Dataset Preview
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
generated_at: string
input_dir: string
output_dir: string
schema_fields: list<item: string>
child 0, item: string
source_files: struct<frontier_papers_2026_quality.jsonl: struct<bad_lines: int64, raw_records: int64, status: stri (... 190 chars omitted)
child 0, frontier_papers_2026_quality.jsonl: struct<bad_lines: int64, raw_records: int64, status: string>
child 0, bad_lines: int64
child 1, raw_records: int64
child 2, status: string
child 1, papers_recent_3y_score_gte_4.jsonl: struct<bad_lines: int64, raw_records: int64, status: string>
child 0, bad_lines: int64
child 1, raw_records: int64
child 2, status: string
child 2, topconf_papers_all.jsonl: struct<bad_lines: int64, raw_records: int64, status: string>
child 0, bad_lines: int64
child 1, raw_records: int64
child 2, status: string
splits: struct<all_curated: struct<bad_lines: int64, deduped_from: list<item: string>, filename: string, rec (... 1665 chars omitted)
child 0, all_curated: struct<bad_lines: int64, deduped_from: list<item: string>, filename: string, record_count: int64, to (... 531 chars omitted)
child 0, bad_lines: int64
child 1, deduped_from: list<item: string>
child 0, item: string
child 2, filename: string
child 3, record_count: int64
child 4, top_venues: struct<AAAI 2024: int64, AAAI 2025: int64, AAAI 2026: int64, CVPR 2023: int64, CVPR 2024: int64, CVP (... 275 chars omitted)
child 0, AAAI 2024:
...
child 13, ICML 2024: int64
child 14, ICML 2025: int64
child 15, ICRA 2026: int64
child 16, IROS 2025: int64
child 17, NeurIPS 2023: int64
child 18, NeurIPS 2024: int64
child 19, NeurIPS 2025: int64
child 4, with_abstract: int64
child 5, with_paper_url: int64
child 6, with_pdf_url: int64
child 7, year_distribution: struct<2023: int64, 2024: int64, 2025: int64, 2026: int64>
child 0, 2023: int64
child 1, 2024: int64
child 2, 2025: int64
child 3, 2026: int64
influential_citation_count: int64
relevance_score: double
metadata_score: null
arxiv_id: string
venue: string
corpus: string
authors: list<item: string>
child 0, item: string
fields_of_study: list<item: string>
child 0, item: string
code_url: string
year: int64
updated_at: string
frontier_score: double
source_file: string
citation_count: int64
pdf_url: string
abstract: string
quality_signals: list<item: string>
child 0, item: string
source_queries: list<item: string>
child 0, item: string
project_url: string
semantic_scholar_id: string
doi: string
title: string
decision: string
sources: list<item: string>
child 0, item: string
hybrid_score: null
union_sources: list<item: string>
child 0, item: string
collected_at: string
categories: list<item: string>
child 0, item: string
paper_id: string
paper_url: string
keywords: list<item: string>
child 0, item: string
dataset_split: string
to
{'abstract': Value('string'), 'arxiv_id': Value('string'), 'authors': List(Value('string')), 'categories': List(Value('string')), 'citation_count': Value('int64'), 'code_url': Value('string'), 'collected_at': Value('string'), 'corpus': Value('string'), 'dataset_split': Value('string'), 'decision': Value('string'), 'doi': Value('string'), 'fields_of_study': List(Value('string')), 'frontier_score': Value('float64'), 'hybrid_score': Value('null'), 'influential_citation_count': Value('int64'), 'keywords': List(Value('string')), 'metadata_score': Value('null'), 'paper_id': Value('string'), 'paper_url': Value('string'), 'pdf_url': Value('string'), 'project_url': Value('string'), 'quality_signals': List(Value('string')), 'relevance_score': Value('float64'), 'semantic_scholar_id': Value('string'), 'source_file': Value('string'), 'source_queries': List(Value('string')), 'sources': List(Value('string')), 'title': Value('string'), 'union_sources': List(Value('string')), 'updated_at': Value('string'), 'venue': Value('string'), 'year': Value('int64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, 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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
generated_at: string
input_dir: string
output_dir: string
schema_fields: list<item: string>
child 0, item: string
source_files: struct<frontier_papers_2026_quality.jsonl: struct<bad_lines: int64, raw_records: int64, status: stri (... 190 chars omitted)
child 0, frontier_papers_2026_quality.jsonl: struct<bad_lines: int64, raw_records: int64, status: string>
child 0, bad_lines: int64
child 1, raw_records: int64
child 2, status: string
child 1, papers_recent_3y_score_gte_4.jsonl: struct<bad_lines: int64, raw_records: int64, status: string>
child 0, bad_lines: int64
child 1, raw_records: int64
child 2, status: string
child 2, topconf_papers_all.jsonl: struct<bad_lines: int64, raw_records: int64, status: string>
child 0, bad_lines: int64
child 1, raw_records: int64
child 2, status: string
splits: struct<all_curated: struct<bad_lines: int64, deduped_from: list<item: string>, filename: string, rec (... 1665 chars omitted)
child 0, all_curated: struct<bad_lines: int64, deduped_from: list<item: string>, filename: string, record_count: int64, to (... 531 chars omitted)
child 0, bad_lines: int64
child 1, deduped_from: list<item: string>
child 0, item: string
child 2, filename: string
child 3, record_count: int64
child 4, top_venues: struct<AAAI 2024: int64, AAAI 2025: int64, AAAI 2026: int64, CVPR 2023: int64, CVPR 2024: int64, CVP (... 275 chars omitted)
child 0, AAAI 2024:
...
child 13, ICML 2024: int64
child 14, ICML 2025: int64
child 15, ICRA 2026: int64
child 16, IROS 2025: int64
child 17, NeurIPS 2023: int64
child 18, NeurIPS 2024: int64
child 19, NeurIPS 2025: int64
child 4, with_abstract: int64
child 5, with_paper_url: int64
child 6, with_pdf_url: int64
child 7, year_distribution: struct<2023: int64, 2024: int64, 2025: int64, 2026: int64>
child 0, 2023: int64
child 1, 2024: int64
child 2, 2025: int64
child 3, 2026: int64
influential_citation_count: int64
relevance_score: double
metadata_score: null
arxiv_id: string
venue: string
corpus: string
authors: list<item: string>
child 0, item: string
fields_of_study: list<item: string>
child 0, item: string
code_url: string
year: int64
updated_at: string
frontier_score: double
source_file: string
citation_count: int64
pdf_url: string
abstract: string
quality_signals: list<item: string>
child 0, item: string
source_queries: list<item: string>
child 0, item: string
project_url: string
semantic_scholar_id: string
doi: string
title: string
decision: string
sources: list<item: string>
child 0, item: string
hybrid_score: null
union_sources: list<item: string>
child 0, item: string
collected_at: string
categories: list<item: string>
child 0, item: string
paper_id: string
paper_url: string
keywords: list<item: string>
child 0, item: string
dataset_split: string
to
{'abstract': Value('string'), 'arxiv_id': Value('string'), 'authors': List(Value('string')), 'categories': List(Value('string')), 'citation_count': Value('int64'), 'code_url': Value('string'), 'collected_at': Value('string'), 'corpus': Value('string'), 'dataset_split': Value('string'), 'decision': Value('string'), 'doi': Value('string'), 'fields_of_study': List(Value('string')), 'frontier_score': Value('float64'), 'hybrid_score': Value('null'), 'influential_citation_count': Value('int64'), 'keywords': List(Value('string')), 'metadata_score': Value('null'), 'paper_id': Value('string'), 'paper_url': Value('string'), 'pdf_url': Value('string'), 'project_url': Value('string'), 'quality_signals': List(Value('string')), 'relevance_score': Value('float64'), 'semantic_scholar_id': Value('string'), 'source_file': Value('string'), 'source_queries': List(Value('string')), 'sources': List(Value('string')), 'title': Value('string'), 'union_sources': List(Value('string')), 'updated_at': Value('string'), 'venue': Value('string'), 'year': Value('int64')}
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 1683, 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 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
abstract string | arxiv_id string | authors list | categories list | citation_count int64 | code_url string | collected_at string | corpus string | dataset_split string | decision string | doi string | fields_of_study list | frontier_score float64 | hybrid_score null | influential_citation_count int64 | keywords list | metadata_score null | paper_id string | paper_url string | pdf_url string | project_url string | quality_signals list | relevance_score float64 | semantic_scholar_id string | source_file string | source_queries list | sources list | title string | union_sources list | updated_at string | venue string | year int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Vision-language models (VLMs) are increasingly being adopted for end-to-end autonomous driving systems due to their exceptional performance in handling long-tail scenarios. However, current VLM-based approaches suffer from two major limitations: 1) Some VLMs directly output planning results without chain-of-thought (Co... | [
"Yuqi Ye",
"Zijian Zhang",
"Junhong Lin",
"Shangkun Sun",
"Changhao Peng",
"Wei Gao"
] | [] | null | 2026-06-12T09:43:49.040901+00:00 | all_curated | rejected | [] | null | null | null | [
"Autonomous Driving; Vision-Language Models; Reinforcement Learning"
] | null | openreview:iclr2026:7ea10b7740f3d0dd | https://openreview.net/forum?id=CMU8GxwpUL | https://openreview.net/pdf?id=CMU8GxwpUL | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | $AutoDrive\text{-}P^3$: Unified Chain of Perception–Prediction–Planning Thought via Reinforcement Fine-Tuning | [
"topconf_all"
] | 2026-06-12T09:43:49.040902+00:00 | ICLR 2026 | 2,026 | ||||||
[
"Ashish Kumar",
"Rajagopalan N Ambasamduram"
] | [] | null | 2026-06-12T09:42:14.025851+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:9181e35df393a981 | https://openaccess.thecvf.com/content/CVPR2026/html/Kumar_L2DGS_Low-Light_Dynamic_Gaussian_Splatting_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Kumar_L2DGS_Low-Light_Dynamic_Gaussian_Splatting_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | $L^{2}DGS$: Low-Light Dynamic Gaussian Splatting | [
"topconf_all"
] | 2026-06-12T09:42:14.025853+00:00 | CVPR 2026 | 2,026 | |||||||
Video generation models have achieved remarkable progress in creating high-quality, photorealistic content. However, their ability to accurately simulate physical phenomena remains a critical and unresolved challenge. This paper presents $PhyWorldBench$ , a comprehensive benchmark designed to evaluate video generation ... | [
"Jing Gu",
"Xian Liu",
"Yu Zeng",
"Ashwin Nagarajan",
"Fangrui Zhu",
"Daniel Hong",
"Yue Fan",
"Qianqi Yan",
"Kaiwen Zhou",
"Ming-Yu Liu",
"Xin Eric Wang"
] | [] | null | 2026-06-12T09:43:49.052482+00:00 | all_curated | rejected | [] | null | null | null | [
"Video Evaluation",
"Video Generation"
] | null | openreview:iclr2026:312f5f4947892222 | https://openreview.net/forum?id=rlZeILv3fm | https://openreview.net/pdf?id=rlZeILv3fm | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | $PhyWorldBench$: A Comprehensive Evaluation of Physical Realism in Text-to-Video Models | [
"topconf_all"
] | 2026-06-12T09:43:49.052483+00:00 | ICLR 2026 | 2,026 | ||||||
We introduce Ψ₀ ( Psi-Zero ), an open foundation model to address challenging humanoid loco-manipulation tasks. While existing approaches often attempt to address this fundamental problem by co-training on large and diverse human and humanoid data, we argue that this strategy is suboptimal due to the fundamental kinema... | [
"Songlin Wei",
"Hongyi Jing",
"Boqian Li",
"Zhenyu Zhao",
"Jiageng Mao",
"Zhenhao Ni",
"Sicheng He",
"Sheng Zang",
"Xiawei Liu",
"Kaidi Kang",
"Jie Liu",
"Weiduo Yuan",
"Marco Pavone",
"Di Huang",
"Yue Wang"
] | [] | null | 2026-06-12T09:54:00.790927+00:00 | all_curated | rejected | [] | null | null | null | [
"Humanoids"
] | null | rss:accepted2026:72acde0cc70e5341 | https://roboticsconference.org/program/papers/21/ | [] | 2 | union:topconf_all | [
"RSS 2026",
"accepted papers"
] | [
"topconf",
"rss_accepted"
] | $\Psi_0$: An Open Foundation Model Towards Universal Humanoid Loco-Manipulation | [
"topconf_all"
] | 2026-06-12T09:54:00.790953+00:00 | RSS 2026 | 2,026 | |||||||
Diffusion models have demonstrated remarkable success in high-fidelity image generation, yet aligning them with human preferences remains challenging. Direct Preference Optimization (DPO) offers a promising framework, but its effectiveness is critically hindered by noisy data arising from mislabeled preference pairs an... | [
"Yang Li",
"Songlin Yang",
"Wei Wang",
"Xiaoxuan Han",
"Jing Dong"
] | [] | null | 2026-06-12T09:43:49.066053+00:00 | all_curated | rejected | [] | null | null | null | [
"diffusion model; preference alignment; noise robustness"
] | null | openreview:iclr2026:2ba0d0d8b046662b | https://openreview.net/forum?id=wqbnA6PcKr | https://openreview.net/pdf?id=wqbnA6PcKr | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | $\alpha$-DPO: Robust Preference Alignment for Diffusion Models via $\alpha$ Divergence | [
"topconf_all"
] | 2026-06-12T09:43:49.066055+00:00 | ICLR 2026 | 2,026 | ||||||
[
"Xinyi Chen",
"Hang Dong",
"Baowei Jiang",
"Shenkun Xu",
"Youqi Guan",
"Kanle Shi",
"Kun Gai",
"Haichuan Song"
] | [] | null | 2026-06-12T09:42:13.917496+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:30566935545f3f95 | https://openaccess.thecvf.com/content/CVPR2026/html/Chen_alphaMatte4K__muMatting_Dataset_and_Model_for_Ultra-Micro_Precision_Alpha_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Chen_alphaMatte4K__muMatting_Dataset_and_Model_for_Ultra-Micro_Precision_Alpha_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | $\alpha$Matte4K & $\mu$Matting: Dataset and Model for Ultra-Micro Precision Alpha Video Matting | [
"topconf_all"
] | 2026-06-12T09:42:13.917498+00:00 | CVPR 2026 | 2,026 | |||||||
We present a novel differentiable grid-based representation for efficiently solving differential equations (DEs). Widely used architectures for neural solvers, such as sinusoidal neural networks, are coordinate-based MLPs that are, both, computationally intensive and slow to train. Although grid-based alternatives for ... | [
"Navami Kairanda",
"Shanthika Naik",
"Marc Habermann",
"Avinash Sharma",
"Christian Theobalt",
"Vladislav Golyanik"
] | [] | null | 2026-06-12T09:43:49.021611+00:00 | all_curated | rejected | [] | null | null | null | [
"Differentiable Equations; Neural Field and Representations; Feature Grid; RBF Interpolation"
] | null | openreview:iclr2026:62dbbaffdb9bf358 | https://openreview.net/forum?id=7G0L4cj452 | https://openreview.net/pdf?id=7G0L4cj452 | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | $\boldsymbol{\partial^\infty}$-Grid: A Neural Differential Equation Solver with Differentiable Feature Grids | [
"topconf_all"
] | 2026-06-12T09:43:49.021613+00:00 | ICLR 2026 | 2,026 | ||||||
Continuous-time graph representation (CTGR) is a widely-used methodology in machine learning, physics, bioinformatics, and social networks. The sequential survival process in a latent space with the squared $\ell_2$ distance is an important ultra-low-dimensional embedding for CTGR. However, the squared $\ell_2$ distanc... | [
"Zhao-Rong Lai",
"Zheng-Sen Zhou",
"Liangda Fang",
"Yongsen Zheng",
"Ziliang Chen"
] | [] | null | 2026-06-12T09:43:49.032262+00:00 | all_curated | rejected | [] | null | null | null | [
"$\\ell_1$ distance",
"graph representation",
"sequential survival process",
"ultra-low-dimensional embedding"
] | null | openreview:iclr2026:516a7f2359464c23 | https://openreview.net/forum?id=pW1Kg9CYyw | https://openreview.net/pdf?id=pW1Kg9CYyw | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | $\ell_1$ Latent Distance based Continuous-time Graph Representation | [
"topconf_all"
] | 2026-06-12T09:43:49.032264+00:00 | ICLR 2026 | 2,026 | ||||||
While the phenomenon of grokking, i.e., delayed generalization, has been studied extensively, it remains an open problem whether there is a mathematical framework that characterizes what kind of features will emerge, how and in which conditions it happens, and is still closely connected with the gradient dynamics of th... | [
"Yuandong Tian"
] | [] | null | 2026-06-12T09:43:49.042266+00:00 | all_curated | rejected | [] | null | null | null | [
"generalization",
"gradient dynamics",
"grokking",
"memorization",
"modular addition",
"scaling laws"
] | null | openreview:iclr2026:25fbaf3fab0178a9 | https://openreview.net/forum?id=ceIBRhJpUr | https://openreview.net/pdf?id=ceIBRhJpUr | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | $\mathbf{Li_2}$: A Framework on Dynamics of Feature Emergence and Delayed Generalization | [
"topconf_all"
] | 2026-06-12T09:43:49.042268+00:00 | ICLR 2026 | 2,026 | ||||||
Learned optimizers (LOs) have the potential to significantly reduce the wall-clock training time of neural networks. However, they can struggle to optimize unseen tasks (*meta-generalize*), especially when training networks wider than those seen during meta-training. To address this, we derive the Maximal Update Parame... | [
"Benjamin Thérien",
"Charles-Étienne Joseph",
"Boris Knyazev",
"Edouard Oyallon",
"Irina Rish",
"Eugene Belilovsky"
] | [] | null | 2026-06-12T09:43:49.073073+00:00 | all_curated | rejected | [] | null | null | null | [
"Learned Optimizer",
"Maximal Update Parameterization",
"Meta Generalization",
"MuP"
] | null | openreview:iclr2026:c09f4832ebefc1cb | https://openreview.net/forum?id=f8z2bzOLK2 | https://openreview.net/pdf?id=f8z2bzOLK2 | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | $\mu$LO: Compute-Efficient Meta-Generalization of Learned Optimizers | [
"topconf_all"
] | 2026-06-12T09:43:49.073075+00:00 | ICLR 2026 | 2,026 | ||||||
Scaling inference-time compute for Large Language Models (LLMs) has unlocked unprecedented reasoning capabilities. However, existing inference-time scaling methods typically rely on inefficient and suboptimal discrete search algorithms or trial-and-error prompting to improve the online policy. In this paper, we propose... | [
"Peihao Wang",
"Ruisi Cai",
"Zhen Wang",
"Hongyuan Mei",
"qiang liu",
"Pan Li",
"Zhangyang Wang"
] | [] | null | 2026-06-12T09:43:49.016151+00:00 | all_curated | rejected | [] | null | null | null | [
"LLM Reasoning",
"Test-Time Training;Textual Optimization"
] | null | openreview:iclr2026:72f8c14444401bcc | https://openreview.net/forum?id=pEJAja73dk | https://openreview.net/pdf?id=pEJAja73dk | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | $\nabla$-Reasoner: LLM Reasoning via Test-Time Gradient Descent in Latent Space | [
"topconf_all"
] | 2026-06-12T09:43:49.016155+00:00 | ICLR 2026 | 2,026 | ||||||
[
"Arnav Devalapally",
"Poornima Jain",
"Kartik Srinivas",
"Vineeth N. Balasubramanian"
] | [] | null | 2026-06-12T09:42:14.086113+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:d32aa0cee5d01a10 | https://openaccess.thecvf.com/content/CVPR2026/html/Devalapally_oslash_Source_Models_Leak_What_They_Shouldnt_nrightarrow_Unlearning_Zero-Shot_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Devalapally_oslash_Source_Models_Leak_What_They_Shouldnt_nrightarrow_Unlearning_Zero-Shot_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | $\oslash$ Source Models Leak What They Shouldn't $\nrightarrow$: Unlearning Zero-Shot Transfer in Domain Adaptation Through Adversarial Optimization | [
"topconf_all"
] | 2026-06-12T09:42:14.086116+00:00 | CVPR 2026 | 2,026 | |||||||
[
"Thanh-Dat Truong",
"Huu-Thien Tran",
"Jackson Cothren",
"Bhiksha Raj",
"Khoa Luu"
] | [] | null | 2026-06-12T09:42:14.049683+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:edd07009d5beea1f | https://openaccess.thecvf.com/content/CVPR2026/html/Truong_phi-DPO_Fairness_Direct_Preference_Optimization_Approach_to_Continual_Learning_in_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Truong_phi-DPO_Fairness_Direct_Preference_Optimization_Approach_to_Continual_Learning_in_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | $\phi$-DPO: Fairness Direct Preference Optimization Approach to Continual Learning in Large Multimodal Models | [
"topconf_all"
] | 2026-06-12T09:42:14.049685+00:00 | CVPR 2026 | 2,026 | |||||||
We introduce $\pi^3$, a feed-forward neural network that offers a novel approach to visual geometry reconstruction, breaking the reliance on a conventional fixed reference view. Previous methods often anchor their reconstructions to a designated viewpoint, an inductive bias that can lead to instability and failures if ... | [
"Yifan Wang",
"Jianjun Zhou",
"Haoyi Zhu",
"Wenzheng Chang",
"Yang Zhou",
"Zizun Li",
"Junyi Chen",
"Jiangmiao Pang",
"Chunhua Shen",
"Tong He"
] | [] | null | 2026-06-12T09:43:49.063983+00:00 | all_curated | rejected | [] | null | null | null | [
"3D reconstruction",
"Camera Pose Estimation",
"Depth Estimation",
"Permutation-Equivariance",
"Reference-Free"
] | null | openreview:iclr2026:2f8bd619d88349eb | https://openreview.net/forum?id=DTQIjngDta | https://openreview.net/pdf?id=DTQIjngDta | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | $\pi^3$: Permutation-Equivariant Visual Geometry Learning | [
"topconf_all"
] | 2026-06-12T09:43:49.063985+00:00 | ICLR 2026 | 2,026 | ||||||
Vision–language–action (VLA) models offer a promising path toward general-purpose robots, but achieving the reliability and speed required for practical deployment remains challenging. We present a general-purpose method, RL with Experience and Corrections via Advantage-conditioned Policies (RECAP) that improves the ef... | [
"Ali Amin",
"Raichelle Aniceto",
"Ashwin Balakrishna",
"Kevin Black",
"Ken Conley",
"Grace B. Connors",
"James Darpinian",
"Karan Dhabalia",
"Jared Di Carlo",
"Danny Driess",
"Michael Robert Equi",
"Adnan Esmail",
"Yunhao Fang",
"Chelsea Finn",
"Catherine Glossop",
"Thomas Godden",
"... | [
"vla"
] | null | 2026-06-12T12:20:24.050607+00:00 | all_curated | likely_embodied_ai | [] | null | null | null | [
"VLA",
"VLA Models"
] | null | rss:accepted2026:2b6643041fcb344b | https://roboticsconference.org/program/papers/87/ | [] | 16 | union:topconf_all | [
"RSS 2026",
"accepted papers"
] | [
"topconf",
"rss_accepted"
] | $\pi^{*}_{0.6}$: a VLA That Learns From Experience | [
"topconf_all"
] | 2026-06-12T12:20:24.050632+00:00 | RSS 2026 | 2,026 | |||||||
Reinforcement learning with verifiable rewards (RLVR) has shown promise in enhancing the reasoning performance of large language models (LLMs) by increasing test-time compute. However, even after extensive RLVR training, such models still tend to generate unnecessary and low-quality steps in their chain-of-thought (CoT... | [
"Pinzheng Wang",
"ShuliXu",
"Juntao Li",
"Yu Luo",
"Dong Li",
"Jianye HAO",
"Min Zhang"
] | [] | null | 2026-06-12T09:43:49.037272+00:00 | all_curated | rejected | [] | null | null | null | [
"LLM Reasoning",
"Re-solving Mechanism",
"Reinforcement learning",
"Test-time Scaling"
] | null | openreview:iclr2026:af563c6ba717d5e5 | https://openreview.net/forum?id=HBOLN5m3qg | https://openreview.net/pdf?id=HBOLN5m3qg | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | $\textbf{Re}^{2}$: Unlocking LLM Reasoning via Reinforcement Learning with Re-solving | [
"topconf_all"
] | 2026-06-12T09:43:49.037274+00:00 | ICLR 2026 | 2,026 | ||||||
Autoregressive policies offer a compelling foundation for scalable robot learning by enabling discrete abstraction, token-level reasoning, and flexible inference. However, applying autoregressive modeling to continuous robot actions requires an effective action tokenization scheme. Existing approaches either rely on an... | [
"Chaoqi Liu",
"Xiaoshen Han",
"Jiawei Gao",
"Yue Zhao",
"Haonan Chen",
"Yilun Du"
] | [
"robot_learning"
] | null | 2026-06-12T12:20:17.273830+00:00 | all_curated | rejected | [] | null | null | null | [
"Imitation learning 1",
"robot learning"
] | null | rss:accepted2026:6dc2398465d01a85 | https://roboticsconference.org/program/papers/75/ | [] | 3.5 | union:topconf_all | [
"RSS 2026",
"accepted papers"
] | [
"topconf",
"rss_accepted"
] | $\textcolor{Maroon}{\textbf{\texttt{OAT}}}$: Ordered Action Tokenization | [
"topconf_all"
] | 2026-06-12T12:20:17.273854+00:00 | RSS 2026 | 2,026 | |||||||
Recent progress in multimodal generation has increasingly combined autoregressive (AR) and diffusion-based approaches, leveraging their complementary strengths: AR models capture long-range dependencies and produce fluent, context-aware outputs, while diffusion models operate in continuous latent spaces to refine high-... | [
"Junhao Chen",
"Yulia Tsvetkov",
"Xiaochuang Han"
] | [] | null | 2026-06-12T09:43:49.059277+00:00 | all_curated | rejected | [] | null | null | null | [
"Autoregressive",
"Continuous Image Generation",
"Diffusion"
] | null | openreview:iclr2026:87efd4e659e5d42b | https://openreview.net/forum?id=9zUJbyR62q | https://openreview.net/pdf?id=9zUJbyR62q | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | $\textit{MADFormer}$: Mixed Autoregressive and Diffusion Transformers for Continuous Image Generation | [
"topconf_all"
] | 2026-06-12T09:43:49.059279+00:00 | ICLR 2026 | 2,026 | ||||||
We present a new robotic foundation model, called $π_{0.7}$, that can enable strong out-of-the-box performance in a wide range of scenarios. $π_{0.7}$ can follow diverse language instructions in unseen environments, including multi-stage tasks with various kitchen appliances, provide zero-shot cross-embodiment generali... | 2604.15483v2 | [
"Physical Intelligence",
"Bo Ai",
"Ali Amin",
"Raichelle Aniceto",
"Ashwin Balakrishna",
"Greg Balke",
"Kevin Black",
"George Bokinsky",
"Shihao Cao",
"Thomas Charbonnier",
"Vedant Choudhary",
"Foster Collins",
"Ken Conley",
"Grace Connors",
"James Darpinian",
"Karan Dhabalia",
"Mait... | [
"frontier_2026",
"multimodal_robotics",
"robot_foundation_model"
] | 32 | 2026-06-11T15:16:28.875050+00:00 | all_curated | frontier_selected | [
"Computer Science",
"Engineering",
"cs.LG",
"cs.RO"
] | 9.479 | null | 1 | [
"agentic/generalist robot",
"compositional task generalization",
"context conditioning",
"multimodal information",
"multimodal robotics",
"robot foundation model",
"robotic foundation model",
"s2_citations",
"s2_influential_citations",
"s2_recommended_from_seed",
"subgoal images"
] | null | arxiv:2604.15483v2 | http://arxiv.org/abs/2604.15483v2 | https://arxiv.org/pdf/2604.15483v2 | [
"s2_citations",
"s2_influential_citations",
"s2_recommended_from_seed"
] | 13.95 | 51b4a73d753dac4be63d07b3d933da8f9418e483 | union:arxiv_recent_3y_score_gte_4,frontier_2026_quality | [
"robot foundation model"
] | [
"arxiv",
"frontier_quality"
] | $π_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities | [
"arxiv_recent_3y_score_gte_4",
"frontier_2026_quality"
] | 2026-06-12T13:07:06.528449+00:00 | arXiv | 2,026 | ||||
Robotic manipulation requires models that generate executable actions while anticipating and evaluating their future consequences before physical execution. We present $τ_0$-World Model ($τ_0$-WM), a unified video-action world model that integrates policy learning, video prediction, and action evaluation within a singl... | 2606.01027v1 | [
"Pengfei Zhou",
"Shengcong Chen",
"Di Chen",
"Jiaxu Wang",
"Rongjun Jin",
"Bingwen Zhu",
"Yike Pan",
"Songen Gu",
"Kuanning Wang",
"Shufeng Nan",
"Xingyu Qiu",
"Chenhao Qiu",
"Pu Yang",
"Yunuo Cai",
"Jianxiong Gao",
"Yifan Li",
"Yanwei Fu",
"Xiangyu Yue",
"Zhi Chen",
"Jianlan L... | [
"frontier_2026",
"robot_learning",
"robot_manipulation",
"sim2real",
"world_model"
] | 0 | 2026-06-11T15:17:08.444861+00:00 | all_curated | frontier_selected | [
"Computer Science",
"Engineering",
"cs.RO"
] | 1.812 | null | 0 | [
"action evaluation",
"action-conditioned video simulator",
"hf_daily_papers",
"policy learning",
"re-denoising consistency",
"robot manipulation",
"robot policy learning",
"robot world model",
"robotic manipulation",
"sim-to-real / real robot",
"test-time computation",
"video action model",
... | null | arxiv:2606.01027v1 | http://arxiv.org/abs/2606.01027v1 | https://arxiv.org/pdf/2606.01027v1 | [
"hf_daily_papers"
] | 16.25 | f872bf4801cf0de5576885ce2a23ebaff5107a5a | union:arxiv_recent_3y_score_gte_4,frontier_2026_quality | [
"robotic manipulation language",
"open vocabulary robotic manipulation"
] | [
"arxiv",
"frontier_quality"
] | $τ_0$-WM: A Unified Video-Action World Model for Robotic Manipulation | [
"arxiv_recent_3y_score_gte_4",
"frontier_2026_quality"
] | 2026-06-12T13:05:41.555528+00:00 | arXiv | 2,026 | ||||
We introduce (U)NFV, a modular neural network architecture that generalizes classical finite volume (FV) methods for solving hyperbolic conservation laws. Hyperbolic partial differential equations (PDEs) are challenging to solve, particularly conservation laws whose physically relevant solutions contain shocks and disc... | [
"Nathan Lichtlé",
"Alexi Canesse",
"Zhe Fu",
"HOSSEIN NICK ZINAT MATIN",
"Maria Laura Delle Monache",
"Alexandre M Bayen"
] | [] | null | 2026-06-12T09:42:09.322254+00:00 | all_curated | rejected | [] | null | null | null | [
"Finite volume methods",
"Hyperbolic conservation laws",
"Neural PDE solvers",
"Physics-informed learning",
"Traffic modeling"
] | null | openreview:iclr2026:f8aeb99a6ef8b4c2 | https://openreview.net/forum?id=AhtDnPyfOE | https://openreview.net/pdf?id=AhtDnPyfOE | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | (U)NFV: (Un)Supervised Neural Finite Volume Methods for Solving Hyperbolic PDEs | [
"topconf_all"
] | 2026-06-12T09:43:55.323252+00:00 | ICLR 2026 | 2,026 | ||||||
[
"Liying Lu",
"Raphael Achddou",
"Sabine Süsstrunk"
] | [] | null | 2026-06-12T09:42:14.078881+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:404cf5c3f127e0ea | https://openaccess.thecvf.com/content/CVPR2026/html/Lu_2-Shots_in_the_Dark_Low-Light_Denoising_with_Minimal_Data_Acquisition_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Lu_2-Shots_in_the_Dark_Low-Light_Denoising_with_Minimal_Data_Acquisition_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 2-Shots in the Dark: Low-Light Denoising with Minimal Data Acquisition | [
"topconf_all"
] | 2026-06-12T09:42:14.078892+00:00 | CVPR 2026 | 2,026 | |||||||
[
"Yeliduosi Xiaokaiti",
"Yakun Chang",
"Yang Bai",
"Zhaojun Huang",
"Peiqi Duan",
"Boxin Shi"
] | [] | null | 2026-06-12T09:42:14.013349+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:ed9dc8881121465f | https://openaccess.thecvf.com/content/CVPR2026/html/Xiaokaiti_240FPS_Stereo_Vision_from_Monocular_Mixed_Spikes_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Xiaokaiti_240FPS_Stereo_Vision_from_Monocular_Mixed_Spikes_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 240FPS Stereo Vision from Monocular Mixed Spikes | [
"topconf_all"
] | 2026-06-12T09:42:14.013352+00:00 | CVPR 2026 | 2,026 | |||||||
[
"Miao Shang",
"Xiaopeng Hong"
] | [] | null | 2026-06-12T09:42:16.799202+00:00 | all_curated | rejected | 10.1609/aaai.v40i11.37836 | [] | null | null | null | [] | null | crossref:aaai2026:9d699c880e42bd98 | https://doi.org/10.1609/aaai.v40i11.37836 | [] | 0.5 | union:topconf_all | [
"AAAI 2026",
"Proceedings of the AAAI Conference on Artificial Intelligence"
] | [
"topconf",
"crossref_aaai"
] | 2D Gaussians Spatial Transport for Point-supervised Density Regression | [
"topconf_all"
] | 2026-06-12T09:42:16.799204+00:00 | AAAI 2026 | 2,026 | |||||||
[
"Longlong Yu",
"Wenxi Li",
"Yaoqi Sun",
"Hang Xu",
"Chenggang Yan",
"Yuchen Guo"
] | [] | null | 2026-06-12T09:42:16.762499+00:00 | all_curated | rejected | 10.1609/aaai.v40i21.38855 | [] | null | null | null | [] | null | crossref:aaai2026:1f472a8af22977bd | https://doi.org/10.1609/aaai.v40i21.38855 | [] | 0.5 | union:topconf_all | [
"AAAI 2026",
"Proceedings of the AAAI Conference on Artificial Intelligence"
] | [
"topconf",
"crossref_aaai"
] | 2D-CrossScan Mamba: Enhancing State Space Models with Spatially Consistent Multi-Path 2D Information Propagation | [
"topconf_all"
] | 2026-06-12T09:42:16.762501+00:00 | AAAI 2026 | 2,026 | |||||||
[
"Mosam Dabhi",
"Irhas Gill",
"László A. Jeni",
"Simon Lucey"
] | [] | null | 2026-06-12T09:42:13.950649+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:6e5a82321c8da02b | https://openaccess.thecvf.com/content/CVPR2026/html/Dabhi_2D-LFM_Lifting_Foundation_Model_without_3D_Supervision_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Dabhi_2D-LFM_Lifting_Foundation_Model_without_3D_Supervision_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 2D-LFM: Lifting Foundation Model without 3D Supervision | [
"topconf_all"
] | 2026-06-12T09:42:13.950652+00:00 | CVPR 2026 | 2,026 | |||||||
[
"Caleb Zheng",
"Eli Shlizerman"
] | [] | null | 2026-06-12T09:42:14.075029+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:66743772681dcc90 | https://openaccess.thecvf.com/content/CVPR2026/html/Zheng_2ndMatch_Finetuning_Pruned_Diffusion_Models_via_Second-Order_Jacobian_Matching_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zheng_2ndMatch_Finetuning_Pruned_Diffusion_Models_via_Second-Order_Jacobian_Matching_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 2ndMatch: Finetuning Pruned Diffusion Models via Second-Order Jacobian Matching | [
"topconf_all"
] | 2026-06-12T09:42:14.075032+00:00 | CVPR 2026 | 2,026 | |||||||
Monocular omnidirectional visual odometry (OVO) systems leverage 360-degree cameras to overcome field-of-view limitations of perspective VO systems. However, existing methods, reliant on handcrafted features or photometric objectives, often lack robustness in challenging scenarios, such as aggressive motion and varying... | 2601.02309 | [
"Guo, Xiaopeng",
"Xu, Yinzhe",
"Huang, Huajian",
"Yeung, Sai-Kit"
] | [] | null | 2026-06-12T09:46:19.892643+00:00 | all_curated | rejected | [
"cs.CV"
] | null | null | null | [] | null | hf:icra2026:d2741fccdb189d8f | http://arxiv.org/abs/2601.02309v2 | https://arxiv.org/pdf/2601.02309v2 | [] | 0.75 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | 360DVO: Deep Visual Odometry for Monocular 360-Degree Camera | [
"topconf_all"
] | 2026-06-12T09:46:19.892647+00:00 | ICRA 2026 | 2,026 | |||||
[
"Xinhua Cheng",
"Haiyang Zhou",
"Wangbo Yu",
"Tanghui Jia",
"Bin Lin",
"Yunyang Ge",
"Weiqi Li",
"Li Yuan"
] | [] | null | 2026-06-12T09:42:16.743379+00:00 | all_curated | rejected | 10.1609/aaai.v40i5.37325 | [] | null | null | null | [] | null | crossref:aaai2026:bb6b993d31d810b0 | https://doi.org/10.1609/aaai.v40i5.37325 | [] | 0.5 | union:topconf_all | [
"AAAI 2026",
"Proceedings of the AAAI Conference on Artificial Intelligence"
] | [
"topconf",
"crossref_aaai"
] | 360Explorer: Exploring 4D Controllable World in Panoramic Videos | [
"topconf_all"
] | 2026-06-12T09:42:16.743381+00:00 | AAAI 2026 | 2,026 | |||||||
We present Spatial Region 3D (SR-3D) aware vision-language model that connects single-view 2D images and multi-view 3D data through a shared visual token space. SR-3D supports flexible region prompting, allowing users to annotate regions with bounding boxes, segmentation masks on any frame, or directly in 3D, without t... | [
"An-Chieh Cheng",
"Yang Fu",
"Yukang Chen",
"Zhijian Liu",
"Xiaolong Li",
"Subhashree Radhakrishnan",
"Song Han",
"Yao Lu",
"Jan Kautz",
"Pavlo Molchanov",
"Hongxu Yin",
"Xiaolong Wang",
"Sifei Liu"
] | [] | null | 2026-06-12T09:43:49.097929+00:00 | all_curated | rejected | [] | null | null | null | [
"Spatial Reasoning",
"Vision Language Models"
] | null | openreview:iclr2026:e09440fb7492f1c6 | https://openreview.net/forum?id=GTpf2NuwtR | https://openreview.net/pdf?id=GTpf2NuwtR | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | 3D Aware Region Prompted Vision Language Model | [
"topconf_all"
] | 2026-06-12T09:43:49.097931+00:00 | ICLR 2026 | 2,026 | ||||||
The incorporation of world modeling into manipulation policy learning has pushed the boundary of manipulation performance. However, existing efforts simply model the 2D visual dynamics, which is insufficient for robust manipulation when target tasks involve prominent depth-wise movement. To address this, we present a 3... | 2502.10028 | [
"He, Yuxin",
"Zhang, Ruihao",
"Wu, Xianzu",
"Zhang, Zhiyuan",
"Ding, Cheng",
"Nie, Qiang"
] | [] | null | 2026-06-12T09:46:19.879396+00:00 | all_curated | rejected | [
"cs.CV",
"cs.RO"
] | null | null | null | [] | null | hf:icra2026:475a72f01fa377a1 | http://arxiv.org/abs/2502.10028v4 | https://arxiv.org/pdf/2502.10028v4 | [] | 0.75 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | 3D Dynamics-Aware Manipulation: Endowing Manipulation Policies with 3D Foresight | [
"topconf_all"
] | 2026-06-12T09:46:19.879397+00:00 | ICRA 2026 | 2,026 | |||||
Decentralized Collaborative Simultaneous Localization And Mapping (C-SLAM) techniques often struggle to identify map overlaps due to significant viewpoint variations among robots. Motivated by recent advancements in 3D foundation models, which can register images despite large viewpoint differences, we propose a robust... | 2602.02430 | [
"Lajoie, Pierre-Yves",
"Ramtoula, Benjamin",
"De Martini, Daniele",
"Beltrame, Giovanni"
] | [] | null | 2026-06-12T09:46:19.859994+00:00 | all_curated | rejected | 10.1109/LRA.2025.3609204 | [
"cs.RO"
] | null | null | null | [] | null | hf:icra2026:6e955ad80930b4d8 | http://arxiv.org/abs/2602.02430v1 | https://arxiv.org/pdf/2602.02430v1 | [] | 2.25 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | 3D Foundation Model-Based Loop Closing for Decentralized Collaborative SLAM | [
"topconf_all"
] | 2026-06-12T09:46:19.859998+00:00 | ICRA 2026 | 2,026 | ||||
Vision-language navigation (VLN) requires an agent to traverse complex 3D environments based on natural language instructions, necessitating a thorough scene understanding. While existing works equip agents with various scene representations to enhance spatial awareness, they often neglect the complex 3D geometry and r... | 2605.26500v1 | [
"Jianzhe Gao",
"Rui Liu",
"Wenguan Wang"
] | [
"frontier_2026",
"navigation"
] | 10 | 2026-06-11T15:18:29.709376+00:00 | all_curated | frontier_selected | 10.1109/ICCV51701.2025.00864 | [
"Computer Science",
"cs.CV"
] | 4.17 | null | 0 | [
"VLN",
"s2_citations"
] | null | arxiv:2605.26500v1 | http://arxiv.org/abs/2605.26500v1 | https://arxiv.org/pdf/2605.26500v1 | [
"s2_citations"
] | 16.25 | 778000332206ccc235b570a31c46c94c28551ae4 | union:arxiv_recent_3y_score_gte_4,frontier_2026_quality | [
"vision language navigation"
] | [
"arxiv",
"frontier_quality"
] | 3D Gaussian Map with Open-Set Semantic Grouping for Vision-Language Navigation | [
"arxiv_recent_3y_score_gte_4",
"frontier_2026_quality"
] | 2026-06-12T13:05:42.179843+00:00 | arXiv | 2,026 | |||
[
"Mingyun Jeong",
"Seongro Yoon",
"Francois Bremond",
"Donghyeon Cho"
] | [] | null | 2026-06-12T09:42:13.941011+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:7d5eb59c24deee98 | https://openaccess.thecvf.com/content/CVPR2026/html/Jeong_3D_Gaussian_Splatting_at_Arbitrary_Resolutions_with_Compact_Proxy_Anchors_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Jeong_3D_Gaussian_Splatting_at_Arbitrary_Resolutions_with_Compact_Proxy_Anchors_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 3D Gaussian Splatting at Arbitrary Resolutions with Compact Proxy Anchors | [
"topconf_all"
] | 2026-06-12T09:42:13.941014+00:00 | CVPR 2026 | 2,026 | |||||||
[
"Yijia Guo",
"Tong Hu",
"Liwen Hu",
"Lei Ma",
"Tiejun Huang"
] | [] | null | 2026-06-12T09:42:13.913910+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:afdcc6648b6c1da8 | https://openaccess.thecvf.com/content/CVPR2026/html/Guo_3D_Gaussian_Splatting_from_Unposed_Spike_Stream_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Guo_3D_Gaussian_Splatting_from_Unposed_Spike_Stream_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 3D Gaussian Splatting from Unposed Spike Stream | [
"topconf_all"
] | 2026-06-12T09:42:13.913912+00:00 | CVPR 2026 | 2,026 | |||||||
[
"Takeshi Noda",
"Yu-Shen Liu",
"Zhizhong Han"
] | [] | null | 2026-06-12T09:42:14.139785+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:b52374d6ae777832 | https://openaccess.thecvf.com/content/CVPR2026/html/Noda_3D_Gaussian_Splatting_with_Self-Constrained_Priors_for_High_Fidelity_Surface_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Noda_3D_Gaussian_Splatting_with_Self-Constrained_Priors_for_High_Fidelity_Surface_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 3D Gaussian Splatting with Self-Constrained Priors for High Fidelity Surface Reconstruction | [
"topconf_all"
] | 2026-06-12T09:42:14.139787+00:00 | CVPR 2026 | 2,026 | |||||||
Embodied AI and robotic systems increasingly depend on scalable, diverse, and physically grounded 3D content for simulation-based training and real-world deployment. While 3D generative modeling has advanced rapidly, embodied applications impose requirements far beyond visual realism: generated objects must carry kinem... | 2604.26509v3 | [
"Tianwei Ye",
"Yifan Mao",
"Minwen Liao",
"Jian Liu",
"Chunchao Guo",
"Dazhao Du",
"Quanxin Shou",
"Fangqi Zhu",
"Song Guo"
] | [
"embodied_agent",
"robot_learning",
"sim2real"
] | null | 2026-06-11T15:15:05.775203+00:00 | all_curated | likely_embodied_ai | [
"cs.RO",
"cs.CV"
] | null | null | null | [
"embodied AI",
"robot learning",
"sim-to-real / real robot"
] | null | arxiv:2604.26509v3 | http://arxiv.org/abs/2604.26509v3 | https://arxiv.org/pdf/2604.26509v3 | [] | 18.95 | union:arxiv_recent_3y_score_gte_4 | [
"embodied AI robot",
"embodied agent robotics",
"VLN embodied",
"world model robotics embodied"
] | [
"arxiv"
] | 3D Generation for Embodied AI and Robotic Simulation: A Survey | [
"arxiv_recent_3y_score_gte_4"
] | 2026-06-11T15:22:09.486725+00:00 | arXiv | 2,026 | |||||
[
"Wang, Zhanwei",
"Huaijin, Chen",
"Zaidi, Syeda Shadab Zehra",
"Roels, Ellen",
"Cools, Hendrik",
"Vanderborght, Bram",
"Terryn, Seppe"
] | [] | null | 2026-06-12T09:46:19.871267+00:00 | all_curated | rejected | [] | null | null | null | [] | null | hf:icra2026:c070efbaf495794d | [] | 0.5 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | 3D Printable Crease-Free Origami Vacuum Bending Actuators for Soft Robots | [
"topconf_all"
] | 2026-06-12T09:46:19.871269+00:00 | ICRA 2026 | 2,026 | |||||||||
Robotics and automation are key enablers to increase throughput in ongoing conservation efforts across various threatened ecosystems. Cataloguing, digitisation, husbandry, and similar activities require the ability to interact with delicate, fragile samples without damaging them. Additionally, learning-based solutions ... | 2509.17389 | [
"Liow, Lois",
"Milford, Jonty",
"Uygun, Emre",
"Farinha, Andre",
"Viswanathan, VinothKumar",
"Pinskier, Joshua",
"Howard, David"
] | [] | null | 2026-06-12T09:46:19.926349+00:00 | all_curated | rejected | [
"cs.RO"
] | null | null | null | [] | null | hf:icra2026:39141adf0aa8b2a7 | http://arxiv.org/abs/2509.17389v1 | https://arxiv.org/pdf/2509.17389v1 | [] | 0.75 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | 3D Printable Soft Liquid Metal Sensors for Delicate Manipulation Tasks | [
"topconf_all"
] | 2026-06-12T09:46:19.926351+00:00 | ICRA 2026 | 2,026 | |||||
We introduce an elastic-driven self-folding approach that fabricates robots directly from flat 3D-printed conductive PLA nets. Elastic bands routed through printed hooks store energy that folds the sheet into programmed 3D geometries, while the flat state allows accurate placement of electronics and magnets before depl... | 2605.04757 | [
"Ge, Gaolin",
"Yang, Qifeng",
"Lu, Haoran",
"Cheng, Tingyu",
"Nisser, Martin",
"Luo, Yiyue"
] | [] | null | 2026-06-12T09:46:19.886787+00:00 | all_curated | rejected | [
"cs.RO",
"cs.HC"
] | null | null | null | [] | null | hf:icra2026:d75966e79b2699c2 | http://arxiv.org/abs/2605.04757v1 | https://arxiv.org/pdf/2605.04757v1 | [] | 0.75 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | 3D Printing of Passively Actuated Self-Folding Robots with Integrated Functional Modules | [
"topconf_all"
] | 2026-06-12T09:46:19.886789+00:00 | ICRA 2026 | 2,026 | |||||
[
"Carlisle, Nicholas",
"Nock, Volker",
"Williams, Martin",
"Whitby, Catherine",
"Chen, Jack L Y",
"Avci, Ebubekir"
] | [] | null | 2026-06-12T09:46:19.872197+00:00 | all_curated | rejected | [] | null | null | null | [] | null | hf:icra2026:a57abaab6d7e882d | [] | 0.5 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | 3D Robotic Control of Micro-Scale Optical Swarms at an Interface | [
"topconf_all"
] | 2026-06-12T09:46:19.872199+00:00 | ICRA 2026 | 2,026 | |||||||||
[
"Xu, Zehui",
"Xu, Xinyue",
"Ceron, Steven"
] | [] | null | 2026-06-12T09:46:19.886904+00:00 | all_curated | rejected | [] | null | null | null | [] | null | hf:icra2026:8fb214aa3ab77ce2 | [] | 0.5 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | 3D Robotic Swarmalators That Reconfigure, Navigate, and Avoid Obstacles | [
"topconf_all"
] | 2026-06-12T09:46:19.886906+00:00 | ICRA 2026 | 2,026 | |||||||||
We present 3DScenePrompt, a framework for camera-controllable video generation that maintains scene consistency when extending arbitrary-length input videos along user-specified trajectories. Unlike existing video generative methods limited to conditioning on a single image or just a few frames, we introduce a dual spa... | [
"JoungBin Lee",
"Jaewoo Jung",
"Jisang Han",
"Takuya Narihira",
"Kazumi Fukuda",
"Junyoung Seo",
"Sunghwan Hong",
"Yuki Mitsufuji",
"Seungryong Kim"
] | [] | null | 2026-06-12T09:43:48.991308+00:00 | all_curated | rejected | [] | null | null | null | [
"Scene-Consistent Video Generation; Camera-Controllable Video Generation; Video Diffusion Models;"
] | null | openreview:iclr2026:3769f7b480ba6dae | https://openreview.net/forum?id=3XxoBwMusJ | https://openreview.net/pdf?id=3XxoBwMusJ | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | 3D Scene Prompting for Scene-Consistent Camera-Controllable Video Generation | [
"topconf_all"
] | 2026-06-12T09:43:48.991310+00:00 | ICLR 2026 | 2,026 | ||||||
[
"Oindrila Saha",
"Vojtech Krs",
"Radomir Mech",
"Subhransu Maji",
"Matheus Gadelha",
"Kevin Blackburn-Matzen"
] | [] | null | 2026-06-12T09:42:13.936733+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:2184078e9b666305 | https://openaccess.thecvf.com/content/CVPR2026/html/Saha_3D_Space_as_a_Scratchpad_for_Editable_Text-to-Image_Generation_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Saha_3D_Space_as_a_Scratchpad_for_Editable_Text-to-Image_Generation_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 3D Space as a Scratchpad for Editable Text-to-Image Generation | [
"topconf_all"
] | 2026-06-12T09:42:13.936735+00:00 | CVPR 2026 | 2,026 | |||||||
[
"Yang, Seungun",
"Nguyen, Kim Tien",
"Kee, Hyeonwoo",
"Lee, Hyoryong",
"Kim, Jayoung",
"Park, Sukho"
] | [] | null | 2026-06-12T09:46:19.894976+00:00 | all_curated | rejected | [] | null | null | null | [] | null | hf:icra2026:d831723a409510c5 | [] | 0.5 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | 3D Targeting of a Magnetic Particle in Blood Vessels Using Field-Free Points in an Open-Type Electromagnetic Actuation System | [
"topconf_all"
] | 2026-06-12T09:46:19.894978+00:00 | ICRA 2026 | 2,026 | |||||||||
[
"Ryousuke Yamada",
"Kohsuke Ide",
"Yoshihiro Fukuhara",
"Hirokatsu Kataoka",
"Gilles Puy",
"Andrei Bursuc",
"Yuki M. Asano"
] | [] | null | 2026-06-12T09:42:13.925515+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:a6754901fe31d183 | https://openaccess.thecvf.com/content/CVPR2026/html/Yamada_3D_sans_3D_Scans_Scalable_Pre-training_from_Video-Generated_Point_Clouds_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Yamada_3D_sans_3D_Scans_Scalable_Pre-training_from_Video-Generated_Point_Clouds_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 3D sans 3D Scans: Scalable Pre-training from Video-Generated Point Clouds | [
"topconf_all"
] | 2026-06-12T09:42:13.925520+00:00 | CVPR 2026 | 2,026 | |||||||
Surface defects are a primary source of yield loss in manufacturing, yet existing anomaly detection methods often fail in real-world deployment due to limited and unrepresentative datasets. To overcome this, we introduce 3D-ADAM, a 3D Anomaly Detection in Additive Manufacturing dataset, that is the first large-scale, i... | 2507.07838 | [
"McHard, Paul Matthew",
"Audonnet, Florent P.",
"Summerell, Oliver",
"Andraos, Sebastian",
"Henderson, Paul",
"Aragon-Camarasa, Gerardo"
] | [] | null | 2026-06-12T09:46:19.884726+00:00 | all_curated | rejected | [
"cs.CV"
] | null | null | null | [] | null | hf:icra2026:7934bdd0a0060967 | http://arxiv.org/abs/2507.07838v2 | https://arxiv.org/pdf/2507.07838v2 | [] | 0.75 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | 3D-ADAM: A Dataset for 3D Anomaly Detection in Additive Manufacturing | [
"topconf_all"
] | 2026-06-12T09:46:19.884728+00:00 | ICRA 2026 | 2,026 | |||||
[
"Yuanmin Huang",
"Wenxuan Li",
"Mi Zhang",
"Xiaohan Zhang",
"Xiaoyu You",
"Min Yang"
] | [] | null | 2026-06-12T09:42:16.800468+00:00 | all_curated | rejected | 10.1609/aaai.v40i7.37434 | [] | null | null | null | [] | null | crossref:aaai2026:9a73a7e5fd571786 | https://doi.org/10.1609/aaai.v40i7.37434 | [] | 0.5 | union:topconf_all | [
"AAAI 2026",
"Proceedings of the AAAI Conference on Artificial Intelligence"
] | [
"topconf",
"crossref_aaai"
] | 3D-ANC: Adaptive Neural Collapse for Robust 3D Point Cloud Recognition | [
"topconf_all"
] | 2026-06-12T09:42:16.800470+00:00 | AAAI 2026 | 2,026 | |||||||
[
"Zhixue Fang",
"Xu He",
"Songlin Tang",
"Haoxian Zhang",
"Qingfeng Li",
"Xiaoqiang Liu",
"Pengfei Wan",
"Kun Gai"
] | [] | null | 2026-06-12T09:42:14.056702+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:17e010723a276db2 | https://openaccess.thecvf.com/content/CVPR2026/html/Fang_3D-Aware_Implicit_Motion_Control_for_View-Adaptive_Human_Video_Generation_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Fang_3D-Aware_Implicit_Motion_Control_for_View-Adaptive_Human_Video_Generation_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 3D-Aware Implicit Motion Control for View-Adaptive Human Video Generation | [
"topconf_all"
] | 2026-06-12T09:42:14.056714+00:00 | CVPR 2026 | 2,026 | |||||||
[
"Xiaoye Wang",
"Chen Tang",
"Xiangyu Yue",
"Wei-Hong Li"
] | [] | null | 2026-06-12T09:42:13.876115+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:577f8c274042dad9 | https://openaccess.thecvf.com/content/CVPR2026/html/Wang_3D-Aware_Multi-Task_Learning_with_Cross-View_Correlations_for_Dense_Scene_Understanding_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Wang_3D-Aware_Multi-Task_Learning_with_Cross-View_Correlations_for_Dense_Scene_Understanding_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 3D-Aware Multi-Task Learning with Cross-View Correlations for Dense Scene Understanding | [
"topconf_all"
] | 2026-06-12T09:42:13.876117+00:00 | CVPR 2026 | 2,026 | |||||||
[
"Ze-Xin Yin",
"Liu Liu",
"Xinjie Wang",
"Wei Sui",
"Zhizhong Su",
"Jian Yang",
"Jin Xie"
] | [] | null | 2026-06-12T09:42:14.025409+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:f6b41e955d5f32d7 | https://openaccess.thecvf.com/content/CVPR2026/html/Yin_3D-Fixer_Coarse-to-Fine_In-place_Completion_for_3D_Scenes_from_a_Single_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Yin_3D-Fixer_Coarse-to-Fine_In-place_Completion_for_3D_Scenes_from_a_Single_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 3D-Fixer: Coarse-to-Fine In-place Completion for 3D Scenes from a Single Image | [
"topconf_all"
] | 2026-06-12T09:42:14.025411+00:00 | CVPR 2026 | 2,026 | |||||||
[
"Chushan Zhang",
"Ruihan Lu",
"Jinguang Tong",
"Yikai Wang",
"Hongdong Li"
] | [] | null | 2026-06-12T09:42:13.938911+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:26212921fe7ed799 | https://openaccess.thecvf.com/content/CVPR2026/html/Zhang_3D-IDE_3D_Implicit_Depth_Emergent_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zhang_3D-IDE_3D_Implicit_Depth_Emergent_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 3D-IDE: 3D Implicit Depth Emergent | [
"topconf_all"
] | 2026-06-12T09:42:13.938913+00:00 | CVPR 2026 | 2,026 | |||||||
[
"Maria Parelli",
"Michael Oechsle",
"Michael Niemeyer",
"Federico Tombari",
"Andreas Geiger"
] | [] | null | 2026-06-12T09:42:14.043143+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:825c7fb1f638fd62 | https://openaccess.thecvf.com/content/CVPR2026/html/Parelli_3D-LATTE_Latent_Space_3D_Editing_from_Textual_Instructions_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Parelli_3D-LATTE_Latent_Space_3D_Editing_from_Textual_Instructions_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 3D-LATTE: Latent Space 3D Editing from Textual Instructions | [
"topconf_all"
] | 2026-06-12T09:42:14.043146+00:00 | CVPR 2026 | 2,026 | |||||||
[
"Agastya Kalra",
"Tim Salzmann",
"Guy Stoppi",
"Dmitrii Marin",
"Rishav Agarwal",
"Vage Taamazyan",
"Martin Bokeloh",
"Stefan Hinterstoisser",
"Anton Boykov",
"Alberto Dall'Olio",
"Pravin Dangol",
"Kartik Venkataraman",
"Huaijin Chen"
] | [] | null | 2026-06-12T09:42:14.081690+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:7e2f72a4705f5bbc | https://openaccess.thecvf.com/content/CVPR2026/html/Kalra_3D-Object_Perception_Transformer_3PT_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Kalra_3D-Object_Perception_Transformer_3PT_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 3D-Object Perception Transformer (3PT) | [
"topconf_all"
] | 2026-06-12T09:42:14.081693+00:00 | CVPR 2026 | 2,026 | |||||||
Large Language Models are increasingly integrated as the cognitive core of 3D embodied agents to enable complex environmental reasoning. However, these agents tend to inherit the critical flaw of hallucination, often failing to ground their responses to their 3D view. While Visual Contrastive Decoding (VCD) is a powerf... | [
"Makanjuola Adekunmi Ogunleye",
"Eman Abdelrahman",
"Ismini Lourentzou"
] | [
"embodied_agent"
] | null | 2026-06-12T09:42:14.114181+00:00 | all_curated | likely_embodied_ai | [] | null | null | null | [
"embodied AI"
] | null | cvf:cvpr2026:d2fe24bc9d965114 | https://openaccess.thecvf.com/content/CVPR2026/html/Ogunleye_3D-VCD_Hallucination_Mitigation_in_3D-LLM_Embodied_Agents_through_Visual_Contrastive_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Ogunleye_3D-VCD_Hallucination_Mitigation_in_3D-LLM_Embodied_Agents_through_Visual_Contrastive_CVPR_2026_paper.pdf | [] | 13.25 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 3D-VCD: Hallucination Mitigation in 3D-LLM Embodied Agents through Visual Contrastive Decoding | [
"topconf_all"
] | 2026-06-12T12:23:38.823366+00:00 | CVPR 2026 | 2,026 | ||||||
Large multimodal models are increasingly used as the reasoning core of embodied agents operating in 3D environments, yet they remain prone to hallucinations that can produce unsafe and ungrounded decisions. Existing inference-time hallucination mitigation methods largely target 2D vision-language settings and do not tr... | 2604.08645v1 | [
"Makanjuola Ogunleye",
"Eman Abdelrahman",
"Ismini Lourentzou"
] | [
"embodied_agent",
"frontier_2026"
] | 1 | 2026-06-11T15:19:09.643848+00:00 | all_curated | frontier_selected | [
"Computer Science",
"cs.AI",
"cs.CV",
"cs.LG",
"cs.RO"
] | 4.431 | null | 0 | [
"3D reasoning",
"3D scene graph",
"embodied AI",
"embodied agents",
"geometric perturbations",
"hallucination mitigation",
"hf_daily_papers",
"hf_upvotes",
"inference-time decoding",
"object-centric representations",
"s2_citations",
"scene graph",
"semantic perturbations",
"visual contrast... | null | arxiv:2604.08645v1 | http://arxiv.org/abs/2604.08645v1 | https://arxiv.org/pdf/2604.08645v1 | [
"s2_citations",
"hf_upvotes",
"hf_daily_papers"
] | 16.25 | d1c3cf3455fb84358c2a6b322776c281d8213777 | union:arxiv_recent_3y_score_gte_4,frontier_2026_quality | [
"VLN embodied"
] | [
"arxiv",
"frontier_quality"
] | 3D-VCD: Hallucination Mitigation in 3D-LLM Embodied Agents through Visual Contrastive Decoding | [
"arxiv_recent_3y_score_gte_4",
"frontier_2026_quality"
] | 2026-06-12T13:05:42.851751+00:00 | arXiv | 2,026 | ||||
Vision-based reinforcement learning can benefit from object-centric scene representation, which factorizes the visual observation into individual objects and their attributes, such as color, shape, size, and position. While such object-centric representations can extract components that generalize well for various mult... | [
"Sungbin Mun",
"Younghwan Lee",
"Cheol-Hui Min",
"Mineui Hong",
"Young Min Kim"
] | [] | null | 2026-06-12T09:43:49.000104+00:00 | all_curated | rejected | [] | null | null | null | [
"compositional generalization",
"goal-conditioned reinforcement learning",
"object-centric learning"
] | null | openreview:iclr2026:b18118468c5f4df8 | https://openreview.net/forum?id=GE0IFoDx8a | https://openreview.net/pdf?id=GE0IFoDx8a | [] | 2.25 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | 3D-aware Disentangled Representation for Compositional Reinforcement Learning | [
"topconf_all"
] | 2026-06-12T09:43:49.000108+00:00 | ICLR 2026 | 2,026 | ||||||
[
"Yunhong He",
"Zhengqing Yuan",
"Zhengzhong Tu",
"Yanfang Ye",
"Lichao Sun"
] | [] | null | 2026-06-12T09:42:16.768637+00:00 | all_curated | rejected | 10.1609/aaai.v40i48.42351 | [] | null | null | null | [] | null | crossref:aaai2026:71f4e42c34704db2 | https://doi.org/10.1609/aaai.v40i48.42351 | [] | 0.5 | union:topconf_all | [
"AAAI 2026",
"Proceedings of the AAAI Conference on Artificial Intelligence"
] | [
"topconf",
"crossref_aaai"
] | 3D4D: An Interactive, Editable, 4D World Model via 3D Video Generation | [
"topconf_all"
] | 2026-06-12T09:42:16.768639+00:00 | AAAI 2026 | 2,026 | |||||||
Molecular representations (MRs) that capture 3D conformations are critical for applications such as reaction prediction, drug design, and material discovery. Yet despite the rapid development of molecular representation models, there is no comprehensive benchmark to evaluate their treatment of 3D conformational informa... | [
"Xi Wang",
"Yang Zhang",
"Yingjia Zhang",
"Yejia Cai",
"Shenji Wan"
] | [] | null | 2026-06-12T09:43:49.033972+00:00 | all_curated | rejected | [] | null | null | null | [
"AI for Science",
"Molecule Benchmark"
] | null | openreview:iclr2026:c4c61c043b0dcba0 | https://openreview.net/forum?id=JAb0y8lkqL | https://openreview.net/pdf?id=JAb0y8lkqL | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | 3DCS: Datasets and Benchmark for Evaluating Conformational Sensitivity in Molecular Representations | [
"topconf_all"
] | 2026-06-12T09:43:49.033973+00:00 | ICLR 2026 | 2,026 | ||||||
[
"Sha, Xuanmeng",
"Zhang, Liyun",
"Mashita, Tomohiro",
"Chiba, Naoya",
"Uranishi, Yuki"
] | [] | null | 2026-06-12T09:46:19.887713+00:00 | all_curated | rejected | [] | null | null | null | [] | null | hf:icra2026:20ea6ef8f45140b5 | [] | 0.5 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | 3DFacePolicy: Speech-Driven 3D Facial Animation Based on Diffusion Policy | [
"topconf_all"
] | 2026-06-12T09:46:19.887715+00:00 | ICRA 2026 | 2,026 | |||||||||
3D Gaussian Splatting (3DGS) achieves an appealing balance between rendering quality and efficiency, but relies on approximating 3D Gaussians as 2D projections—an assumption that degrades accuracy, especially under generic large field-of-view (FoV) cameras. Despite recent extensions, no prior work has simultaneously ac... | [
"Zixun Huang",
"Cho-Ying Wu",
"Yuliang Guo",
"Xinyu Huang",
"Liu Ren"
] | [] | null | 2026-06-12T09:43:49.095560+00:00 | all_curated | rejected | [] | null | null | null | [
"Differentiable Rendering",
"Neural Reconstruction",
"Novel View Synthesis",
"Radiance Fields",
"Volumetric Rendering"
] | null | openreview:iclr2026:bb0c121984c1b509 | https://openreview.net/forum?id=4voMNlRWI7 | https://openreview.net/pdf?id=4voMNlRWI7 | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | 3DGEER: 3D Gaussian Rendering Made Exact and Efficient for Generic Cameras | [
"topconf_all"
] | 2026-06-12T09:43:49.095561+00:00 | ICLR 2026 | 2,026 | ||||||
[
"Rizvi, Syed Muhammad Raza",
"Weng, Huaiyuan",
"Yeum, Chul Min"
] | [] | null | 2026-06-12T09:46:19.877945+00:00 | all_curated | rejected | [] | null | null | null | [] | null | hf:icra2026:9e06011fc9c0a746 | [] | 0.5 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | 3DGS-Holo-Inspector: A Mixed Reality UAV Controller with 3D Gaussian Splatting Localization for Infrastructure Inspection | [
"topconf_all"
] | 2026-06-12T09:46:19.877946+00:00 | ICRA 2026 | 2,026 | |||||||||
[
"Yuan, Hengyi",
"Cheng, Zesheng",
"Chen, Huiru",
"Shixuan, Wang"
] | [] | null | 2026-06-12T09:46:19.866078+00:00 | all_curated | rejected | [] | null | null | null | [] | null | hf:icra2026:65714b980adae036 | [] | 0.5 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | 3DME: Dual-Branch Encoder with Progressive Masking for 3D Medical Foundation Encoding Model | [
"topconf_all"
] | 2026-06-12T09:46:19.866079+00:00 | ICRA 2026 | 2,026 | |||||||||
[
"Zhicheng Liang",
"Haoyi Yu",
"Boyan Li",
"Dayou Zhang",
"Zijian Cao",
"Tianyi Gong",
"Junhua Liu",
"Shuguang Cui",
"Fangxin Wang"
] | [] | null | 2026-06-12T09:42:14.128404+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:2cf3799fb6b36e46 | https://openaccess.thecvf.com/content/CVPR2026/html/Liang_3DReflecNet_A_Large-Scale_Dataset_for_3D_Reconstruction_of_Reflective_Transparent_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Liang_3DReflecNet_A_Large-Scale_Dataset_for_3D_Reconstruction_of_Reflective_Transparent_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 3DReflecNet: A Large-Scale Dataset for 3D Reconstruction of Reflective, Transparent, and Low-Texture Objects | [
"topconf_all"
] | 2026-06-12T09:42:14.128406+00:00 | CVPR 2026 | 2,026 | |||||||
The sparse unordered structure of point clouds causes unnecessary computation and energy consumption in deep models. Conventionally, the Transformer architecture is leveraged to model global relationships in point clouds, however, its quadratic complexity restricts scalability. Although the Mamba architecture enables e... | [
"Zhiming Zhou",
"Yong He",
"Qiaoyun Wu",
"Chaoxu Mu",
"Ajmal Saeed Mian"
] | [] | null | 2026-06-12T09:43:49.062393+00:00 | all_curated | rejected | [] | null | null | null | [
"Point Cloud Analysis",
"Spiking Local Offset Attention",
"Spiking Mamba Block",
"Spiking neural network"
] | null | openreview:iclr2026:41120dc42d229606 | https://openreview.net/forum?id=KkoS6y0pHP | https://openreview.net/pdf?id=KkoS6y0pHP | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | 3DSMT: A Hybrid Spiking Mamba-Transformer for Point Cloud Analysis | [
"topconf_all"
] | 2026-06-12T09:43:49.062394+00:00 | ICLR 2026 | 2,026 | ||||||
[
"Zhiguo Lu",
"Jianwen Lou",
"Mingjun Ma",
"Hairong Jin",
"Youyi Zheng",
"Kun Zhou"
] | [] | null | 2026-06-12T09:42:16.722874+00:00 | all_curated | rejected | 10.1609/aaai.v40i9.37702 | [] | null | null | null | [] | null | crossref:aaai2026:ecbe9a8508e20a49 | https://doi.org/10.1609/aaai.v40i9.37702 | [] | 0.5 | union:topconf_all | [
"AAAI 2026",
"Proceedings of the AAAI Conference on Artificial Intelligence"
] | [
"topconf",
"crossref_aaai"
] | 3DTeethSAM: Taming SAM2 for 3D Teeth Segmentation | [
"topconf_all"
] | 2026-06-12T09:42:16.722875+00:00 | AAAI 2026 | 2,026 | |||||||
[
"Hongcan Xiao",
"Xinyue Xiao",
"Yilin Wang",
"Yue Zhang",
"Yonggang Qi"
] | [] | null | 2026-06-12T09:42:13.961136+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:9f3e26a101c50b56 | https://openaccess.thecvf.com/content/CVPR2026/html/Xiao_3DrawAgent_Teaching_LLM_to_Draw_in_3D_with_Early_Contrastive_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Xiao_3DrawAgent_Teaching_LLM_to_Draw_in_3D_with_Early_Contrastive_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 3DrawAgent: Teaching LLM to Draw in 3D with Early Contrastive Experience | [
"topconf_all"
] | 2026-06-12T09:42:13.961139+00:00 | CVPR 2026 | 2,026 | |||||||
[
"Minchong Chen",
"Xiaoyun Yuan",
"Junzhe Wan",
"Jianing Zhang",
"Jun Zhang"
] | [] | null | 2026-06-12T09:42:13.983297+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:08fde7ae739b8c62 | https://openaccess.thecvf.com/content/CVPR2026/html/Chen_3M-TI_High-Quality_Mobile_Thermal_Imaging_via_Calibration-free_Multi-Camera_Cross-Modal_Diffusion_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Chen_3M-TI_High-Quality_Mobile_Thermal_Imaging_via_Calibration-free_Multi-Camera_Cross-Modal_Diffusion_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 3M-TI: High-Quality Mobile Thermal Imaging via Calibration-free Multi-Camera Cross-Modal Diffusion | [
"topconf_all"
] | 2026-06-12T09:42:13.983308+00:00 | CVPR 2026 | 2,026 | |||||||
[
"Junsheng Zhou",
"Zhifan Yang",
"Liang Han",
"Wenyuan Zhang",
"Kanle Shi",
"Shenkun Xu",
"Yu-Shen Liu"
] | [] | null | 2026-06-12T09:42:14.081439+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:bf194338f9c609fa | https://openaccess.thecvf.com/content/CVPR2026/html/Zhou_4C4D_4_Camera_4D_Gaussian_Splatting_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zhou_4C4D_4_Camera_4D_Gaussian_Splatting_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 4C4D: 4 Camera 4D Gaussian Splatting | [
"topconf_all"
] | 2026-06-12T09:42:14.081442+00:00 | CVPR 2026 | 2,026 | |||||||
[
"Jiaxun Guo",
"Wentao Fan",
"Manar Amayri",
"Nizar Bouguila"
] | [] | null | 2026-06-12T09:42:13.924252+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:74767f1da8d45110 | https://openaccess.thecvf.com/content/CVPR2026/html/Guo_4D_Local_Modeling_Toward_Dynamic_Global_Perception_for_Ambiguity-free_Rotation-Invariant_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Guo_4D_Local_Modeling_Toward_Dynamic_Global_Perception_for_Ambiguity-free_Rotation-Invariant_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 4D Local Modeling Toward Dynamic Global Perception for Ambiguity-free Rotation-Invariant Point Cloud Analysis | [
"topconf_all"
] | 2026-06-12T09:42:13.924255+00:00 | CVPR 2026 | 2,026 | |||||||
[
"Kirill Mazur",
"Marwan Taher",
"Andrew J. Davison"
] | [] | null | 2026-06-12T09:42:14.143109+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:7e2331c16c933b55 | https://openaccess.thecvf.com/content/CVPR2026/html/Mazur_4D_Primitive-Mache_Glueing_Primitives_for_Persistent_4D_Scene_Reconstruction_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Mazur_4D_Primitive-Mache_Glueing_Primitives_for_Persistent_4D_Scene_Reconstruction_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 4D Primitive-Mache: Glueing Primitives for Persistent 4D Scene Reconstruction | [
"topconf_all"
] | 2026-06-12T09:42:14.143113+00:00 | CVPR 2026 | 2,026 | |||||||
[
"Xiao, Renxiang",
"Zhang, Yuanfan",
"Liu, Wei",
"Dong, Guangzhong",
"Lou, Yunjiang",
"Hu, Liang"
] | [] | null | 2026-06-12T09:46:19.907255+00:00 | all_curated | rejected | [] | null | null | null | [] | null | hf:icra2026:2fdfb0bbc3518c59 | [] | 0.5 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | 4D Radar Diffusion with Adaptive Visual-Aided Condition for Point Cloud Enhancement | [
"topconf_all"
] | 2026-06-12T09:46:19.907257+00:00 | ICRA 2026 | 2,026 | |||||||||
[
"Woong Oh Cho",
"In Cho",
"Seoha Kim",
"Jeongmin Bae",
"Youngjung Uh",
"Seon Joo Kim"
] | [] | null | 2026-06-12T09:42:16.761772+00:00 | all_curated | rejected | 10.1609/aaai.v40i5.37332 | [] | null | null | null | [] | null | crossref:aaai2026:2291b74f6d3011c6 | https://doi.org/10.1609/aaai.v40i5.37332 | [] | 0.5 | union:topconf_all | [
"AAAI 2026",
"Proceedings of the AAAI Conference on Artificial Intelligence"
] | [
"topconf",
"crossref_aaai"
] | 4D Scaffold Gaussian Splatting with Dynamic-Aware Anchor Growing for Efficient and High-Fidelity Dynamic Scene Reconstruction | [
"topconf_all"
] | 2026-06-12T09:42:16.761774+00:00 | AAAI 2026 | 2,026 | |||||||
[
"Chiao-An Yang",
"Ryo Hachiuma",
"Sifei Liu",
"Subhashree Radhakrishnan",
"Raymond A. Yeh",
"Yu-Chiang Frank Wang",
"Min-Hung Chen"
] | [] | null | 2026-06-12T09:42:14.083312+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:124ad137598b06d7 | https://openaccess.thecvf.com/content/CVPR2026/html/Yang_4D-RGPT_Toward_Region-level_4D_Understanding_via_Perceptual_Distillation_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Yang_4D-RGPT_Toward_Region-level_4D_Understanding_via_Perceptual_Distillation_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 4D-RGPT: Toward Region-level 4D Understanding via Perceptual Distillation | [
"topconf_all"
] | 2026-06-12T09:42:14.083314+00:00 | CVPR 2026 | 2,026 | |||||||
[
"Jin Lyu",
"Liang An",
"Pujin Cheng",
"Yebin Liu",
"Xiaoying Tang"
] | [] | null | 2026-06-12T09:42:13.947125+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:87cf88ad65d4a542 | https://openaccess.thecvf.com/content/CVPR2026/html/Lyu_4DEquine_Disentangling_Motion_and_Appearance_for_4D_Equine_Reconstruction_from_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Lyu_4DEquine_Disentangling_Motion_and_Appearance_for_4D_Equine_Reconstruction_from_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 4DEquine: Disentangling Motion and Appearance for 4D Equine Reconstruction from Monocular Video | [
"topconf_all"
] | 2026-06-12T09:42:13.947130+00:00 | CVPR 2026 | 2,026 | |||||||
[
"Seokju Cho",
"Abhishek Badki",
"Hang Su",
"Jindong Jiang",
"Ziyao Zeng",
"Seungryong Kim",
"Sifei Liu",
"Orazio Gallo"
] | [] | null | 2026-06-12T09:42:14.111494+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:545be1712860b3f6 | https://openaccess.thecvf.com/content/CVPR2026/html/Cho_4DP-QA_Scalable_QA_for_4D_Perception_in_Vision_Language_Models_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Cho_4DP-QA_Scalable_QA_for_4D_Perception_in_Vision_Language_Models_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 4DP-QA: Scalable QA for 4D Perception in Vision Language Models | [
"topconf_all"
] | 2026-06-12T09:42:14.111496+00:00 | CVPR 2026 | 2,026 | |||||||
Place recognition is crucial for loop closure detection and global localization in robotics. Although mainstream algorithms typically rely on cameras and LiDAR, these sensors are susceptible to adverse weather conditions. Fortunately, the recently developed 4D millimeter-wave radar (4D radar) offers a promising solutio... | 2603.26206 | [
"Huang, Ningyuan",
"Li, Zhiheng",
"Fang, Zheng"
] | [] | null | 2026-06-12T09:46:19.919868+00:00 | all_curated | rejected | [
"cs.CV",
"cs.RO"
] | null | null | null | [] | null | hf:icra2026:bd7d9a6258f24f79 | http://arxiv.org/abs/2603.26206v1 | https://arxiv.org/pdf/2603.26206v1 | [] | 0.75 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | 4DRaL: Bridging 4D Radar with LiDAR for Place Recognition Using Knowledge Distillation | [
"topconf_all"
] | 2026-06-12T09:46:19.919869+00:00 | ICRA 2026 | 2,026 | |||||
[
"Renjie Wu",
"Hongdong Li",
"Jose M. Alvarez",
"Miaomiao Liu"
] | [] | null | 2026-06-12T09:42:14.109504+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:def0ac63f35b7e77 | https://openaccess.thecvf.com/content/CVPR2026/html/Wu_4DSurf_High-Fidelity_Dynamic_Scene_Surface_Reconstruction_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Wu_4DSurf_High-Fidelity_Dynamic_Scene_Surface_Reconstruction_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 4DSurf: High-Fidelity Dynamic Scene Surface Reconstruction | [
"topconf_all"
] | 2026-06-12T09:42:14.109516+00:00 | CVPR 2026 | 2,026 | |||||||
[
"Yiting Lu",
"Wei Luo",
"Peiyan Tu",
"Haoran Li",
"Hanxin Zhu",
"Zihao Yu",
"Xingrui Wang",
"Xinyi Chen",
"Xinge Peng",
"Xin Li",
"Zhibo Chen"
] | [] | null | 2026-06-12T09:42:14.018493+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:646a24f60706f979 | https://openaccess.thecvf.com/content/CVPR2026/html/Lu_4DWorldBench_A_Comprehensive_Evaluation_Framework_for_3D4D_World_Generation_Models_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Lu_4DWorldBench_A_Comprehensive_Evaluation_Framework_for_3D4D_World_Generation_Models_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | 4DWorldBench: A Comprehensive Evaluation Framework for 3D/4D World Generation Models | [
"topconf_all"
] | 2026-06-12T09:42:14.018495+00:00 | CVPR 2026 | 2,026 | |||||||
Embodied agents, which couple intelligent decision-making with physical actuation in the real world, impose far more stringent and heterogeneous communication requirements than purely software-based agents. While 6G promises sub-millisecond latency, ultra-high reliability, native intelligence, and integrated sensing, s... | 2605.23263v1 | [
"Lipeng Dai",
"Luping Xiang",
"Kun Yang"
] | [
"embodied_agent"
] | null | 2026-06-11T15:15:46.968229+00:00 | all_curated | likely_embodied_ai | [
"cs.RO",
"cs.AI",
"eess.SP",
"eess.SY"
] | null | null | null | [
"embodied AI"
] | null | arxiv:2605.23263v1 | http://arxiv.org/abs/2605.23263v1 | https://arxiv.org/pdf/2605.23263v1 | [] | 14.75 | union:arxiv_recent_3y_score_gte_4 | [
"embodied agent robotics",
"VLN embodied"
] | [
"arxiv"
] | 6G Communication Networks Enabling Embodied Agents: Architecture and Prototype | [
"arxiv_recent_3y_score_gte_4"
] | 2026-06-11T15:19:10.971246+00:00 | arXiv | 2,026 | |||||
Large language models show great potential in unstructured data understanding, but still face significant challenges with graphs due to their structural hallucination. Existing approaches mainly either verbalize graphs into natural language, which leads to excessive token consumption and scattered attention, or transfo... | [
"Jingyao Wu",
"Bin Lu",
"Zijun Di",
"Xiaoying Gan",
"Meng Jin",
"Luoyi Fu",
"Xinbing Wang",
"Chenghu Zhou"
] | [] | null | 2026-06-12T09:43:49.043689+00:00 | all_curated | rejected | [] | null | null | null | [
"Graph Structure Learning",
"LLM for Graph",
"Structure Hallucination"
] | null | openreview:iclr2026:7465eb5c17eeaa3c | https://openreview.net/forum?id=eXidGkRUFt | https://openreview.net/pdf?id=eXidGkRUFt | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | <SO$G_k$>: One LLM Token for Explicit Graph Structural Understanding | [
"topconf_all"
] | 2026-06-12T09:43:49.043691+00:00 | ICLR 2026 | 2,026 | ||||||
[
"Kim, Donggeon",
"Kiefer, Kira",
"Veits, Luisa",
"Ulbl, Laura",
"Morgado-Vega, Necolle",
"Kim, Tae-Hyoung",
"Kim, Yeongmi"
] | [] | null | 2026-06-12T09:46:19.904024+00:00 | all_curated | rejected | [] | null | null | null | [] | null | hf:icra2026:21533b9d36b16659 | [] | 0.5 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | A 2-DoF Ankle Rehabilitation Platform Based on an Inclined Dual-Cylinder Mechanism | [
"topconf_all"
] | 2026-06-12T09:46:19.904029+00:00 | ICRA 2026 | 2,026 | |||||||||
[
"Dragusanu, Mihai",
"Guinet, Nicolas",
"Suthar, Bhivraj",
"Lisini Baldi, Tommaso",
"Prattichizzo, Domenico",
"Malvezzi, Monica"
] | [] | null | 2026-06-12T09:46:19.883500+00:00 | all_curated | rejected | [] | null | null | null | [] | null | hf:icra2026:690e3c2ca6713431 | [] | 0.5 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | A 3-Degrees-Of-Freedom Lightweight Flexible Twisted String Actuators (TSAs)-Based Exoskeleton for Wrist Rehabilitation | [
"topconf_all"
] | 2026-06-12T09:46:19.883502+00:00 | ICRA 2026 | 2,026 | |||||||||
Although Large Language Models (LLMs) have demonstrated impressive formal reasoning abilities, they often break down when problems require complex proof planning. One promising approach for improving LLM reasoning abilities involves translating problems into formal logic and using a logic solver. Although off-the-shelf... | [
"Joseph Cotnareanu",
"Didier Chételat",
"Yingxue Zhang",
"Mark Coates"
] | [] | null | 2026-06-12T09:43:49.017309+00:00 | all_curated | rejected | [] | null | null | null | [
"Abduction",
"Artificial Intelligence",
"Large Language Models",
"Logic",
"Reasoning"
] | null | openreview:iclr2026:7c9d4ad1eda512c1 | https://openreview.net/forum?id=RCsBoUr72G | https://openreview.net/pdf?id=RCsBoUr72G | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | A Balanced Neuro-Symbolic Approach for Commonsense Abductive Logic | [
"topconf_all"
] | 2026-06-12T09:43:49.017311+00:00 | ICLR 2026 | 2,026 | ||||||
Autonomous robot navigation in complex environments requires robust perception as well as high-level scene understanding due to perceptual challenges, such as occlusions, and uncertainty introduced by robot movement. For example, a robot climbing a cluttered staircase can misinterpret clutter as a step, misrepresenting... | 2501.04170 | [
"Sriganesh, Prasanna",
"Shirose, Burhanuddin",
"Travers, Matthew"
] | [
"sim2real"
] | null | 2026-06-12T09:46:19.867423+00:00 | all_curated | rejected | 10.1109/LRA.2025.3549662 | [
"cs.RO"
] | null | null | null | [
"sim-to-real / real robot"
] | null | hf:icra2026:00a9261b89def375 | http://arxiv.org/abs/2501.04170v2 | https://arxiv.org/pdf/2501.04170v2 | [] | 1.95 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | A Bayesian Modeling Framework for Estimation and Ground Segmentation of Cluttered Staircases | [
"topconf_all"
] | 2026-06-12T09:46:19.867425+00:00 | ICRA 2026 | 2,026 | ||||
Disentangled representation learning aims to identify and organize the underlying sources of variation in observed data. However, learning disentangled representations from observational data alone without any additional supervision necessitates inductive biases to solve the fundamental identifiability problem of uniqu... | [
"Vaishnavi S Patil",
"Siddhi Patil",
"Matthew Evanusa",
"Amit Kumar Kundu",
"Cornelia Fermuller",
"Joseph JaJa"
] | [] | null | 2026-06-12T09:42:09.314417+00:00 | all_curated | rejected | [] | null | null | null | [
"disentangled representations",
"nonparametric methods",
"representation learning",
"unsupervised learning"
] | null | openreview:iclr2026:caa15bf69ec50545 | https://openreview.net/forum?id=GVOLiaENgU | https://openreview.net/pdf?id=GVOLiaENgU | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | A Bayesian Nonparametric Framework For Learning Disentangled Representations | [
"topconf_all"
] | 2026-06-12T09:43:50.976981+00:00 | ICLR 2026 | 2,026 | ||||||
A fundamental challenge in data synthesis is protecting the fairness and privacy of the individual, particularly in data-scarce environments where underrepresented groups are at risk of further marginalization by reproducing the biases inherent in the data modeling process. We introduce a privacy- and fairness-aware fo... | [
"Forough Fazeli-Asl",
"Michael Minyi Zhang",
"Linglong Kong",
"Bei Jiang"
] | [] | null | 2026-06-12T09:43:49.002540+00:00 | all_curated | rejected | [] | null | null | null | [
"Bayesian nonparametric",
"Differential privacy",
"Dirichlet process",
"Tabular data generation"
] | null | openreview:iclr2026:b3ab1662981f253b | https://openreview.net/forum?id=j0czDrEnFc | https://openreview.net/pdf?id=j0czDrEnFc | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | A Bayesian Nonparametric Framework for Private, Fair, and Balanced Tabular Data Synthesis | [
"topconf_all"
] | 2026-06-12T09:43:49.002542+00:00 | ICLR 2026 | 2,026 | ||||||
Autonomous robots deployed in mass casualty incidents (MCI) face the challenge of making critical decisions based on incomplete and noisy perceptual data. We present an autonomous robotic system for casualty assessment that fuses outputs from multiple vision-based algorithms, estimating signs of severe hemorrhage, visi... | 2604.21568 | [
"Rusiecki, Szymon",
"Morales, Cecilia",
"Störy, Pia",
"Elenberg, Kimberly",
"Weiss, Leonard",
"Dubrawski, Artur"
] | [] | null | 2026-06-12T09:46:19.877452+00:00 | all_curated | rejected | [
"cs.RO"
] | null | null | null | [] | null | hf:icra2026:4c541e893de61ef8 | http://arxiv.org/abs/2604.21568v1 | https://arxiv.org/pdf/2604.21568v1 | [] | 2.25 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | A Bayesian Reasoning Framework for Robotic Systems in Autonomous Casualty Triage | [
"topconf_all"
] | 2026-06-12T09:46:19.877456+00:00 | ICRA 2026 | 2,026 | |||||
Large language model (LLM)-based agents are increasingly used to solve complex tasks involving tool use, such as web browsing, code execution, and data analysis. However, current evaluation benchmarks do not adequately assess their ability to solve real-world tasks that require synthesizing information from multiple so... | [
"Debjit Paul",
"Daniel Murphy",
"Milan Gritta",
"Ronald Cardenas",
"Victor Prokhorov",
"Lena Sophia Bolliger",
"Aysim Toker",
"Roy Miles",
"Andreea-Maria Oncescu",
"Jasivan Alex Sivakumar",
"Philipp Borchert",
"Ismail Elezi",
"Meiru Zhang",
"Ka Yiu Lee",
"Guchun Zhang",
"Jun Wang",
"... | [] | null | 2026-06-12T09:42:09.330542+00:00 | all_curated | rejected | [] | null | null | null | [
"AI agents",
"Benchmark",
"Deep Information Synthesis",
"Deep Research",
"LLM agents"
] | null | openreview:iclr2026:c44ab1ca91239ad5 | https://openreview.net/forum?id=0Dhpt9aY3n | https://openreview.net/pdf?id=0Dhpt9aY3n | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | A Benchmark for Deep Information Synthesis | [
"topconf_all"
] | 2026-06-12T09:43:57.814341+00:00 | ICLR 2026 | 2,026 | ||||||
We present a benchmarking study of vision-based robotic grasping algorithms with distinct approaches, and provide a comparative analysis. In particular, we compare two machine-learning-based and two analytical algorithms using an existing benchmarking protocol from the literature and determine the algorithm's strengths... | 2503.11163 | [
"Ramesh Babu, Bharath Kumar",
"Sreenivasarao Balakrishna, Sumukh",
"Flynn, Brian",
"Kapoor, Vinayak",
"Norton, Adam",
"Yanco, Holly",
"Calli, Berk"
] | [
"robot_manipulation",
"sim2real"
] | null | 2026-06-12T09:46:19.872640+00:00 | all_curated | maybe_related | [
"cs.RO",
"cs.CV"
] | null | null | null | [
"robot manipulation",
"sim-to-real / real robot"
] | null | hf:icra2026:58b2828cca5946c4 | http://arxiv.org/abs/2503.11163v2 | https://arxiv.org/pdf/2503.11163v2 | [] | 5.25 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | A Benchmarking Study of Vision-Based Robotic Grasping Algorithms | [
"topconf_all"
] | 2026-06-12T09:46:19.872642+00:00 | ICRA 2026 | 2,026 | |||||
[
"Yile Chen",
"Zeyi Wen",
"Jian Chen",
"Jin Huang"
] | [] | null | 2026-06-12T09:42:16.797122+00:00 | all_curated | rejected | 10.1609/aaai.v40i36.40284 | [] | null | null | null | [] | null | crossref:aaai2026:79c302350cd65580 | https://doi.org/10.1609/aaai.v40i36.40284 | [] | 0.5 | union:topconf_all | [
"AAAI 2026",
"Proceedings of the AAAI Conference on Artificial Intelligence"
] | [
"topconf",
"crossref_aaai"
] | A Better Start: Sensitivity-Aware Warm-Up for Robust and Efficient Fine-Tuning | [
"topconf_all"
] | 2026-06-12T09:42:16.797125+00:00 | AAAI 2026 | 2,026 | |||||||
Krotov and Hopfield (2021) proposed a biologically plausible two-layer associative memory network with memory storage capacity exponential in the number of visible neurons. However, the capacity was only linear in the number of hidden neurons. This limitation arose from the choice of nonlinearity between the visible an... | [
"Mohadeseh Shafiei Kafraj",
"Dmitry Krotov",
"Peter E. Latham"
] | [] | null | 2026-06-12T09:42:09.330586+00:00 | all_curated | rejected | [] | null | null | null | [
"Dense Associative Memory",
"Hopfield Network"
] | null | openreview:iclr2026:4b6f2fa6519b36b7 | https://openreview.net/forum?id=mRZOayQL1i | https://openreview.net/pdf?id=mRZOayQL1i | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | A Biologically Plausible Dense Associative Memory with Exponential Capacity | [
"topconf_all"
] | 2026-06-12T09:43:57.830180+00:00 | ICLR 2026 | 2,026 | ||||||
[
"Kanchana Vaishnavi Gandikota",
"Michael Moeller",
"Andreas Kolb",
"Bhaskar Choubey",
"Paramanand Chandramouli"
] | [] | null | 2026-06-12T09:42:13.953546+00:00 | all_curated | rejected | [] | null | null | null | [] | null | cvf:cvpr2026:72f2c81a57d5771a | https://openaccess.thecvf.com/content/CVPR2026/html/Gandikota_A_Bit_is_All_You_Need_Efficient_Video_Capture_via_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Gandikota_A_Bit_is_All_You_Need_Efficient_Video_Capture_via_CVPR_2026_paper.pdf | [] | 0.75 | union:topconf_all | [
"CVPR2026"
] | [
"topconf",
"cvf"
] | A Bit is All You Need! Efficient Video Capture via Single Bit Imaging | [
"topconf_all"
] | 2026-06-12T09:42:13.953549+00:00 | CVPR 2026 | 2,026 | |||||||
Nonsmooth composite optimization with orthogonality constraints has a wide range of applications in statistical learning and data science. However, this problem is challenging due to its nonsmooth objective and computationally expensive, non-convex constraints. In this paper, we propose a new approach called \textbf{OB... | [
"Ganzhao Yuan"
] | [] | null | 2026-06-12T09:43:49.011398+00:00 | all_curated | rejected | [] | null | null | null | [
"Orthogonality Constraints; Nonconvex Optimization; Nonsmooth Composite Optimization; Block Coordinate Descent; Convergence Analysis"
] | null | openreview:iclr2026:aa88a148e3b18f78 | https://openreview.net/forum?id=L3Or2mhuCH | https://openreview.net/pdf?id=L3Or2mhuCH | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | A Block Coordinate Descent Method for Nonsmooth Composite Optimization under Orthogonality Constraints | [
"topconf_all"
] | 2026-06-12T09:43:49.011399+00:00 | ICLR 2026 | 2,026 | ||||||
[
"Zhao, Hanqing",
"Pacheco, Alexandre",
"Beltrame, Giovanni",
"Liu, Xue",
"Dorigo, Marco",
"Dudek, Gregory"
] | [] | null | 2026-06-12T09:46:19.883832+00:00 | all_curated | rejected | [] | null | null | null | [] | null | hf:icra2026:463008bdd7a249f6 | [] | 0.5 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | A Blockchain Framework for Equitable and Secure Task Allocation in Robot Swarms | [
"topconf_all"
] | 2026-06-12T09:46:19.883834+00:00 | ICRA 2026 | 2,026 | |||||||||
[
"Kailun Lyu",
"Zehan Li",
"Fu Zhang",
"Jingwei Cheng"
] | [] | null | 2026-06-12T09:42:16.728642+00:00 | all_curated | rejected | 10.1609/aaai.v40i38.40515 | [] | null | null | null | [] | null | crossref:aaai2026:7bd0270fd0aa8408 | https://doi.org/10.1609/aaai.v40i38.40515 | [] | 0.5 | union:topconf_all | [
"AAAI 2026",
"Proceedings of the AAAI Conference on Artificial Intelligence"
] | [
"topconf",
"crossref_aaai"
] | A Boundary Token Graph for Zero-Shot Relation Triplet Extraction Involving Discontinuous Entities | [
"topconf_all"
] | 2026-06-12T09:42:16.728644+00:00 | AAAI 2026 | 2,026 | |||||||
As large language models (LLMs) continue to revolutionize AI research, there is a growing interest in building large-scale brain foundation models to advance neuroscience. While most existing brain foundation models are pre-trained on time-series signals or connectome features, we propose a novel graph-based pre-traini... | [
"Xinxu Wei",
"kanhao zhao",
"Yong Jiao",
"Lifang He",
"Yu Zhang"
] | [] | null | 2026-06-12T09:43:49.016670+00:00 | all_curated | rejected | [] | null | null | null | [
"Brain Graph Foundation Model",
"Fine-Tuning",
"Functional Magnetic Resonance Imaging (fMRI)",
"Graph Pre-Training",
"Neuroscience",
"Prompt Learning"
] | null | openreview:iclr2026:38c65ee98331495c | https://openreview.net/forum?id=PeGHkAaRxs | https://openreview.net/pdf?id=PeGHkAaRxs | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | A Brain Graph Foundation Model: Pre-Training and Prompt-Tuning across Broad Atlases and Disorders | [
"topconf_all"
] | 2026-06-12T09:43:49.016672+00:00 | ICLR 2026 | 2,026 | ||||||
While spiking neural networks (SNNs) provide a biologically inspired and energy-efficient computational framework, their robustness and the dynamic advantages inherent to biological neurons remain significantly underutilized owing to oversimplified neuron models. In particular, conventional leaky integrate-and-fire (LI... | [
"Qianyi Bai",
"Haiteng Wang",
"Qiang Yu"
] | [] | null | 2026-06-12T09:43:49.035956+00:00 | all_curated | rejected | [] | null | null | null | [
"Brain-Inspired Computing",
"Dynamic Gated Neurons",
"Noise Robustness",
"Spiking Neural Networks (SNNs)"
] | null | openreview:iclr2026:e2baa3f98db61d4b | https://openreview.net/forum?id=5h741EyfQM | https://openreview.net/pdf?id=5h741EyfQM | [] | 0.75 | union:topconf_all | [
"ICLR 2026",
"ICLR.cc/2026/Conference"
] | [
"topconf",
"openreview"
] | A Brain-Inspired Gating Mechanism Unlocks Robust Computation in Spiking Neural Networks | [
"topconf_all"
] | 2026-06-12T09:43:49.035958+00:00 | ICLR 2026 | 2,026 | ||||||
[
"Tafuro, Alessandra",
"Guarini, Marco",
"Mineo, Angelo",
"Zanchettin, Andrea Maria",
"Rocco, Paolo"
] | [] | null | 2026-06-12T09:46:19.908311+00:00 | all_curated | rejected | [] | null | null | null | [] | null | hf:icra2026:f17ea5239a6bd3a7 | [] | 2 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | A CAD-Free Vision-Guided Framework for Robotic Deburring of Flexible Shoe Soles | [
"topconf_all"
] | 2026-06-12T09:46:19.908313+00:00 | ICRA 2026 | 2,026 | |||||||||
[
"Zhou, Zhanfeng",
"Zuo, Runze",
"Du, Matthew",
"Wang, Shaojia",
"Levy, Sebastian",
"Sun, Yu",
"Liu, Xinyu"
] | [] | null | 2026-06-12T09:46:19.892749+00:00 | all_curated | rejected | [] | null | null | null | [] | null | hf:icra2026:7c75651f21f1efb7 | [] | 0.5 | union:topconf_all | [
"ICRA 2026",
"ai-conferences/ICRA2026"
] | [
"topconf",
"hf_icra"
] | A Cable-Driven Soft Robotic Hand with an In-Hand RGB-D Camera for Dexterous Grasping and Manipulation | [
"topconf_all"
] | 2026-06-12T09:46:19.892751+00:00 | ICRA 2026 | 2,026 |
End of preview.
Embodied AI Literature Metadata
This dataset contains normalized paper metadata collected for an Embodied AI / Vision-Language-Action literature assistant. It is intended for metadata search, paper triage, and PDF retrieval before PaperQA-style evidence reading.
Generated at: 2026-07-04T04:32:33.578443+00:00
Splits
| Split | Records | With abstract | With PDF URL |
|---|---|---|---|
topconf_all |
79068 | 24519 | 62689 |
frontier_2026_quality |
351 | 351 | 351 |
arxiv_recent_3y_score_gte_4 |
1625 | 1625 | 1625 |
all_curated |
80693 | 26144 | 64314 |
Files
data/all_curated.jsonl: de-duplicated union of the main curated sources.data/topconf_all.jsonl: top conference / venue metadata collected by the project.data/frontier_2026_quality.jsonl: 2026 frontier / trending-quality candidates.data/arxiv_recent_3y_score_gte_4.jsonl: recent arXiv-style metadata subset.metadata_manifest.json: counts, coverage, and generation provenance.
Schema
Each JSONL row uses a compact schema:
paper_id, title, authors, year, venue, corpus, abstract, keywords, categories,
fields_of_study, paper_url, pdf_url, doi, arxiv_id, semantic_scholar_id,
code_url, project_url, sources, source_queries, quality_signals, decision,
relevance_score, frontier_score, metadata_score, hybrid_score, citation_count,
influential_citation_count, collected_at, updated_at, dataset_split, source_file
Provider-specific raw payloads were intentionally removed to keep the dataset small and easy to preview.
Intended Use
Use this dataset as a lightweight metadata index for:
- literature search and candidate recall;
- LLM-based paper triage;
- retrieving PDF URLs for downstream PaperQA reading;
- reproducing the demo workflow in the repository.
The metadata can contain duplicates across non-union splits because the splits
represent different collection policies. Use all_curated for a de-duplicated
view.
- Downloads last month
- 127