Pith. sign in

Paper Citation Record · LEDGER

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions

As of 8 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 1 inbound Pith citation observation for arXiv:2506.22679.

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

pith.paper-citation-record.v1
2506.22679 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:06:05.320718Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:06:02.530416Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T22:06:05.772852Z

Reference resolution

32 of 32 outbound references displayed

  • verified exact1
  • verified fuzzy21
  • unresolved9
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3368d40e-5550-4a2e-9cfb-097dace898b6 · outbound

This paper cites an unresolved cited work.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Unresolved cited work

Reference 1

Resolution
unresolved
raw_fallback, observed 2026-08-06T22:06:10.115031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:02.457055Z digest=sha256:541cbcf120f9a1be5ac0925f933988bfff2a16d374196447c23d579dab416918

Observation 47c6269a-a8ba-472b-ac21-1e6917a3e487 · outbound

This paper cites Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions

Reference 2

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T22:06:05.829656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:02.530416Z digest=sha256:7d5eec68e6da75f55fdaf100423ee21b1903765188673a3eaf4ac306d5af54f8

Observation 825ca329-bd37-4b1b-aaaa-06648824315f · outbound

This paper cites National Aeronautics and Space Administration (NASA).

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions National Aeronautics and Space Administration (NASA)

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:06:09.995163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:02.596581Z digest=sha256:3f825fdcefef4414d6ae3096f3d2e4b646611903b36f9b55ca17f930d9b0d4ab

Observation 7c55fb9a-3497-4fb6-b81d-0f5d0c27e67a · outbound

This paper cites an unresolved cited work.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Unresolved cited work

Reference 4

Resolution
unresolved
raw_fallback, observed 2026-08-06T22:06:09.857140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:02.662717Z digest=sha256:0a45b4fa5e998c52e6aa385e51482e0532ddfb0dc4fdd8de9d48836b83807555

Observation d0ab2201-24ed-44d7-9186-45a91768477b · outbound

This paper cites Fine-tuning on in-domain data improves performance.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Fine-tuning on in-domain data improves performance

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:06:09.703439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:02.768698Z digest=sha256:49ebf30da99fd8ada27f19f413d6078e5e0a68a75f46c64cbd7e6bb916dff760

Observation 34d0d2d2-5e92-4b42-9a5c-373109f260e3 · outbound

This paper cites an unresolved cited work.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Unresolved cited work

Reference 6

Resolution
unresolved
raw_fallback, observed 2026-08-06T22:06:09.588284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:02.847128Z digest=sha256:fdc88f3e06e5db1582c2128d483af57bc6769f712b4d2e739f26e30b315c9428

Observation 054dbda8-8a92-41fb-a818-c453448fecbd · outbound

This paper cites Are llms robust for spoken dialogues?.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Are llms robust for spoken dialogues?

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:06:09.425042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:02.937758Z digest=sha256:a0710aa317eda533f804cd7f8fea7f0371fc834fe90cc01c86aefde835613c3b

Observation 233b42e3-9126-4d21-9c5d-298d3e7afa3d · outbound

This paper cites Zero-shot spoken language understanding via large language models: A preliminary study,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Zero-shot spoken language understanding via large language models: A preliminary study,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:06:09.235187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:02.994728Z digest=sha256:292c120a397c8bb1f314c075a8599616d6d948a4ebe0c0143cc68382b90dd507

Observation 84ef243d-3a97-4d99-a3e9-778ed73496da · outbound

This paper cites Can ChatGPT detect intent? evaluat- ing large language models for spoken language understanding,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Can ChatGPT detect intent? evaluat- ing large language models for spoken language understanding,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:06:09.083233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:03.066448Z digest=sha256:08b1a0af5cbc20067773d750ac26bdfe62ba09851c412eeddb03754405ce7277

Observation 751b0907-1313-46c0-af51-6c806eccd9e0 · outbound

This paper cites Language Models are Few-Shot Learners.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Language Models are Few-Shot Learners

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T22:06:03.111953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:06:03.111953Z digest=sha256:bfeff43881e3271951aadc093b97601d739d0090a44bd55e39f6b9c9d8c78834

Observation b69ee10f-3ca0-4c83-9b0b-1d0c6a8c2394 · outbound

This paper cites Chain-of-thought prompting elic- its reasoning in large language models,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Chain-of-thought prompting elic- its reasoning in large language models,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:06:08.943249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:03.195999Z digest=sha256:13be5144099b65ce970a97ccc9a3064f82b24abccb062ef03b15e1bcf559435e

Observation 25b5a211-06ac-4e64-93c5-1c53ac020e05 · outbound

This paper cites Can generative artificial intelli- gence productivity tools support workplace learning? a qualitative study on employee perceptions in a multinational corporation,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Can generative artificial intelli- gence productivity tools support workplace learning? a qualitative study on employee perceptions in a multinational corporation,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:06:08.790506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:03.303517Z digest=sha256:e76dcba555b62b209d4f87cd26ba91c6fe1a999733529f6d0e2e49b67f68bfdd

Observation 785594cd-6300-4c71-8afd-c925f49f5838 · outbound

This paper cites LLM-based Smart Reply (LSR): Enhancing Collaborative Performance with ChatGPT-mediated Smart Reply System.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions LLM-based Smart Reply (LSR): Enhancing Collaborative Performance with ChatGPT-mediated Smart Reply System

Reference 13

Resolution
verified exact
local_arxiv, observed 2026-08-06T22:06:05.650856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:03.380544Z digest=sha256:50fdcf142d1303b09dca2a361cf4dc8fe4f8c3917dafdd95909b88920c1b9995

Observation 6fd7507f-f248-4d48-8b09-3b897c63311e · outbound

This paper cites Conversational ai as the new employee liaison: Llm-powered chatbots in enhancing workplace collaboration and inclusion,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Conversational ai as the new employee liaison: Llm-powered chatbots in enhancing workplace collaboration and inclusion,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:06:08.636680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:03.458071Z digest=sha256:d983483a78b7bf81499ccaf5a7819b9c2856ba025af0a378d99f4c9b0568f326

Observation f59babff-ddc9-442f-b0ef-b3bdb741b3d5 · outbound

This paper cites Selective incivility as modern discrimination in organi- zations: Evidence and impact,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Selective incivility as modern discrimination in organi- zations: Evidence and impact,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:06:08.452153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:03.557329Z digest=sha256:568d10f04681752a35326d56b0a10c9a14bb3f78e2e40cb1c093971f4e437d4b

Observation 92b04616-1e5d-4f56-b9cd-3273752ffcc8 · outbound

This paper cites Microaggressions, everyday dis- crimination, workplace incivilities, and other subtle slights at work: A meta-synthesis,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Microaggressions, everyday dis- crimination, workplace incivilities, and other subtle slights at work: A meta-synthesis,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:06:08.269138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:03.635534Z digest=sha256:7236b83eb8d426ab0f33b6041be470cb9cc0f2a12d2323dcaf2c0771b1428afa

Observation 258cd884-7e04-4b1f-b694-05ef5769cc02 · outbound

This paper cites What’s that supposed to mean? capturing micro- behaviors in teams,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions What’s that supposed to mean? capturing micro- behaviors in teams,

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:06:08.094021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:03.749291Z digest=sha256:222b62231a9e1c0c05db6278a43bf0f5f88cd80167eb9a113127dcdb5c7ab910

Observation 72111356-2dc5-4686-9768-f0081ee1dfbe · outbound

This paper cites an unresolved cited work.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Unresolved cited work

Reference 18

Resolution
unresolved
raw_fallback, observed 2026-08-06T22:06:07.906306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:03.815422Z digest=sha256:0d6a2e5acfcf2b00ecda0f53f706c33bc8872ffafd9d72af0f3f28d1e3403756

Observation 226bd455-75e3-4651-9852-0073d9431911 · outbound

This paper cites Automated detection of racial microaggressions using machine learning,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Automated detection of racial microaggressions using machine learning,

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:06:07.711369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:03.919136Z digest=sha256:ccb8bddcf873690449af52e67c953573ec7edd5f1f96766a5538169e0d1a9dab

Observation 197fcce8-4b2b-48f8-baf7-e1c14172575f · outbound

This paper cites Finding mi- croaggressions in the wild: A case for locating elusive phenom- ena in social media posts,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Finding mi- croaggressions in the wild: A case for locating elusive phenom- ena in social media posts,

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:06:07.500086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:04.063320Z digest=sha256:ca1d256abc6fa1e2a031e1ab2f262c9d35f1357ca50ded0fec96733e6eff5f9c

Observation 13ac2896-d5d0-4c30-ae69-d173d637255a · outbound

This paper cites Leveraging bias in pre- trained word embeddings for unsupervised microaggression de- tection,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Leveraging bias in pre- trained word embeddings for unsupervised microaggression de- tection,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:06:07.330739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:04.174213Z digest=sha256:73f83a1d606853adc42d2cfe6f16d7372b8c92eefd334740789811b03d860f2f

Observation 7933776a-bb73-472c-aac7-0cf34361fe0d · outbound

This paper cites Overview of machine learning algorithms for detect- ing microaggression in written text,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Overview of machine learning algorithms for detect- ing microaggression in written text,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:06:07.134677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:04.288844Z digest=sha256:e4348c3ee48f4aed51379c94d523a2ba39aeebc4e2f8cda6236a06a6f9d2b761

Observation 04785774-ac7a-407e-bb12-221e6c70f268 · outbound

This paper cites Towards identification of microaggressions in real-life and scripted conversations, using context-aware machine learning techniques,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Towards identification of microaggressions in real-life and scripted conversations, using context-aware machine learning techniques,

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:06:06.905442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:04.371648Z digest=sha256:e8af2f218dc3f4d4a59f134403286be21b20b73ee8515faf80238cfdd18108ea

Observation b033f9bb-5c5d-45b7-bd2c-814dae26ce57 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-06T22:06:04.482451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:06:04.482451Z digest=sha256:9bdcd5603aea634f7c98c172304a4421355b7e9fc1a68a8198f21f7dd31e7335

Observation 2a4196e5-0a42-4eba-b12c-faff25b0c39e · outbound

This paper cites Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:06:06.703788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:04.597187Z digest=sha256:f1d584e5cf0c2edc6ce449cd11c2a9f32e1644722ce3309629d16e309390c0ae

Observation 6ce26adb-9182-4877-a878-6b1c65e0a5d4 · outbound

This paper cites Twitter-roberta-base for sentiment analysis - up- dated (2022),.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Twitter-roberta-base for sentiment analysis - up- dated (2022),

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:06:06.503963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:04.811965Z digest=sha256:f51f5d47f6ef72eebbbf5ae42ea29f4a4ccc2d43653368565993abd60a646d23

Observation 2cccb562-2711-4a9d-a42a-c98585cded95 · outbound

This paper cites Distilbert base uncased finetuned sst-2,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Distilbert base uncased finetuned sst-2,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:06:06.365888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:04.906620Z digest=sha256:2de7923f71af2babebf7ec6baaf6d1f38daaeeadaef73130d12e36d85dd18ec8

Observation f348cbb2-e5f1-44fa-9d92-be49f41e6219 · outbound

This paper cites Pegasus: Pre-training with extracted gap-sentences for abstractive summarization,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Pegasus: Pre-training with extracted gap-sentences for abstractive summarization,

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:06:06.211616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:05.010157Z digest=sha256:c85fbb33eb0fdb9e44eda2af1dd0fae151b5e3fe481c2aee6a1ea808129ee9d1

Observation 452955ac-d86d-4042-90e7-10ae54aa0448 · outbound

This paper cites PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-06T22:06:05.111204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:06:05.111204Z digest=sha256:1cd2e0b45d6b7f037075cf42ad3b527d5298fea4963b7adddc4006726685ab5b

Observation c7323a0a-3598-49fd-bcf2-434f4c9dc53c · outbound

This paper cites A survey on in-context learning,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions A survey on in-context learning,

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:06:06.015784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:05.216960Z digest=sha256:533a9473ebb7e5e5597207114164ab0f09d1130e3b9da8e71abba17a5e318fa8

Observation 26a3cbe6-38e9-461f-9905-ff2854211263 · outbound

This paper cites The Llama 3 Herd of Models.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions The Llama 3 Herd of Models

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-06T22:06:05.320718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:06:05.320718Z digest=sha256:35a594e4f9c2492f701edcc17b2904f2b46ded432a04bb695d4a2d0c13636282

Observation 4085e5b1-c5fc-4f24-8ebd-677af39be5b1 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-06T22:06:04.712196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:06:04.712196Z digest=sha256:ee3c411b8785a977e0e7d64dc8925d014b0582afdae33d553e22a78978a88cea

Pith citing papers

Observation 47c6269a-a8ba-472b-ac21-1e6917a3e487 · inbound

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions cites this paper.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions

Reference 2

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T22:06:05.829656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:06:02.530416Z digest=sha256:7d5eec68e6da75f55fdaf100423ee21b1903765188673a3eaf4ac306d5af54f8