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Paper Citation Record · LEDGER

RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Models

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2406.11020.

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

pith.paper-citation-record.v1
2406.11020 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:36:46.216027Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T19:07:17.629184Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
  • unresolved0
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  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6966cf7e-bc05-4e2f-82bd-9e14df86f8a6 · inbound

Generative Agents for Multi-Agent Autoformalization of Interaction Scenarios cites this paper.

Generative Agents for Multi-Agent Autoformalization of Interaction Scenarios RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Models

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-11T17:37:05.300917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:37:05.300917Z digest=sha256:382117434bf8a68b9f2374a20ef8bb47fcc0dc1b9616fb762f25cb3eba3ecf6c

Observation 4aeaf685-397b-4fd3-a57d-7c85cfd8e104 · inbound

Investigating the Robustness of Deductive Reasoning with Large Language Models cites this paper.

Investigating the Robustness of Deductive Reasoning with Large Language Models RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Models

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-09T12:00:32.978837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T12:00:32.978837Z digest=sha256:6ce0e144b83d2a295cf69d36da3ad4e51f55a2a4cbf92f8dac9fc297cf1926c4

Observation aa4dcf69-077d-49ba-8cdc-3ef7994a1ff0 · inbound

Statistical Runtime Verification for LLMs via Robustness Estimation cites this paper.

Statistical Runtime Verification for LLMs via Robustness Estimation RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Models

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-16T10:36:46.216027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:36:46.216027Z digest=sha256:004b37a58d0c5ca20a5a6dae0f305fc5c9f61c77caf3678bc617826a5fc40a99

Observation 4ce7da9e-3f97-470c-a0c5-91b59f47040e · inbound

A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models cites this paper.

A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Models

Reference 181

Resolution
unresolved
no resolver link, observed 2026-08-05T10:39:07.110033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:39:07.110033Z digest=sha256:d5d52a9979fca45f932c950f29011a32874c7c6114da2717671b176a5cca86e0

Observation cb2b3ddc-6b9f-499f-924c-71350f93ab6e · inbound

ReasonBENCH: Benchmarking the (In)Stability of LLM Reasoning cites this paper.

ReasonBENCH: Benchmarking the (In)Stability of LLM Reasoning RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Models

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-03T17:53:56.270756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T17:53:56.270756Z digest=sha256:e6ac2140d582c459f7c636dadd19b0c337ae35ca11db7f6469f4d3485801ea8d

Observation ed6e48bb-5ca5-437e-bea2-6117ceec8fb8 · inbound

Towards a Science of AI Agent Reliability cites this paper.

Towards a Science of AI Agent Reliability RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Models

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-02T22:32:02.429944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T22:32:02.429944Z digest=sha256:f73ecb2680ee0bd3a9f420626cd20d3b040d302a776745cc1989755e2698eca3

Observation 786d5343-04c4-40a3-87fe-ee41f2fe97df · inbound

GSM-SEM: Benchmark and Framework for Generating Semantically Variant Augmentations cites this paper.

GSM-SEM: Benchmark and Framework for Generating Semantically Variant Augmentations RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Models

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-11T04:55:59.848096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-05-11T00:57:55.616036Z digest=sha256:e908b7e6ab34cad35f208288afdb03d8095fdffc8fe285ffe8688397e78dcdce

Observation 632b6b7c-1189-41fa-a37c-072bbc04f0e9 · inbound

GSM-SEM: Benchmark and Framework for Generating Semantically Variant Augmentations cites this paper.

GSM-SEM: Benchmark and Framework for Generating Semantically Variant Augmentations RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Models

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-06-30T23:45:07.992954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-06-30T23:42:00.965554Z digest=sha256:4c83e7ae6a8e002b12afa77385e77802c8b58f5d554d4cb3a3622c42f30631ca

Observation d13b6aa6-beb6-4b4b-80fb-57dd5289f287 · inbound

Seir\^enes: Adversarial Self-Play with Evolving Distractions for LLM Reasoning cites this paper.

Seir\^enes: Adversarial Self-Play with Evolving Distractions for LLM Reasoning RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Models

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-05-13T01:22:01.650069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-13T01:19:49.761472Z digest=sha256:859188af4d5863fa416c95d2210d6880c7004abd3a1e12ac6746adfcace85931

Observation 2b5dec2f-bf4f-42e9-a639-87bacd7473c5 · inbound

When Large Language Models Fail in Healthcare: Evaluating Sensitivity to Prompt Variations cites this paper.

When Large Language Models Fail in Healthcare: Evaluating Sensitivity to Prompt Variations RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Models

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-07-02T19:07:17.630636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-27T21:41:20.354463Z digest=sha256:4b120dc3e3729294d548400b822b1494613652dad25817686694b6b9abaa6b99

Observation 32d1364f-0811-4c0b-b22c-bc37564cea51 · inbound

Implicit Reasoning Steering via Concept Chaining cites this paper.

Implicit Reasoning Steering via Concept Chaining RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Models

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-02T02:44:22.745849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:44:22.745849Z digest=sha256:15f583f9133351274d785a756815cf61dd72ede1cb5d3e3741dbf5fab302c2ff

Observation 525f9cf0-1081-4e5c-9915-8614244d99de · inbound

Implicit Reasoning Steering via Concept Chaining cites this paper.

Implicit Reasoning Steering via Concept Chaining RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Models

Reference 173

Resolution
unresolved
no resolver link, observed 2026-08-02T02:44:36.232661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:44:36.232661Z digest=sha256:6183cf5d69e10e0c671e52eb102c8bc709741af9d3420a7af580ff6d14edf9eb

Observation 65bb48e2-b5b4-44b8-8708-0e44caf57646 · inbound

Understanding the Impact of Linguistic Realization Choices on LLM Stance with Causal Tracing cites this paper.

Understanding the Impact of Linguistic Realization Choices on LLM Stance with Causal Tracing RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Models

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-01T10:47:04.252559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T10:47:04.252559Z digest=sha256:14b0187c4aeaea30c80a8bd92763628c1316752cf4781021e1e63a9901d41876

Observation bbcbfa2d-e625-4dfc-81af-1e632d639675 · inbound

Wrong and More Confident: A Field Experiment on Large Language Models Taking a Graduate Economics Exam cites this paper.

Wrong and More Confident: A Field Experiment on Large Language Models Taking a Graduate Economics Exam RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Models

Reference 59

Resolution
unresolved
no resolver link, observed 2026-07-30T22:40:44.589052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T22:40:44.589052Z digest=sha256:d29f25b8153aa6a91d3b973248e79a9216bab945e90ae9538f62b7947597761b

Observation 66ad5cfa-ba6c-4d63-835b-bfe1016bfa12 · inbound

Wrong and More Confident: A Field Experiment on Large Language Models Taking a Graduate Economics Exam cites this paper.

Wrong and More Confident: A Field Experiment on Large Language Models Taking a Graduate Economics Exam RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Models

Reference 59

Resolution
unresolved
no resolver link, observed 2026-07-31T23:42:26.014902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-31T23:42:26.014902Z digest=sha256:f458f48d095f763b3ebef01b48db1637c6f0f3f4ec7fe2b73f9e61b9627f9870

Observation b58994f9-d4e6-4c6d-94d9-884a55ac8053 · inbound

Wrong and More Confident: A Field Experiment on Large Language Models Taking a Graduate Economics Exam cites this paper.

Wrong and More Confident: A Field Experiment on Large Language Models Taking a Graduate Economics Exam RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Models

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-03T01:52:38.577316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T01:52:38.577316Z digest=sha256:ba63eebcf45618c44a0b350b90c50e6f3913dd73ca8013cc0d62457ef11f5164

Observation 311eb501-d071-4230-9228-4c7e3f35a4eb · inbound

Benchmarking the Benchmarks: Testing the Predictive Validity of Commonsense Benchmarks cites this paper.

Benchmarking the Benchmarks: Testing the Predictive Validity of Commonsense Benchmarks RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Models

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-05T20:45:24.247556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T20:45:24.247556Z digest=sha256:6fe88c0d2222304daddff12c7e76f1693b1b12a44a51ffa84dd30f74e294545c