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Datasets:
cmwqfcmwqf
/
DeepReview-Bench

Tasks:
Text Classification
Text Generation
Modalities:
Document
Image
Languages:
English
Size:
10K<n<100K
Tags:
peer-review
scientific-papers
iclr
llm-evaluation
License:
Dataset card Data Studio Files
xet
 Community
1
Dataset Preview
Duplicate
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
id: string
title: string
venue: string
venue_year: int64
date: timestamp[s]
decision: string
source_file: string
source_index: int64
review_time_pdf_url: string
markdown_source: string
markdown_chars: int64
pdf_time_verified_at: null
conversion_report: string
openreview_review_count: int64
review_time_revision_cdate: int64
downloaded_at: string
modes: list<item: string>
  child 0, item: string
processed_at: string
source_pdf_content_field: string
selected_pdf_mdate: int64
review_time_revision_mdate: int64
openreview_forum: string
openreview_submission_note_id: string
pdf_time_verification_status: string
review_time_candidate_count: int64
paper_source_tex: string
first_official_review_cdate: int64
pdf_sha256: string
paper_pdf: string
selected_pdf_cdate: int64
openreview_edit_revision_count: int64
markdown_backend: string
selected_pdf_mdate_iso: string
review_time_pdf_edit_id: null
years: list<item: string>
  child 0, item: string
metadata: string
review_time_revision_mdate_iso: string
pdf_bytes: int64
openreview_revision_count: int64
ratings: list<item: string>
  child 0, item: string
selected_pdf_cdate_iso: string
review_time_revision_source: string
review: string
review_time_revision_cdate_iso: string
markdown_rebuilt_at: string
splits: list<item: string>
  child 0, item: string
review_time_pdf_note_id: string
paper_md: string
first_official_review_cdate_iso: string
to
{'id': Value('string'), 'title': Value('string'), 'decision': Value('string'), 'years': List(Value('string')), 'modes': List(Value('string')), 'ratings': List(Value('string')), 'splits': List(Value('string')), 'paper_pdf': Value('string'), 'paper_md': Value('string'), 'paper_source_tex': Value('string'), 'metadata': Value('string'), 'review': Value('string'), 'conversion_report': Value('string'), 'pdf_sha256': Value('string'), 'pdf_bytes': Value('int64'), 'markdown_chars': Value('int64'), 'markdown_source': Value('string'), 'markdown_backend': Value('string'), 'markdown_rebuilt_at': Value('string'), 'openreview_forum': Value('string'), 'openreview_submission_note_id': Value('string'), 'review_time_pdf_note_id': Value('string'), 'review_time_pdf_url': Value('string'), 'review_time_revision_source': Value('string'), 'review_time_pdf_edit_id': Value('null'), 'source_pdf_content_field': Value('string'), 'openreview_review_count': Value('int64'), 'openreview_revision_count': Value('int64'), 'openreview_edit_revision_count': Value('int64'), 'review_time_candidate_count': Value('int64'), 'review_time_revision_cdate': Value('int64'), 'review_time_revision_cdate_iso': Value('string'), 'review_time_revision_mdate': Value('int64'), 'review_time_revision_mdate_iso': Value('string'), 'first_official_review_cdate': Value('int64'), 'first_official_review_cdate_iso': Value('string'), 'selected_pdf_cdate': Value('int64'), 'selected_pdf_cdate_iso': Value('string'), 'selected_pdf_mdate': Value('int64'), 'selected_pdf_mdate_iso': Value('string'), 'downloaded_at': Value('string'), 'processed_at': Value('string'), 'pdf_time_verified_at': Value('null'), 'pdf_time_verification_status': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              id: string
              title: string
              venue: string
              venue_year: int64
              date: timestamp[s]
              decision: string
              source_file: string
              source_index: int64
              review_time_pdf_url: string
              markdown_source: string
              markdown_chars: int64
              pdf_time_verified_at: null
              conversion_report: string
              openreview_review_count: int64
              review_time_revision_cdate: int64
              downloaded_at: string
              modes: list<item: string>
                child 0, item: string
              processed_at: string
              source_pdf_content_field: string
              selected_pdf_mdate: int64
              review_time_revision_mdate: int64
              openreview_forum: string
              openreview_submission_note_id: string
              pdf_time_verification_status: string
              review_time_candidate_count: int64
              paper_source_tex: string
              first_official_review_cdate: int64
              pdf_sha256: string
              paper_pdf: string
              selected_pdf_cdate: int64
              openreview_edit_revision_count: int64
              markdown_backend: string
              selected_pdf_mdate_iso: string
              review_time_pdf_edit_id: null
              years: list<item: string>
                child 0, item: string
              metadata: string
              review_time_revision_mdate_iso: string
              pdf_bytes: int64
              openreview_revision_count: int64
              ratings: list<item: string>
                child 0, item: string
              selected_pdf_cdate_iso: string
              review_time_revision_source: string
              review: string
              review_time_revision_cdate_iso: string
              markdown_rebuilt_at: string
              splits: list<item: string>
                child 0, item: string
              review_time_pdf_note_id: string
              paper_md: string
              first_official_review_cdate_iso: string
              to
              {'id': Value('string'), 'title': Value('string'), 'decision': Value('string'), 'years': List(Value('string')), 'modes': List(Value('string')), 'ratings': List(Value('string')), 'splits': List(Value('string')), 'paper_pdf': Value('string'), 'paper_md': Value('string'), 'paper_source_tex': Value('string'), 'metadata': Value('string'), 'review': Value('string'), 'conversion_report': Value('string'), 'pdf_sha256': Value('string'), 'pdf_bytes': Value('int64'), 'markdown_chars': Value('int64'), 'markdown_source': Value('string'), 'markdown_backend': Value('string'), 'markdown_rebuilt_at': Value('string'), 'openreview_forum': Value('string'), 'openreview_submission_note_id': Value('string'), 'review_time_pdf_note_id': Value('string'), 'review_time_pdf_url': Value('string'), 'review_time_revision_source': Value('string'), 'review_time_pdf_edit_id': Value('null'), 'source_pdf_content_field': Value('string'), 'openreview_review_count': Value('int64'), 'openreview_revision_count': Value('int64'), 'openreview_edit_revision_count': Value('int64'), 'review_time_candidate_count': Value('int64'), 'review_time_revision_cdate': Value('int64'), 'review_time_revision_cdate_iso': Value('string'), 'review_time_revision_mdate': Value('int64'), 'review_time_revision_mdate_iso': Value('string'), 'first_official_review_cdate': Value('int64'), 'first_official_review_cdate_iso': Value('string'), 'selected_pdf_cdate': Value('int64'), 'selected_pdf_cdate_iso': Value('string'), 'selected_pdf_mdate': Value('int64'), 'selected_pdf_mdate_iso': Value('string'), 'downloaded_at': Value('string'), 'processed_at': Value('string'), 'pdf_time_verified_at': Value('null'), 'pdf_time_verification_status': Value('string')}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

id
string
title
string
decision
string
years
list
modes
list
ratings
list
splits
list
paper_pdf
string
paper_md
string
paper_source_tex
string
metadata
string
review
string
conversion_report
string
pdf_sha256
string
pdf_bytes
int64
markdown_chars
int64
markdown_source
string
markdown_backend
string
markdown_rebuilt_at
string
openreview_forum
string
openreview_submission_note_id
string
review_time_pdf_note_id
string
review_time_pdf_url
string
review_time_revision_source
string
review_time_pdf_edit_id
null
source_pdf_content_field
string
openreview_review_count
int64
openreview_revision_count
int64
openreview_edit_revision_count
int64
review_time_candidate_count
int64
review_time_revision_cdate
int64
review_time_revision_cdate_iso
string
review_time_revision_mdate
int64
review_time_revision_mdate_iso
string
first_official_review_cdate
int64
first_official_review_cdate_iso
string
selected_pdf_cdate
int64
selected_pdf_cdate_iso
string
selected_pdf_mdate
int64
selected_pdf_mdate_iso
string
downloaded_at
string
processed_at
string
pdf_time_verified_at
null
pdf_time_verification_status
string
00SnKBGTsz
DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback
Accept
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[8, 8, 6, 8]" ]
[ "data/train.csv" ]
papers/00SnKBGTsz/paper.pdf
papers/00SnKBGTsz/paper.md
papers/00SnKBGTsz/paper.source.tex
papers/00SnKBGTsz/metadata.json
papers/00SnKBGTsz/review.json
papers/00SnKBGTsz/conversion_report.json
f7f12759d7b8249e69907e3d2af9804376c5516acf73daffe5bf7c4d2b77da9b
4,264,017
79,626
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:16.136080+00:00
00SnKBGTsz
00SnKBGTsz
00SnKBGTsz
https://arxiv.org/pdf/2410.06215v1
arxiv
null
https://arxiv.org/pdf/2410.06215v1
4
1
null
1
1,728,408,037,000
2024-10-08T17:20:37+00:00
1,728,408,037,000
2024-10-08T17:20:37+00:00
1,730,472,742,428
2024-11-01T14:52:22.428000+00:00
1,728,408,037,000
2024-10-08T17:20:37+00:00
1,728,408,037,000
2024-10-08T17:20:37+00:00
2026-08-05T11:29:29.426016+00:00
2026-08-05T11:29:29.426016+00:00
null
verified_arxiv_version_before_review
00ezkB2iZf
MedFuzz: Exploring the Robustness of Large Language Models in Medical Question Answering
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[3, 6, 3, 5]" ]
[ "data/train.csv" ]
papers/00ezkB2iZf/paper.pdf
papers/00ezkB2iZf/paper.md
papers/00ezkB2iZf/paper.source.tex
papers/00ezkB2iZf/metadata.json
papers/00ezkB2iZf/review.json
papers/00ezkB2iZf/conversion_report.json
1d7a47be37d78857b8c860e77fa68dd28ac18c7d7f8a450c18f9785b6ba6d657
549,184
68,042
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:16.162412+00:00
00ezkB2iZf
00ezkB2iZf
00ezkB2iZf
https://arxiv.org/pdf/2406.06573v2
arxiv
null
https://arxiv.org/pdf/2406.06573v2
4
1
null
1
1,725,219,482,000
2024-09-01T19:38:02+00:00
1,725,219,482,000
2024-09-01T19:38:02+00:00
1,730,166,452,694
2024-10-29T01:47:32.694000+00:00
1,725,219,482,000
2024-09-01T19:38:02+00:00
1,725,219,482,000
2024-09-01T19:38:02+00:00
2026-08-05T11:29:30.479703+00:00
2026-08-05T11:29:30.479703+00:00
null
verified_arxiv_version_before_review
01wMplF8TL
INSTRUCTION-FOLLOWING LLMS FOR TIME SERIES PREDICTION: A TWO-STAGE MULTIMODAL APPROACH
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[5, 3, 5, 5]" ]
[ "data/train.csv" ]
papers/01wMplF8TL/paper.pdf
papers/01wMplF8TL/paper.md
papers/01wMplF8TL/paper.source.tex
papers/01wMplF8TL/metadata.json
papers/01wMplF8TL/review.json
papers/01wMplF8TL/conversion_report.json
fc828f78d9ec09092fb0beb52415d1b15f7d8c21785a6c4d210ab5d9cce3c545
2,857,892
22,409
dataset
dataset_source_lossless
2026-08-05T17:15:41.586106+00:00
01wMplF8TL
01wMplF8TL
01wMplF8TL
https://openreview.net/pdf/a81d207169793b5c164d4491b942f69c4daec4fa.pdf
null
null
/pdf/a81d207169793b5c164d4491b942f69c4daec4fa.pdf
4
1
null
1
1,727,444,438,002
2024-09-27T13:40:38.002000+00:00
1,738,735,813,851
2025-02-05T06:10:13.851000+00:00
1,730,199,891,836
2024-10-29T11:04:51.836000+00:00
1,727,444,438,002
2024-09-27T13:40:38.002000+00:00
1,738,735,813,851
2025-02-05T06:10:13.851000+00:00
2026-08-05T11:29:32.181837+00:00
2026-08-05T11:29:32.181837+00:00
null
verified_revision_cdate_before_review
029hDSVoXK
Dynamic Neural Fortresses: An Adaptive Shield for Model Extraction Defense
Accept
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[8, 6, 6, 6, 8]" ]
[ "data/train.csv" ]
papers/029hDSVoXK/paper.pdf
papers/029hDSVoXK/paper.md
papers/029hDSVoXK/paper.source.tex
papers/029hDSVoXK/metadata.json
papers/029hDSVoXK/review.json
papers/029hDSVoXK/conversion_report.json
a400d949b4eb732fe98a35ab4074c364e88fc2c76bd441615c2f148d891837ad
1,734,221
42,049
dataset
dataset_source_lossless
2026-08-05T17:15:41.591145+00:00
029hDSVoXK
029hDSVoXK
029hDSVoXK
https://openreview.net/pdf/3f444b54245e1e0b307583740df184d35ba5cd2c.pdf
null
null
/pdf/3f444b54245e1e0b307583740df184d35ba5cd2c.pdf
5
1
null
1
1,727,301,949,954
2024-09-25T22:05:49.954000+00:00
1,746,247,148,279
2025-05-03T04:39:08.279000+00:00
1,730,301,204,098
2024-10-30T15:13:24.098000+00:00
1,727,301,949,954
2024-09-25T22:05:49.954000+00:00
1,746,247,148,279
2025-05-03T04:39:08.279000+00:00
2026-08-05T11:29:33.303408+00:00
2026-08-05T11:29:33.303408+00:00
null
verified_revision_cdate_before_review
02DCEU6vSU
Gen-LRA: Towards a Principled Membership Inference Attack for Generative Models
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[8, 3, 5, 3, 5]" ]
[ "data/train.csv" ]
papers/02DCEU6vSU/paper.pdf
papers/02DCEU6vSU/paper.md
papers/02DCEU6vSU/paper.source.tex
papers/02DCEU6vSU/metadata.json
papers/02DCEU6vSU/review.json
papers/02DCEU6vSU/conversion_report.json
cc96450f033c709e62d4b9fb8db5992b8e3c5a6db745cccd93dcc509630fdcc0
695,849
33,627
dataset
dataset_source_lossless
2026-08-05T17:15:41.595191+00:00
02DCEU6vSU
02DCEU6vSU
02DCEU6vSU
https://openreview.net/pdf/bcad18f87958725e9b50970906e168913dcdf521.pdf
null
null
/pdf/bcad18f87958725e9b50970906e168913dcdf521.pdf
5
1
null
1
1,727,487,667,754
2024-09-28T01:41:07.754000+00:00
1,732,725,402,868
2024-11-27T16:36:42.868000+00:00
1,730,152,041,539
2024-10-28T21:47:21.539000+00:00
1,727,487,667,754
2024-09-28T01:41:07.754000+00:00
1,732,725,402,868
2024-11-27T16:36:42.868000+00:00
2026-08-05T11:29:34.147692+00:00
2026-08-05T11:29:34.147692+00:00
null
verified_revision_cdate_before_review
02Od16GFRW
Ensembles provably learn equivariance through data augmentation
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[3, 6, 6]" ]
[ "data/train.csv" ]
papers/02Od16GFRW/paper.pdf
papers/02Od16GFRW/paper.md
papers/02Od16GFRW/paper.source.tex
papers/02Od16GFRW/metadata.json
papers/02Od16GFRW/review.json
papers/02Od16GFRW/conversion_report.json
5c851a1ef6eae76ebc1df5926032767fac341e4e5e2b92b94f4fd9cc02f8ab15
2,336,538
91,569
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:16.199304+00:00
02Od16GFRW
02Od16GFRW
02Od16GFRW
https://arxiv.org/pdf/2410.01452v1
arxiv
null
https://arxiv.org/pdf/2410.01452v1
3
1
null
1
1,727,870,563,000
2024-10-02T12:02:43+00:00
1,727,870,563,000
2024-10-02T12:02:43+00:00
1,730,497,417,502
2024-11-01T21:43:37.502000+00:00
1,727,870,563,000
2024-10-02T12:02:43+00:00
1,727,870,563,000
2024-10-02T12:02:43+00:00
2026-08-05T11:30:34.704633+00:00
2026-08-05T11:30:34.704633+00:00
null
verified_arxiv_version_before_review
02haSpO453
VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation
Accept
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[8, 6, 6, 6]" ]
[ "data/train.csv" ]
papers/02haSpO453/paper.pdf
papers/02haSpO453/paper.md
papers/02haSpO453/paper.source.tex
papers/02haSpO453/metadata.json
papers/02haSpO453/review.json
papers/02haSpO453/conversion_report.json
561fbae4b481f44a57b15c6619cb455e60670688d701722c6483eb7f1f2408ec
18,990,906
45,484
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:16.216901+00:00
02haSpO453
02haSpO453
02haSpO453
https://arxiv.org/pdf/2409.04429v2
arxiv
null
https://arxiv.org/pdf/2409.04429v2
4
1
null
1
1,729,701,726,000
2024-10-23T16:42:06+00:00
1,729,701,726,000
2024-10-23T16:42:06+00:00
1,730,093,669,491
2024-10-28T05:34:29.491000+00:00
1,729,701,726,000
2024-10-23T16:42:06+00:00
1,729,701,726,000
2024-10-23T16:42:06+00:00
2026-08-05T11:30:38.680003+00:00
2026-08-05T11:30:38.680003+00:00
null
verified_arxiv_version_before_review
02kZwCo0C3
SAIL: Self-improving Efficient Online Alignment of Large Language Models
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 6, 8, 3]" ]
[ "data/train.csv" ]
papers/02kZwCo0C3/paper.pdf
papers/02kZwCo0C3/paper.md
papers/02kZwCo0C3/paper.source.tex
papers/02kZwCo0C3/metadata.json
papers/02kZwCo0C3/review.json
papers/02kZwCo0C3/conversion_report.json
8f384281fb34788198a7e583e066095b008cd9ec8e87e89af90674cb14028b89
1,728,205
90,185
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:16.252823+00:00
02kZwCo0C3
02kZwCo0C3
02kZwCo0C3
https://arxiv.org/pdf/2406.15567v1
arxiv
null
https://arxiv.org/pdf/2406.15567v1
4
1
null
1
1,718,993,135,000
2024-06-21T18:05:35+00:00
1,718,993,135,000
2024-06-21T18:05:35+00:00
1,729,772,623,071
2024-10-24T12:23:43.071000+00:00
1,718,993,135,000
2024-06-21T18:05:35+00:00
1,718,993,135,000
2024-06-21T18:05:35+00:00
2026-08-05T11:30:40.192258+00:00
2026-08-05T11:30:40.192258+00:00
null
verified_arxiv_version_before_review
03EkqSCKuO
Port-Hamiltonian Architectural Bias for Long-Range Propagation in Deep Graph Networks
Accept
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[8, 5, 8]" ]
[ "data/train.csv" ]
papers/03EkqSCKuO/paper.pdf
papers/03EkqSCKuO/paper.md
papers/03EkqSCKuO/paper.source.tex
papers/03EkqSCKuO/metadata.json
papers/03EkqSCKuO/review.json
papers/03EkqSCKuO/conversion_report.json
ff876de8e33ab714c249df6d0932d03ae4bc390a7dcdd0e342172250a0fa1b57
1,504,285
109,626
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:16.297120+00:00
03EkqSCKuO
03EkqSCKuO
03EkqSCKuO
https://arxiv.org/pdf/2405.17163v1
arxiv
null
https://arxiv.org/pdf/2405.17163v1
3
1
null
1
1,716,817,010,000
2024-05-27T13:36:50+00:00
1,716,817,010,000
2024-05-27T13:36:50+00:00
1,729,822,898,690
2024-10-25T02:21:38.690000+00:00
1,716,817,010,000
2024-05-27T13:36:50+00:00
1,716,817,010,000
2024-05-27T13:36:50+00:00
2026-08-05T11:30:41.593171+00:00
2026-08-05T11:30:41.593171+00:00
null
verified_arxiv_version_before_review
03u7pbpyeN
BEATS: Optimizing LLM Mathematical Capabilities with BackVerify and Adaptive Disambiguate based Efficient Tree Search
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[3, 3, 6, 5]" ]
[ "data/train.csv" ]
papers/03u7pbpyeN/paper.pdf
papers/03u7pbpyeN/paper.md
papers/03u7pbpyeN/paper.source.tex
papers/03u7pbpyeN/metadata.json
papers/03u7pbpyeN/review.json
papers/03u7pbpyeN/conversion_report.json
e5de7c48131798b7348b82e4aa1c1a5fcda211436e20f1285bdfb2975cee7f5d
3,728,410
48,924
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:16.374376+00:00
03u7pbpyeN
03u7pbpyeN
03u7pbpyeN
https://arxiv.org/pdf/2409.17972v2
arxiv
null
https://arxiv.org/pdf/2409.17972v2
4
1
null
1
1,727,612,668,000
2024-09-29T12:24:28+00:00
1,727,612,668,000
2024-09-29T12:24:28+00:00
1,730,287,257,817
2024-10-30T11:20:57.817000+00:00
1,727,612,668,000
2024-09-29T12:24:28+00:00
1,727,612,668,000
2024-09-29T12:24:28+00:00
2026-08-05T11:30:43.708334+00:00
2026-08-05T11:30:43.708334+00:00
null
verified_arxiv_version_before_review
04RGjODVj3
From Rest to Action: Adaptive Weight Generation for Motor Imagery Classification from Resting-State EEG Using Hypernetworks
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[1, 5, 3, 3]" ]
[ "data/train.csv" ]
papers/04RGjODVj3/paper.pdf
papers/04RGjODVj3/paper.md
papers/04RGjODVj3/paper.source.tex
papers/04RGjODVj3/metadata.json
papers/04RGjODVj3/review.json
papers/04RGjODVj3/conversion_report.json
c8337974e95e3c1b061ef7cfd0b710a33b64168a75c6fbdb7324c169248ac146
223,269
22,180
dataset
dataset_source_lossless
2026-08-05T17:15:41.630678+00:00
04RGjODVj3
04RGjODVj3
04RGjODVj3
https://openreview.net/pdf/fe67b84fb9f3855a93c69b0b64c4bac3103faf9d.pdf
null
null
/pdf/fe67b84fb9f3855a93c69b0b64c4bac3103faf9d.pdf
4
1
null
1
1,727,519,598,998
2024-09-28T10:33:18.998000+00:00
1,738,735,884,874
2025-02-05T06:11:24.874000+00:00
1,730,243,876,620
2024-10-29T23:17:56.620000+00:00
1,727,519,598,998
2024-09-28T10:33:18.998000+00:00
1,738,735,884,874
2025-02-05T06:11:24.874000+00:00
2026-08-05T11:30:44.435423+00:00
2026-08-05T11:30:44.435423+00:00
null
verified_revision_cdate_before_review
04RLVxDvig
NanoMoE: Scaling Mixture of Experts to Individual Layers for Parameter-Efficient Deep Learning
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[3, 3, 3, 3]" ]
[ "data/train.csv" ]
papers/04RLVxDvig/paper.pdf
papers/04RLVxDvig/paper.md
papers/04RLVxDvig/paper.source.tex
papers/04RLVxDvig/metadata.json
papers/04RLVxDvig/review.json
papers/04RLVxDvig/conversion_report.json
2c2c61180620ee50c608dd27a49517ef751d1d36153ce503da90d4c4a789980b
397,121
25,354
dataset
dataset_source_lossless
2026-08-05T17:15:41.634407+00:00
04RLVxDvig
04RLVxDvig
04RLVxDvig
https://openreview.net/pdf/4aa38d228c8912d3aa307666a911d282faf7ee53.pdf
null
null
/pdf/4aa38d228c8912d3aa307666a911d282faf7ee53.pdf
4
1
null
1
1,727,451,195,453
2024-09-27T15:33:15.453000+00:00
1,738,735,824,695
2025-02-05T06:10:24.695000+00:00
1,730,044,512,970
2024-10-27T15:55:12.970000+00:00
1,727,451,195,453
2024-09-27T15:33:15.453000+00:00
1,738,735,824,695
2025-02-05T06:10:24.695000+00:00
2026-08-05T11:30:45.694014+00:00
2026-08-05T11:30:45.694014+00:00
null
verified_revision_cdate_before_review
04TRw4pYSV
Dual-Modality Guided Prompt for Continual Learning of Large Multimodal Models
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[5, 3, 3, 3]" ]
[ "data/train.csv" ]
papers/04TRw4pYSV/paper.pdf
papers/04TRw4pYSV/paper.md
papers/04TRw4pYSV/paper.source.tex
papers/04TRw4pYSV/metadata.json
papers/04TRw4pYSV/review.json
papers/04TRw4pYSV/conversion_report.json
22b539369fe59c26273b1248c0e7c85de025d228c9d1052d180547284fea3642
1,348,605
46,939
dataset
dataset_source_lossless
2026-08-05T17:15:41.639391+00:00
04TRw4pYSV
04TRw4pYSV
04TRw4pYSV
https://openreview.net/pdf/f557d72e5e53556a321bc19c7bcf56100cd4753a.pdf
null
null
/pdf/f557d72e5e53556a321bc19c7bcf56100cd4753a.pdf
4
1
null
1
1,727,317,529,950
2024-09-26T02:25:29.950000+00:00
1,731,652,601,618
2024-11-15T06:36:41.618000+00:00
1,730,016,439,970
2024-10-27T08:07:19.970000+00:00
1,727,317,529,950
2024-09-26T02:25:29.950000+00:00
1,731,652,601,618
2024-11-15T06:36:41.618000+00:00
2026-08-05T11:30:46.637700+00:00
2026-08-05T11:30:46.637700+00:00
null
verified_revision_cdate_before_review
04c5uWq9SA
A False Sense of Privacy: Evaluating Textual Data Sanitization Beyond Surface-level Privacy Leakage
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[8, 5, 5, 5]" ]
[ "data/train.csv" ]
papers/04c5uWq9SA/paper.pdf
papers/04c5uWq9SA/paper.md
papers/04c5uWq9SA/paper.source.tex
papers/04c5uWq9SA/metadata.json
papers/04c5uWq9SA/review.json
papers/04c5uWq9SA/conversion_report.json
9259f231e6d4878d2d0c55312a7def1db494fc546a3af6bce87140c3f7eb189e
11,310,208
31,928
dataset
dataset_source_lossless
2026-08-05T17:15:41.643087+00:00
04c5uWq9SA
04c5uWq9SA
04c5uWq9SA
https://openreview.net/pdf/4ecb6117ec293be1b481b25d0050bad293f57707.pdf
null
null
/pdf/4ecb6117ec293be1b481b25d0050bad293f57707.pdf
4
1
null
1
1,727,407,419,934
2024-09-27T03:23:39.934000+00:00
1,738,735,777,982
2025-02-05T06:09:37.982000+00:00
1,729,587,475,750
2024-10-22T08:57:55.750000+00:00
1,727,407,419,934
2024-09-27T03:23:39.934000+00:00
1,738,735,777,982
2025-02-05T06:09:37.982000+00:00
2026-08-05T11:30:48.294276+00:00
2026-08-05T11:30:48.294276+00:00
null
verified_revision_cdate_before_review
04qx93Viwj
Holistically Evaluating the Environmental Impact of Creating Language Models
Accept
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[8, 8, 6]" ]
[ "data/train.csv" ]
papers/04qx93Viwj/paper.pdf
papers/04qx93Viwj/paper.md
papers/04qx93Viwj/paper.source.tex
papers/04qx93Viwj/metadata.json
papers/04qx93Viwj/review.json
papers/04qx93Viwj/conversion_report.json
3bebd3b2e53305a79c34825db4fafdab38621b99b7bfdae19379dbadb9984dc4
463,263
36,342
dataset
dataset_source_lossless
2026-08-05T17:15:41.647352+00:00
04qx93Viwj
04qx93Viwj
04qx93Viwj
https://openreview.net/pdf/7d64c75721b0a23176a356a6e38aa7f4843e4372.pdf
null
null
/pdf/7d64c75721b0a23176a356a6e38aa7f4843e4372.pdf
3
1
null
1
1,727,481,254,071
2024-09-27T23:54:14.071000+00:00
1,741,040,249,789
2025-03-03T22:17:29.789000+00:00
1,730,518,728,567
2024-11-02T03:38:48.567000+00:00
1,727,481,254,071
2024-09-27T23:54:14.071000+00:00
1,741,040,249,789
2025-03-03T22:17:29.789000+00:00
2026-08-05T11:30:49.040469+00:00
2026-08-05T11:30:49.040469+00:00
null
verified_revision_cdate_before_review
063FuFYQQd
LLaVA-Surg: Towards Multimodal Surgical Assistant via Structured Lecture Learning
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[5, 5, 5, 3, 6]" ]
[ "data/train.csv" ]
papers/063FuFYQQd/paper.pdf
papers/063FuFYQQd/paper.md
papers/063FuFYQQd/paper.source.tex
papers/063FuFYQQd/metadata.json
papers/063FuFYQQd/review.json
papers/063FuFYQQd/conversion_report.json
cba3598e9ffd08169573c32ada2f967f65c574424c3d2e695b62bd8f21cf48b9
4,361,031
40,112
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:16.390680+00:00
063FuFYQQd
063FuFYQQd
063FuFYQQd
https://arxiv.org/pdf/2408.07981v1
arxiv
null
https://arxiv.org/pdf/2408.07981v1
5
1
null
1
1,723,705,220,000
2024-08-15T07:00:20+00:00
1,723,705,220,000
2024-08-15T07:00:20+00:00
1,730,361,702,171
2024-10-31T08:01:42.171000+00:00
1,723,705,220,000
2024-08-15T07:00:20+00:00
1,723,705,220,000
2024-08-15T07:00:20+00:00
2026-08-05T11:30:50.272580+00:00
2026-08-05T11:30:50.272580+00:00
null
verified_arxiv_version_before_review
06B23UkNid
MV-CLAM: Multi-View Molecular Interpretation with Cross-Modal Projection via Language Model
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[3, 5, 5, 3]" ]
[ "data/train.csv" ]
papers/06B23UkNid/paper.pdf
papers/06B23UkNid/paper.md
papers/06B23UkNid/paper.source.tex
papers/06B23UkNid/metadata.json
papers/06B23UkNid/review.json
papers/06B23UkNid/conversion_report.json
8229880c9dff7240555a729dce46d075d1c776b342664db8941544a7b6396529
1,545,670
35,306
dataset
dataset_source_lossless
2026-08-05T17:15:41.654693+00:00
06B23UkNid
06B23UkNid
06B23UkNid
https://openreview.net/pdf/8b980bbb0ff72341091780d9a0d3079915a1dfbd.pdf
null
null
/pdf/8b980bbb0ff72341091780d9a0d3079915a1dfbd.pdf
4
1
null
1
1,727,497,068,849
2024-09-28T04:17:48.849000+00:00
1,738,735,871,456
2025-02-05T06:11:11.456000+00:00
1,730,282,154,723
2024-10-30T09:55:54.723000+00:00
1,727,497,068,849
2024-09-28T04:17:48.849000+00:00
1,738,735,871,456
2025-02-05T06:11:11.456000+00:00
2026-08-05T11:30:51.157326+00:00
2026-08-05T11:30:51.157326+00:00
null
verified_revision_cdate_before_review
06GH83hDIv
Auction-Based Regulation for Artificial Intelligence
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 5, 5, 5]" ]
[ "data/train.csv" ]
papers/06GH83hDIv/paper.pdf
papers/06GH83hDIv/paper.md
papers/06GH83hDIv/paper.source.tex
papers/06GH83hDIv/metadata.json
papers/06GH83hDIv/review.json
papers/06GH83hDIv/conversion_report.json
74f4e30eaa059695d475b3cd4f6ff37a6b3d058f1a2a4b956b24a53bd64203c5
4,377,241
81,902
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:16.421684+00:00
06GH83hDIv
06GH83hDIv
06GH83hDIv
https://arxiv.org/pdf/2410.01871v1
arxiv
null
https://arxiv.org/pdf/2410.01871v1
4
1
null
1
1,727,891,822,000
2024-10-02T17:57:02+00:00
1,727,891,822,000
2024-10-02T17:57:02+00:00
1,729,630,206,684
2024-10-22T20:50:06.684000+00:00
1,727,891,822,000
2024-10-02T17:57:02+00:00
1,727,891,822,000
2024-10-02T17:57:02+00:00
2026-08-05T11:30:52.274918+00:00
2026-08-05T11:30:52.274918+00:00
null
verified_arxiv_version_before_review
06lrITXVAx
Dropout Enhanced Bilevel Training
Accept
[ "2024" ]
[ "best", "fast", "standard" ]
[ "[6, 8, 8, 6]" ]
[ "data/train.csv" ]
papers/06lrITXVAx/paper.pdf
papers/06lrITXVAx/paper.md
papers/06lrITXVAx/paper.source.tex
papers/06lrITXVAx/metadata.json
papers/06lrITXVAx/review.json
papers/06lrITXVAx/conversion_report.json
1a8b0bd15358858e93f98a393d458b34142905fcd6719e5e70fb47fe2aa620f3
837,802
24,050
dataset
dataset_source_lossless
2026-08-05T17:15:41.666163+00:00
06lrITXVAx
06lrITXVAx
06lrITXVAx
https://openreview.net/pdf/09304d5bf3e31448450004ee461830870db26085.pdf
null
null
/pdf/09304d5bf3e31448450004ee461830870db26085.pdf
4
1
null
1
1,695,415,248,638
2023-09-22T20:40:48.638000+00:00
1,710,217,378,699
2024-03-12T04:22:58.699000+00:00
1,698,624,070,166
2023-10-30T00:01:10.166000+00:00
1,695,415,248,638
2023-09-22T20:40:48.638000+00:00
1,710,217,378,699
2024-03-12T04:22:58.699000+00:00
2026-08-05T11:30:54.329923+00:00
2026-08-05T11:30:54.329923+00:00
null
verified_revision_cdate_before_review
06mzMua9Rw
A Trust Region Approach for Few-Shot Sim-to-Real Reinforcement Learning
Reject
[ "2024" ]
[ "best", "fast", "standard" ]
[ "[5, 3, 5, 3]" ]
[ "data/train.csv" ]
papers/06mzMua9Rw/paper.pdf
papers/06mzMua9Rw/paper.md
papers/06mzMua9Rw/paper.source.tex
papers/06mzMua9Rw/metadata.json
papers/06mzMua9Rw/review.json
papers/06mzMua9Rw/conversion_report.json
e0da03e983cff53086d44de735ab6d07f3bc3ee765d6d7d3c266eb3de9a93be7
1,354,416
39,580
dataset
dataset_source_lossless
2026-08-05T17:15:41.670802+00:00
06mzMua9Rw
06mzMua9Rw
06mzMua9Rw
https://openreview.net/pdf/ed2e9b4bb7354781d580350d96e49129b8bbe67b.pdf
null
null
/pdf/ed2e9b4bb7354781d580350d96e49129b8bbe67b.pdf
4
1
null
1
1,695,282,764,927
2023-09-21T07:52:44.927000+00:00
1,713,469,415,527
2024-04-18T19:43:35.527000+00:00
1,697,899,450,133
2023-10-21T14:44:10.133000+00:00
1,695,282,764,927
2023-09-21T07:52:44.927000+00:00
1,713,469,415,527
2024-04-18T19:43:35.527000+00:00
2026-08-05T11:30:55.149203+00:00
2026-08-05T11:30:55.149203+00:00
null
verified_revision_cdate_before_review
070DFUdNh7
GraphGPT: Graph Learning with Generative Pre-trained Transformers
Reject
[ "2024" ]
[ "best", "fast", "standard" ]
[ "[5, 3, 5, 5]" ]
[ "data/train.csv" ]
papers/070DFUdNh7/paper.pdf
papers/070DFUdNh7/paper.md
papers/070DFUdNh7/paper.source.tex
papers/070DFUdNh7/metadata.json
papers/070DFUdNh7/review.json
papers/070DFUdNh7/conversion_report.json
ea639967b48a70b5b0bef9574dc9cd7e69ba129fc165f465946f47ce41190b4d
1,885,052
40,040
dataset
dataset_source_lossless
2026-08-05T17:15:41.675612+00:00
070DFUdNh7
070DFUdNh7
070DFUdNh7
https://openreview.net/pdf/d6659600b4c7b032755f853d7a7a637df4890ac3.pdf
null
null
/pdf/d6659600b4c7b032755f853d7a7a637df4890ac3.pdf
4
1
null
1
1,695,369,235,520
2023-09-22T07:53:55.520000+00:00
1,707,625,716,559
2024-02-11T04:28:36.559000+00:00
1,698,670,948,519
2023-10-30T13:02:28.519000+00:00
1,695,369,235,520
2023-09-22T07:53:55.520000+00:00
1,707,625,716,559
2024-02-11T04:28:36.559000+00:00
2026-08-05T11:30:56.139253+00:00
2026-08-05T11:30:56.139253+00:00
null
verified_revision_cdate_before_review
07ZaA3MiL0
Consistent Iterative Denoising for Robot Manipulation
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[3, 6, 5, 3]" ]
[ "data/train.csv" ]
papers/07ZaA3MiL0/paper.pdf
papers/07ZaA3MiL0/paper.md
papers/07ZaA3MiL0/paper.source.tex
papers/07ZaA3MiL0/metadata.json
papers/07ZaA3MiL0/review.json
papers/07ZaA3MiL0/conversion_report.json
1debe962da90555b93e442cff1479e5c4054b5797b8fbbd65af1faef93026b1c
1,062,584
32,191
dataset
dataset_source_lossless
2026-08-05T17:15:41.679701+00:00
07ZaA3MiL0
07ZaA3MiL0
07ZaA3MiL0
https://openreview.net/pdf/444137bba6fc98bf0f71907783db1f09bf5eeaef.pdf
null
null
/pdf/444137bba6fc98bf0f71907783db1f09bf5eeaef.pdf
4
1
null
1
1,727,270,793,210
2024-09-25T13:26:33.210000+00:00
1,732,619,999,966
2024-11-26T11:19:59.966000+00:00
1,730,570,141,916
2024-11-02T17:55:41.916000+00:00
1,727,270,793,210
2024-09-25T13:26:33.210000+00:00
1,732,619,999,966
2024-11-26T11:19:59.966000+00:00
2026-08-05T11:30:56.950522+00:00
2026-08-05T11:30:56.950522+00:00
null
verified_revision_cdate_before_review
07cehZ97Xb
How to Build a Pre-trained Multimodal model for Simultaneously Chatting and Decision-making?
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[3, 3, 5]" ]
[ "data/train.csv" ]
papers/07cehZ97Xb/paper.pdf
papers/07cehZ97Xb/paper.md
papers/07cehZ97Xb/paper.source.tex
papers/07cehZ97Xb/metadata.json
papers/07cehZ97Xb/review.json
papers/07cehZ97Xb/conversion_report.json
0d2d85fd5a145b5c34cd754d603bbde24fafc6327e8e13c82445ce5110a4d627
1,214,936
41,151
dataset
dataset_source_lossless
2026-08-05T17:15:41.684450+00:00
07cehZ97Xb
07cehZ97Xb
07cehZ97Xb
https://openreview.net/pdf/55d50e8073e31524d6e9cef3c6f6f2b6202a851a.pdf
null
null
/pdf/55d50e8073e31524d6e9cef3c6f6f2b6202a851a.pdf
3
1
null
1
1,727,082,647,833
2024-09-23T09:10:47.833000+00:00
1,732,888,082,071
2024-11-29T13:48:02.071000+00:00
1,730,529,852,342
2024-11-02T06:44:12.342000+00:00
1,727,082,647,833
2024-09-23T09:10:47.833000+00:00
1,732,888,082,071
2024-11-29T13:48:02.071000+00:00
2026-08-05T11:30:57.729922+00:00
2026-08-05T11:30:57.729922+00:00
null
verified_revision_cdate_before_review
07xuZw59uB
Bridging the Fairness Divide: Achieving Group and Individual Fairness in Graph Neural Networks
Reject
[ "2024" ]
[ "best", "fast", "standard" ]
[ "[3, 5, 3, 1]" ]
[ "data/train.csv" ]
papers/07xuZw59uB/paper.pdf
papers/07xuZw59uB/paper.md
papers/07xuZw59uB/paper.source.tex
papers/07xuZw59uB/metadata.json
papers/07xuZw59uB/review.json
papers/07xuZw59uB/conversion_report.json
563b7208595a5408689216fe10359a9587abb00f87ec3b3f83c6cd841e538344
487,658
42,062
dataset
dataset_source_lossless
2026-08-05T17:15:41.689694+00:00
07xuZw59uB
07xuZw59uB
07xuZw59uB
https://openreview.net/pdf/224b5ca40bc76a025f51ca8d7d2cd11917de91d4.pdf
null
null
/pdf/224b5ca40bc76a025f51ca8d7d2cd11917de91d4.pdf
4
1
null
1
1,695,508,766,423
2023-09-23T22:39:26.423000+00:00
1,707,625,763,239
2024-02-11T04:29:23.239000+00:00
1,697,129,215,125
2023-10-12T16:46:55.125000+00:00
1,695,508,766,423
2023-09-23T22:39:26.423000+00:00
1,707,625,763,239
2024-02-11T04:29:23.239000+00:00
2026-08-05T11:30:58.892227+00:00
2026-08-05T11:30:58.892227+00:00
null
verified_revision_cdate_before_review
07yvxWDSla
Synthetic continued pretraining
Accept
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[8, 8, 8, 8]" ]
[ "data/train.csv" ]
papers/07yvxWDSla/paper.pdf
papers/07yvxWDSla/paper.md
papers/07yvxWDSla/paper.source.tex
papers/07yvxWDSla/metadata.json
papers/07yvxWDSla/review.json
papers/07yvxWDSla/conversion_report.json
dd1557027bda29dc8672375bf978b697827c6d8dff06ccee11142f80973c4878
1,719,577
124,893
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:16.468378+00:00
07yvxWDSla
07yvxWDSla
07yvxWDSla
https://arxiv.org/pdf/2409.07431v2
arxiv
null
https://arxiv.org/pdf/2409.07431v2
4
1
null
1
1,727,960,845,000
2024-10-03T13:07:25+00:00
1,727,960,845,000
2024-10-03T13:07:25+00:00
1,730,157,910,400
2024-10-28T23:25:10.400000+00:00
1,727,960,845,000
2024-10-03T13:07:25+00:00
1,727,960,845,000
2024-10-03T13:07:25+00:00
2026-08-05T11:30:59.695463+00:00
2026-08-05T11:30:59.695463+00:00
null
verified_arxiv_version_before_review
0823rvTIhs
Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors
Accept
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 8, 6, 5]" ]
[ "data/train.csv" ]
papers/0823rvTIhs/paper.pdf
papers/0823rvTIhs/paper.md
papers/0823rvTIhs/paper.source.tex
papers/0823rvTIhs/metadata.json
papers/0823rvTIhs/review.json
papers/0823rvTIhs/conversion_report.json
26434cf81dcdf83613ff44773a6cfa5f681d5dce2b9416f67a292a706d28428e
3,206,875
38,623
dataset
dataset_source_lossless
2026-08-05T17:15:41.700646+00:00
0823rvTIhs
0823rvTIhs
0823rvTIhs
https://openreview.net/pdf/2551c99500000f40bd07dc3c4c3d1111d11b9c1a.pdf
null
null
/pdf/2551c99500000f40bd07dc3c4c3d1111d11b9c1a.pdf
4
1
null
1
1,726,734,516,318
2024-09-19T08:28:36.318000+00:00
1,740,674,301,655
2025-02-27T16:38:21.655000+00:00
1,730,466,025,928
2024-11-01T13:00:25.928000+00:00
1,726,734,516,318
2024-09-19T08:28:36.318000+00:00
1,740,674,301,655
2025-02-27T16:38:21.655000+00:00
2026-08-05T11:31:00.856491+00:00
2026-08-05T11:31:00.856491+00:00
null
verified_revision_cdate_before_review
09JVxsEZPf
Towards Comprehensive and Efficient Post Safety Alignment of Large Language Models via Safety Patching
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[5, 3, 6, 5, 5]" ]
[ "data/train.csv" ]
papers/09JVxsEZPf/paper.pdf
papers/09JVxsEZPf/paper.md
papers/09JVxsEZPf/paper.source.tex
papers/09JVxsEZPf/metadata.json
papers/09JVxsEZPf/review.json
papers/09JVxsEZPf/conversion_report.json
9fd49c89d5adc7822e3c87d3aaab0d7c7acefd42dab3edacb258af040ac04735
1,282,750
38,128
dataset
dataset_source_lossless
2026-08-05T17:15:41.704720+00:00
09JVxsEZPf
09JVxsEZPf
09JVxsEZPf
https://openreview.net/pdf/b28f0d1191a8411bdc4d03df6e9a2f113daa2a14.pdf
null
null
/pdf/b28f0d1191a8411bdc4d03df6e9a2f113daa2a14.pdf
5
1
null
1
1,727,340,072,523
2024-09-26T08:41:12.523000+00:00
1,733,815,823,483
2024-12-10T07:30:23.483000+00:00
1,730,116,144,451
2024-10-28T11:49:04.451000+00:00
1,727,340,072,523
2024-09-26T08:41:12.523000+00:00
1,733,815,823,483
2024-12-10T07:30:23.483000+00:00
2026-08-05T11:31:01.828430+00:00
2026-08-05T11:31:01.828430+00:00
null
verified_revision_cdate_before_review
09LEjbLcZW
AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[5, 5, 5]" ]
[ "data/train.csv" ]
papers/09LEjbLcZW/paper.pdf
papers/09LEjbLcZW/paper.md
papers/09LEjbLcZW/paper.source.tex
papers/09LEjbLcZW/metadata.json
papers/09LEjbLcZW/review.json
papers/09LEjbLcZW/conversion_report.json
3d0ad600cf99818647658a6a40a093049fb7dc9a130e1d2fcf708b77fc281ad5
1,772,077
135,365
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:16.538441+00:00
09LEjbLcZW
09LEjbLcZW
09LEjbLcZW
https://arxiv.org/pdf/2410.20424v2
arxiv
null
https://arxiv.org/pdf/2410.20424v2
3
1
null
1
1,730,222,763,000
2024-10-29T17:26:03+00:00
1,730,222,763,000
2024-10-29T17:26:03+00:00
1,730,586,049,754
2024-11-02T22:20:49.754000+00:00
1,730,222,763,000
2024-10-29T17:26:03+00:00
1,730,222,763,000
2024-10-29T17:26:03+00:00
2026-08-05T11:31:02.670381+00:00
2026-08-05T11:31:02.670381+00:00
null
verified_arxiv_version_before_review
09TI1yUo9K
Noise is More Than Just Interference: Information Infusion Networks for Anomaly Detection
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[5, 5, 5, 3]" ]
[ "data/train.csv" ]
papers/09TI1yUo9K/paper.pdf
papers/09TI1yUo9K/paper.md
papers/09TI1yUo9K/paper.source.tex
papers/09TI1yUo9K/metadata.json
papers/09TI1yUo9K/review.json
papers/09TI1yUo9K/conversion_report.json
7479f1cb716b698cd58aee74d1c65dfd91e97f7a5bdcc92569338e17747a9ef1
5,346,753
30,223
dataset
dataset_source_lossless
2026-08-05T17:15:41.713435+00:00
09TI1yUo9K
09TI1yUo9K
09TI1yUo9K
https://openreview.net/pdf/9ae64af9f3e13bb39bc76f276404a4bb1ef10d05.pdf
null
null
/pdf/9ae64af9f3e13bb39bc76f276404a4bb1ef10d05.pdf
4
1
null
1
1,726,312,337,200
2024-09-14T11:12:17.200000+00:00
1,731,662,386,990
2024-11-15T09:19:46.990000+00:00
1,729,787,206,957
2024-10-24T16:26:46.957000+00:00
1,726,312,337,200
2024-09-14T11:12:17.200000+00:00
1,731,662,386,990
2024-11-15T09:19:46.990000+00:00
2026-08-05T11:31:04.078721+00:00
2026-08-05T11:31:04.078721+00:00
null
verified_revision_cdate_before_review
09iOdaeOzp
Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning
Accept
[ "2024" ]
[ "best", "fast", "standard" ]
[ "[5, 8, 5, 6]" ]
[ "data/train.csv" ]
papers/09iOdaeOzp/paper.pdf
papers/09iOdaeOzp/paper.md
papers/09iOdaeOzp/paper.source.tex
papers/09iOdaeOzp/metadata.json
papers/09iOdaeOzp/review.json
papers/09iOdaeOzp/conversion_report.json
077fae9d32c77564438635717ab2208d380bf895c10f4375f0540c48e0605c3a
1,190,696
77,454
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:16.568370+00:00
09iOdaeOzp
09iOdaeOzp
09iOdaeOzp
https://arxiv.org/pdf/2310.06694v1
arxiv
null
https://arxiv.org/pdf/2310.06694v1
4
1
null
1
1,696,950,810,000
2023-10-10T15:13:30+00:00
1,696,950,810,000
2023-10-10T15:13:30+00:00
1,698,605,206,336
2023-10-29T18:46:46.336000+00:00
1,696,950,810,000
2023-10-10T15:13:30+00:00
1,696,950,810,000
2023-10-10T15:13:30+00:00
2026-08-05T11:31:04.828915+00:00
2026-08-05T11:31:04.828915+00:00
null
verified_arxiv_version_before_review
0A5o6dCKeK
NExT-GPT: Any-to-Any Multimodal LLM
Reject
[ "2024" ]
[ "best", "fast", "standard" ]
[ "[6, 5, 5, 8]" ]
[ "data/train.csv" ]
papers/0A5o6dCKeK/paper.pdf
papers/0A5o6dCKeK/paper.md
papers/0A5o6dCKeK/paper.source.tex
papers/0A5o6dCKeK/metadata.json
papers/0A5o6dCKeK/review.json
papers/0A5o6dCKeK/conversion_report.json
70d061cda98abeab02061c6f423c05713227ba6bb25652bb73ef0cfb24f28e42
7,019,422
48,445
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:16.622475+00:00
0A5o6dCKeK
0A5o6dCKeK
0A5o6dCKeK
https://arxiv.org/pdf/2309.05519v2
arxiv
null
https://arxiv.org/pdf/2309.05519v2
4
1
null
1
1,694,623,774,000
2023-09-13T16:49:34+00:00
1,694,623,774,000
2023-09-13T16:49:34+00:00
1,698,440,717,308
2023-10-27T21:05:17.308000+00:00
1,694,623,774,000
2023-09-13T16:49:34+00:00
1,694,623,774,000
2023-09-13T16:49:34+00:00
2026-08-05T11:31:07.197926+00:00
2026-08-05T11:31:07.197926+00:00
null
verified_arxiv_version_before_review
0A6f1b66pE
Unleashing the Power of Selective State Space Models in Vision-Language Models
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 5, 3, 3, 6]" ]
[ "data/train.csv" ]
papers/0A6f1b66pE/paper.pdf
papers/0A6f1b66pE/paper.md
papers/0A6f1b66pE/paper.source.tex
papers/0A6f1b66pE/metadata.json
papers/0A6f1b66pE/review.json
papers/0A6f1b66pE/conversion_report.json
cf05b7e8ddf99fa844af2cb4ef41f5132d10ebb63d4cbace2962132389421ce8
911,815
32,160
dataset
dataset_source_lossless
2026-08-05T17:15:41.729980+00:00
0A6f1b66pE
0A6f1b66pE
0A6f1b66pE
https://openreview.net/pdf/1183a88cc6949631a83ff61a73451d323b4592a6.pdf
null
null
/pdf/1183a88cc6949631a83ff61a73451d323b4592a6.pdf
5
1
null
1
1,727,251,002,717
2024-09-25T07:56:42.717000+00:00
1,731,505,202,370
2024-11-13T13:40:02.370000+00:00
1,730,573,521,913
2024-11-02T18:52:01.913000+00:00
1,727,251,002,717
2024-09-25T07:56:42.717000+00:00
1,731,505,202,370
2024-11-13T13:40:02.370000+00:00
2026-08-05T11:31:07.966221+00:00
2026-08-05T11:31:07.966221+00:00
null
verified_revision_cdate_before_review
0AHkdAtFW8
Sum-of-Squares Programming for Ma-Trudinger-Wang Regularity of Optimal Transport Maps
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 6, 6, 5, 5]" ]
[ "data/train.csv" ]
papers/0AHkdAtFW8/paper.pdf
papers/0AHkdAtFW8/paper.md
papers/0AHkdAtFW8/paper.source.tex
papers/0AHkdAtFW8/metadata.json
papers/0AHkdAtFW8/review.json
papers/0AHkdAtFW8/conversion_report.json
17e62b12c51bd47f36f07f328b21bc214b1767aacdc380e9e5a4990c8c1bca9d
1,993,297
31,931
dataset
dataset_source_lossless
2026-08-05T17:15:41.734558+00:00
0AHkdAtFW8
0AHkdAtFW8
0AHkdAtFW8
https://openreview.net/pdf/7ba01cd8e178b1b9d9c85d7a656d92b42998f0e4.pdf
submission_note
null
/pdf/7ba01cd8e178b1b9d9c85d7a656d92b42998f0e4.pdf
5
1
0
1
1,727,061,545,646
2024-09-23T03:19:05.646000+00:00
1,738,735,654,428
2025-02-05T06:07:34.428000+00:00
1,729,125,722,049
2024-10-17T00:42:02.049000+00:00
1,727,061,545,646
2024-09-23T03:19:05.646000+00:00
1,738,735,654,428
2025-02-05T06:07:34.428000+00:00
2026-08-05T12:25:29.741823+00:00
2026-08-05T12:25:29.741823+00:00
null
verified_revision_cdate_before_review
0Ag8FQ5Rr3
The Super Weight in Large Language Models
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 5, 1, 5, 6]" ]
[ "data/train.csv" ]
papers/0Ag8FQ5Rr3/paper.pdf
papers/0Ag8FQ5Rr3/paper.md
papers/0Ag8FQ5Rr3/paper.source.tex
papers/0Ag8FQ5Rr3/metadata.json
papers/0Ag8FQ5Rr3/review.json
papers/0Ag8FQ5Rr3/conversion_report.json
2207636a1e1acfdd677e3719ef14eb7d12cb165b09341f90cfe425db7a1aec0f
693,717
41,160
dataset
dataset_source_lossless
2026-08-05T17:15:41.743270+00:00
0Ag8FQ5Rr3
0Ag8FQ5Rr3
0Ag8FQ5Rr3
https://openreview.net/pdf/aa257378a186435f12c82d822bfff89ec29b1ed9.pdf
submission_note
null
/pdf/aa257378a186435f12c82d822bfff89ec29b1ed9.pdf
5
1
0
1
1,726,214,080,601
2024-09-13T07:54:40.601000+00:00
1,738,735,614,256
2025-02-05T06:06:54.256000+00:00
1,730,485,357,609
2024-11-01T18:22:37.609000+00:00
1,726,214,080,601
2024-09-13T07:54:40.601000+00:00
1,738,735,614,256
2025-02-05T06:06:54.256000+00:00
2026-08-05T12:25:38.528753+00:00
2026-08-05T12:25:38.528753+00:00
null
verified_revision_cdate_before_review
0ApkwFlCxq
ComputAgeBench: Epigenetic Aging Clocks Benchmark
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[8, 6, 5, 6]" ]
[ "data/train.csv" ]
papers/0ApkwFlCxq/paper.pdf
papers/0ApkwFlCxq/paper.md
papers/0ApkwFlCxq/paper.source.tex
papers/0ApkwFlCxq/metadata.json
papers/0ApkwFlCxq/review.json
papers/0ApkwFlCxq/conversion_report.json
c58b4d72f5f7f822a8001bb58b4d7e95556072e6dc0d9138d0cbf1d0918c131e
3,786,550
37,078
dataset
dataset_source_lossless
2026-08-05T17:15:41.747600+00:00
0ApkwFlCxq
0ApkwFlCxq
0ApkwFlCxq
https://openreview.net/pdf/7a52c8a332f5e6eb6af094b02525b05def41b636.pdf
submission_note
null
/pdf/7a52c8a332f5e6eb6af094b02525b05def41b636.pdf
4
1
0
1
1,727,378,343,750
2024-09-26T19:19:03.750000+00:00
1,738,735,758,620
2025-02-05T06:09:18.620000+00:00
1,730,577,881,483
2024-11-02T20:04:41.483000+00:00
1,727,378,343,750
2024-09-26T19:19:03.750000+00:00
1,738,735,758,620
2025-02-05T06:09:18.620000+00:00
2026-08-05T12:25:48.149621+00:00
2026-08-05T12:25:48.149621+00:00
null
verified_revision_cdate_before_review
0BBzwpLVpm
Learning Identifiable Concepts for Compositional Image Generation
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 3, 5, 3]" ]
[ "data/train.csv" ]
papers/0BBzwpLVpm/paper.pdf
papers/0BBzwpLVpm/paper.md
papers/0BBzwpLVpm/paper.source.tex
papers/0BBzwpLVpm/metadata.json
papers/0BBzwpLVpm/review.json
papers/0BBzwpLVpm/conversion_report.json
6fb75c9406924c476fe75983b4f0f73db351f4f090b6bad2fb1f5d9e2aa7c737
15,266,987
33,893
dataset
dataset_source_lossless
2026-08-05T17:15:41.751815+00:00
0BBzwpLVpm
0BBzwpLVpm
0BBzwpLVpm
https://openreview.net/pdf/1746275f4e677f128e95eb27c5e373f2d90afb6b.pdf
submission_note
null
/pdf/1746275f4e677f128e95eb27c5e373f2d90afb6b.pdf
4
1
0
1
1,727,400,198,580
2024-09-27T01:23:18.580000+00:00
1,731,665,408,570
2024-11-15T10:10:08.570000+00:00
1,729,726,760,674
2024-10-23T23:39:20.674000+00:00
1,727,400,198,580
2024-09-27T01:23:18.580000+00:00
1,731,665,408,570
2024-11-15T10:10:08.570000+00:00
2026-08-05T12:21:58.085493+00:00
2026-08-05T12:21:58.085493+00:00
null
verified_revision_cdate_before_review
0BujOfTqab
AdvWave: Stealthy Adversarial Jailbreak Attack against Large Audio-Language Models
Accept
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[8, 8, 3, 3]" ]
[ "data/train.csv" ]
papers/0BujOfTqab/paper.pdf
papers/0BujOfTqab/paper.md
papers/0BujOfTqab/paper.source.tex
papers/0BujOfTqab/metadata.json
papers/0BujOfTqab/review.json
papers/0BujOfTqab/conversion_report.json
e1611922b83a8823577a7d64e0841e0ea8cc80f1eafcc516f828b95645ea9dd6
735,136
55,342
dataset
dataset_source_lossless
2026-08-05T17:15:41.757987+00:00
0BujOfTqab
0BujOfTqab
0BujOfTqab
https://openreview.net/pdf/68256be57a67d525b48727aaf8ea8a96dab1f286.pdf
submission_note
null
/pdf/68256be57a67d525b48727aaf8ea8a96dab1f286.pdf
4
1
0
1
1,727,451,257,120
2024-09-27T15:34:17.120000+00:00
1,740,870,948,924
2025-03-01T23:15:48.924000+00:00
1,729,429,291,334
2024-10-20T13:01:31.334000+00:00
1,727,451,257,120
2024-09-27T15:34:17.120000+00:00
1,740,870,948,924
2025-03-01T23:15:48.924000+00:00
2026-08-05T12:25:57.407923+00:00
2026-08-05T12:25:57.407923+00:00
null
verified_revision_cdate_before_review
0Ce3c9l7G1
Learning Multi-Agent Communication using Regularized Attention Messages
Reject
[ "2024" ]
[ "best", "fast", "standard" ]
[ "[3, 3, 5, 5]" ]
[ "data/train.csv" ]
papers/0Ce3c9l7G1/paper.pdf
papers/0Ce3c9l7G1/paper.md
papers/0Ce3c9l7G1/paper.source.tex
papers/0Ce3c9l7G1/metadata.json
papers/0Ce3c9l7G1/review.json
papers/0Ce3c9l7G1/conversion_report.json
2c0c0a2169adb455e0c36470a9f740fcdab8efa6535f7a64071e8a7a931419ae
28,872,842
34,859
dataset
dataset_source_lossless
2026-08-05T17:15:41.762163+00:00
0Ce3c9l7G1
0Ce3c9l7G1
0Ce3c9l7G1
https://openreview.net/pdf/7b144301f1540f0d6933189880e2a10a75d5f80f.pdf
submission_note
null
/pdf/7b144301f1540f0d6933189880e2a10a75d5f80f.pdf
4
1
0
1
1,695,512,898,135
2023-09-23T23:48:18.135000+00:00
1,707,625,764,650
2024-02-11T04:29:24.650000+00:00
1,698,698,141,758
2023-10-30T20:35:41.758000+00:00
1,695,512,898,135
2023-09-23T23:48:18.135000+00:00
1,707,625,764,650
2024-02-11T04:29:24.650000+00:00
2026-08-05T12:22:10.910390+00:00
2026-08-05T12:22:10.910390+00:00
null
verified_revision_cdate_before_review
0CieWy9ONY
Neural Eulerian Scene Flow Fields
Accept
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 8, 6, 6]" ]
[ "data/train.csv" ]
papers/0CieWy9ONY/paper.pdf
papers/0CieWy9ONY/paper.md
papers/0CieWy9ONY/paper.source.tex
papers/0CieWy9ONY/metadata.json
papers/0CieWy9ONY/review.json
papers/0CieWy9ONY/conversion_report.json
1d107ab2b5ed9e10f4bd6bf95dc8909eaee2839f9b401aaad4c7114639255cd0
11,306,722
68,837
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:16.673163+00:00
0CieWy9ONY
0CieWy9ONY
0CieWy9ONY
https://arxiv.org/pdf/2410.02031v1
arxiv
null
https://arxiv.org/pdf/2410.02031v1
4
1
0
1
1,727,902,605,000
2024-10-02T20:56:45+00:00
1,727,902,605,000
2024-10-02T20:56:45+00:00
1,729,899,279,036
2024-10-25T23:34:39.036000+00:00
1,727,902,605,000
2024-10-02T20:56:45+00:00
1,727,902,605,000
2024-10-02T20:56:45+00:00
2026-08-05T12:26:07.370271+00:00
2026-08-05T12:26:07.370271+00:00
null
verified_arxiv_version_before_review
0CtIt485ew
Brain-inspired continual pre-trained learner via silent synaptic consolidation
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[5, 3, 3, 5]" ]
[ "data/train.csv" ]
papers/0CtIt485ew/paper.pdf
papers/0CtIt485ew/paper.md
papers/0CtIt485ew/paper.source.tex
papers/0CtIt485ew/metadata.json
papers/0CtIt485ew/review.json
papers/0CtIt485ew/conversion_report.json
aaf7c369b6474978da84c549c27d515229a23218f8a54e97e1b5d4564a32bbd2
827,952
73,015
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:16.702262+00:00
0CtIt485ew
0CtIt485ew
0CtIt485ew
https://arxiv.org/pdf/2410.05899v1
arxiv
null
https://arxiv.org/pdf/2410.05899v1
4
1
0
1
1,728,384,979,000
2024-10-08T10:56:19+00:00
1,728,384,979,000
2024-10-08T10:56:19+00:00
1,730,131,587,081
2024-10-28T16:06:27.081000+00:00
1,728,384,979,000
2024-10-08T10:56:19+00:00
1,728,384,979,000
2024-10-08T10:56:19+00:00
2026-08-05T12:22:21.114244+00:00
2026-08-05T12:22:21.114244+00:00
null
verified_arxiv_version_before_review
0CvJYiOo2b
Revisiting PCA for Time Series Reduction in Temporal Dimension
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[5, 5, 3, 5]" ]
[ "data/train.csv" ]
papers/0CvJYiOo2b/paper.pdf
papers/0CvJYiOo2b/paper.md
papers/0CvJYiOo2b/paper.source.tex
papers/0CvJYiOo2b/metadata.json
papers/0CvJYiOo2b/review.json
papers/0CvJYiOo2b/conversion_report.json
5d885188f93d59ed2466092f78e1dc4bc26cc1c814a12980c3d3fd2f11126406
1,539,350
35,867
dataset
dataset_source_lossless
2026-08-05T17:15:41.777113+00:00
0CvJYiOo2b
0CvJYiOo2b
0CvJYiOo2b
https://openreview.net/pdf/fc4c20a8da885d40d61aaf10250033789daa53b2.pdf
submission_note
null
/pdf/fc4c20a8da885d40d61aaf10250033789daa53b2.pdf
4
1
0
1
1,727,356,412,679
2024-09-26T13:13:32.679000+00:00
1,738,735,739,432
2025-02-05T06:08:59.432000+00:00
1,729,971,248,029
2024-10-26T19:34:08.029000+00:00
1,727,356,412,679
2024-09-26T13:13:32.679000+00:00
1,738,735,739,432
2025-02-05T06:08:59.432000+00:00
2026-08-05T12:26:16.988526+00:00
2026-08-05T12:26:16.988526+00:00
null
verified_revision_cdate_before_review
0DZEs8NpUH
Personality Alignment of Large Language Models
Accept
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[5, 8, 5]" ]
[ "data/train.csv" ]
papers/0DZEs8NpUH/paper.pdf
papers/0DZEs8NpUH/paper.md
papers/0DZEs8NpUH/paper.source.tex
papers/0DZEs8NpUH/metadata.json
papers/0DZEs8NpUH/review.json
papers/0DZEs8NpUH/conversion_report.json
d762099d6cd0f0b78941d08f478bd787f6d78c5ff4d7b93d6628d6eb94fafec6
4,264,336
109,651
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:16.742996+00:00
0DZEs8NpUH
0DZEs8NpUH
0DZEs8NpUH
https://arxiv.org/pdf/2408.11779v1
arxiv
null
https://arxiv.org/pdf/2408.11779v1
3
1
0
1
1,724,260,140,000
2024-08-21T17:09:00+00:00
1,724,260,140,000
2024-08-21T17:09:00+00:00
1,730,000,679,458
2024-10-27T03:44:39.458000+00:00
1,724,260,140,000
2024-08-21T17:09:00+00:00
1,724,260,140,000
2024-08-21T17:09:00+00:00
2026-08-05T12:26:26.628973+00:00
2026-08-05T12:26:26.628973+00:00
null
verified_arxiv_version_before_review
0EP01yhDlg
Faster Language Models with Better Multi-Token Prediction Using Tensor Decomposition
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[5, 5, 5, 5]" ]
[ "data/train.csv" ]
papers/0EP01yhDlg/paper.pdf
papers/0EP01yhDlg/paper.md
papers/0EP01yhDlg/paper.source.tex
papers/0EP01yhDlg/metadata.json
papers/0EP01yhDlg/review.json
papers/0EP01yhDlg/conversion_report.json
9d47d0373ad0a48c7f351388d665271ee69cbeadb744aacacb9737727c6fbc08
552,710
44,089
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:16.760172+00:00
0EP01yhDlg
0EP01yhDlg
0EP01yhDlg
https://arxiv.org/pdf/2410.17765v1
arxiv
null
https://arxiv.org/pdf/2410.17765v1
4
1
0
1
1,729,681,596,000
2024-10-23T11:06:36+00:00
1,729,681,596,000
2024-10-23T11:06:36+00:00
1,730,339,862,666
2024-10-31T01:57:42.666000+00:00
1,729,681,596,000
2024-10-23T11:06:36+00:00
1,729,681,596,000
2024-10-23T11:06:36+00:00
2026-08-05T12:22:37.037021+00:00
2026-08-05T12:22:37.037021+00:00
null
verified_arxiv_version_before_review
0F1rIKppTf
Through the Looking Glass: Mirror Schrödinger Bridges
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 5, 6, 6]" ]
[ "data/train.csv" ]
papers/0F1rIKppTf/paper.pdf
papers/0F1rIKppTf/paper.md
papers/0F1rIKppTf/paper.source.tex
papers/0F1rIKppTf/metadata.json
papers/0F1rIKppTf/review.json
papers/0F1rIKppTf/conversion_report.json
f14fd7a35b259070a85cd388dee9898f52bd12abb06aaf8f06af004356ac8b61
24,953,586
38,069
dataset
dataset_source_lossless
2026-08-05T17:15:41.788756+00:00
0F1rIKppTf
0F1rIKppTf
0F1rIKppTf
https://openreview.net/pdf/a4471c1470ef1989b92a5f982c981f8b01a6505c.pdf
submission_note
null
/pdf/a4471c1470ef1989b92a5f982c981f8b01a6505c.pdf
4
1
0
1
1,727,480,036,051
2024-09-27T23:33:56.051000+00:00
1,738,735,856,919
2025-02-05T06:10:56.919000+00:00
1,730,299,396,061
2024-10-30T14:43:16.061000+00:00
1,727,480,036,051
2024-09-27T23:33:56.051000+00:00
1,738,735,856,919
2025-02-05T06:10:56.919000+00:00
2026-08-05T12:26:38.170266+00:00
2026-08-05T12:26:38.170266+00:00
null
verified_revision_cdate_before_review
0FK6tzqV76
RTDiff: Reverse Trajectory Synthesis via Diffusion for Offline Reinforcement Learning
Accept
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 6, 6, 5]" ]
[ "data/train.csv" ]
papers/0FK6tzqV76/paper.pdf
papers/0FK6tzqV76/paper.md
papers/0FK6tzqV76/paper.source.tex
papers/0FK6tzqV76/metadata.json
papers/0FK6tzqV76/review.json
papers/0FK6tzqV76/conversion_report.json
dd24c2cf7c350dbd37af70b1174dc8fc6defcf71e91f5ab0e806d65f3746c512
1,000,706
37,309
dataset
dataset_source_lossless
2026-08-05T17:15:41.793225+00:00
0FK6tzqV76
0FK6tzqV76
0FK6tzqV76
https://openreview.net/pdf/148b8387321b7e4b640b74802074ee7b608c5435.pdf
submission_note
null
/pdf/148b8387321b7e4b640b74802074ee7b608c5435.pdf
4
1
0
1
1,727,276,902,516
2024-09-25T15:08:22.516000+00:00
1,740,898,414,848
2025-03-02T06:53:34.848000+00:00
1,729,776,059,003
2024-10-24T13:20:59.003000+00:00
1,727,276,902,516
2024-09-25T15:08:22.516000+00:00
1,740,898,414,848
2025-03-02T06:53:34.848000+00:00
2026-08-05T12:26:46.943804+00:00
2026-08-05T12:26:46.943804+00:00
null
verified_revision_cdate_before_review
0FbzC7B9xI
Improved Sampling Of Diffusion Models In Fluid Dynamics With Tweedie's Formula
Accept
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 8, 8, 5, 6]" ]
[ "data/train.csv" ]
papers/0FbzC7B9xI/paper.pdf
papers/0FbzC7B9xI/paper.md
papers/0FbzC7B9xI/paper.source.tex
papers/0FbzC7B9xI/metadata.json
papers/0FbzC7B9xI/review.json
papers/0FbzC7B9xI/conversion_report.json
34d36683e21cd85796344dda727b75d95dd55b8f0501369d73b541e474302a3a
3,626,409
34,937
dataset
dataset_source_lossless
2026-08-05T17:15:41.797399+00:00
0FbzC7B9xI
0FbzC7B9xI
0FbzC7B9xI
https://openreview.net/pdf/485bbd7bf5fb24c69f5e5d50e296a55b22984c61.pdf
submission_note
null
/pdf/485bbd7bf5fb24c69f5e5d50e296a55b22984c61.pdf
5
1
0
1
1,727,273,756,612
2024-09-25T14:15:56.612000+00:00
1,742,828,200,847
2025-03-24T14:56:40.847000+00:00
1,730,111,803,484
2024-10-28T10:36:43.484000+00:00
1,727,273,756,612
2024-09-25T14:15:56.612000+00:00
1,742,828,200,847
2025-03-24T14:56:40.847000+00:00
2026-08-05T12:26:56.185458+00:00
2026-08-05T12:26:56.185458+00:00
null
verified_revision_cdate_before_review
0FxnSZJPmh
Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems
Accept
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 5, 6]" ]
[ "data/train.csv" ]
papers/0FxnSZJPmh/paper.pdf
papers/0FxnSZJPmh/paper.md
papers/0FxnSZJPmh/paper.source.tex
papers/0FxnSZJPmh/metadata.json
papers/0FxnSZJPmh/review.json
papers/0FxnSZJPmh/conversion_report.json
5fb1807e2e8db178f3d637f0b90d4497b93bff6b176dd1f4a1396b112162ef63
2,250,989
44,205
dataset
dataset_source_lossless
2026-08-05T17:15:41.803038+00:00
0FxnSZJPmh
0FxnSZJPmh
0FxnSZJPmh
https://openreview.net/pdf/b56f166dad491faab7b756b572988dae7aa557cc.pdf
submission_note
null
/pdf/b56f166dad491faab7b756b572988dae7aa557cc.pdf
3
1
0
1
1,727,334,335,454
2024-09-26T07:05:35.454000+00:00
1,742,956,413,210
2025-03-26T02:33:33.210000+00:00
1,729,685,885,260
2024-10-23T12:18:05.260000+00:00
1,727,334,335,454
2024-09-26T07:05:35.454000+00:00
1,742,956,413,210
2025-03-26T02:33:33.210000+00:00
2026-08-05T12:27:05.670881+00:00
2026-08-05T12:27:05.670881+00:00
null
verified_revision_cdate_before_review
0G6rRLYcxm
Maximum Next-State Entropy for Efficient Reinforcement Learning
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 3, 5, 6]" ]
[ "data/train.csv" ]
papers/0G6rRLYcxm/paper.pdf
papers/0G6rRLYcxm/paper.md
papers/0G6rRLYcxm/paper.source.tex
papers/0G6rRLYcxm/metadata.json
papers/0G6rRLYcxm/review.json
papers/0G6rRLYcxm/conversion_report.json
157acec94988a45524b55c7e732a44f6582cd76e7cb5ee8155d2252be93f1ddf
911,565
35,699
dataset
dataset_source_lossless
2026-08-05T17:15:41.807443+00:00
0G6rRLYcxm
0G6rRLYcxm
0G6rRLYcxm
https://openreview.net/pdf/986ea5954f43048b6c58657f22fa0f60ec22c0b9.pdf
submission_note
null
/pdf/986ea5954f43048b6c58657f22fa0f60ec22c0b9.pdf
4
1
0
1
1,727,319,394,328
2024-09-26T02:56:34.328000+00:00
1,733,715,419,890
2024-12-09T03:36:59.890000+00:00
1,730,089,987,352
2024-10-28T04:33:07.352000+00:00
1,727,319,394,328
2024-09-26T02:56:34.328000+00:00
1,733,715,419,890
2024-12-09T03:36:59.890000+00:00
2026-08-05T12:27:14.847020+00:00
2026-08-05T12:27:14.847020+00:00
null
verified_revision_cdate_before_review
0GC81gpjOo
Cognitive Insights and Stable Coalition Matching for Fostering Multi-Agent Cooperation
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 6, 3, 3]" ]
[ "data/train.csv" ]
papers/0GC81gpjOo/paper.pdf
papers/0GC81gpjOo/paper.md
papers/0GC81gpjOo/paper.source.tex
papers/0GC81gpjOo/metadata.json
papers/0GC81gpjOo/review.json
papers/0GC81gpjOo/conversion_report.json
728a480cb4caee8bc86390beb45a4bb4afe7f1bc8d2b5b8f63b5151521ed2385
1,018,355
68,604
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:16.785533+00:00
0GC81gpjOo
0GC81gpjOo
0GC81gpjOo
https://arxiv.org/pdf/2405.18044v1
arxiv
null
https://arxiv.org/pdf/2405.18044v1
4
1
0
1
1,716,893,973,000
2024-05-28T10:59:33+00:00
1,716,893,973,000
2024-05-28T10:59:33+00:00
1,730,565,153,575
2024-11-02T16:32:33.575000+00:00
1,716,893,973,000
2024-05-28T10:59:33+00:00
1,716,893,973,000
2024-05-28T10:59:33+00:00
2026-08-05T12:27:23.736481+00:00
2026-08-05T12:27:23.736481+00:00
null
verified_arxiv_version_before_review
0GZ1Bq4Tfr
Layer-wise Pre-weight Decay
Reject
[ "2024" ]
[ "best", "fast", "standard" ]
[ "[3, 6, 3, 3]" ]
[ "data/train.csv" ]
papers/0GZ1Bq4Tfr/paper.pdf
papers/0GZ1Bq4Tfr/paper.md
papers/0GZ1Bq4Tfr/paper.source.tex
papers/0GZ1Bq4Tfr/metadata.json
papers/0GZ1Bq4Tfr/review.json
papers/0GZ1Bq4Tfr/conversion_report.json
f13b62a70492b8e88360bad56a603fe40a5c9f41d7ea9e5ffe8172b3b5bb56c1
3,289,142
25,717
dataset
dataset_source_lossless
2026-08-05T17:15:41.815600+00:00
0GZ1Bq4Tfr
0GZ1Bq4Tfr
0GZ1Bq4Tfr
https://openreview.net/pdf/98b7b8743b96c8699d6ce101a2d2534c318cd8c3.pdf
submission_note
null
/pdf/98b7b8743b96c8699d6ce101a2d2534c318cd8c3.pdf
4
1
0
1
1,695,538,356,642
2023-09-24T06:52:36.642000+00:00
1,764,878,773,699
2025-12-04T20:06:13.699000+00:00
1,697,420,131,087
2023-10-16T01:35:31.087000+00:00
1,695,538,356,642
2023-09-24T06:52:36.642000+00:00
1,764,878,773,699
2025-12-04T20:06:13.699000+00:00
2026-08-05T12:27:32.762256+00:00
2026-08-05T12:27:32.762256+00:00
null
verified_revision_cdate_before_review
0GzqVqCKns
Probing the Latent Hierarchical Structure of Data via Diffusion Models
Accept
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 6, 8, 6]" ]
[ "data/train.csv" ]
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arxiv
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null
verified_arxiv_version_before_review
0HWAbWgI3T
A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings
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null
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MLGLP: Multi-Scale Line-Graph Link Prediction based on Graph Neural Networks
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verified_revision_cdate_before_review
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On the Embedding Collapse When Scaling up Recommendation Models
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verified_arxiv_version_before_review
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Inference of Sequential Patterns for Neural Message Passing in Temporal Graphs
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arxiv
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null
verified_arxiv_version_before_review
0IqriWHWYy
Watch Out!! Your Confidence Might be a Reason for Vulnerability
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null
verified_revision_cdate_before_review
0JTwZ30qPH
Task-Oriented Multi-View Representation Learning
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[ "[3, 3, 6, 3, 5]" ]
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null
verified_revision_cdate_before_review
0JWVWUlobv
4D Tensor Multi-task Continual Learning for Disease Dynamic Prediction
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[ "[5, 5, 6, 5]" ]
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null
verified_revision_cdate_before_review
0JcPJ0CLbx
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null
verified_revision_cdate_before_review
0JjsZC0w8x
COrAL: Order-Agnostic Language Modeling for Efficient Iterative Refinement
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[ "[3, 6, 8, 6]" ]
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arxiv
null
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null
verified_arxiv_version_before_review
0JnaN0Crlz
Enhancing Adversarial Robustness on Categorical Data via Attribution Smoothing
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null
verified_revision_cdate_before_review
0JwxMqKGxa
Reinforcement Learning on Synthetic Navigation Data allows Safe Navigation in Blind Digital Twins
Reject
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[ "best", "fast", "standard" ]
[ "[6, 5, 1, 3, 3, 1]" ]
[ "data/train.csv" ]
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0JwxMqKGxa
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null
verified_revision_cdate_before_review
0KFwhDqTQ6
PSHead: 3D Head Reconstruction from a Single Image with Diffusion Prior and Self-Enhancement
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[ "2025" ]
[ "best", "fast", "standard" ]
[ "[5, 3, 3, 5]" ]
[ "data/train.csv" ]
papers/0KFwhDqTQ6/paper.pdf
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0KFwhDqTQ6
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null
verified_revision_cdate_before_review
0KHW6yXdiZ
An End-to-End Model For Logits Based Large Language Models Watermarking
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 6, 6, 3]" ]
[ "data/train.csv" ]
papers/0KHW6yXdiZ/paper.pdf
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1,776,850,956,325
2026-04-22T09:42:36.325000+00:00
2026-08-05T12:29:45.077213+00:00
2026-08-05T12:29:45.077213+00:00
null
verified_revision_cdate_before_review
0L8wZ9WRah
Attention-aware Post-training Quantization without Backpropagation
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 3, 3, 3]" ]
[ "data/train.csv" ]
papers/0L8wZ9WRah/paper.pdf
papers/0L8wZ9WRah/paper.md
papers/0L8wZ9WRah/paper.source.tex
papers/0L8wZ9WRah/metadata.json
papers/0L8wZ9WRah/review.json
papers/0L8wZ9WRah/conversion_report.json
5be9aabf4f74a2c35e724dd58f9d9847f1ac2c7f5d37499d83a8f20891ccb8a0
921,721
97,777
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:16.953297+00:00
0L8wZ9WRah
0L8wZ9WRah
0L8wZ9WRah
https://arxiv.org/pdf/2406.13474v1
arxiv
null
https://arxiv.org/pdf/2406.13474v1
4
1
0
1
1,718,798,001,000
2024-06-19T11:53:21+00:00
1,718,798,001,000
2024-06-19T11:53:21+00:00
1,730,205,505,002
2024-10-29T12:38:25.002000+00:00
1,718,798,001,000
2024-06-19T11:53:21+00:00
1,718,798,001,000
2024-06-19T11:53:21+00:00
2026-08-05T12:29:53.948313+00:00
2026-08-05T12:29:53.948313+00:00
null
verified_arxiv_version_before_review
0MhlzybvAp
Balanced Learning for Domain Adaptive Semantic Segmentation
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[5, 6, 5, 6]" ]
[ "data/train.csv" ]
papers/0MhlzybvAp/paper.pdf
papers/0MhlzybvAp/paper.md
papers/0MhlzybvAp/paper.source.tex
papers/0MhlzybvAp/metadata.json
papers/0MhlzybvAp/review.json
papers/0MhlzybvAp/conversion_report.json
8243bdd66a06e3ca255262ab9ef5d2143944c79964371a845b974d08b35f10e3
15,386,330
37,563
dataset
dataset_source_lossless
2026-08-05T17:15:41.897399+00:00
0MhlzybvAp
0MhlzybvAp
0MhlzybvAp
https://openreview.net/pdf/67b1309f612e81a1e718ebe9e30dfe26aa26f00b.pdf
submission_note
null
/pdf/67b1309f612e81a1e718ebe9e30dfe26aa26f00b.pdf
4
1
0
1
1,727,428,422,671
2024-09-27T09:13:42.671000+00:00
1,738,735,796,795
2025-02-05T06:09:56.795000+00:00
1,729,956,278,476
2024-10-26T15:24:38.476000+00:00
1,727,428,422,671
2024-09-27T09:13:42.671000+00:00
1,738,735,796,795
2025-02-05T06:09:56.795000+00:00
2026-08-05T12:30:17.054820+00:00
2026-08-05T12:30:17.054820+00:00
null
verified_revision_cdate_before_review
0NAVeUm7sk
Variational Bayesian Pseudo-Coreset
Accept
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[8, 6, 8, 5]" ]
[ "data/train.csv" ]
papers/0NAVeUm7sk/paper.pdf
papers/0NAVeUm7sk/paper.md
papers/0NAVeUm7sk/paper.source.tex
papers/0NAVeUm7sk/metadata.json
papers/0NAVeUm7sk/review.json
papers/0NAVeUm7sk/conversion_report.json
d1dec5cecc2ad5bd34c421737e36b0c64144b0e39f5a1808ed09f40f3e98e11f
17,557,137
65,351
dataset
dataset_source_lossless
2026-08-05T17:15:41.905581+00:00
0NAVeUm7sk
0NAVeUm7sk
0NAVeUm7sk
https://openreview.net/pdf/b32a6f1e80c190793d90622b2b3d53cb3acb9ed7.pdf
submission_note
null
/pdf/b32a6f1e80c190793d90622b2b3d53cb3acb9ed7.pdf
4
1
0
1
1,727,356,444,256
2024-09-26T13:14:04.256000+00:00
1,740,358,394,196
2025-02-24T00:53:14.196000+00:00
1,729,436,049,135
2024-10-20T14:54:09.135000+00:00
1,727,356,444,256
2024-09-26T13:14:04.256000+00:00
1,740,358,394,196
2025-02-24T00:53:14.196000+00:00
2026-08-05T12:30:27.553940+00:00
2026-08-05T12:30:27.553940+00:00
null
verified_revision_cdate_before_review
0NEjIZlEhP
Verified Relative Output Margins for Neural Network Twins
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[5, 3, 6, 3, 3]" ]
[ "data/train.csv" ]
papers/0NEjIZlEhP/paper.pdf
papers/0NEjIZlEhP/paper.md
papers/0NEjIZlEhP/paper.source.tex
papers/0NEjIZlEhP/metadata.json
papers/0NEjIZlEhP/review.json
papers/0NEjIZlEhP/conversion_report.json
a9a03cc668fcf70248622bd194c77035fead82e2457a63cd066dedcfb5b929ed
1,382,111
45,241
dataset
dataset_source_lossless
2026-08-05T17:15:41.911384+00:00
0NEjIZlEhP
0NEjIZlEhP
0NEjIZlEhP
https://openreview.net/pdf/bc645c187a5e61f687d2be520692f1ea98785891.pdf
submission_note
null
/pdf/bc645c187a5e61f687d2be520692f1ea98785891.pdf
5
1
0
1
1,727,427,745,008
2024-09-27T09:02:25.008000+00:00
1,738,735,796,407
2025-02-05T06:09:56.407000+00:00
1,729,190,093,356
2024-10-17T18:34:53.356000+00:00
1,727,427,745,008
2024-09-27T09:02:25.008000+00:00
1,738,735,796,407
2025-02-05T06:09:56.407000+00:00
2026-08-05T12:30:36.757988+00:00
2026-08-05T12:30:36.757988+00:00
null
verified_revision_cdate_before_review
0NruoU6s5Z
CompoDiff: Versatile Composed Image Retrieval With Latent Diffusion
Reject
[ "2024" ]
[ "best", "fast", "standard" ]
[ "[5, 5, 6, 5]" ]
[ "data/train.csv" ]
papers/0NruoU6s5Z/paper.pdf
papers/0NruoU6s5Z/paper.md
papers/0NruoU6s5Z/paper.source.tex
papers/0NruoU6s5Z/metadata.json
papers/0NruoU6s5Z/review.json
papers/0NruoU6s5Z/conversion_report.json
ebf0b15bf0b8dbe4400c9ae36b0aafc55808b59821592d803d64fc9d17f39c7e
5,325,590
95,726
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:16.987367+00:00
0NruoU6s5Z
0NruoU6s5Z
0NruoU6s5Z
https://arxiv.org/pdf/2303.11916v2
arxiv
null
https://arxiv.org/pdf/2303.11916v2
4
1
0
1
1,696,434,870,000
2023-10-04T15:54:30+00:00
1,696,434,870,000
2023-10-04T15:54:30+00:00
1,698,671,129,617
2023-10-30T13:05:29.617000+00:00
1,696,434,870,000
2023-10-04T15:54:30+00:00
1,696,434,870,000
2023-10-04T15:54:30+00:00
2026-08-05T12:30:46.778948+00:00
2026-08-05T12:30:46.778948+00:00
null
verified_arxiv_version_before_review
0Nui91LBQS
Making LLaMA SEE and Draw with SEED Tokenizer
Accept
[ "2024" ]
[ "best", "fast", "standard" ]
[ "[5, 6, 8]" ]
[ "data/train.csv" ]
papers/0Nui91LBQS/paper.pdf
papers/0Nui91LBQS/paper.md
papers/0Nui91LBQS/paper.source.tex
papers/0Nui91LBQS/metadata.json
papers/0Nui91LBQS/review.json
papers/0Nui91LBQS/conversion_report.json
063cdc5508cc2773782ca535b13f9a3bb9ec48847c44c06c328996ac65cef6a1
23,657,339
73,722
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:17.017613+00:00
0Nui91LBQS
0Nui91LBQS
0Nui91LBQS
https://arxiv.org/pdf/2310.01218v1
arxiv
null
https://arxiv.org/pdf/2310.01218v1
3
1
0
1
1,696,255,382,000
2023-10-02T14:03:02+00:00
1,696,255,382,000
2023-10-02T14:03:02+00:00
1,698,705,889,770
2023-10-30T22:44:49.770000+00:00
1,696,255,382,000
2023-10-02T14:03:02+00:00
1,696,255,382,000
2023-10-02T14:03:02+00:00
2026-08-05T12:30:58.015224+00:00
2026-08-05T12:30:58.015224+00:00
null
verified_arxiv_version_before_review
0NvSMb7xgC
Auditing Predictive Models for Intersectional Biases
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 3, 5, 5]" ]
[ "data/train.csv" ]
papers/0NvSMb7xgC/paper.pdf
papers/0NvSMb7xgC/paper.md
papers/0NvSMb7xgC/paper.source.tex
papers/0NvSMb7xgC/metadata.json
papers/0NvSMb7xgC/review.json
papers/0NvSMb7xgC/conversion_report.json
7c38e3f0bd8665532951c38ed71efcf7dc560470dc8c5c7b6acb5350e26b41b5
3,711,211
191,646
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:17.086722+00:00
0NvSMb7xgC
0NvSMb7xgC
0NvSMb7xgC
https://arxiv.org/pdf/2306.13064v1
arxiv
null
https://arxiv.org/pdf/2306.13064v1
4
1
0
1
1,687,455,132,000
2023-06-22T17:32:12+00:00
1,687,455,132,000
2023-06-22T17:32:12+00:00
1,729,623,680,298
2024-10-22T19:01:20.298000+00:00
1,687,455,132,000
2023-06-22T17:32:12+00:00
1,687,455,132,000
2023-06-22T17:32:12+00:00
2026-08-05T12:31:07.234114+00:00
2026-08-05T12:31:07.234114+00:00
null
verified_arxiv_version_before_review
0OB3RVmTXE
Unstable Unlearning: The Hidden Risk of Concept Resurgence in Diffusion Models
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[3, 3, 5, 5]" ]
[ "data/train.csv" ]
papers/0OB3RVmTXE/paper.pdf
papers/0OB3RVmTXE/paper.md
papers/0OB3RVmTXE/paper.source.tex
papers/0OB3RVmTXE/metadata.json
papers/0OB3RVmTXE/review.json
papers/0OB3RVmTXE/conversion_report.json
9e0be8bcc07e148ec462cd5f56d10f19a752c2e243bbc494d493ae5cdf9db50e
50,772,389
62,000
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:17.110372+00:00
0OB3RVmTXE
0OB3RVmTXE
0OB3RVmTXE
https://arxiv.org/pdf/2410.08074v1
arxiv
null
https://arxiv.org/pdf/2410.08074v1
4
1
0
1
1,728,576,627,000
2024-10-10T16:10:27+00:00
1,728,576,627,000
2024-10-10T16:10:27+00:00
1,730,113,798,820
2024-10-28T11:09:58.820000+00:00
1,728,576,627,000
2024-10-10T16:10:27+00:00
1,728,576,627,000
2024-10-10T16:10:27+00:00
2026-08-05T12:31:16.133185+00:00
2026-08-05T12:31:16.133185+00:00
null
verified_arxiv_version_before_review
0OzDMjPHa3
Efficient Visualization of Implicit Neural Representations via Weight Matrix Analysis
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[3, 3, 5, 3]" ]
[ "data/train.csv" ]
papers/0OzDMjPHa3/paper.pdf
papers/0OzDMjPHa3/paper.md
papers/0OzDMjPHa3/paper.source.tex
papers/0OzDMjPHa3/metadata.json
papers/0OzDMjPHa3/review.json
papers/0OzDMjPHa3/conversion_report.json
364eb3f32577ed186c4eb714bd58543fcfab40259f16db58dad9a37075309963
7,037,091
30,377
dataset
dataset_source_lossless
2026-08-05T17:15:41.940809+00:00
0OzDMjPHa3
0OzDMjPHa3
0OzDMjPHa3
https://openreview.net/pdf/0f7e3f349278734d5011ee9384088949e288faaa.pdf
submission_note
null
/pdf/0f7e3f349278734d5011ee9384088949e288faaa.pdf
4
1
0
1
1,727,453,483,902
2024-09-27T16:11:23.902000+00:00
1,738,735,827,417
2025-02-05T06:10:27.417000+00:00
1,730,228,425,255
2024-10-29T19:00:25.255000+00:00
1,727,453,483,902
2024-09-27T16:11:23.902000+00:00
1,738,735,827,417
2025-02-05T06:10:27.417000+00:00
2026-08-05T12:31:25.514345+00:00
2026-08-05T12:31:25.514345+00:00
null
verified_revision_cdate_before_review
0PC9goPpuz
Compatibility-aware Single-cell Continual Annotation
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[5, 3, 3]" ]
[ "data/train.csv" ]
papers/0PC9goPpuz/paper.pdf
papers/0PC9goPpuz/paper.md
papers/0PC9goPpuz/paper.source.tex
papers/0PC9goPpuz/metadata.json
papers/0PC9goPpuz/review.json
papers/0PC9goPpuz/conversion_report.json
9a2c48d25e51b83831d49f1a1073323d4ab081a4b0049a8aa8d9961644d2dfc7
9,941,491
38,991
dataset
dataset_source_lossless
2026-08-05T17:15:41.945480+00:00
0PC9goPpuz
0PC9goPpuz
0PC9goPpuz
https://openreview.net/pdf/d2f8cb4c3925fd4538249805e99719f43dd9cc36.pdf
submission_note
null
/pdf/d2f8cb4c3925fd4538249805e99719f43dd9cc36.pdf
3
1
0
1
1,727,314,934,846
2024-09-26T01:42:14.846000+00:00
1,731,432,432,012
2024-11-12T17:27:12.012000+00:00
1,730,338,873,690
2024-10-31T01:41:13.690000+00:00
1,727,314,934,846
2024-09-26T01:42:14.846000+00:00
1,731,432,432,012
2024-11-12T17:27:12.012000+00:00
2026-08-05T12:31:35.292629+00:00
2026-08-05T12:31:35.292629+00:00
null
verified_revision_cdate_before_review
0PxLpVURTl
MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Masked Image Modeling Representations
Accept
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 6, 8, 6]" ]
[ "data/train.csv" ]
papers/0PxLpVURTl/paper.pdf
papers/0PxLpVURTl/paper.md
papers/0PxLpVURTl/paper.source.tex
papers/0PxLpVURTl/metadata.json
papers/0PxLpVURTl/review.json
papers/0PxLpVURTl/conversion_report.json
3554d4e12057b59e0815b1879bb736689c99d9a3b867f5674c89d243f8cc33f0
1,412,459
137,101
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:17.164219+00:00
0PxLpVURTl
0PxLpVURTl
0PxLpVURTl
https://arxiv.org/pdf/2402.10093v3
arxiv
null
https://arxiv.org/pdf/2402.10093v3
4
1
0
1
1,725,814,114,000
2024-09-08T16:48:34+00:00
1,725,814,114,000
2024-09-08T16:48:34+00:00
1,730,523,774,637
2024-11-02T05:02:54.637000+00:00
1,725,814,114,000
2024-09-08T16:48:34+00:00
1,725,814,114,000
2024-09-08T16:48:34+00:00
2026-08-05T12:31:44.120294+00:00
2026-08-05T12:31:44.120294+00:00
null
verified_arxiv_version_before_review
0Q1mBvUgmt
VIPER: Vibrant Period Representation for Robust and Efficient Time Series Forecasting
Reject
[ "2024" ]
[ "best", "fast", "standard" ]
[ "[3, 3, 3]" ]
[ "data/train.csv" ]
papers/0Q1mBvUgmt/paper.pdf
papers/0Q1mBvUgmt/paper.md
papers/0Q1mBvUgmt/paper.source.tex
papers/0Q1mBvUgmt/metadata.json
papers/0Q1mBvUgmt/review.json
papers/0Q1mBvUgmt/conversion_report.json
26f10704a59bdb3ed98210fa19a3d3b2d49c5ea13a9d3c6346955099bdf1f5f0
1,973,289
32,100
dataset
dataset_source_lossless
2026-08-05T17:15:41.953049+00:00
0Q1mBvUgmt
0Q1mBvUgmt
0Q1mBvUgmt
https://openreview.net/pdf/f92048e2484dab7a77e29a3996d62b108c33153d.pdf
submission_note
null
/pdf/f92048e2484dab7a77e29a3996d62b108c33153d.pdf
3
1
0
1
1,695,129,152,586
2023-09-19T13:12:32.586000+00:00
1,707,625,676,554
2024-02-11T04:27:56.554000+00:00
1,698,428,979,639
2023-10-27T17:49:39.639000+00:00
1,695,129,152,586
2023-09-19T13:12:32.586000+00:00
1,707,625,676,554
2024-02-11T04:27:56.554000+00:00
2026-08-05T12:31:53.253673+00:00
2026-08-05T12:31:53.253673+00:00
null
verified_revision_cdate_before_review
0QJPszYxpo
Extended Flow Matching : a Method of Conditional Generation with Generalized Continuity Equation
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[3, 6, 6, 5, 5]" ]
[ "data/train.csv" ]
papers/0QJPszYxpo/paper.pdf
papers/0QJPszYxpo/paper.md
papers/0QJPszYxpo/paper.source.tex
papers/0QJPszYxpo/metadata.json
papers/0QJPszYxpo/review.json
papers/0QJPszYxpo/conversion_report.json
92fd1eb85d04f3ddb27534c4344c1602d8120c353e5722ac3e6fd3f22629e278
33,032,313
86,276
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:17.196916+00:00
0QJPszYxpo
0QJPszYxpo
0QJPszYxpo
https://arxiv.org/pdf/2402.18839v6
arxiv
null
https://arxiv.org/pdf/2402.18839v6
5
1
0
1
1,720,181,949,000
2024-07-05T12:19:09+00:00
1,720,181,949,000
2024-07-05T12:19:09+00:00
1,729,370,314,185
2024-10-19T20:38:34.185000+00:00
1,720,181,949,000
2024-07-05T12:19:09+00:00
1,720,181,949,000
2024-07-05T12:19:09+00:00
2026-08-05T12:32:13.994590+00:00
2026-08-05T12:32:13.994590+00:00
null
verified_arxiv_version_before_review
0QePvFoqY6
IncEventGS: Pose-Free Gaussian Splatting from a Single Event Camera
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[5, 5, 5, 3]" ]
[ "data/train.csv" ]
papers/0QePvFoqY6/paper.pdf
papers/0QePvFoqY6/paper.md
papers/0QePvFoqY6/paper.source.tex
papers/0QePvFoqY6/metadata.json
papers/0QePvFoqY6/review.json
papers/0QePvFoqY6/conversion_report.json
22464e2eeb36d5fc4ac4df0074dd8a3669ab68a0bd5091e74d3aa329eb824396
10,736,560
56,117
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:17.218311+00:00
0QePvFoqY6
0QePvFoqY6
0QePvFoqY6
https://arxiv.org/pdf/2410.08107v1
arxiv
null
https://arxiv.org/pdf/2410.08107v1
4
1
0
1
1,728,579,263,000
2024-10-10T16:54:23+00:00
1,728,579,263,000
2024-10-10T16:54:23+00:00
1,729,018,928,951
2024-10-15T19:02:08.951000+00:00
1,728,579,263,000
2024-10-10T16:54:23+00:00
1,728,579,263,000
2024-10-10T16:54:23+00:00
2026-08-05T12:32:23.623237+00:00
2026-08-05T12:32:23.623237+00:00
null
verified_arxiv_version_before_review
0QkVAxJ5iZ
FacLens: Transferable Probe for Foreseeing Non-Factuality in Large Language Models
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[3, 5, 8, 5]" ]
[ "data/train.csv" ]
papers/0QkVAxJ5iZ/paper.pdf
papers/0QkVAxJ5iZ/paper.md
papers/0QkVAxJ5iZ/paper.source.tex
papers/0QkVAxJ5iZ/metadata.json
papers/0QkVAxJ5iZ/review.json
papers/0QkVAxJ5iZ/conversion_report.json
79dd5e105b42f8e36ee2c43648f300d5cd81326165ae30243a66892471965a95
2,435,394
45,327
dataset
dataset_source_lossless
2026-08-05T17:15:41.972951+00:00
0QkVAxJ5iZ
0QkVAxJ5iZ
0QkVAxJ5iZ
https://openreview.net/pdf/e2297ed06ca065d361ec3f28961b352c3377db10.pdf
submission_note
null
/pdf/e2297ed06ca065d361ec3f28961b352c3377db10.pdf
4
1
0
1
1,727,427,181,292
2024-09-27T08:53:01.292000+00:00
1,738,735,795,933
2025-02-05T06:09:55.933000+00:00
1,730,648,962,825
2024-11-03T15:49:22.825000+00:00
1,727,427,181,292
2024-09-27T08:53:01.292000+00:00
1,738,735,795,933
2025-02-05T06:09:55.933000+00:00
2026-08-05T12:32:32.968899+00:00
2026-08-05T12:32:32.968899+00:00
null
verified_revision_cdate_before_review
0QvLISYIKM
Pointwise Information Measures as Confidence Estimators in Deep Neural Networks: A Comparative Study
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[5, 6, 6, 6, 3]" ]
[ "data/train.csv" ]
papers/0QvLISYIKM/paper.pdf
papers/0QvLISYIKM/paper.md
papers/0QvLISYIKM/paper.source.tex
papers/0QvLISYIKM/metadata.json
papers/0QvLISYIKM/review.json
papers/0QvLISYIKM/conversion_report.json
e20c6dd6f4c050d25e53836846fcb250d104b8a5190f3018861c4a417d3b8805
8,559,634
40,877
dataset
dataset_source_lossless
2026-08-05T17:15:41.982087+00:00
0QvLISYIKM
0QvLISYIKM
0QvLISYIKM
https://openreview.net/pdf/af5c190b1d3276dad97943f5cea6c0b2118829da.pdf
submission_note
null
/pdf/af5c190b1d3276dad97943f5cea6c0b2118829da.pdf
5
1
0
1
1,727,406,079,623
2024-09-27T03:01:19.623000+00:00
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2025-02-05T06:09:36.805000+00:00
1,730,380,925,442
2024-10-31T13:22:05.442000+00:00
1,727,406,079,623
2024-09-27T03:01:19.623000+00:00
1,738,735,776,805
2025-02-05T06:09:36.805000+00:00
2026-08-05T12:32:51.514200+00:00
2026-08-05T12:32:51.514200+00:00
null
verified_revision_cdate_before_review
0Qyxw0cCuu
CONTROL: A Contrastive Learning Framework for Open World Semi-Supervised Learning
Reject
[ "2024" ]
[ "best", "fast", "standard" ]
[ "[5, 5, 5, 5]" ]
[ "data/train.csv" ]
papers/0Qyxw0cCuu/paper.pdf
papers/0Qyxw0cCuu/paper.md
papers/0Qyxw0cCuu/paper.source.tex
papers/0Qyxw0cCuu/metadata.json
papers/0Qyxw0cCuu/review.json
papers/0Qyxw0cCuu/conversion_report.json
676156024b65940172a94574fe195f601dcf8c4235b817fa7b27074e69aa25ee
958,055
35,052
dataset
dataset_source_lossless
2026-08-05T17:15:41.986317+00:00
0Qyxw0cCuu
0Qyxw0cCuu
0Qyxw0cCuu
https://openreview.net/pdf/e57a5d24dc9dc57bfd2d78347f4dc775e847f6b8.pdf
submission_note
null
/pdf/e57a5d24dc9dc57bfd2d78347f4dc775e847f6b8.pdf
4
1
0
1
1,695,279,908,806
2023-09-21T07:05:08.806000+00:00
1,707,625,693,355
2024-02-11T04:28:13.355000+00:00
1,698,072,337,556
2023-10-23T14:45:37.556000+00:00
1,695,279,908,806
2023-09-21T07:05:08.806000+00:00
1,707,625,693,355
2024-02-11T04:28:13.355000+00:00
2026-08-05T12:33:00.366314+00:00
2026-08-05T12:33:00.366314+00:00
null
verified_revision_cdate_before_review
0R3ha8oNPU
SecCodePLT: A Unified Platform for Evaluating the Security of Code GenAI
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 3, 5, 6]" ]
[ "data/train.csv" ]
papers/0R3ha8oNPU/paper.pdf
papers/0R3ha8oNPU/paper.md
papers/0R3ha8oNPU/paper.source.tex
papers/0R3ha8oNPU/metadata.json
papers/0R3ha8oNPU/review.json
papers/0R3ha8oNPU/conversion_report.json
3d4a40e2c8998fbe0cb94a35e53034ed2424c4e6e76d4dca7b7905163e2dc710
1,479,466
45,260
dataset
dataset_source_lossless
2026-08-05T17:15:41.991215+00:00
0R3ha8oNPU
0R3ha8oNPU
0R3ha8oNPU
https://openreview.net/pdf/edfeeced60ba4052749c781a06ae52d00814c712.pdf
submission_note
null
/pdf/edfeeced60ba4052749c781a06ae52d00814c712.pdf
4
1
0
1
1,727,486,009,357
2024-09-28T01:13:29.357000+00:00
1,738,735,862,663
2025-02-05T06:11:02.663000+00:00
1,730,507,200,313
2024-11-02T00:26:40.313000+00:00
1,727,486,009,357
2024-09-28T01:13:29.357000+00:00
1,738,735,862,663
2025-02-05T06:11:02.663000+00:00
2026-08-05T12:33:10.017433+00:00
2026-08-05T12:33:10.017433+00:00
null
verified_revision_cdate_before_review
0R8JUzjSdq
LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause Analysis
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[5, 6, 5, 5]" ]
[ "data/train.csv" ]
papers/0R8JUzjSdq/paper.pdf
papers/0R8JUzjSdq/paper.md
papers/0R8JUzjSdq/paper.source.tex
papers/0R8JUzjSdq/metadata.json
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papers/0R8JUzjSdq/conversion_report.json
0af54b1f95683ffded1a17c3d56a8558ec344e70efa684a8e8e19ad43e8ea9cd
2,773,690
79,151
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:17.248499+00:00
0R8JUzjSdq
0R8JUzjSdq
0R8JUzjSdq
https://arxiv.org/pdf/2406.05375v2
arxiv
null
https://arxiv.org/pdf/2406.05375v2
4
1
0
1
1,727,390,569,000
2024-09-26T22:42:49+00:00
1,727,390,569,000
2024-09-26T22:42:49+00:00
1,729,910,722,237
2024-10-26T02:45:22.237000+00:00
1,727,390,569,000
2024-09-26T22:42:49+00:00
1,727,390,569,000
2024-09-26T22:42:49+00:00
2026-08-05T12:33:19.078146+00:00
2026-08-05T12:33:19.078146+00:00
null
verified_arxiv_version_before_review
0RHMnPj8no
Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 8, 5, 5]" ]
[ "data/train.csv" ]
papers/0RHMnPj8no/paper.pdf
papers/0RHMnPj8no/paper.md
papers/0RHMnPj8no/paper.source.tex
papers/0RHMnPj8no/metadata.json
papers/0RHMnPj8no/review.json
papers/0RHMnPj8no/conversion_report.json
678d29823d6a5e27903aa3a41fbd20625a2b2c5c15cbfbb02c2a19b33a7743d8
391,771
80,113
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:17.279190+00:00
0RHMnPj8no
0RHMnPj8no
0RHMnPj8no
https://arxiv.org/pdf/2410.05880v1
arxiv
null
https://arxiv.org/pdf/2410.05880v1
4
1
0
1
1,728,382,549,000
2024-10-08T10:15:49+00:00
1,728,382,549,000
2024-10-08T10:15:49+00:00
1,730,546,992,205
2024-11-02T11:29:52.205000+00:00
1,728,382,549,000
2024-10-08T10:15:49+00:00
1,728,382,549,000
2024-10-08T10:15:49+00:00
2026-08-05T12:33:27.912590+00:00
2026-08-05T12:33:27.912590+00:00
null
verified_arxiv_version_before_review
0Ra0E43kK0
CaLMol: Disentangled Causal Graph LLM for Molecular Relational Learning
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[5, 3, 3, 3]" ]
[ "data/train.csv" ]
papers/0Ra0E43kK0/paper.pdf
papers/0Ra0E43kK0/paper.md
papers/0Ra0E43kK0/paper.source.tex
papers/0Ra0E43kK0/metadata.json
papers/0Ra0E43kK0/review.json
papers/0Ra0E43kK0/conversion_report.json
4ef533ca1eab76e10704e2f7e7dfac4d98b6e882fbdb6c7cf456bf0e99be524f
941,874
29,395
dataset
dataset_source_lossless
2026-08-05T17:15:42.007453+00:00
0Ra0E43kK0
0Ra0E43kK0
0Ra0E43kK0
https://openreview.net/pdf/ee3422892152cea6a7a044c1bf0ef4893decf78d.pdf
submission_note
null
/pdf/ee3422892152cea6a7a044c1bf0ef4893decf78d.pdf
4
1
0
1
1,727,431,562,391
2024-09-27T10:06:02.391000+00:00
1,738,735,801,505
2025-02-05T06:10:01.505000+00:00
1,730,355,921,965
2024-10-31T06:25:21.965000+00:00
1,727,431,562,391
2024-09-27T10:06:02.391000+00:00
1,738,735,801,505
2025-02-05T06:10:01.505000+00:00
2026-08-05T12:33:36.668502+00:00
2026-08-05T12:33:36.668502+00:00
null
verified_revision_cdate_before_review
0RgLIMh94b
Diffusion Curriculum: Synthetic-to-Real Data Curriculum via Image-Guided Diffusion
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[5, 3, 3, 5]" ]
[ "data/train.csv" ]
papers/0RgLIMh94b/paper.pdf
papers/0RgLIMh94b/paper.md
papers/0RgLIMh94b/paper.source.tex
papers/0RgLIMh94b/metadata.json
papers/0RgLIMh94b/review.json
papers/0RgLIMh94b/conversion_report.json
9564c1567859aa58da4880cb57d6a8a0147b74524d5da308c66e46f10218e2be
8,932,332
92,963
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:17.313613+00:00
0RgLIMh94b
0RgLIMh94b
0RgLIMh94b
https://arxiv.org/pdf/2410.13674v2
arxiv
null
https://arxiv.org/pdf/2410.13674v2
4
1
0
1
1,729,222,118,000
2024-10-18T03:28:38+00:00
1,729,222,118,000
2024-10-18T03:28:38+00:00
1,730,083,869,496
2024-10-28T02:51:09.496000+00:00
1,729,222,118,000
2024-10-18T03:28:38+00:00
1,729,222,118,000
2024-10-18T03:28:38+00:00
2026-08-05T12:33:46.334784+00:00
2026-08-05T12:33:46.334784+00:00
null
verified_arxiv_version_before_review
0SpkBUPjL3
Unremovable Watermarks for Open-Source Language Models
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[3, 3, 3, 6]" ]
[ "data/train.csv" ]
papers/0SpkBUPjL3/paper.pdf
papers/0SpkBUPjL3/paper.md
papers/0SpkBUPjL3/paper.source.tex
papers/0SpkBUPjL3/metadata.json
papers/0SpkBUPjL3/review.json
papers/0SpkBUPjL3/conversion_report.json
75c39dcfa963941de334100fbd5657450d766a4ac2cb07bc08e3c23698120573
3,571,720
50,623
dataset
dataset_source_lossless
2026-08-05T17:15:42.026515+00:00
0SpkBUPjL3
0SpkBUPjL3
0SpkBUPjL3
https://openreview.net/pdf/1c718e63c1d4bb45e5c294633410633ec7605b3e.pdf
submission_note
null
/pdf/1c718e63c1d4bb45e5c294633410633ec7605b3e.pdf
4
1
0
1
1,727,426,518,213
2024-09-27T08:41:58.213000+00:00
1,738,735,795,293
2025-02-05T06:09:55.293000+00:00
1,729,416,563,995
2024-10-20T09:29:23.995000+00:00
1,727,426,518,213
2024-09-27T08:41:58.213000+00:00
1,738,735,795,293
2025-02-05T06:09:55.293000+00:00
2026-08-05T12:34:13.977838+00:00
2026-08-05T12:34:13.977838+00:00
null
verified_revision_cdate_before_review
0T8vCKa7yu
LLM Compression with Convex Optimization—Part 1: Weight Quantization
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[3, 3, 3, 3]" ]
[ "data/train.csv" ]
papers/0T8vCKa7yu/paper.pdf
papers/0T8vCKa7yu/paper.md
papers/0T8vCKa7yu/paper.source.tex
papers/0T8vCKa7yu/metadata.json
papers/0T8vCKa7yu/review.json
papers/0T8vCKa7yu/conversion_report.json
f0de1eef9791d94a93a5157d0ed982a5ad931a4707b338865a09ee2d058b4420
1,719,870
38,640
dataset
dataset_source_lossless
2026-08-05T17:15:42.037072+00:00
0T8vCKa7yu
0T8vCKa7yu
0T8vCKa7yu
https://openreview.net/pdf/2879ae1f1b003fa5fb7e54d60c0bed9b70d99553.pdf
submission_note
null
/pdf/2879ae1f1b003fa5fb7e54d60c0bed9b70d99553.pdf
4
1
0
1
1,726,430,690,138
2024-09-15T20:04:50.138000+00:00
1,738,735,626,332
2025-02-05T06:07:06.332000+00:00
1,729,428,954,670
2024-10-20T12:55:54.670000+00:00
1,726,430,690,138
2024-09-15T20:04:50.138000+00:00
1,738,735,626,332
2025-02-05T06:07:06.332000+00:00
2026-08-05T12:34:32.323829+00:00
2026-08-05T12:34:32.323829+00:00
null
verified_revision_cdate_before_review
0TSAIUCwpp
Diffusion-based Extreme Image Compression with Compressed Feature Initialization
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[3, 3, 6, 5]" ]
[ "data/train.csv" ]
papers/0TSAIUCwpp/paper.pdf
papers/0TSAIUCwpp/paper.md
papers/0TSAIUCwpp/paper.source.tex
papers/0TSAIUCwpp/metadata.json
papers/0TSAIUCwpp/review.json
papers/0TSAIUCwpp/conversion_report.json
b78520916c6bb50d707d15748c20fd40a19db0daf2873370b4fabce43496c971
24,600,494
38,537
dataset
dataset_source_lossless
2026-08-05T17:15:42.041737+00:00
0TSAIUCwpp
0TSAIUCwpp
0TSAIUCwpp
https://openreview.net/pdf/4764d5c724b2d36924c7357e0f8dfb4f24d195c6.pdf
submission_note
null
/pdf/4764d5c724b2d36924c7357e0f8dfb4f24d195c6.pdf
4
1
0
1
1,727,080,071,764
2024-09-23T08:27:51.764000+00:00
1,737,791,572,169
2025-01-25T07:52:52.169000+00:00
1,730,101,613,961
2024-10-28T07:46:53.961000+00:00
1,727,080,071,764
2024-09-23T08:27:51.764000+00:00
1,737,791,572,169
2025-01-25T07:52:52.169000+00:00
2026-08-05T12:34:43.629433+00:00
2026-08-05T12:34:43.629433+00:00
null
verified_revision_cdate_before_review
0TZs6WOs16
Hyperbolic Embeddings in Sequential Self-Attention for Improved Next-Item Recommendations
Reject
[ "2024" ]
[ "best", "fast", "standard" ]
[ "[3, 3, 3, 5]" ]
[ "data/train.csv" ]
papers/0TZs6WOs16/paper.pdf
papers/0TZs6WOs16/paper.md
papers/0TZs6WOs16/paper.source.tex
papers/0TZs6WOs16/metadata.json
papers/0TZs6WOs16/review.json
papers/0TZs6WOs16/conversion_report.json
c73ed211d93ab288333a8c30a281d8da5bae688c0e56ae68f69325adb5f8db5a
1,089,586
38,159
dataset
dataset_source_lossless
2026-08-05T17:15:42.046129+00:00
0TZs6WOs16
0TZs6WOs16
0TZs6WOs16
https://openreview.net/pdf/de39fe39a335287d49f605e8d11e1a750ef5d0df.pdf
submission_note
null
/pdf/de39fe39a335287d49f605e8d11e1a750ef5d0df.pdf
4
1
0
1
1,695,491,937,577
2023-09-23T17:58:57.577000+00:00
1,707,625,757,899
2024-02-11T04:29:17.899000+00:00
1,698,785,279,076
2023-10-31T20:47:59.076000+00:00
1,695,491,937,577
2023-09-23T17:58:57.577000+00:00
1,707,625,757,899
2024-02-11T04:29:17.899000+00:00
2026-08-05T12:34:52.942029+00:00
2026-08-05T12:34:52.942029+00:00
null
verified_revision_cdate_before_review
0Th6bCZwKt
Gaussian Mixture Models Based Augmentation Enhances GNN Generalization
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[1, 5, 6, 6]" ]
[ "data/train.csv" ]
papers/0Th6bCZwKt/paper.pdf
papers/0Th6bCZwKt/paper.md
papers/0Th6bCZwKt/paper.source.tex
papers/0Th6bCZwKt/metadata.json
papers/0Th6bCZwKt/review.json
papers/0Th6bCZwKt/conversion_report.json
c2112882971a4fa57625e1fd3bab755046fe28689e8b0308b0383d55150824fb
454,624
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dataset
dataset_source_lossless
2026-08-05T17:15:42.051710+00:00
0Th6bCZwKt
0Th6bCZwKt
0Th6bCZwKt
https://openreview.net/pdf/be21f6df098139a97b2a0ba6d79199fbd6cec4fb.pdf
submission_note
null
/pdf/be21f6df098139a97b2a0ba6d79199fbd6cec4fb.pdf
4
1
0
1
1,727,471,600,615
2024-09-27T21:13:20.615000+00:00
1,738,735,848,618
2025-02-05T06:10:48.618000+00:00
1,730,018,607,026
2024-10-27T08:43:27.026000+00:00
1,727,471,600,615
2024-09-27T21:13:20.615000+00:00
1,738,735,848,618
2025-02-05T06:10:48.618000+00:00
2026-08-05T12:35:01.731854+00:00
2026-08-05T12:35:01.731854+00:00
null
verified_revision_cdate_before_review
0UCkWfcfb9
OPTune: Efficient Online Preference Tuning
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[5, 5, 5, 3, 3]" ]
[ "data/train.csv" ]
papers/0UCkWfcfb9/paper.pdf
papers/0UCkWfcfb9/paper.md
papers/0UCkWfcfb9/paper.source.tex
papers/0UCkWfcfb9/metadata.json
papers/0UCkWfcfb9/review.json
papers/0UCkWfcfb9/conversion_report.json
18d66c7b9316c9d91e0a85c8204854730caed162ccdb64c9d8d09e225d7d6818
833,098
59,215
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:17.336298+00:00
0UCkWfcfb9
0UCkWfcfb9
0UCkWfcfb9
https://arxiv.org/pdf/2406.07657v1
arxiv
null
https://arxiv.org/pdf/2406.07657v1
5
1
0
1
1,718,132,104,000
2024-06-11T18:55:04+00:00
1,718,132,104,000
2024-06-11T18:55:04+00:00
1,730,404,068,575
2024-10-31T19:47:48.575000+00:00
1,718,132,104,000
2024-06-11T18:55:04+00:00
1,718,132,104,000
2024-06-11T18:55:04+00:00
2026-08-05T12:35:10.551982+00:00
2026-08-05T12:35:10.551982+00:00
null
verified_arxiv_version_before_review
0ULf242ApE
From Context to Concept: Concept Encoding in In-Context Learning
Reject
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[5, 6, 8, 5]" ]
[ "data/train.csv" ]
papers/0ULf242ApE/paper.pdf
papers/0ULf242ApE/paper.md
papers/0ULf242ApE/paper.source.tex
papers/0ULf242ApE/metadata.json
papers/0ULf242ApE/review.json
papers/0ULf242ApE/conversion_report.json
24a7a1f8f317c0515251b55389718fecfc7a8349e56edfdfb622af4a55800232
3,746,238
33,850
dataset
dataset_source_lossless
2026-08-05T17:15:42.059974+00:00
0ULf242ApE
0ULf242ApE
0ULf242ApE
https://openreview.net/pdf/33009ab030338f037b7dc4b083890d66bdbd9f09.pdf
submission_note
null
/pdf/33009ab030338f037b7dc4b083890d66bdbd9f09.pdf
4
1
0
1
1,727,332,575,632
2024-09-26T06:36:15.632000+00:00
1,738,735,722,843
2025-02-05T06:08:42.843000+00:00
1,729,511,749,732
2024-10-21T11:55:49.732000+00:00
1,727,332,575,632
2024-09-26T06:36:15.632000+00:00
1,738,735,722,843
2025-02-05T06:08:42.843000+00:00
2026-08-05T12:35:20.073049+00:00
2026-08-05T12:35:20.073049+00:00
null
verified_revision_cdate_before_review
0UO1mH3Iwv
Edge-aware Image Smoothing with Relative Wavelet Domain Representation
Accept
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 6, 6]" ]
[ "data/train.csv" ]
papers/0UO1mH3Iwv/paper.pdf
papers/0UO1mH3Iwv/paper.md
papers/0UO1mH3Iwv/paper.source.tex
papers/0UO1mH3Iwv/metadata.json
papers/0UO1mH3Iwv/review.json
papers/0UO1mH3Iwv/conversion_report.json
28ee32c7919be3a9a77890b2281916d9811296302a9ec241e87ac81035caadec
49,872,861
29,050
dataset
dataset_source_lossless
2026-08-05T17:15:42.063779+00:00
0UO1mH3Iwv
0UO1mH3Iwv
0UO1mH3Iwv
https://openreview.net/pdf/3c16314b92dc0ba33a00d3f20e5c0e622bcb6cd2.pdf
submission_note
null
/pdf/3c16314b92dc0ba33a00d3f20e5c0e622bcb6cd2.pdf
3
1
0
1
1,727,164,086,766
2024-09-24T07:48:06.766000+00:00
1,740,890,321,984
2025-03-02T04:38:41.984000+00:00
1,730,511,723,728
2024-11-02T01:42:03.728000+00:00
1,727,164,086,766
2024-09-24T07:48:06.766000+00:00
1,740,890,321,984
2025-03-02T04:38:41.984000+00:00
2026-08-05T12:35:33.616241+00:00
2026-08-05T12:35:33.616241+00:00
null
verified_revision_cdate_before_review
0UvlnHgaii
Toward Exploratory Inverse Constraint Inference with Generative Diffusion Verifiers
Accept
[ "2025" ]
[ "best", "fast", "standard" ]
[ "[6, 6, 6]" ]
[ "data/train.csv" ]
papers/0UvlnHgaii/paper.pdf
papers/0UvlnHgaii/paper.md
papers/0UvlnHgaii/paper.source.tex
papers/0UvlnHgaii/metadata.json
papers/0UvlnHgaii/review.json
papers/0UvlnHgaii/conversion_report.json
7ac46fa59f41360fd1bd9b902b8c5f3ae3afeadc98619dba0973a442182ca151
6,181,354
42,565
dataset
dataset_source_lossless
2026-08-05T17:15:42.068786+00:00
0UvlnHgaii
0UvlnHgaii
0UvlnHgaii
https://openreview.net/pdf/93382d6a85d87cda83e88ec95bbffdabc6d2e2e4.pdf
submission_note
null
/pdf/93382d6a85d87cda83e88ec95bbffdabc6d2e2e4.pdf
3
1
0
1
1,727,335,139,649
2024-09-26T07:18:59.649000+00:00
1,740,824,839,339
2025-03-01T10:27:19.339000+00:00
1,730,328,123,971
2024-10-30T22:42:03.971000+00:00
1,727,335,139,649
2024-09-26T07:18:59.649000+00:00
1,740,824,839,339
2025-03-01T10:27:19.339000+00:00
2026-08-05T12:35:42.995966+00:00
2026-08-05T12:35:42.995966+00:00
null
verified_revision_cdate_before_review
0V5TVt9bk0
Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision
Accept
[ "2024" ]
[ "best", "fast", "standard" ]
[ "[6, 8, 8]" ]
[ "data/train.csv" ]
papers/0V5TVt9bk0/paper.pdf
papers/0V5TVt9bk0/paper.md
papers/0V5TVt9bk0/paper.source.tex
papers/0V5TVt9bk0/metadata.json
papers/0V5TVt9bk0/review.json
papers/0V5TVt9bk0/conversion_report.json
a31eed7d2f4c580711edb02ec7376aa1b061277f8d9c130bcdb46dec3c98b661
6,048,451
96,415
arxiv
arxiv_source_flattened_tex_lossless
2026-08-09T03:24:17.372479+00:00
0V5TVt9bk0
0V5TVt9bk0
0V5TVt9bk0
https://arxiv.org/pdf/2309.14181v2
arxiv
null
https://arxiv.org/pdf/2309.14181v2
3
1
0
1
1,695,918,143,000
2023-09-28T16:22:23+00:00
1,695,918,143,000
2023-09-28T16:22:23+00:00
1,698,762,994,673
2023-10-31T14:36:34.673000+00:00
1,695,918,143,000
2023-09-28T16:22:23+00:00
1,695,918,143,000
2023-09-28T16:22:23+00:00
2026-08-05T12:35:52.413509+00:00
2026-08-05T12:35:52.413509+00:00
null
verified_arxiv_version_before_review
0VKEJKKLvr
A GRAPH-BASED REPRESENTATION LEARNING APPROACH FOR BREAST CANCER RISK PREDICTION USING GENOTYPE DATA
Reject
[ "2024" ]
[ "best", "fast", "standard" ]
[ "[3, 3, 3, 3]" ]
[ "data/train.csv" ]
papers/0VKEJKKLvr/paper.pdf
papers/0VKEJKKLvr/paper.md
papers/0VKEJKKLvr/paper.source.tex
papers/0VKEJKKLvr/metadata.json
papers/0VKEJKKLvr/review.json
papers/0VKEJKKLvr/conversion_report.json
ca1963c9d89e3b89fed38523adad610bf8d7002c9cfe75ea15edac081b3ab6f0
1,157,317
19,474
dataset
dataset_source_lossless
2026-08-05T17:15:42.077144+00:00
0VKEJKKLvr
0VKEJKKLvr
0VKEJKKLvr
https://openreview.net/pdf/086f94a0474eebb59892ad3f80450d9852e0fa15.pdf
submission_note
null
/pdf/086f94a0474eebb59892ad3f80450d9852e0fa15.pdf
4
1
0
1
1,695,420,653,275
2023-09-22T22:10:53.275000+00:00
1,707,625,736,864
2024-02-11T04:28:56.864000+00:00
1,698,828,103,996
2023-11-01T08:41:43.996000+00:00
1,695,420,653,275
2023-09-22T22:10:53.275000+00:00
1,707,625,736,864
2024-02-11T04:28:56.864000+00:00
2026-08-05T12:36:01.338645+00:00
2026-08-05T12:36:01.338645+00:00
null
verified_revision_cdate_before_review
End of preview.

DeepReview-Bench

A benchmark package built from DeepReview-13K. Each completed paper directory contains the selected review-time PDF, a Markdown conversion generated from the DeepReview-13K embedded source text, human-review data, and provenance metadata.

Contents

papers/<paper_id>/:

file description
paper.pdf selected PDF revision for review-time use
paper.md Markdown converted from DeepReview-13K embedded source text
paper.source.tex embedded source text used to build paper.md
review.json OpenReview official reviews when available
metadata.json title, venue, decision, PDF/review timestamps, provenance
conversion_report.json conversion fidelity/audit metadata

Top-level:

  • splits/ — canonical split manifests: train.jsonl, test_2024.jsonl, test_2025.jsonl, and all_completed.jsonl.
  • timestamps_manifest.jsonl — one row per completed paper with selected PDF revision time (review_time_revision_* / selected_pdf_*), first official review time when available, download/processing time, PDF URL, and SHA-256 hash.
  • process_manifest.jsonl and upload_manifest.jsonl — pipeline progress and upload provenance.

Version Timing

The selected PDF timestamp fields are review_time_revision_cdate and review_time_revision_mdate; aliases selected_pdf_cdate and selected_pdf_mdate are included in split manifests for downstream code. first_official_review_cdate is present only when the official review note time was available from OpenReview. pdf_time_verification_status records whether the selected PDF creation time is verified to be before the first official review time.
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