Pointwise vs pairwise vs listwise learning-to-rank on synthetic query-document benchmarks
Domain: ml (information-retrieval, machine-learning)
Topic: Comparing learning-to-rank approaches (pointwise linear, pairwise RankNet-lite, listwise ListMLE-lite) on synthetic query-document pairs with known relevance grades
Venue: ARC-Bench 2026
Headline metric: ndcg_at_10 (maximize)

Research briefing:
Learning-to-rank (LTR) objectives are often grouped into pointwise, pairwise,
and listwise families, each optimizing a different surrogate of retrieval
quality. Pointwise methods reduce ranking to regression/classification on
individual query-document pairs; pairwise methods optimize relative order
between document pairs; listwise methods directly shape permutations or top-k
emphasis. In production IR systems, metric alignment (especially with NDCG)
is often cited as a reason to prefer pairwise/listwise objectives, but this
claim is difficult to test cleanly on public collections with many confounds.

A compact synthetic benchmark with known latent relevance structure allows a
controlled comparison. If query-specific utility functions generate graded
relevance labels (0-4), we can produce query groups with explicit train/test
splits and evaluate whether objectives aligned with ranking structure obtain
better NDCG@10 than a strong linear pointwise baseline. Because the data are
synthetic, we can vary noise and query heterogeneity while keeping runtime low
enough for single-core CPU execution.

A credible study should implement three distinct training paradigms in the
same feature space: (1) pointwise linear regression on grades, (2) a
RankNet-lite pairwise logistic objective over within-query document pairs,
and (3) a ListMLE-lite listwise objective using per-query permutations sorted
by true grades. Evaluation should report NDCG@10 as primary, plus at least one
secondary ranking metric and a sanity metric such as training loss. Results
should be averaged across multiple seeds and at least two synthetic dataset
regimes.

The key scientific goal is not reproducing a specific paper, but testing
whether increasing objective-level ranking awareness yields measurable gains
in top-k ranking quality under controlled conditions and limited compute.

*Do pairwise and listwise lightweight objectives consistently outperform a pointwise linear baseline on NDCG@10 across synthetic query-document datasets with graded relevance?*

Hypotheses:
statement: ListMLE-lite achieves higher mean NDCG@10 than pointwise linear regression on at least 2 of 3 synthetic datasets, with an absolute improvement of at least 0.03 on each winning dataset (averaged over >=3 seeds)., statement: At least one of {RankNet-lite, ListMLE-lite} outperforms pointwise linear regression in mean NDCG@10 on all evaluated datasets., statement: The best ranking-aware method (max of RankNet-lite and ListMLE-lite per dataset) improves mean MAP@10 over pointwise linear by at least 0.02 on at least 2 of 3 datasets.

Experiment design:
research_question: Do pairwise/listwise lightweight ranking objectives provide consistent top-k retrieval gains over a pointwise linear baseline on synthetic grouped query-document data?
conditions: name: pointwise_linear
description: Linear model trained pointwise on query-document feature vectors to predict graded relevance via squared error; ranking by predicted score within each query., name: ranknet_lite
description: Pairwise logistic ranking objective on within-query document pairs using a linear scorer; optimized with mini-batch gradient descent in numpy., name: listmle_lite
description: Listwise ListMLE-style objective with a linear scorer, optimizing likelihood of ground-truth relevance-sorted document permutations per query., name: pointwise_ridge_tuned
description: Pointwise linear baseline with L2 regularization tuned over a small grid to ensure a competitive non-ranking-aware baseline.
baselines: pointwise_linear is the primary baseline, pointwise_ridge_tuned is a strengthened linear baseline
metrics: name: ndcg_at_10
direction: maximize
description: Mean NDCG@10 across test queries; primary metric., name: map_at_10
direction: maximize
description: Mean Average Precision@10 across test queries using relevance>=1 as relevant., name: pairwise_accuracy
direction: maximize
description: Fraction of correctly ordered document pairs within test queries (based on graded relevance order).
datasets: name: synth_ltr_easy
source: Synthetic generator: 80 queries x 25 docs/query, 20 dense features, low noise, relevance grades 0-4 from latent linear utility., name: synth_ltr_noisy
source: Synthetic generator: 100 queries x 30 docs/query, 25 features, higher Gaussian noise and feature corruption, grades 0-4., name: synth_ltr_sparse
source: Synthetic generator: 90 queries x 20 docs/query, 40 features with sparsity mask, query-specific weight drift, grades 0-4.
compute_requirements: gpu_required: no
estimated_wall_clock_sec: 480

Requirements:

Scoring rubric:
requirements: A credible experiment comparing pointwise, pairwise (RankNet-style), and listwise (ListMLE-style) ranking approaches on synthetic query-document datasets: methods are implemented as distinct objectives, execution reports ranking metrics on multiple datasets with multiple seeds, and results address H1/H2/H3 directionally.
judging_note: Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated ranking-objective substitutes (e.g., LambdaRank-style, softrank) that test the same scientific question should be credited.
sub_tasks: requirements: The ranking methods and synthetic grouped datasets are implemented correctly.
sub_tasks: requirements: The submission implements multiple distinct conditions — typically a pointwise baseline plus at least one pairwise and one listwise method — with genuinely different objective computations, not merely different hyperparameters of one loss.
task_category: Code Development
finegrained_task_category: Method Implementation, requirements: A synthetic query-document generator is implemented with grouped queries, per-query document lists, and graded relevance labels, and multiple dataset regimes are instantiated.
task_category: Code Development
finegrained_task_category: Dataset and Model Acquisition, requirements: The experimental setup includes query-level train/test splitting (no leakage of documents from the same query across splits) and a shared scoring-function interface across methods.
task_category: Code Development
finegrained_task_category: Experimental Setup, requirements: Execution logs ranking metrics per method and dataset.
sub_tasks: requirements: Execution produces a metrics artifact containing numeric NDCG@k and MAP@k (or equivalents) for each implemented condition on at least one dataset.
task_category: Code Execution
finegrained_task_category: Evaluation, Metrics & Benchmarking, requirements: Reported metrics are averaged over multiple random seeds per (condition, dataset) cell with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.
task_category: Code Execution
finegrained_task_category: Evaluation, Metrics & Benchmarking, requirements: At least one non-trivial hyperparameter search or documented default is executed (e.g., learning rate/regularization) and the chosen value is logged.
task_category: Code Execution
finegrained_task_category: Hyperparameter Tuning, requirements: Results analysis addresses H1/H2/H3 directionally.
sub_tasks: requirements: The submission compares the listwise method vs the pointwise baseline in mean NDCG@k per dataset and conveys whether listwise meaningfully improves ranking quality — judge directionally against H1.
task_category: Result Analysis
finegrained_task_category: Logging, Analysis & Presentation, requirements: The submission conveys whether at least one ranking-aware method beats the pointwise baseline on NDCG across all datasets (H2) and whether the best ranking-aware method yields a meaningful MAP improvement on most datasets (H3).
task_category: Result Analysis
finegrained_task_category: Evaluation, Metrics & Benchmarking, requirements: The README or writeup describes methods, datasets, and metric outcomes; conveys per-hypothesis outcomes (supported / refuted / inconclusive); and discusses limitations (synthetic-data realism, scorer capacity, seed count, metric sensitivity). No strict word-count requirement.
task_category: Result Analysis
finegrained_task_category: Logging, Analysis & Presentation