PeiyangLiu/ascp-context-attribution

ASCP: Causal Context Attribution and Probe Benchmark

Released artifacts for The Laws of Context Allocation: Causal Measurement and Closed-Loop Orchestration in Generative Search.

• 📄 Paper: https://arxiv.org/abs/2608.23252
• 💻 Code: https://github.com/PeiYangLiu/ascp

Retrieval-augmented generation is usually measured with relevance proxies — BM25, query–document cosine, output overlap — that score how related a passage looks, not whether the generator used it. This dataset ships the measurements behind two claims of the paper:

1. Those proxies look near-perfect only because standard evaluation pools are padded with off-query distractors. Under same-query hard negatives that entail no answer, they collapse toward chance while causal leave-one-out probes are essentially unchanged.
2. Once attribution is measured causally, a fixed inference budget is better spent on several narrow contexts than on one wide context.

Each leave-one-out score costs k+1 teacher-forced forward passes, so the attribution records here represent tens of thousands of GPU forward passes that are impractical to regenerate from scratch.

Configurations

probe_benchmark — 11,520 rows

The evaluation that produces the paper's headline finding. For every case the necessary answer-bearing set is known by construction, so estimators can be scored directly against ground truth as the negative population is swapped.

field	meaning
regime	distractor (off-query padding), samequery (hard negatives), duplicate, mixed
protocol	fixed (target held constant) or free (padding may reshape the response)
k	context width, one of 2, 3, 5, 8, 12, 24
m	size of the designed necessary set (always 2)
loo, contextcite, sim_gen, sim_query, bm25_query, overlap_gen, random	per-estimator recall / false-positive rate at five thresholds
loo_necessary_scores, loo_null_scores	raw probe scores for necessary and padding documents
groups	likelihood drop when dropping the necessary set, the padding, all, or half

Balanced by design: 2,880 rows per regime, 5,760 per protocol, 5,760 per generator, 5,760 per task.

attribution — 54,100 rows

Per-round causal attribution over the full policy grid: 4 tasks × 2 generators × 16 scheduling policies. attribution_raw[i] is the per-token log-likelihood drop when doc_ids[i] is ablated from that round's context, i.e. the quantity in Eq. (3) of the paper.

Tasks: ASQA, QAMPARI, ELI5, Recipes (Spanish, cross-cultural). Policies: vanilla, mmr, xquad, pm2, dpp, carriage, carriage_narrow, rotate, ascp, and their -integrate / width variants.

utilization — 1,200 rows

The subset carrying full generations alongside the matrices the scheduler consumes: the round-by-round utilization matrix, the document–facet matrix doc_facet_matrix, facet importance facet_importance, gold answers, and induced subtopics. This is the frame behind the judge meta-evaluation (179 portfolios, 858 document-level judgments) in the paper.

Usage

from datasets import load_dataset

# the estimator benchmark
probe = load_dataset("PeiyangLiu/ascp-context-attribution", "probe_benchmark", split="train")

# does a proxy survive the swap from off-query to same-query padding?
off = probe.filter(lambda r: r["regime"] == "distractor" and r["protocol"] == "fixed")
hard = probe.filter(lambda r: r["regime"] == "samequery" and r["protocol"] == "fixed")

# causal attribution over every policy
attr = load_dataset("PeiyangLiu/ascp-context-attribution", "attribution", split="train")

Records join across configurations on qid (plus task, generator, policy).

Reproducing the paper from these files

Averaging estimator AUC over context widths under the fixed protocol reproduces the paper's Figure 1(c), the swap from off-query to same-query padding:

estimator	off-query	same-query	Δ
leave-one-out probe	0.852	0.845	−0.01
output overlap	0.952	0.831	−0.14
output–document cosine	0.980	0.766	−0.23
query–document cosine	0.999	0.564	−0.52
BM25	0.992	0.568	−0.54

Every table and figure in the paper is regenerated by the analysis scripts in the GitHub repository, which ships the aggregated per-query rows and needs no GPU.

Source data and licensing

Queries and retrieval pools are built on ALCE (ASQA, QAMPARI, ELI5). The ALCE passage text is deliberately not redistributed here: records reference source passages by their position in the evaluation pool (doc_ids) and, where useful, by title. Reconstruct the pools from ALCE directly; scripts/build_pool.py in the code repository does this.

The Recipes split follows the cross-cultural recipe setting of Hu et al. Please observe the upstream licenses for both.

The annotations released here — attribution scores, utilization matrices, facet matrices, estimator scores — are our own and are licensed CC BY 4.0.

Limitations

• Probe validation covers 7–8B generators (Qwen2.5-7B, Llama-3.1-8B); the paper's scale checks at 14B and 32B are reported from the aggregated rows in the code repository rather than here.
• utilization covers Qwen2.5-7B on ASQA and QAMPARI only, because it backs the judge meta-evaluation rather than the main grid.
• Attribution is a leave-one-out likelihood drop on an already-produced response. It measures reliance of that generation, not counterfactual answer correctness.

Citation

@article{liu2026laws,
  title   = {The Laws of Context Allocation: Causal Measurement and Closed-Loop
             Orchestration in Generative Search},
  author  = {Liu, Peiyang and Wang, Xi and Liang, Di and Ye, Wei},
  journal = {arXiv preprint arXiv:2608.23252},
  year    = {2026},
  url     = {https://arxiv.org/abs/2608.23252}
}