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Paper Citation Record · LEDGER

V-FiLLM: Verified Financial LLM Reasoning Benchmark

As of 12 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2608.11047.

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

pith.paper-citation-record.v1
2608.11047 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:27:14.740756Z

measured 55 of 55 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

55 of 55 outbound references displayed

  • verified exact7
  • verified fuzzy5
  • unresolved39
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a82a223f-ce73-4e1a-86c8-524e11e4605b · outbound

This paper cites Royal Society Open Science , author =.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Royal Society Open Science , author =

Reference 1

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verified fuzzy
raw_fallback, observed 2026-08-12T11:27:15.814555Z

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=2026-08-12T11:27:14.465181Z digest=sha256:3cf6b0257bdde003767718690d46e038da0ad40b2ecd5cf492a6abc20b7f62d8

Observation 18c20d07-9891-43d6-8914-389ee9b160c2 · outbound

This paper cites an unresolved cited work.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Unresolved cited work

Reference 2

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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=2026-08-12T11:27:14.472921Z digest=sha256:10332bead083b6e1b039dea538a88d83de73b8b0574362f3ebbdfcc49f21e515

Observation d3d4b963-4817-483b-ad8c-f8a3a56859bd · outbound

This paper cites Journal of Financial Data Science , author =.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Journal of Financial Data Science , author =

Reference 3

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raw_fallback, observed 2026-08-12T11:27:15.784013Z

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=2026-08-12T11:27:14.478110Z digest=sha256:16a09c9e0c476b9c70ff31be8b9828e3974a37fc2fb52632f945e69730ac204f

Observation 21207e7a-cb1e-403f-a346-6a4f00d03b27 · outbound

This paper cites GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models.

V-FiLLM: Verified Financial LLM Reasoning Benchmark GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:27:14.482978Z digest=sha256:1df02688645dbae8e438a859d0f7bc769d628be516bc8e5544bb14ffb77ccacd

Observation 1be2fae3-78d6-474f-b0d8-ca1693951683 · outbound

This paper cites and Schärli, Nathanael and Zhou, Denny , month = jul, year =.

V-FiLLM: Verified Financial LLM Reasoning Benchmark and Schärli, Nathanael and Zhou, Denny , month = jul, year =

Reference 5

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raw_fallback, observed 2026-08-12T11:27:15.768285Z

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=2026-08-12T11:27:14.488851Z digest=sha256:d50c9a2c008b77375bbeb5cba1c428bf4c616b37c10a3dec9725467ed6f775b8

Observation 8a2e1531-9206-4fea-8023-4fd74050eb7a · outbound

This paper cites Proceedings of the 2024.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Proceedings of the 2024

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:27:14.494069Z digest=sha256:3f8b19383adcc7a8655b9442840eb8d59275a5c2845d02b61b4e293dcc7cb69f

Observation 11901028-0c03-4170-a2d9-03d864c444ab · outbound

This paper cites Proceedings of the 61st.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Proceedings of the 61st

Reference 7

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source=arxiv_source observed=2026-08-12T11:27:14.499162Z digest=sha256:c1dc7ed2ad6cef4496b0ad3f1617b729244d0fe91a503ba4bceee28b59887161

Observation 8275cf26-577b-4e5c-90f2-ee5a54ca28ed · outbound

This paper cites an unresolved cited work.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Unresolved cited work

Reference 8

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parse uncertain
no resolver link, observed 2026-08-12T11:27:14.504228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:27:14.504228Z digest=sha256:a82f61fa01aa54642161703ef9be96329e6b4659cd2a60b5fdc761e8d03a5cb9

Observation 638d1d04-55ae-4633-9f53-0766c56e991e · outbound

This paper cites Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 9

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source=arxiv_source observed=2026-08-12T11:27:14.508896Z digest=sha256:a4fba03cb460cbe0b311d8b031e0c0d3253fce3f7e28201a0681286ec04d373d

Observation 6070be9a-045b-4b3d-b50c-a1f8e4f02ad1 · outbound

This paper cites arXiv.org , author =.

V-FiLLM: Verified Financial LLM Reasoning Benchmark arXiv.org , author =

Reference 10

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verified fuzzy
raw_fallback, observed 2026-08-12T11:27:15.742432Z

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=2026-08-12T11:27:14.514276Z digest=sha256:60d824b107966fda4405a530f0e29ef5462180c083e6c4cdc8680d3f00dd2773

Observation 295d2c15-6841-4434-89b4-817a26a17ff7 · outbound

This paper cites an unresolved cited work.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Unresolved cited work

Reference 11

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

source=arxiv_source observed=2026-08-12T11:27:14.519444Z digest=sha256:41bbe17c0ec1fb8dd58959f6ed574a7b09328a24f42212bb62152f0286f727c7

Observation d68f7240-e2a6-436b-881e-57c7c7fcb871 · outbound

This paper cites an unresolved cited work.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Unresolved cited work

Reference 12

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:27:14.523907Z digest=sha256:a00f2edb881d88894f186579478c4f7b6ce0b34caeb419f255688379a21a18c5

Observation 6dc34919-0876-49ad-8c58-b9e45926ec96 · outbound

This paper cites State of.

V-FiLLM: Verified Financial LLM Reasoning Benchmark State of

Reference 13

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source=arxiv_source observed=2026-08-12T11:27:14.528646Z digest=sha256:bb8d7b7e07f8892b91853a505c0b4afc5310c71f3e20372a56e3a7e2c4b50724

Observation b1df4501-1674-4086-84be-0c3ad29511a4 · outbound

This paper cites Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 14

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source=arxiv_source observed=2026-08-12T11:27:14.533197Z digest=sha256:e2a514c15ede4cd4bfcb05ace04e464efa6ea3ae7c44d38e4667c27309a0e2f1

Observation 2e070690-7432-4f89-9411-eadca9a51fac · outbound

This paper cites LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models.

V-FiLLM: Verified Financial LLM Reasoning Benchmark LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 15

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no resolver link, observed 2026-08-12T11:27:14.538468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:27:14.538468Z digest=sha256:f8775879f89f1f18579c3096bfc19115bc08d5a2981b13a2b39d84697ac1dfa7

Observation 9a7d4f31-e938-485a-b948-ddb02759a5a4 · outbound

This paper cites Parameter-Efficient Transfer Learning for NLP.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Parameter-Efficient Transfer Learning for NLP

Reference 16

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source=arxiv_source observed=2026-08-12T11:27:14.543528Z digest=sha256:95a7b45e56eb575303d339f267a89d1e7f2e395f8967a42fcc21dbe15c2bffb9

Observation 1fa46fb9-7413-44e4-8bdf-539da2ea1bae · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

V-FiLLM: Verified Financial LLM Reasoning Benchmark LoRA: Low-Rank Adaptation of Large Language Models

Reference 17

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no resolver link, observed 2026-08-12T11:27:14.548592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:27:14.548592Z digest=sha256:aa7117a7404ac46b94c6aedff9d26b85f592232f1896ad7a66b85b6e9cf10978

Observation f8aa8649-599e-4745-9a40-a696840490cf · outbound

This paper cites Faith and Fate: Limits of Transformers on Compositionality.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Faith and Fate: Limits of Transformers on Compositionality

Reference 18

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source=arxiv_source observed=2026-08-12T11:27:14.554690Z digest=sha256:776b92eff88b20f7c6453eb7626622e488a02621771ebce16c4a075a51d2cadc

Observation 8cfed1da-fdef-44d5-9985-0b5c4f905fae · outbound

This paper cites Compositional.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Compositional

Reference 19

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source=arxiv_source observed=2026-08-12T11:27:14.559559Z digest=sha256:a7a8e0ad8bb5efa0cf22c60990b81f9afc6dc4fda9d241f4188df0fb98c17ab6

Observation dd3966e0-d8c0-4322-870a-048d2854dae9 · outbound

This paper cites Findings of the.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Findings of the

Reference 20

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source=arxiv_source observed=2026-08-12T11:27:14.564906Z digest=sha256:99fc7a9c4dad5ec4e20bcb97a5cb4cb8334191834c9692c08177c6c7c32be456

Observation 91bf9fc0-82d3-427b-aa8d-ebad0bdf38b9 · outbound

This paper cites DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks.

V-FiLLM: Verified Financial LLM Reasoning Benchmark DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks

Reference 21

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source=arxiv_source observed=2026-08-12T11:27:14.569773Z digest=sha256:c8eed52a80e8c918e056b4b7a2ba8cb48db97e35d1627e5c9748a707d63eb1d9

Observation 24d6dea3-d680-4ed2-ad79-01097e3172ee · outbound

This paper cites BizBench: A Quantitative Reasoning Benchmark for Business and Finance.

V-FiLLM: Verified Financial LLM Reasoning Benchmark BizBench: A Quantitative Reasoning Benchmark for Business and Finance

Reference 22

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source=arxiv_source observed=2026-08-12T11:27:14.574849Z digest=sha256:5f9fdd92641b719c54e3bbd494fcc8ad9cd2b8f5a12a3726ce312c3938c534a8

Observation 7f11c845-30cd-4694-ad99-33752ee8ad1e · outbound

This paper cites MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data.

V-FiLLM: Verified Financial LLM Reasoning Benchmark MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data

Reference 23

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source=arxiv_source observed=2026-08-12T11:27:14.579823Z digest=sha256:a3c651812662260d68c84a1b9ee02b0ed674639d372f58ffefe607832b69adad

Observation ce0b6129-7434-4486-baee-307b062af219 · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

V-FiLLM: Verified Financial LLM Reasoning Benchmark GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 24

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source=arxiv_source observed=2026-08-12T11:27:14.584718Z digest=sha256:1b57b723ba8d7742b0925985266c15315d637da5f9ee492fcf34ad8795866eb4

Observation d8e4b936-32ed-49d7-8968-d03e5810c415 · outbound

This paper cites Findings of the.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Findings of the

Reference 25

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source=arxiv_source observed=2026-08-12T11:27:14.590042Z digest=sha256:fe0ee66e01692bf1095aa8e31cb23e8f711abfe82e40482c2fa3f6c4ba6a5390

Observation a4c2a1a7-ee44-4d37-8fbb-5b987b6af374 · outbound

This paper cites TabFact: A Large-scale Dataset for Table-based Fact Verification.

V-FiLLM: Verified Financial LLM Reasoning Benchmark TabFact: A Large-scale Dataset for Table-based Fact Verification

Reference 26

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source=arxiv_source observed=2026-08-12T11:27:14.594944Z digest=sha256:649b92f2fa087f450c17cbe1e01a6b8932e01ac5b84cb47b082d9ca1f7fbe519

Observation f1adfcf5-f805-47ce-beca-cfd2b880a6bb · outbound

This paper cites doi:10.48550/arXiv.2509.25160 , abstract =.

V-FiLLM: Verified Financial LLM Reasoning Benchmark doi:10.48550/arXiv.2509.25160 , abstract =

Reference 27

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doi, observed 2026-08-12T11:27:15.425169Z

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

source=arxiv_source observed=2026-08-12T11:27:14.600117Z digest=sha256:fb58af1a20a702ff05f99d299303781005da96408b775940990996899578c063

Observation e3a3cd35-f541-4820-a96e-53553c5be3c1 · outbound

This paper cites DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.

V-FiLLM: Verified Financial LLM Reasoning Benchmark DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

Reference 28

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source=arxiv_source observed=2026-08-12T11:27:14.604742Z digest=sha256:14fa5c3acb1310536f2443b125f98a91e2f44dd4c7847021648de47bcf09c6ea

Observation 42a7500a-9b27-4ba5-b9bb-d96447bfe60d · outbound

This paper cites TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance.

V-FiLLM: Verified Financial LLM Reasoning Benchmark TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance

Reference 29

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source=arxiv_source observed=2026-08-12T11:27:14.609994Z digest=sha256:50262c06f458e17b9474025fdca77cd0ac4a6d141c685260cf6a6f77484a2d24

Observation 8500b8c7-4524-488f-aecf-0c407067f575 · outbound

This paper cites DORO: Distributional and Outlier Robust Optimization.

V-FiLLM: Verified Financial LLM Reasoning Benchmark DORO: Distributional and Outlier Robust Optimization

Reference 30

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local_arxiv, observed 2026-08-12T11:27:15.309985Z

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=2026-08-12T11:27:14.615115Z digest=sha256:e0c2e7ed2a39678b2f12ca4ca8572485066fe3340a2119b75a0cadee4d275991

Observation 17ebcb4b-0bde-442b-839a-eb79b1d79672 · outbound

This paper cites and Haghgoo, Behzad and Chen, Annie S.

V-FiLLM: Verified Financial LLM Reasoning Benchmark and Haghgoo, Behzad and Chen, Annie S

Reference 31

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raw_fallback, observed 2026-08-12T11:27:15.710880Z

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=2026-08-12T11:27:14.620342Z digest=sha256:f43499f88c9c2dc6fdb072f49653b48d5253209dd84144eaf6a8af708c916f41

Observation 893c57f4-7fcf-481f-8125-1d612ac6cda5 · outbound

This paper cites Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Reference 32

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source=arxiv_source observed=2026-08-12T11:27:14.625334Z digest=sha256:de8115748c1257b97dc21a907c3ba426a696c30919964ff3d9dcd81375503eb1

Observation c5b2f6bc-9153-46e8-b5f2-fa4d890b401a · outbound

This paper cites Evaluating.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Evaluating

Reference 33

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source=arxiv_source observed=2026-08-12T11:27:14.630485Z digest=sha256:00ccabd24d32622f08c034a711a056697ae96cc4e5699f0dbdf7579f40dadf1e

Observation 6a11c748-9428-41a6-be10-d3b0609dc4f5 · outbound

This paper cites Enabling.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Enabling

Reference 34

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source=arxiv_source observed=2026-08-12T11:27:14.635401Z digest=sha256:59ceb09b1cc24055051f13d3b9b8ef929393c142178eeca943ec81ffa79384ef

Observation 70fd02e5-b0f8-4348-a893-5c06c9662ec1 · outbound

This paper cites Measuring.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Measuring

Reference 35

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source=arxiv_source observed=2026-08-12T11:27:14.640336Z digest=sha256:6f16fe118d7e8ac587f4c4e7992509ed3e60dc10eb71386855d1cb8e5f28390f

Observation 435d29ca-81d3-40cf-8d41-72191df3d903 · outbound

This paper cites Advances in Neural Information Processing Systems , author =.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Advances in Neural Information Processing Systems , author =

Reference 36

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source=arxiv_source observed=2026-08-12T11:27:14.645389Z digest=sha256:3c158d8221ec5cd3d45d0353e8a315e97103a4f9475d78a43ab748ec44647113

Observation 6ae0a78c-693f-4c9c-a4ba-6c5ab9043d4e · outbound

This paper cites DeepSciVerify: Verifying Scientific Claim--Citation Alignment via LLM-Driven Evidence Escalation.

V-FiLLM: Verified Financial LLM Reasoning Benchmark DeepSciVerify: Verifying Scientific Claim--Citation Alignment via LLM-Driven Evidence Escalation

Reference 37

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local_arxiv, observed 2026-08-12T11:27:15.239546Z

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=2026-08-12T11:27:14.649731Z digest=sha256:04486118370cdb952794ff6024ec77b593b05e7372ba7bf4f57be1a4339a307d

Observation 0786b739-323b-406d-81cf-1a8b92555a4d · outbound

This paper cites Evaluating Large Language Models at Evaluating Instruction Following.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Evaluating Large Language Models at Evaluating Instruction Following

Reference 38

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unresolved
no resolver link, observed 2026-08-12T11:27:14.654832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:27:14.654832Z digest=sha256:b712d5bec24334c990a524f007faeed87fe901ad00e50b492aa04965e2b98fc4

Observation 04ce2feb-6030-4475-83fc-3616d40e9a35 · outbound

This paper cites Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-12T11:27:14.659813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:27:14.659813Z digest=sha256:dbae475b6c03140b8f7f80a956547a933c605657e68360c77fda246124b8572e

Observation 372cb7e0-17b5-4fbf-9a42-f20d3b8c57c3 · outbound

This paper cites an unresolved cited work.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Unresolved cited work

Reference 40

Resolution
verified exact
doi, observed 2026-08-12T11:27:15.181180Z

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=2026-08-12T11:27:14.664613Z digest=sha256:e3789a9f219a120a4fe7ee3f4b0e7998456379d7e3c841e85d8a97c32440cead

Observation 28a4c5a3-e92b-42c4-953e-6024c09ca0c6 · outbound

This paper cites Proceedings of the 63rd.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Proceedings of the 63rd

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-12T11:27:14.669429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:27:14.669429Z digest=sha256:8c11f259d1db4aeef177e7f9952aed6c08cc6fc81987072233de45457999d2d2

Observation 3e170810-b0cb-4fdf-893b-cedca4f5aa9b · outbound

This paper cites an unresolved cited work.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Unresolved cited work

Reference 42

Resolution
verified exact
doi, observed 2026-08-12T11:27:15.100351Z

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=2026-08-12T11:27:14.675157Z digest=sha256:ce42238365b239db348be151e595e7484cd23171d1c461c0728c9a5929314799

Observation a744e5b3-94de-4f9c-abd5-b48e66509e8b · outbound

This paper cites On the Limits of LLM-as-Judge for Scientific Novelty Assessment.

V-FiLLM: Verified Financial LLM Reasoning Benchmark On the Limits of LLM-as-Judge for Scientific Novelty Assessment

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-12T11:27:14.679948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:27:14.679948Z digest=sha256:43a7b1dff2cbb99a9b5ae7f9fbef4684748a996c86fcfec19b4e4917ef1ddc88

Observation 25cb44d5-be12-404e-bdc6-331dd3e08d6b · outbound

This paper cites and Goel, Pranav and Stoehr, Niklas and Ash, Elliott and Hoyle, Alexander Miserlis , editor =.

V-FiLLM: Verified Financial LLM Reasoning Benchmark and Goel, Pranav and Stoehr, Niklas and Ash, Elliott and Hoyle, Alexander Miserlis , editor =

Reference 44

Resolution
verified exact
doi, observed 2026-08-12T11:27:14.973197Z

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=2026-08-12T11:27:14.684978Z digest=sha256:d20184adbb11450a7963dbc1b6090113f5e796098763ac1829040da76fd6b9c9

Observation c4fff4a4-17d5-4146-894e-98057756c811 · outbound

This paper cites Cited but Not Verified: Parsing and Evaluating Source Attribution in LLM Deep Research Agents.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Cited but Not Verified: Parsing and Evaluating Source Attribution in LLM Deep Research Agents

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-12T11:27:14.690819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:27:14.690819Z digest=sha256:ff7844d9adb84acae769ccf77b38d856fbcb1b635ce122d43f55b3e80105d0f2

Observation 9743ab4e-951d-4903-8156-94fb349244f4 · outbound

This paper cites AURA: Adaptive Uncertainty-aware Refinement for LLM-as-a-Judge Auditing.

V-FiLLM: Verified Financial LLM Reasoning Benchmark AURA: Adaptive Uncertainty-aware Refinement for LLM-as-a-Judge Auditing

Reference 46

Resolution
metadata mismatch
local_arxiv, observed 2026-08-12T11:27:14.941244Z

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=2026-08-12T11:27:14.695950Z digest=sha256:18a2fe182e2d7ce973a572de452377732b7ff17bf1ea60fd60e5ff934b2d9997

Observation c487bd08-d28b-4bf6-966a-6ce5fbfbb600 · outbound

This paper cites Rethinking Literature Search Evaluation: Deep Research Helps, and Human Citation Lists Are Not a Ground Truth.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Rethinking Literature Search Evaluation: Deep Research Helps, and Human Citation Lists Are Not a Ground Truth

Reference 47

Resolution
verified exact
local_arxiv, observed 2026-08-12T11:27:14.919387Z

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=2026-08-12T11:27:14.700894Z digest=sha256:a2990140b053b99c90316d765070564fa9d35b66d4c34a93f1ea16173bd1f8fe

Observation 71e4d072-acbd-4332-aee2-764f35a263fd · outbound

This paper cites Citation Grounding Measures the Oracle: Graph Coverage Determines Reported LLM Hallucination Rates in Law.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Citation Grounding Measures the Oracle: Graph Coverage Determines Reported LLM Hallucination Rates in Law

Reference 48

Resolution
verified exact
local_arxiv, observed 2026-08-12T11:27:14.897265Z

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=2026-08-12T11:27:14.705829Z digest=sha256:c0a0c9adb3eb04b3f6daf2e22486cb925b6ca5e12a7349b3adbeed22639890be

Observation a5c890be-981e-49d4-b186-c5aec3ffd088 · outbound

This paper cites LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models.

V-FiLLM: Verified Financial LLM Reasoning Benchmark LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-12T11:27:14.710985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:27:14.710985Z digest=sha256:6bfff0bda216f4c426ccf4e49ca2cc3ad7687650f7fbe1fdf2e2086ab9a2e7d4

Observation 6f21bfed-a74f-4d0c-bbe5-4c08ef2704e8 · outbound

This paper cites Findings of the.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Findings of the

Reference 50

Resolution
verified exact
doi, observed 2026-08-12T11:27:14.858172Z

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=2026-08-12T11:27:14.716472Z digest=sha256:24d5d5d8e5c913333b866e7511e6e5138c8ee3f93d3f0842c35ec1edbc8e889c

Observation b9d8cbf2-89a2-4078-8134-c665b339f574 · outbound

This paper cites FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets.

V-FiLLM: Verified Financial LLM Reasoning Benchmark FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-12T11:27:14.721155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:27:14.721155Z digest=sha256:1fc34321be69090b16595d2784e2cf79e1a19f2a82daf7dba43eaf582c3076db

Observation 3e500578-578c-4cfb-892c-0023d18ce1c8 · outbound

This paper cites FinanceBench: A New Benchmark for Financial Question Answering.

V-FiLLM: Verified Financial LLM Reasoning Benchmark FinanceBench: A New Benchmark for Financial Question Answering

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-12T11:27:14.726217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:27:14.726217Z digest=sha256:31c3e0c7480c15ba0f7d202ffd741817e045575d1fdae6ab2c2bb9cefa953b9f

Observation df4e0999-fe47-4441-a68f-e71cba2bccb3 · outbound

This paper cites Beyond Knowledge to Agency: Evaluating Expertise, Autonomy, and Integrity in Finance with CNFinBench.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Beyond Knowledge to Agency: Evaluating Expertise, Autonomy, and Integrity in Finance with CNFinBench

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-12T11:27:14.730913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:27:14.730913Z digest=sha256:9030d3c0c15ceeb1cab38a813709f267312224bd416b14aaa8faa9af75c77816

Observation 788d2f23-0e55-4f54-9dbb-b24f9e08b1af · outbound

This paper cites ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering.

V-FiLLM: Verified Financial LLM Reasoning Benchmark ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-12T11:27:14.735752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:27:14.735752Z digest=sha256:633711a855d2e04d63aa189d71fe546b8d433685fea06e705703a414253bbb97

Observation 1e72da66-2209-4f13-bfee-8d4b5c18002b · outbound

This paper cites FinQA: A Dataset of Numerical Reasoning over Financial Data.

V-FiLLM: Verified Financial LLM Reasoning Benchmark FinQA: A Dataset of Numerical Reasoning over Financial Data

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-12T11:27:14.740756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:27:14.740756Z digest=sha256:c1b2dc983fcdebc7bd6d4ae47396e09e2a8a0ac1088ab723e4494e96f75fd8ff

Pith citing papers

No inbound Pith citation observations are available.