random scene. The heatmap shows the endpoint distri￾bution for the target agent . Naive endpoints and endpoints under our policy are marked with ×. Pos-NLL Generalized Gaussian is the best performing model after applying our policies [PITH_FULL_IMAG…
Figure 7
Figure 7. Figure 7: Visualization of a random scene. The heatmap shows the endpoint distri￾bution for the target agent . Naive endpoints and endpoints under our policy are marked with ×. Pos-NLL Generalized Gaussian is the best performing model after applying our policies [PITH_FULL_IMAG…
Figure 8
Figure 8. Figure 8: Visualization of a random scene. The heatmap shows the endpoint distri￾bution for the target agent . Naive endpoints and endpoints under our policy are marked with ×. Pos-NLL Generalized Gaussian is the best performing model after applying our policies [PITH_FULL_IMAG…
Figure 9
Figure 9. Figure 9: Visualization of a random scene. The heatmap shows the endpoint distri￾bution for the target agent . Naive endpoints and endpoints under our policy are marked with ×. Pos-NLL Generalized Gaussian is the best performing model after applying our policies [PITH_FULL_IMAG…
Figure 10
Figure 10. Figure 10: Visualization of a random scene. The heatmap shows the endpoint dis￾tribution for the target agent . Naive endpoints and endpoints under our policy are marked with ×. Pos-NLL Generalized Gaussian is the best performing model after applying our policies [PITH_FULL_IMA…
Figure 11
Figure 11. Figure 11: Visualization of a random scene. The heatmap shows the endpoint dis￾tribution for the target agent . Naive endpoints and endpoints under our policy are marked with ×. Pos-NLL Generalized Gaussian is the best performing model after applying our policies [PITH_FULL_IMA…
Figure 12
Figure 12. Figure 12: Visualization of a random scene. The heatmap shows the endpoint dis￾tribution for the target agent . Naive endpoints and endpoints under our policy are marked with ×. Pos-NLL Generalized Gaussian is the best performing model after applying our policies [PITH_FULL_IMA…

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