---
license: other
license_name: per-partition-source-licenses
license_link: https://github.com/aimsresearchlab/seam/blob/main/DATA_LICENSES.md
language:
- en
pretty_name: SEAM
size_categories:
- 1K<n<10K
task_categories:
- text-generation
tags:
- llm-evaluation
- benchmark
- prompt-boundaries
- instruction-data-separation
- editing
configs:
- config_name: permissive
  default: true
  data_files: benchmark/seam_v4_permissive.jsonl
- config_name: cc-by-sa
  data_files: benchmark/restricted/seam_v4_cc-by-sa.jsonl
- config_name: cc-by-nc-sa
  data_files: benchmark/restricted/seam_v4_cc-by-nc-sa.jsonl
---

# SEAM: absorption at unmarked prompt seams

A user pastes text into a chat, asks for an edit, and keeps typing a comment below the paste. The model receives one flat message. SEAM measures **absorption**: the model returns that trailing comment inside the edited text, even though the comment is benign and asks for no insertion.

This is the benchmark from [Can LLMs Separate Pasted Artifacts from User Speech? Absorption at Unmarked Prompt Seams](https://arxiv.org/abs/2610.04210) (Sugam Panthi, Muhaiminul Yeamin, and Rabab Abdelfattah, arXiv:2610.04210).

- Paper: https://arxiv.org/abs/2610.04210
- Code, scorer, and per-model case-level labels: https://github.com/aimsresearchlab/seam
- Leaderboard: https://aimsresearchlab.com/seam/leaderboard.html
- Companion post: https://spanthi.com/blog/where-does-the-paste-end/

Across 20 models, absorption at a bare newline ranges from 7.7% to 66.7%. Adding a blank line does not significantly reduce absorption in any model, while boundary markers reduce it in 19 of 20 models. Comments that fit the pasted text, such as a code comment typed after code, are absorbed significantly more often in 17 of 20 models.

## Structure

300 composition clusters, each under six matched conditions, 1,800 rows in total. A cluster is one task instruction and one pasted artifact; rows that share `composition_event_id` are matched.

| condition | what follows the pasted artifact |
|---|---|
| `clean` | nothing; the model's own output for the cluster, used as the comparison |
| `newline` | the comment after a bare newline |
| `blank` | the comment after a blank line |
| `boundary` | the comment after explicit boundary markers |
| `mitigation` | boundary markers plus one instruction line |
| `newline_R` | a comment rewritten to fit the genre of the pasted text (called artifact-native in the paper) |

Conditions are scored within a cluster against that cluster's `clean` output, so a signal counts only when it is absent from the model's unprompted output. The scorer is in the GitHub repository.

## Configurations and licenses

The examples reuse text from six public editing corpora, so the data is split by source license. Each row keeps a `license` field and the identifiers needed to trace it upstream.

| config | rows | sources | license |
|---|---:|---|---|
| `permissive` (default) | 1,200 | CanItEdit (MIT), CoEdIT (Apache-2.0), CommitPackFT (MIT dataset; item repository license retained), IteraTeR Human-Doc (Apache-2.0) | per source, permissive |
| `cc-by-sa` | 300 | Stack Exchange | CC BY-SA (version from the item revision date) |
| `cc-by-nc-sa` | 300 | ParaRev | CC BY-NC-SA 4.0, noncommercial |

```python
from datasets import load_dataset

seam = load_dataset("vein05/seam", "permissive", split="train")
```

Public availability of a source text does not by itself grant redistribution rights, and item-level retrieval dates and URLs are not recorded consistently across all sources. Check the upstream terms before redistributing a partition. [DATA_LICENSES.md](https://github.com/aimsresearchlab/seam/blob/main/DATA_LICENSES.md) has the details. The occurrence analysis in the paper uses WildChat and LMSYS-Chat-1M; that conversation text is not included here.

## Main fields

| field | meaning |
|---|---|
| `composition_event_id` | cluster identifier |
| `condition` | one of the six conditions above |
| `task_instruction` | typed instruction before the simulated paste |
| `artifact` | source material designated as pasted |
| `afterthought` | typed text after the paste; null in `clean` |
| `forbidden_proposition` | meaning that must not enter the returned artifact |
| `witness_tokens` | distinctive afterthought substrings, verified absent from the artifact, reference, and instruction; null in `clean` |
| `message` | the flattened user message the model receives |
| `segments` | typed and pasted origins with exact character offsets |
| `reference` | upstream reference edit, where the source provides one |
| `source`, `license`, `url` | upstream corpus and licensing metadata |

## Scope

The examples are constructed, English, first-turn editing requests with equal source weighting and repeated comment pools. SEAM measures unintended inclusion of benign user speech in a returned artifact. It contains no adversarial instructions or executable payloads, and it does not establish behavior for sensitive content, other languages, or later conversational turns.

## Citation

```bibtex
@misc{panthi2026seam,
  title         = {Can {LLMs} Separate Pasted Artifacts from User Speech? Absorption at Unmarked Prompt Seams},
  author        = {Panthi, Sugam and Yeamin, Muhaiminul and Abdelfattah, Rabab},
  year          = {2026},
  eprint        = {2610.04210},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  doi           = {10.48550/arXiv.2610.04210},
  url           = {https://arxiv.org/abs/2610.04210}
}
```