Paper Citation Record · LEDGER

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation

As of 10 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 1 inbound Pith citation observation for arXiv:2505.17099.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.17099 v1

Coverage vector

measured 62 of 62 reference resolution

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One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

62 of 62 outbound references displayed

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External citation measurements

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Outbound references

Observation abb02a55-717a-4fd9-887b-0ac27b8d9de8 · outbound

This paper cites Decoding mental states from brain activity in humans.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Decoding mental states from brain activity in humans

Reference 1

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Observation 2508e1ff-18ef-47f1-8875-196fbd2db262 · outbound

This paper cites Decoding the brain: From neural representations to mechanistic models.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Decoding the brain: From neural representations to mechanistic models

Reference 2

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Observation 67754e52-6a1f-42d7-aaed-538652d914a4 · outbound

This paper cites Performance-optimized hierarchical models predict neural responses in higher visual cortex.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Performance-optimized hierarchical models predict neural responses in higher visual cortex

Reference 3

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Observation 8ee29453-0d48-47b7-b9a2-b8ff193750ad · outbound

This paper cites Decoding speech perception from non-invasive brain recordings.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Decoding speech perception from non-invasive brain recordings

Reference 4

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Observation 6d546039-dc72-4305-9231-a5d3e64f5e58 · outbound

This paper cites Brain diffusion for visual exploration: Cortical discovery using large scale generative models.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Brain diffusion for visual exploration: Cortical discovery using large scale generative models

Reference 5

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Observation e76109a0-f388-4e79-ba26-6df5323fa663 · outbound

This paper cites Brain decoding: toward real-time reconstruction of visual perception.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Brain decoding: toward real-time reconstruction of visual perception

Reference 6

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Observation d7238b4f-1bc6-4ac2-9898-f4694e2f5df8 · outbound

This paper cites Seeing beyond the brain: Conditional diffusion model with sparse masked modeling for vision decoding.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Seeing beyond the brain: Conditional diffusion model with sparse masked modeling for vision decoding

Reference 7

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Observation e16da1c5-7ac2-46a3-8c72-e75c5ed78396 · outbound

This paper cites High-resolution image reconstruction with latent diffusion models from human brain activity.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation High-resolution image reconstruction with latent diffusion models from human brain activity

Reference 8

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Observation c3dad1cf-c299-4850-8c8d-13ad19c5d62a · outbound

This paper cites Semantic reconstruction of continuous language from non-invasive brain recordings.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Semantic reconstruction of continuous language from non-invasive brain recordings

Reference 9

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Observation 02176eae-9223-4869-b949-dd88d2d7fe59 · outbound

This paper cites Open vocabulary electroencephalography-to-text decoding and zero-shot sentiment classification.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Open vocabulary electroencephalography-to-text decoding and zero-shot sentiment classification

Reference 10

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Observation ebc55401-8607-4fbc-b784-f66bd71a5568 · outbound

This paper cites Non-invasive brain-computer interfaces: state of the art and trends.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Non-invasive brain-computer interfaces: state of the art and trends

Reference 11

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Observation 28d0eb6a-5c50-405d-a328-d26ea8024c85 · outbound

This paper cites Dissociating language and thought in large language models.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Dissociating language and thought in large language models

Reference 12

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Observation 79f88d4b-ebd3-48ba-9eec-78e086dd0ea5 · outbound

This paper cites Onellm: One framework to align all modalities with language.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Onellm: One framework to align all modalities with language

Reference 13

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Observation c2b0c1aa-fa69-4cad-9915-2cdbc889d263 · outbound

This paper cites Unveiling thoughts: A review of advancements in eeg brain signal decoding into text.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Unveiling thoughts: A review of advancements in eeg brain signal decoding into text

Reference 14

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Observation 9edb42e9-b3d1-4056-9595-537be3e6701b · outbound

This paper cites Dewave: Discrete encoding of EEG waves for EEG to text translation.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Dewave: Discrete encoding of EEG waves for EEG to text translation

Reference 15

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Observation e3bdae26-5727-448b-b201-c130eb54a606 · outbound

This paper cites Enhancing eeg-to-text decoding through transferable representations from pre-trained contrastive eeg- text masked autoencoder.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Enhancing eeg-to-text decoding through transferable representations from pre-trained contrastive eeg- text masked autoencoder

Reference 16

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Observation c46af8e7-3fdf-49c6-8f7a-847e1313767b · outbound

This paper cites Are EEG-to-Text Models Working?.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Are EEG-to-Text Models Working?

Reference 17

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This paper cites Neural discrete representation learning.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Neural discrete representation learning

Reference 18

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This paper cites Zuco, a simultaneous eeg and eye-tracking resource for natural sentence reading.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Zuco, a simultaneous eeg and eye-tracking resource for natural sentence reading

Reference 19

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This paper cites ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotation.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotation

Reference 20

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Observation 8bc814c4-afcf-4866-acd2-38c00ab2c26b · outbound

This paper cites A Survey of Hallucination in Large Foundation Models.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation A Survey of Hallucination in Large Foundation Models

Reference 21

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Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Survey of hallucination in natural language generation

Reference 22

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Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Evaluating Object Hallucination in Large Vision-Language Models

Reference 23

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This paper cites Hallucination of Multimodal Large Language Models: A Survey.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Hallucination of Multimodal Large Language Models: A Survey

Reference 24

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This paper cites Estimating the hallucination rate of generative ai.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Estimating the hallucination rate of generative ai

Reference 25

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This paper cites Brainbits: How much of the brain are generative reconstruction methods using? Advances in Neural Information Processing Systems, 37:54396–54420, 2024.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Brainbits: How much of the brain are generative reconstruction methods using? Advances in Neural Information Processing Systems, 37:54396–54420, 2024

Reference 26

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This paper cites Spurious reconstruction from brain activity.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Spurious reconstruction from brain activity

Reference 27

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This paper cites Z-forcing: Training stochastic recurrent networks.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Z-forcing: Training stochastic recurrent networks

Reference 28

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This paper cites A survey on multimodal large language models.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation A survey on multimodal large language models

Reference 29

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This paper cites Deep neural networks rival the representation of primate it cortex for core visual object recognition.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Deep neural networks rival the representation of primate it cortex for core visual object recognition

Reference 30

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This paper cites Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence

Reference 31

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Observation fa625260-64d1-4cd6-ad28-ff2f7ecbe04b · outbound

This paper cites Convolutional neural network-based encoding and decoding of visual object recognition in space and time.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Convolutional neural network-based encoding and decoding of visual object recognition in space and time

Reference 32

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This paper cites Evidence of a predictive coding hierarchy in the human brain listening to speech.Nature human behaviour, 7(3):430–441,.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Evidence of a predictive coding hierarchy in the human brain listening to speech.Nature human behaviour, 7(3):430–441,

Reference 33

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This paper cites The neural architecture of language: Integrative modeling converges on predictive processing.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation The neural architecture of language: Integrative modeling converges on predictive processing

Reference 34

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This paper cites Low-dimensional structure in the space of language representations is reflected in brain responses.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Low-dimensional structure in the space of language representations is reflected in brain responses

Reference 35

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This paper cites Incorporating context into language encoding models for fmri.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Incorporating context into language encoding models for fmri

Reference 36

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This paper cites Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain).

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)

Reference 37

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This paper cites Predictive coding or just feature discovery? an alternative account of why language models fit brain data.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Predictive coding or just feature discovery? an alternative account of why language models fit brain data

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:14.437980Z

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Observation 64c46a49-80e5-4931-ba5b-32eeb903e014 · outbound

This paper cites Layer by Layer: Uncovering Hidden Representations in Language Models.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Layer by Layer: Uncovering Hidden Representations in Language Models

Reference 39

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This paper cites Recursive deep models for semantic compositionality over a sentiment treebank.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Recursive deep models for semantic compositionality over a sentiment treebank

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:14.341666Z

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This paper cites Integrating probabilistic extraction models and data mining to discover relations and patterns in text.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Integrating probabilistic extraction models and data mining to discover relations and patterns in text

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:14.225583Z

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Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:14.136211Z

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Observation 512140a0-b676-4d27-a507-d0ad4fb60846 · outbound

This paper cites Towards large-scale 3d representation learning with multi-dataset point prompt training.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Towards large-scale 3d representation learning with multi-dataset point prompt training

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:14.001025Z

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Observation 313c03bf-74b3-45e9-b53b-595e3d568f6a · outbound

This paper cites Transfer learning in brain-computer interfaces.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Transfer learning in brain-computer interfaces

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:13.865482Z

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Observation eab0e833-b280-4f8a-9c9a-d1c22ea3ff9b · outbound

This paper cites Inter-subject deep transfer learning for motor imagery eeg decoding.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Inter-subject deep transfer learning for motor imagery eeg decoding

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:13.734020Z

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Observation 1264b80d-11a3-4197-ac45-a2f2f7288a0c · outbound

This paper cites Brief segments of neurophysiological activity enable individual differentiation.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Brief segments of neurophysiological activity enable individual differentiation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:13.609376Z

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Observation 75b02fef-7982-478f-8ca9-b8cd4791e7c0 · outbound

This paper cites Scalable diffusion models with transformers.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Scalable diffusion models with transformers

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:13.323565Z

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Observation 72799861-2675-4c50-981b-6e49be81a701 · outbound

This paper cites Scaling instruction-finetuned language models.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Scaling instruction-finetuned language models

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:13.044322Z

Source-reported events for the cited work

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Observation 73db1cf3-0d77-4c3a-920a-800c49fb4150 · outbound

This paper cites BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Reference 49

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Observation e99a50b3-c9cb-404c-a110-366ee9bcf485 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:12.922324Z

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Observation 0c88117b-04e1-48a2-8544-e07e0d9a3c12 · outbound

This paper cites Extraphrase: Efficient data augmentation for abstractive summarization.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Extraphrase: Efficient data augmentation for abstractive summarization

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:12.828063Z

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Observation de68c36f-763f-4eb2-82cf-affdd82974db · outbound

This paper cites Learning transferable visual models from natural language supervision.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Learning transferable visual models from natural language supervision

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:12.715105Z

Source-reported events for the cited work

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Observation f8b0762e-df5a-4278-9d49-781ab6684213 · outbound

This paper cites Recent progress in wearable brain–computer interface (bci) devices based on electroencephalogram (eeg) for medical applications: a review.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Recent progress in wearable brain–computer interface (bci) devices based on electroencephalogram (eeg) for medical applications: a review

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:12.599720Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d56a2727-ea20-4190-907f-cfd0a14c4811 · outbound

This paper cites Brain-to-Text Decoding: A Non-invasive Approach via Typing.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Brain-to-Text Decoding: A Non-invasive Approach via Typing

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T15:28:10.725473Z

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Observation 613fb601-aea8-4e3d-a575-c325f75aafdb · outbound

This paper cites Thinking out loud, an open-access eeg-based bci dataset for inner speech recognition.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Thinking out loud, an open-access eeg-based bci dataset for inner speech recognition

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:12.499205Z

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Observation 2a3a4cb0-2d27-4169-b75a-94f10915a452 · outbound

This paper cites Pretraining Large Brain Language Model for Active BCI: Silent Speech.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation Pretraining Large Brain Language Model for Active BCI: Silent Speech

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:28:11.672292Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1883854c-7f17-4a27-97b9-eae0d5a751af · outbound

This paper cites The Llama 3 Herd of Models.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation The Llama 3 Herd of Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T15:28:10.894177Z

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Observation 947931a7-34fd-4779-a5c5-cb54827a8f67 · outbound

This paper cites You that read wrong again! a transposed- word effect in grammaticality judgments.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation You that read wrong again! a transposed- word effect in grammaticality judgments

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:12.367443Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b94ed2d8-01a5-4127-bd4a-b135e93f37e3 · outbound

This paper cites McGowan, Mahmoud M Elsherif, Michael G Cutter, Jingxin Wang, Zhiwei Liu, and Kevin B Paterson.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation McGowan, Mahmoud M Elsherif, Michael G Cutter, Jingxin Wang, Zhiwei Liu, and Kevin B Paterson

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:12.232479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1f7ce2c5-348f-462c-a8a0-6aa20e04ba6b · outbound

This paper cites When classifying grammatical role, bert doesn’t care about word order.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation When classifying grammatical role, bert doesn’t care about word order

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:28:12.107765Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 09266096-57cc-42f1-9cc3-67bb2d0e319e · outbound

This paper cites When does word order matter and when doesn't it?.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation When does word order matter and when doesn't it?

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:28:11.479328Z

Source-reported events for the cited work

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Observation aa4f04f2-b106-4db8-b90f-87587ece1f3d · outbound

This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T15:28:11.203385Z

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Pith citing papers

Observation fc69bf25-b60a-4d2a-b214-bf26b5747834 · inbound

Escaping the BLEU Trap: A Signal-Grounded Framework with Decoupled Semantic Guidance for EEG-to-Text Decoding cites this paper.

Escaping the BLEU Trap: A Signal-Grounded Framework with Decoupled Semantic Guidance for EEG-to-Text Decoding Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T03:27:34.519327Z

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