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Paper Citation Record · LEDGER

On Adversarial Robustness and Out-of-Distribution Robustness of Large Language Models

As of 13 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 1 inbound Pith citation observation for arXiv:2412.10535.

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

pith.paper-citation-record.v1
2412.10535 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:55:06.883874Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-08T03:39:30.528601Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T22:01:10.933034Z

Reference resolution

14 of 14 outbound references displayed

  • verified exact1
  • verified fuzzy1
  • unresolved11
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a45614df-493d-4ef7-a1fe-c183979d2a31 · outbound

This paper cites Mixtral of Experts.

On Adversarial Robustness and Out-of-Distribution Robustness of Large Language Models Mixtral of Experts

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-11T15:55:06.820556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T15:55:06.820556Z digest=sha256:27a53e1caeb68bfb859fbf081f7a55d172f03e81b9f18d643c201c9f88f82619

Observation d18c6f4e-b00c-4d2e-ae26-ea5897ad85f4 · outbound

This paper cites Out-of-Distribution Generalization via Risk Extrapolation (REx).

On Adversarial Robustness and Out-of-Distribution Robustness of Large Language Models Out-of-Distribution Generalization via Risk Extrapolation (REx)

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-11T15:55:06.826133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T15:55:06.826133Z digest=sha256:79526366fb97d9e4a01aedac1a6e4178d98faf4a828f2eb30b4f9b22621387eb

Observation dee32ef6-5c7c-49c9-b85b-9f2d9d9251d5 · outbound

This paper cites Translation of Gulmanelli's seminar notes "On a theory of isotopic spin" (1954).

On Adversarial Robustness and Out-of-Distribution Robustness of Large Language Models Translation of Gulmanelli's seminar notes "On a theory of isotopic spin" (1954)

Reference 3

Resolution
metadata mismatch
local_arxiv, observed 2026-08-11T15:55:07.074438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T15:55:06.831210Z digest=sha256:f7a84c897228e3b5e94963979c7bd835fb3910db763fef65d9aa5f921a5fda04

Observation fd75035f-d97b-4674-bbd4-e60da90720f1 · outbound

This paper cites Adversarial NLI: A New Benchmark for Natural Language Understanding.

On Adversarial Robustness and Out-of-Distribution Robustness of Large Language Models Adversarial NLI: A New Benchmark for Natural Language Understanding

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-11T15:55:06.835683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T15:55:06.835683Z digest=sha256:fde28be164f26a325a1751cd9cf800af1f7823176881af4e7ab9f5c3b9c1c609

Observation b67bdc66-e63d-452c-a3a7-3dac7a341a8d · outbound

This paper cites Improving Black-box Robustness with In-Context Rewriting.

On Adversarial Robustness and Out-of-Distribution Robustness of Large Language Models Improving Black-box Robustness with In-Context Rewriting

Reference 5

Resolution
verified exact
local_arxiv, observed 2026-08-11T15:55:07.035627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T15:55:06.840598Z digest=sha256:9a71208fc66286163796ea829a038cc573bb3cf151e71e10251951faae73822d

Observation 849c325d-3440-4c18-930c-e6230fb0abe9 · outbound

This paper cites DDXPlus: A New Dataset For Automatic Medical Diagnosis.

On Adversarial Robustness and Out-of-Distribution Robustness of Large Language Models DDXPlus: A New Dataset For Automatic Medical Diagnosis

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-11T15:55:06.845410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T15:55:06.845410Z digest=sha256:6e64e86192645e815faefd54104d485a40ef6835d3797ec8599f36b9a7c835b8

Observation 384bc2ec-3938-4ca0-8dc7-9ceb2bb638cb · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

On Adversarial Robustness and Out-of-Distribution Robustness of Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-11T15:55:06.851028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T15:55:06.851028Z digest=sha256:bc6347891663b3a7032f45da7ef553dfa20ee12cd9140c58045c37693010d950

Observation 061586fe-c6ab-46b9-bfeb-669bf94aaefe · outbound

This paper cites and Thummar, M.

On Adversarial Robustness and Out-of-Distribution Robustness of Large Language Models and Thummar, M

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:55:07.130052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T15:55:06.855872Z digest=sha256:b278ce0bd13ff93bf74a47581cc3ca927b5892ec9b02f349e72daa907fc29ffe

Observation bef0e7fc-336f-4b0a-962b-42dce9246ae8 · outbound

This paper cites Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models.

On Adversarial Robustness and Out-of-Distribution Robustness of Large Language Models Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-11T15:55:06.860412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T15:55:06.860412Z digest=sha256:a27bb75518ad4274d2dd1f752aad4e15f8fa81f876759f5d6c09c242b5d51770

Observation 7dd7d6ac-b7da-484a-8eb2-b093f7fc072f · outbound

This paper cites DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models.

On Adversarial Robustness and Out-of-Distribution Robustness of Large Language Models DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-11T15:55:06.865174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T15:55:06.865174Z digest=sha256:6a7fee474284da356f12129d398adb4f79504d4f5366254a37d91310a277c827

Observation b7309f94-17e1-4fa0-8881-c61190db2f63 · outbound

This paper cites On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective.

On Adversarial Robustness and Out-of-Distribution Robustness of Large Language Models On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-11T15:55:06.869867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T15:55:06.869867Z digest=sha256:29662ae966a09cffe55762a7478e9323ea4c1486ca5dbb83368b8d4653ded61b

Observation 919ea228-6e2a-4058-81a0-a9d1e190473c · outbound

This paper cites Improving the Robustness of Large Language Models via Consistency Alignment.

On Adversarial Robustness and Out-of-Distribution Robustness of Large Language Models Improving the Robustness of Large Language Models via Consistency Alignment

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-11T15:55:06.873942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T15:55:06.873942Z digest=sha256:81f54511559601a7f72d42f0064a76a95daf0125744c4e4913ee4a0494efe39e

Observation 5e5aa873-6119-4f0b-8d07-1ae22f9c5112 · outbound

This paper cites PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts.

On Adversarial Robustness and Out-of-Distribution Robustness of Large Language Models PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-11T15:55:06.879492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T15:55:06.879492Z digest=sha256:d6a607980fc52079ec274ade158b870b4903cfce1f55e262d69a3e5e985bc84e

Observation f3e39df0-f87e-44f7-8ae2-e79eb9e36b5d · outbound

This paper cites write newline.

On Adversarial Robustness and Out-of-Distribution Robustness of Large Language Models write newline

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-11T15:55:06.883874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T15:55:06.883874Z digest=sha256:df0d707a6298b1e76218701e1154911b1b5ddf09c4e57d61ed5cdf52063ab93e

Pith citing papers

Observation c871ab23-4477-4b01-ae08-aefdff1dd953 · inbound

STELLAR-E: a Synthetic, Tailored, End-to-end LLM Application Rigorous Evaluator cites this paper.

STELLAR-E: a Synthetic, Tailored, End-to-end LLM Application Rigorous Evaluator On Adversarial Robustness and Out-of-Distribution Robustness of Large Language Models

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-05-11T22:01:10.936182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-08T03:39:30.528601Z digest=sha256:0ea0577a7bb7cdf40504d26fa62d16d39620ca53d3a621b709685a5cd356042c