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The dataset generation failed because of a cast error

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 2 new columns ({'label', 'idx'})

This happened while the json dataset builder was generating data using

hf://datasets/forza61/academic-rag-data/metadatas.jsonl (at revision 7ea1e91a31d2c7b7056415ead288128b221f49d3)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              idx: int64
              chunk_id: string
              paper_id: string
              title: string
              section_title: string
              section_path: list<item: string>
                child 0, item: string
              para_index: int64
              reference_ids: list<item: int64>
                child 0, item: int64
              inline_citations: list<item: struct<raw: string, authors: string, year: string>>
                child 0, item: struct<raw: string, authors: string, year: string>
                    child 0, raw: string
                    child 1, authors: string
                    child 2, year: string
              references: list<item: struct<id: int64, text: string>>
                child 0, item: struct<id: int64, text: string>
                    child 0, id: int64
                    child 1, text: string
              year: int64
              url: string
              venue: null
              authors: list<item: struct<firstname: string, surname: string, email: string>>
                child 0, item: struct<firstname: string, surname: string, email: string>
                    child 0, firstname: string
                    child 1, surname: string
                    child 2, email: string
              text: string
              label: int64
              to
              {'chunk_id': Value('string'), 'paper_id': Value('string'), 'title': Value('string'), 'section_title': Value('string'), 'section_path': List(Value('string')), 'para_index': Value('int64'), 'text': Value('string'), 'reference_ids': List(Value('int64')), 'inline_citations': List({'raw': Value('string'), 'authors': Value('string'), 'year': Value('string')}), 'year': Value('int64'), 'venue': Value('null'), 'url': Value('string'), 'authors': List({'firstname': Value('string'), 'surname': Value('string'), 'email': Value('string')}), 'references': List({'id': Value('int64'), 'text': Value('string')})}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1339, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 972, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1833, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 2 new columns ({'label', 'idx'})
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/forza61/academic-rag-data/metadatas.jsonl (at revision 7ea1e91a31d2c7b7056415ead288128b221f49d3)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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chunk_id string	paper_id string	title string	section_title string	section_path list	para_index int64	text string	reference_ids list	inline_citations list	year int64	venue null	url string	authors list	references list
Artificial Intelligence_1_abstract_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	Abstract	[ "Abstract" ]	0	Biosignals offer valuable insights into the physiological states of the human body. Although biosignal modalities differ in functionality, signal fidelity, sensor comfort, and cost, they are often intercorrelated, reflecting the holistic and interconnected nature of human physiology. This opens up the possibility of pe...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec0_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	INTRODUCTION	[ "INTRODUCTION" ]	0	Biosignals, such as electrocardiogram (ECG), electroencephalogram (EEG), and photoplethysmography (PPG), provide critical insights into the underlying physiological states of individuals. They are essential tools in modern healthcare and have often been considered the gold standard for diagnostics (Rosenberg & Van Hout...	[]	[ { "raw": "Rosenberg & Van Hout, 2013", "authors": "Rosenberg & Van Hout", "year": "2013" }, { "raw": "Stracina et al., 2022", "authors": "Stracina et al.", "year": "2022" }, { "raw": "Mentis et al., 2024", "authors": "Mentis et al.", "year": "2024" }, { "raw": "Mo...	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec0_p1	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	INTRODUCTION	[ "INTRODUCTION" ]	1	A promising direction is to harness the correlations between different biosignal modalities and perform the same tasks using alternative modalities, making health monitoring systems more accessible, practical, and flexible (Wang et al., 2023;Yang et al., 2023). For example, being able to perform the same tasks using si...	[]	[ { "raw": "Wang et al., 2023", "authors": "Wang et al.", "year": "2023" }, { "raw": "Yang et al., 2023", "authors": "Yang et al.", "year": "2023" } ]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec0_p2	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	INTRODUCTION	[ "INTRODUCTION" ]	2	Unsupervised cross-modal knowledge transfer stands out as a practical solution to address the aforementioned needs. Existing methods can be divided into two categories: data translation and knowledge distillation. As illustrated in Figure 1(a), data translation directly translates data from the new modality to the old ...	[]	[ { "raw": "Sarkar & Etemad, 2021", "authors": "Sarkar & Etemad", "year": "2021" }, { "raw": "Abbaspourazad et al., 2024b", "authors": "Abbaspourazad et al.", "year": "2024b" }, { "raw": "Zhang et al., 2024", "authors": "Zhang et al.", "year": "2024" }, { "raw": "Co...	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec0_p3	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	INTRODUCTION	[ "INTRODUCTION" ]	3	To this end, we propose BioX-Bridge, a new framework for unsupervised cross-modal knowledge transfer via model bridging, as illustrated in Figure 1(c). The core idea is to construct a bridge that projects intermediate representations from one biosignal model to another, leveraging the powerful representational capabili...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec0_p4	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	INTRODUCTION	[ "INTRODUCTION" ]	4	To enable effective projection between high-dimensional spaces, we design a prototype network composed of a learnable prototype set and a low-rank approximation module to compute aggregation weights. Notably, only the bridge network requires training to enable interoperability between models of different modalities. We...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec0_p5	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	INTRODUCTION	[ "INTRODUCTION" ]	5	Extensive ablation studies further confirm the robustness of the proposed framework under various conditions. Our contributions can be summarized as follows:	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec0_p6	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	INTRODUCTION	[ "INTRODUCTION" ]	6	• We propose BioX-Bridge, a novel unsupervised model bridging framework that enables crossmodal knowledge transfer through information flow between biosignal models.	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec0_p7	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	INTRODUCTION	[ "INTRODUCTION" ]	7	• We introduce key components to support the framework, including an efficient two-stage strategy for selecting bridge positions and a prototype network with low-rank approximation for effective high-dimensional projection.	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec0_p8	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	INTRODUCTION	[ "INTRODUCTION" ]	8	• We demonstrate the efficiency of BioX-Bridge through experiments on three biosignal datasets, four modalities, and six transfer directions, demonstrating robustness through comprehensive ablation studies.	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec1_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	RELATED WORKS	[ "RELATED WORKS" ]	0	Unsupervised Cross-modal Knowledge Transfer Existing methods can be divided into two categories: knowledge distillation and data translation.	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec1_p1	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	RELATED WORKS	[ "RELATED WORKS" ]	1	Knowledge distillation was introduced as a model compression technique, where a smaller student model learns to mimic a larger and high-performing teacher model by matching its output distributions (Hinton et al., 2015). The concept has since been extended to cross-modal knowledge transfer. Early efforts focused on com...	[]	[ { "raw": "Hinton et al., 2015", "authors": "Hinton et al.", "year": "2015" }, { "raw": "Garcia et al., 2018", "authors": "Garcia et al.", "year": "2018" }, { "raw": "Gupta et al., 2016", "authors": "Gupta et al.", "year": "2016" }, { "raw": "Hoffman et al., 2016",...	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec1_p2	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	RELATED WORKS	[ "RELATED WORKS" ]	2	Data translation aims to achieve unsupervised cross-modal knowledge transfer by directly translating raw data from one modality to another. Generative adversarial networks (GAN) (Goodfellow et al., 2020) and their variants (Mirza & Osindero, 2014;Zhu et al., 2017) have been widely adopted for modality translation tasks...	[]	[ { "raw": "Goodfellow et al., 2020", "authors": "Goodfellow et al.", "year": "2020" }, { "raw": "Mirza & Osindero, 2014", "authors": "Mirza & Osindero", "year": "2014" }, { "raw": "Zhu et al., 2017", "authors": "Zhu et al.", "year": "2017" }, { "raw": "Duan et al.,...	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec1_p3	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	RELATED WORKS	[ "RELATED WORKS" ]	3	Biosignals Foundation Models Inspired by the recent success of large-scale pre-training in natural language processing (Achiam et al., 2023) and computer vision (Dosovitskiy et al., 2020), the development of foundation models for biosignals has garnered much interest (Han et al., 2024;Lai et al., 2025). Through large-s...	[]	[ { "raw": "Achiam et al., 2023", "authors": "Achiam et al.", "year": "2023" }, { "raw": "Dosovitskiy et al., 2020", "authors": "Dosovitskiy et al.", "year": "2020" }, { "raw": "Han et al., 2024", "authors": "Han et al.", "year": "2024" }, { "raw": "Lai et al., 2025...	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec1_p4	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	RELATED WORKS	[ "RELATED WORKS" ]	4	pre-training, these models struggle with generalization to unseen modalities due to mismatches in input dimensions and data distributions (Liu et al., 2024).	[]	[ { "raw": "Liu et al., 2024", "authors": "Liu et al.", "year": "2024" } ]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec1_p5	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	RELATED WORKS	[ "RELATED WORKS" ]	5	We provide further discussion on model stitching and domain adaptation in Appendix C.	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec2_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	METHODS	[ "METHODS" ]	0	The core concept of our proposed BioX-Bridge framework is to build a bridging network that facilitates efficient and effective projection between intermediate representations of biosignal models. This allows the framework to harness the strong representational power of one model while integrating the task-specific know...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec3_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	PROBLEM DEFINITION	[ "PROBLEM DEFINITION" ]	0	Assume that we are given an annotated dataset from an old biosignal modality, old) \| labeled samples for a specific task, and a corresponding model,	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec3_p1	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	PROBLEM DEFINITION	[ "PROBLEM DEFINITION" ]	1	, where f (old) θ is a pre-trained encoder parametrized by θ followed by a task head g (old) ω parametrized by ω. We also have an un-annotated dataset from a new modality,	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec3_p2	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	PROBLEM DEFINITION	[ "PROBLEM DEFINITION" ]	2	, which shares the same underlying label set with D (old) . We further have a disjoint, un-annotated paired dataset D (pair) = {(x	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec3_p3	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	PROBLEM DEFINITION	[ "PROBLEM DEFINITION" ]	3	. The unsupervised cross-modal knowledge transfer problem aims to obtain a model f , such that f can make predictions on D (new) .	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec4_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	MODEL BRIDGING	[ "MODEL BRIDGING" ]	0	Let f (old) θ be the model for the old modality, parametrized by θ of L layers, and let f (new) ϕ be the model for the new modality, parametrized by ϕ of M layers. The intermediate representations from the m-th layer of the new modality model can then be extracted as:	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec4_p1	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	MODEL BRIDGING	[ "MODEL BRIDGING" ]	1	where x (new) denotes a biosignal time series sample from the new modality. f (new) ϕ ≤m denotes the subset of the new modality model consisting of its first m layers, subject to the constraint Next, we introduce a bridge network to enable the information flow between the new and old modality models by projecting repre...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec4_p2	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	MODEL BRIDGING	[ "MODEL BRIDGING" ]	2	where b ψ denotes the bridge network parametrized by ψ. h(old) l denotes the projected representation from the new modality to the old modality. Note that the projected representation is designed to mimic the intermediate representation from the l-th layer of the old modality model, defined as h (old) old) , where x (o...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec4_p3	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	MODEL BRIDGING	[ "MODEL BRIDGING" ]	3	Finally, we can obtain predictions using the old modality model starting from the (l + 1)-th layer:	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec4_p4	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	MODEL BRIDGING	[ "MODEL BRIDGING" ]	4	where m and l are also known as the bridge input and output positions, • denotes function composition.	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec5_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	BRIDGE POSITION SELECTION	[ "BRIDGE POSITION SELECTION" ]	0	There are L × M possible locations where the bridge can be constructed between the layers of the two models. Although a brute-force search would yield the optimal bridge position, it is computationally expensive. In particular, the choice of the bridge position is one of the most influential factors affecting transfer ...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec5_p1	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	BRIDGE POSITION SELECTION	[ "BRIDGE POSITION SELECTION" ]	1	Stage 1: Bridge Input Position (m) Selection The bridge serves to project new modality representations to the old modality representation space, enabling the bridged model to mimic the behavior of the old modality model. As the saying "garbage in, garbage out" suggests, it is important to select discriminative new moda...	[]	[ { "raw": "Alain & Bengio, 2016", "authors": "Alain & Bengio", "year": "2016" } ]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec5_p2	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	BRIDGE POSITION SELECTION	[ "BRIDGE POSITION SELECTION" ]	2	where ) denotes the pseudo label. L probe denotes the empirical loss for the linear prober.	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec5_p3	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	BRIDGE POSITION SELECTION	[ "BRIDGE POSITION SELECTION" ]	3	, we can ease the transformation process by selecting h (old) l to be as similar as possible to h (new) m . We select linear CKA (Kornblith et al., 2019)	[]	[ { "raw": "Kornblith et al., 2019", "authors": "Kornblith et al.", "year": "2019" } ]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec5_p4	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	BRIDGE POSITION SELECTION	[ "BRIDGE POSITION SELECTION" ]	4	For detailed formulation of CKA linear , please refer to the appendix.	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec5_p5	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	BRIDGE POSITION SELECTION	[ "BRIDGE POSITION SELECTION" ]	5	Algorithm 1: BioX-Bridge learning procedure	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec5_p6	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	BRIDGE POSITION SELECTION	[ "BRIDGE POSITION SELECTION" ]	6	Input: Old modality model f (old) θ ; New modality model f (new) ϕ ; Task head g (old) ω ; Paired dataset	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec5_p7	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	BRIDGE POSITION SELECTION	[ "BRIDGE POSITION SELECTION" ]	7	Select bridge input position, m, using Eq. ( 4) Select bridge output position, l, using Eq. ( 5)	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec5_p8	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	BRIDGE POSITION SELECTION	[ "BRIDGE POSITION SELECTION" ]	8	using Eq. ( 7) whose intermediate representation exhibits the strongest linear association with the pseudolabels. For bridge output position, we select the layer from f (old) θ whose representation is most similar to that of the bridge input layer.	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec6_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	BRIDGE ARCHITECTURE	[ "BRIDGE ARCHITECTURE" ]	0	Models of different modalities operate in distinct representational spaces. The bridge network should be sufficiently parametrized to enable the projection and alignment of the two spaces. A naive bridge architecture is a full-rank linear layer, but this is prohibitively expensive because of the highdimensional project...	[]	[ { "raw": "Jiang et al., 2024", "authors": "Jiang et al.", "year": "2024" }, { "raw": "Coppola et al., 2024", "authors": "Coppola et al.", "year": "2024" } ]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec6_p1	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	BRIDGE ARCHITECTURE	[ "BRIDGE ARCHITECTURE" ]	1	7 billion parameters. To address the challenge of high-dimensional projection, we propose a prototype network. The prototype network consists of two modules, a prototype set, and a low-rank approximation module. Specifically, the prototype set, P ∈ R Np×d (old) l , consisting of N p prototype vectors with embedding dim...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec6_p2	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	BRIDGE ARCHITECTURE	[ "BRIDGE ARCHITECTURE" ]	2	where Pool(•) denotes a pooling operation along the N (new) m dimension, and Reshape N (old) l ×Np (•) denotes the reshape operation to the specified output dimensions.	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec7_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	BRIDGE TRAINING	[ "BRIDGE TRAINING" ]	0	As the difference between h (old) l and h(old) l approaches zero, the bridged model yields predictions identical to those of the old modality model. Formally:	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec7_p1	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	BRIDGE TRAINING	[ "BRIDGE TRAINING" ]	1	Naturally, the training objective for the bridge network is to align the intermediate representations in the L-th layer2 :	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec7_p2	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	BRIDGE TRAINING	[ "BRIDGE TRAINING" ]	2	where L align denotes the loss function, such as cosine loss and mean absolute error loss. The learning process of BioX-Bridge is presented in Algorithm 1. Given the unbalanced nature of the datasets, we report Balanced Accuracy, F1-Weighted, and F1-Macro, following (Jiang et al., 2024;Pillai et al., 2025).	[]	[ { "raw": "Jiang et al., 2024", "authors": "Jiang et al.", "year": "2024" }, { "raw": "Pillai et al., 2025", "authors": "Pillai et al.", "year": "2025" } ]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec8_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	EXPERIMENTS	[ "EXPERIMENTS" ]	0	Backbone Foundation Models For EEG, we adopt the base version of the LaBraM architecture with 5.8M parameters (Jiang et al., 2024). For ECG, we adopt the small version of the HuBERT-ECG architecture with 30.4M parameters (Coppola et al., 2024). For PPG, we adopt the small version of the PaPaGei architecture with 5.7M p...	[]	[ { "raw": "Jiang et al., 2024", "authors": "Jiang et al.", "year": "2024" }, { "raw": "Coppola et al., 2024", "authors": "Coppola et al.", "year": "2024" }, { "raw": "Pillai et al., 2025", "authors": "Pillai et al.", "year": "2025" }, { "raw": "Luo et al., 2024", ...	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec8_p1	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	EXPERIMENTS	[ "EXPERIMENTS" ]	1	Baselines We compare our method with the following baselines evaluated on D (new) : (i) Random denotes a model that produces predictions at random. (ii) CardioGAN uses GAN to synthesize ECG from PPG (Sarkar & Etemad, 2021), and we translate the new modality data (PPG) to the old modality (ECG) for evaluation. (iii) KD ...	[]	[ { "raw": "Sarkar & Etemad, 2021", "authors": "Sarkar & Etemad", "year": "2021" }, { "raw": "Hinton et al., 2015", "authors": "Hinton et al.", "year": "2015" }, { "raw": "Abbaspourazad et al., 2024b", "authors": "Abbaspourazad et al.", "year": "2024b" }, { "raw": "...	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec9_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER PERFORMANCE	[ "UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER PERFORMANCE" ]	0	Experiment results on the ISRUC, FOG, and WESAD dataset are presented in Table 1. We observe that BioX-Bridge significantly reduces the number of trainable parameters by 87.9-99.1% and continues to achieve performance comparable to or better than that of the baseline methods. For example, for WESAD (PPG → ECG), BioX-Br...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec9_p1	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER PERFORMANCE	[ "UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER PERFORMANCE" ]	1	We also observe that the knowledge transfer performance gap compared to Oracle varies across datasets and knowledge transfer directions. For example, on the ISRUC dataset, we observe approximately 20% balanced accuracy gap between BioX-Bridge (60.11%) and Oracle ( On another note, BioX-Bridge and KD-Contrast achieved h...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec10_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	ABLATION STUDIES	[ "ABLATION STUDIES" ]	0	We conduct ablation studies on the WESAD dataset and the direction of knowledge transfer (PPG → ECG). Additional results are presented in the appendix.	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec11_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	Bridge Rank and Prototype Set	[ "Bridge Rank and Prototype Set" ]	0	We study the impact of different hyperparameters for the prototype network in Figures 4a and4b. A performance drop is observed when the approximation rank and prototype set size are too small or too large, likely due to under-/over-parameterization of the bridge network. In particular, the performance peaks at around 0...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec11_p1	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	Bridge Rank and Prototype Set	[ "Bridge Rank and Prototype Set" ]	1	Dataset Size We reduce the size of the paired dataset for bridge training. We observe in Figure 4c that the transfer performance slowly decays by around 2% at 20% dataset size, showcasing the robustness of the bridge under the low-data regime. Bridge Position Selection To show that the bridge position selection strateg...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec12_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	Foundation Model	[ "Foundation Model" ]	0	We further analyze the impact of using different foundation models for crossmodal knowledge transfer. In Table 3, we replace the HuBERT-ECG foundation model (Coppola et al., 2024) with ECG-FM (McKeen et al., 2024). Notably, due to the large number of trainable parameters (90M), the knowledge distillation methods with E...	[]	[ { "raw": "Coppola et al., 2024", "authors": "Coppola et al.", "year": "2024" }, { "raw": "McKeen et al., 2024", "authors": "McKeen et al.", "year": "2024" } ]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec13_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	CONCLUSION	[ "CONCLUSION" ]	0	We present BioX-Bridge as an efficient framework for unsupervised cross-modal knowledge transfer across biosignals. To address the challenges of high-dimensional projection between biosignal foundation models, we design a prototype-based architecture for parameter-efficient learning of transformations between represent...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec14_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	ETHICAL AND REPRODUCIBILITY STATEMENT	[ "ETHICAL AND REPRODUCIBILITY STATEMENT" ]	0	This study makes use of datasets involving human subjects (ISRUC, WESAD, and FOG). All datasets employed are publicly available, and we follow the usage terms and ethical guidelines specified by the original data providers. No new data were collected for this work, and all analyses were conducted on de-identified, prev...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec14_p1	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	ETHICAL AND REPRODUCIBILITY STATEMENT	[ "ETHICAL AND REPRODUCIBILITY STATEMENT" ]	1	To ensure reproducibility, we provide detailed descriptions of our experimental setups, including data preprocessing steps, model architectures, hyperparameters, and training procedures, in Section 4.1 and Appendix D. Our code implementation is available in the supplementary materials and will be available in a dedicat...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec15_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	APPENDIX TABLE OF CONTENTS	[ "APPENDIX TABLE OF CONTENTS" ]	0	A l denote the matrix of old modality representations from the l-th layer. The CKA linear operator introduced in Eq. 5 is formulated as follows (Kornblith et al., 2019):	[]	[ { "raw": "Kornblith et al., 2019", "authors": "Kornblith et al.", "year": "2019" } ]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec15_p1	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	APPENDIX TABLE OF CONTENTS	[ "APPENDIX TABLE OF CONTENTS" ]	1	HSIC is the Hilbert-Schmidt Independence Criterion. H is the centering matrix. Note that while this formulation uses the entire H (new) m and H (old) l to compute similarity between representations of the old and new modalities, it is also possible to improve efficiency by computing similarity using only a subset o...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec16_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	B LIMITATIONS AND FUTURE WORK	[ "B LIMITATIONS AND FUTURE WORK" ]	0	Although BioX-Bridge greatly reduces training computational requirements and improves the efficiency of cross-modal knowledge transfer, it depends on the availability of pre-trained models for each biosignal modality, an assumption that may not hold for emerging or underexplored biosignals. Furthermore, depending on th...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec16_p1	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	B LIMITATIONS AND FUTURE WORK	[ "B LIMITATIONS AND FUTURE WORK" ]	1	WESAD In this dataset, ECG signals are sampled at 700Hz and PPG signals at 64Hz. For ECG, which uses HuBERT-ECG as its foundation model, we first downsample to 500Hz, apply a finite impulse response (FIR) bandpass filter between 0.05-47Hz, resample to 100Hz, and then perform channel-wise z-score normalization. For PPG,...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec16_p2	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	B LIMITATIONS AND FUTURE WORK	[ "B LIMITATIONS AND FUTURE WORK" ]	2	FOG In this dataset, both EEG and EMG signals were collected at 1000Hz, downsampled to 500Hz with a notch and bandpass filter already applied. For EEG, which uses LaBraM as its foundation model, we downsample to 200Hz and convert the unit to 0.1mV. For EMG, which uses NormWear as its foundation model, we downsample to ...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec16_p3	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	B LIMITATIONS AND FUTURE WORK	[ "B LIMITATIONS AND FUTURE WORK" ]	3	ISRUC In this dataset, EEG signals are sampled at 200Hz with a notch and bandpass filter already applied, while PPG signals are also sampled at 200Hz with a notch filter applied. For EEG, which uses LaBraM as its foundation model, no resampling is required; we only convert the unit to 0.1mV. For ECG, which uses HuBERT-...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec17_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	D.1.2 DATASET SPLIT	[ "D.1.2 DATASET SPLIT" ]	0	Dataset split is summarized in Figure A1. The datasets contain synchronized data from the old and new modalities. We perform a subject-wise split for WESAD and ISRUC and sample-wise split for FOG (Zhang et al., 2022) to obtain four subsets D (old) , D (new) , D (val) , and D (pair) , at a ratio of 33%, 22%, 11%, and 33...	[]	[ { "raw": "Zhang et al., 2022", "authors": "Zhang et al.", "year": "2022" } ]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec18_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	New modality data	[ "New modality data" ]	0	Old modality data	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec20_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	≈33% ≈22%	[ "≈33% ≈22%" ]	0	Evaluate Unused	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec21_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	≈11%	[ "≈11%" ]	0	Hyperparameter Selection Unsupervised Training Figure A1: Illustration of Dataset Split. The dataset is divided into four subject-independent subsets. We first use the old modality data from D (old) to train the linear prober g (old) ω for experiment setup, followed by unsupervised training on D (pair) . The subsets ...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec22_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	D.1.3 BIOX-BRIDGE AND BASELINE IMPLEMENTATION DETAILS	[ "D.1.3 BIOX-BRIDGE AND BASELINE IMPLEMENTATION DETAILS" ]	0	BioX-Bridge Implementation Details To prepare f (old) θ and g (old) ω for evaluation, we adapt the pre-trained foundation models for the classification tasks using D (old) , we apply mean pooling to the last-layer representations and add a linear layer for classification. The weights of the foundation model are froze...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec22_p1	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	D.1.3 BIOX-BRIDGE AND BASELINE IMPLEMENTATION DETAILS	[ "D.1.3 BIOX-BRIDGE AND BASELINE IMPLEMENTATION DETAILS" ]	1	To select the bridge rank and number of prototypes, we perform a grid search over r = {4, 8, 16, 32} and N p = {50, 100, 150, 200, 250, 300}. For compatibility across various network architectures without modifying the forward functions, we retrieve and replace intermediate representations using forward hooks and forwa...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec22_p2	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	D.1.3 BIOX-BRIDGE AND BASELINE IMPLEMENTATION DETAILS	[ "D.1.3 BIOX-BRIDGE AND BASELINE IMPLEMENTATION DETAILS" ]	2	Baseline Implementation Details For the Random baseline, we simulate a model that randomly assigns labels to samples uniformly across all classes. For CardioGAN, we adopt the pre-trained weights provided by the original publication. We do not perform any fine-tuning as CardioGAN has been trained with WESAD as one of it...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec22_p3	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	D.1.3 BIOX-BRIDGE AND BASELINE IMPLEMENTATION DETAILS	[ "D.1.3 BIOX-BRIDGE AND BASELINE IMPLEMENTATION DETAILS" ]	3	For knowledge distillation (KD), we append a linear layer to map mean-pooled representations from the last layer of the foundation model to classwise probabilities. We then fine-tune the entire foundation model with a learning rate of 1e-4 for LaBraM, 1e-4 for PaPaGei, 1e-4 for NormWear, and 1e-5 for HuBERT-ECG, as sug...	[]	[ { "raw": "Abbaspourazad et al., 2024b", "authors": "Abbaspourazad et al.", "year": "2024b" } ]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec23_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	D.2 MAIN RESULTS WITH STANDARD DEVIATIONS	[ "D.2 MAIN RESULTS WITH STANDARD DEVIATIONS" ]	0	Tables 1-3 only report the average performance for five seeds due to space constraints. In Table A1-A5, we present the full results with standard deviation.	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec24_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	D.3 ADDITIONAL ABLATION STUDIES D.3.1 BIOX-BRIDGE FRAMEWORK WITH TRADITIONAL BIOSIGNAL MODELS	[ "D.3 ADDITIONAL ABLATION STUDIES D.3.1 BIOX-BRIDGE FRAMEWORK WITH TRADITIONAL BIOSIGNAL MODELS" ]	0	All results presented in the main body focused on building a bridge between biosignal foundation models to better support ongoing research in this area. To show that BioX-Bridge is also compatible with traditional biosignal models, we replace HuBERT-ECG with a pre-trained ECG model, ECG-DualNet (Rohr et al., 2022), in ...	[]	[ { "raw": "Rohr et al., 2022", "authors": "Rohr et al.", "year": "2022" } ]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_1__sec25_p0	Artificial Intelligence_1	BIOX-BRIDGE: MODEL BRIDGING FOR UNSUPERVISED CROSS-MODAL KNOWLEDGE TRANSFER ACROSS BIOSIGNALS	D.3.2 IMPACT OF FOUNDATION MODEL SIZE	[ "D.3.2 IMPACT OF FOUNDATION MODEL SIZE" ]	0	The HubERT-ECG family of models is available in three sizes, with varying numbers of parameters. We replace the small version of HuBERT-ECG (30M) with base (93M) and large (183M) versions, and report both baseline and our results in Table A7. We observe that the best transfer performance for BioX-Bridge is achieved usi...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02276	[ { "firstname": "Chenqi", "surname": "Li", "email": "chenqi.li@eng.ox.ac.uk" }, { "firstname": "Yu", "surname": "Liu", "email": "yu.liu@eng.ox.ac.uk" }, { "firstname": "Timothy", "surname": "Denison", "email": "timothy.denison@eng.ox.ac.uk" }, { "firstname": "Tingt...	[]
Artificial Intelligence_10_abstract_p0	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	Abstract	[ "Abstract" ]	0	In tabular anomaly detection (AD), textual semantics often carry critical signals, as the definition of an anomaly is closely tied to domain-specific context. However, existing benchmarks provide only raw data points without semantic context, overlooking rich textual metadata such as feature descriptions and domain kno...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec0_p0	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	INTRODUCTION	[ "INTRODUCTION" ]	0	Anomaly detection (AD), the task of identifying patterns that diverge from the distribution of normal data, represents a fundamental challenge across diverse domains such as finance (Al-Hashedi & Magalingam, 2021), cybersecurity (Landauer et al., 2023), manufacturing (Kharitonov et al., 2022), and healthcare (Fernando ...	[]	[ { "raw": "Al-Hashedi & Magalingam, 2021", "authors": "Al-Hashedi & Magalingam", "year": "2021" }, { "raw": "Landauer et al., 2023", "authors": "Landauer et al.", "year": "2023" }, { "raw": "Kharitonov et al., 2022", "authors": "Kharitonov et al.", "year": "2022" }, { ...	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec0_p1	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	INTRODUCTION	[ "INTRODUCTION" ]	1	DAMI Repository (Campos et al., 2016) and ADBench (Han et al., 2022) have made significant contributions to tabular AD by establishing standardized benchmarks that enabled fair and rigorous comparisons across AD algorithms. Despite this contribution, these benchmarks were developed under earlier detection paradigms tha...	[]	[ { "raw": "Campos et al., 2016", "authors": "Campos et al.", "year": "2016" }, { "raw": "Han et al., 2022", "authors": "Han et al.", "year": "2022" }, { "raw": "Chandola et al., 2009", "authors": "Chandola et al.", "year": "2009" }, { "raw": "Pang et al., 2021", ...	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec0_p2	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	INTRODUCTION	[ "INTRODUCTION" ]	2	Motivated by these considerations, we introduce ReTabAD, the first context-aware tabular AD benchmark that incorporates rich textual metadata into AD, enabling models to ground their predictions in semantic context rather than purely numerical patterns. As highlighted in Table 1, our benchmark prioritizes quality by cu...	[]	[ { "raw": "Han et al., 2022", "authors": "Han et al.", "year": "2022" } ]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec0_p3	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	INTRODUCTION	[ "INTRODUCTION" ]	3	To sum up, our contributions are summarized as follows:	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec0_p4	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	INTRODUCTION	[ "INTRODUCTION" ]	4	• The first context-aware tabular AD benchmark: We present ReTabAD, which provides 20 carefully curated tabular datasets enriched with textual metadata and faithful anomaly definitions, and enables standardized evaluation across 16 unsupervised algorithms, including classical, deep learning, and LLM-based approaches.	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec0_p5	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	INTRODUCTION	[ "INTRODUCTION" ]	5	• Zero-shot tabular AD framework leveraging textual metadata: We introduce and evaluate a zero-shot LLM framework that establishes not only a strong and competitive baseline but also a new evaluation paradigm for context-aware tabular AD. By leveraging semantic context without task-specific training, ReTabAD provides a...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec0_p6	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	INTRODUCTION	[ "INTRODUCTION" ]	6	• Insights into context-aware tabular AD: We systematically analyze how incorporating textual metadata affects not only model performance but also reasoning quality. Specifically, we evaluate alignment between predicted key features and supervised attributions, and qualitatively examine model-generated reasoning texts,...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec1_p0	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	LIMITATIONS ON PREVIOUS TABULAR AD BENCHMARKS	[ "LIMITATIONS ON PREVIOUS TABULAR AD BENCHMARKS" ]	0	Benchmarking efforts in tabular AD have primarily aimed to provide reproducible and systematic comparisons of algorithms across diverse datasets. The comparative studies (Goldstein & Uchida, 2016;Campos et al., 2016;Ruff et al., 2021b) established the first generation of evaluation resources, offering systematic compar...	[]	[ { "raw": "Goldstein & Uchida, 2016", "authors": "Goldstein & Uchida", "year": "2016" }, { "raw": "Campos et al., 2016", "authors": "Campos et al.", "year": "2016" }, { "raw": "Ruff et al., 2021b", "authors": "Ruff et al.", "year": "2021b" }, { "raw": "Campos et al...	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec2_p0	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	RETABAD: TABULAR AD BENCHMARK RESTORING SEMANTIC CONTEXT	[ "RETABAD: TABULAR AD BENCHMARK RESTORING SEMANTIC CONTEXT" ]	0	To overcome these limitations as described in Section 2, we introduce ReTabAD, a next-generation benchmark that focuses exclusively on genuinely tabular datasets while systematically integrating rich semantic information. This design enables the systematic evaluation of context-aware AD. Label: Binary label (0: Normal,...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec3_p0	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	DATASETS	[ "DATASETS" ]	0	Data Collection and Annotation. ReTabAD covers 20 tabular datasets drawn from widely used repositories and real-world application domains. Building upon the comprehensive coverage of ADBench (Han et al., 2022), we curate a subset of datasets and enrich them with textual metadata according to the following criteria: (1)...	[]	[ { "raw": "Han et al., 2022", "authors": "Han et al.", "year": "2022" } ]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec3_p1	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	DATASETS	[ "DATASETS" ]	1	Semantically-rich Tabular Data. To incorporate rich semantic information into tabular data, we consider the following two principles:	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec3_p2	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	DATASETS	[ "DATASETS" ]	2	• Raw Numerical Preservation: ReTabAD preserves numerical features in their original scales rather than applying unspecified normalization or standardization. Maintaining raw values retains domain-meaningful interpretations that support intuitive reasoning about anomalies. For instance, a resting heart rate of 200 bpm ...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec3_p3	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	DATASETS	[ "DATASETS" ]	3	• Categorical Text Restoration: Existing benchmarks often provide categorical features in inconsistent formats (e.g., integer-encoded, text-embeddings) or apply inappropriate normalization that discards semantic meaning. Contrary to this practice, ReTabAD restores categorical attributes to their original textual values...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec3_p4	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	DATASETS	[ "DATASETS" ]	4	Structured Text Metadata. We provide each dataset supplemented with a JSON-formatted metadata file, containing structured semantic descriptions omitted in previous benchmarks. To ensure reliability, we provide direct links to the original data sources, enabling researchers to verify our interpretations.	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec3_p5	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	DATASETS	[ "DATASETS" ]	5	The metadata is organized into three categories:	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec3_p6	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	DATASETS	[ "DATASETS" ]	6	• Dataset-Level Descriptions: High-level descriptors, including dataset name, purpose, origin, and collection methodology. Each entry includes links to original publications or repositories to enhance credibility.	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec3_p7	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	DATASETS	[ "DATASETS" ]	7	• Column-Level Descriptions: For each column, ReTabAD provides its name, human-readable description, logical type (e.g., numerical, categorical, ordinal, binary), measurement units, if available. All descriptions are cross-referenced with the original dataset documentation.	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec3_p8	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	DATASETS	[ "DATASETS" ]	8	• Label-Level Descriptions: For each dataset, ReTabAD clearly specifies which classes are considered normal and which are treated as anomalies. These anomaly definitions are derived directly from the original dataset documentation, with full source attribution.	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec4_p0	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	ALGORITHMS	[ "ALGORITHMS" ]	0	To provide a comprehensive comparison, we evaluate 16 representative unsupervised AD models, with full algorithmic details provided in Appendix B. These models are grouped into three categories:	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec4_p1	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	ALGORITHMS	[ "ALGORITHMS" ]	1	(1) Classical methods: We include a diverse range of foundational algorithms that remain widely used. These encompass distance-based (e.g., KNN (Ramaswamy et al., 2000)), density-based (e.g., LOF (Breunig et al., 2000)), boundary-based (e.g., OCSVM (Schölkopf et al., 1999)), and ensemble techniques (e.g., Isolation For...	[]	[ { "raw": "Ramaswamy et al., 2000", "authors": "Ramaswamy et al.", "year": "2000" }, { "raw": "Breunig et al., 2000", "authors": "Breunig et al.", "year": "2000" }, { "raw": "Schölkopf et al., 1999", "authors": "Schölkopf et al.", "year": "1999" }, { "raw": "Liu et...	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec5_p0	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	ZERO-SHOT LLM FRAMEWORK FOR CONTEXT-AWARE AD	[ "ZERO-SHOT LLM FRAMEWORK FOR CONTEXT-AWARE AD" ]	0	Motivation. Despite the importance of textual metadata in real-world tabular datasets, there has been no systematic attempt to leverage such information. Conventional training-based AD models are designed to process data as numerical or categorical vectors, making it structurally difficult to directly integrate textual...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec5_p1	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	ZERO-SHOT LLM FRAMEWORK FOR CONTEXT-AWARE AD	[ "ZERO-SHOT LLM FRAMEWORK FOR CONTEXT-AWARE AD" ]	1	Our Design. To utilize LLMs for tabular AD, we propose a zero-shot LLM framework as a strong baseline for context-aware AD, as illustrated in Figure 2. We employ a carefully designed prompt that integrates domain knowledge and structured reasoning guidelines. The prompt for each data instance x i consists of three key ...	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec6_p0	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	System Prompt (S):	[ "System Prompt (S):" ]	0	The system prompt provides the necessary context for the task. It is constructed by concatenating the following information(detailed templates are in the Appendix D.3):	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec6_p1	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	System Prompt (S):	[ "System Prompt (S):" ]	1	• Role and Task Definition (S role ): Assigns the LLM an expert persona for each dataset's domain (e.g., "You are a domain expert in finance"). { "anomaly_score": s, "key_features": F, "reasoning": e } where s is the anomaly score in [0, 1], F is a list of key features, and e is the explanation in text.	[ 0, 1 ]	[]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[ { "id": 1, "text": "Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019\n\t\t\n\t\t\tAl-HashediKhaled Gubran\n\t\t\n\t\t\n\t\t\tPritheegaMagalingam\n\t\t\n\t\n\t\n\t\tComputer Science Review\n\t\t\n\t\t\t40\n\t\t\t100402\n\t\t\t2021" } ]
Artificial Intelligence_10__sec7_p0	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	EVALUATION	[ "EVALUATION" ]	0	Problem Setup. The concept of normality, a ground-truth law of normal behavior for a given task (Ruff et al., 2021a), is modeled through a probability distribution P with corresponding density function p(x). Conventional tabular anomaly detectors map each data instance x ∈ R K to a class label y ∈ {0, 1} by learning th...	[]	[ { "raw": "Ruff et al., 2021a", "authors": "Ruff et al.", "year": "2021a" } ]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec7_p1	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	EVALUATION	[ "EVALUATION" ]	1	Hyperparameter Optimization. We follow the default model architectures and tune key hyperparameters, including preprocessing settings. Hyperparameters for training-based methods are faithfully searched, and detailed configurations are provided in the Appendix C for reproducibility.	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec7_p2	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	EVALUATION	[ "EVALUATION" ]	2	Evaluator LLMs. We evaluate a set of leading LLMs in a zero-shot AD setting to assess their general semantic reasoning capabilities. Specifically, we include state-of-the-art reasoning-oriented models-GPT-4.1 (Fachada et al., 2025), Claude-3.7-sonnet (Anthropic, 2025), Qwen3-235B (Yang et al., 2025), and Gemini-2.5-pro...	[]	[ { "raw": "Fachada et al., 2025", "authors": "Fachada et al.", "year": "2025" }, { "raw": "Anthropic, 2025", "authors": "Anthropic", "year": "2025" }, { "raw": "Yang et al., 2025", "authors": "Yang et al.", "year": "2025" }, { "raw": "Comanici et al., 2025", "a...	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[ { "id": 2, "text": "Anthropic\n\t\t\n\t\t\n\t\t\n\t\t\tAugust 2025" }, { "id": 23, "text": "GPT-4o, DALL-E, and Whisper\n\t\t\n\t\t\tShimonIfrah\n\t\t\n\t\t10.1007/979-8-8688-0599-8_6\n\t\t\n\t\n\t\n\t\tGetting Started with Azure OpenAI\n\t\t\n\t\t\tApress\n\t\t\t2024" } ]
Artificial Intelligence_10__sec7_p3	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	EVALUATION	[ "EVALUATION" ]	3	Evaluation Protocol. ReTabAD follows the one-class classification setting, where the training set consists of 50% of the normal samples, while the test set consists of the remaining normal samples and all anomalous samples, as in previous studies (Bergman & Hoshen, 2020;Livernoche et al., 2023). For performance evaluat...	[]	[ { "raw": "Bergman & Hoshen, 2020", "authors": "Bergman & Hoshen", "year": "2020" }, { "raw": "Livernoche et al., 2023", "authors": "Livernoche et al.", "year": "2023" }, { "raw": "Ruff et al., 2018", "authors": "Ruff et al.", "year": "2018" }, { "raw": "Saito & Re...	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec8_p0	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	EXPERIMENTS ON RETABAD BENCHMARK	[ "EXPERIMENTS ON RETABAD BENCHMARK" ]	0	This section explores the potential of context-aware approaches in tabular AD. Specifically, we design our experiments to investigate the following research questions:	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]
Artificial Intelligence_10__sec8_p1	Artificial Intelligence_10	RETABAD: A BENCHMARK FOR RESTORING SEMAN-TIC CONTEXT IN TABULAR ANOMALY DETECTION	EXPERIMENTS ON RETABAD BENCHMARK	[ "EXPERIMENTS ON RETABAD BENCHMARK" ]	1	• Does semantic context M improve AD performance? ( §4.1)	[]	[]	2,025	null	https://arxiv.org//pdf/2510.02060	[ { "firstname": "Sanghyu", "surname": "Yoon", "email": "sanghyu.yoon@lgresearch.ai" }, { "firstname": "Dongmin", "surname": "Kim", "email": "dmkim@lgresearch.ai" }, { "firstname": "Suhee", "surname": "Yoon", "email": "suhee.yoon@lgresearch.ai" }, { "firstname": "Ye...	[]

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