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Paper Citation Record · LEDGER

Lexical Hints of Accuracy in LLM Reasoning Chains

As of 10 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 2 inbound Pith citation observations for arXiv:2508.15842.

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

pith.paper-citation-record.v1
2508.15842 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:48:55.479301Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T15:22:55.705722Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T08:06:28.393138Z

Reference resolution

35 of 35 outbound references displayed

  • verified exact2
  • verified fuzzy11
  • unresolved22
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8cddd00c-28e2-4870-81b2-422d8464ed48 · outbound

This paper cites Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection.

Lexical Hints of Accuracy in LLM Reasoning Chains Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:48:50.786671Z digest=sha256:a7273804a51649137ad876c05f76ad8b63c2b2c5de9e7548f1112022699b1a16

Observation 1bc81062-1a6c-4fe0-9237-9da26b29fbe2 · outbound

This paper cites Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models.

Lexical Hints of Accuracy in LLM Reasoning Chains Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models

Reference 2

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no resolver link, observed 2026-08-05T18:48:50.863975Z

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source=pdf_text observed=2026-08-05T18:48:50.863975Z digest=sha256:21041c893ec4cc7dcae425c1bc78975724bb2d205d9be0d60825214f4f667743

Observation 5ed479fd-84da-4618-bb7c-dd9fd1fdd78c · outbound

This paper cites Goodfellow, Jonathon Shlens, and Chris- tian Szegedy.

Lexical Hints of Accuracy in LLM Reasoning Chains Goodfellow, Jonathon Shlens, and Chris- tian Szegedy

Reference 3

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verified fuzzy
raw_fallback, observed 2026-08-05T18:48:59.951981Z

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.

source=pdf_text observed=2026-08-05T18:48:50.990382Z digest=sha256:2ba7ca1ad7ee75ee09332fb66d92551eb7c31b63ceb5a59185ffc33d5286e02c

Observation dc0f4cea-8784-4782-8bee-980daaa94640 · outbound

This paper cites Towards evaluating the robustness of neural networks.

Lexical Hints of Accuracy in LLM Reasoning Chains Towards evaluating the robustness of neural networks

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:48:51.146384Z digest=sha256:10a369a138e4117fa653dee15c251ad5b48169809f473fdd7ddf60d04e71935c

Observation 9dc8fd3f-6326-45ba-9e50-df1e29c58a14 · outbound

This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.

Lexical Hints of Accuracy in LLM Reasoning Chains Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 5

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:48:51.313157Z digest=sha256:a4821fa208a00fe7cb34de876bdd2d0657ec693acdaa24b45ff1e7e8ccdc9d92

Observation 42bea56d-127a-4cba-8ef5-c45f5814a891 · outbound

This paper cites Towards Understanding Sycophancy in Language Models.

Lexical Hints of Accuracy in LLM Reasoning Chains Towards Understanding Sycophancy in Language Models

Reference 6

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no resolver link, observed 2026-08-05T18:48:51.499083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:48:51.499083Z digest=sha256:dc7c8ab68eb46f68d0135993a6057b81e2c8673c8d238c42d0f27f05900f4dad

Observation 2ea971e8-edf2-41a6-8a66-1a51494c4db2 · outbound

This paper cites Hallucination is Inevitable: An Innate Limitation of Large Language Models.

Lexical Hints of Accuracy in LLM Reasoning Chains Hallucination is Inevitable: An Innate Limitation of Large Language Models

Reference 7

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no resolver link, observed 2026-08-05T18:48:51.655868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:48:51.655868Z digest=sha256:1dfc7d5e39379c38e4442e72c4069a22526b57161967d6fe8fa6d0c1c9705475

Observation a5c2612b-25f9-491e-9fee-c933b5ec96a5 · outbound

This paper cites Prompt Injection attack against LLM-integrated Applications.

Lexical Hints of Accuracy in LLM Reasoning Chains Prompt Injection attack against LLM-integrated Applications

Reference 8

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no resolver link, observed 2026-08-05T18:48:51.798763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:48:51.798763Z digest=sha256:dd9a970c314764851b4bcc7ad02cbae0d915f9e1e12706180928b02a69b7ed19

Observation d7ec56af-1696-4abd-a490-5b82e13facb2 · outbound

This paper cites Automatic and Universal Prompt Injection Attacks against Large Language Models.

Lexical Hints of Accuracy in LLM Reasoning Chains Automatic and Universal Prompt Injection Attacks against Large Language Models

Reference 9

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no resolver link, observed 2026-08-05T18:48:51.940993Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:48:51.940993Z digest=sha256:5d2e41e011d42b7df6be1466ab0a7ad3b5df06d79f4a758b9b360a569295fecb

Observation 4eb29adb-1642-424c-9233-eb9ab704962b · outbound

This paper cites Concrete Problems in AI Safety.

Lexical Hints of Accuracy in LLM Reasoning Chains Concrete Problems in AI Safety

Reference 10

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no resolver link, observed 2026-08-05T18:48:52.069538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:48:52.069538Z digest=sha256:e290f0d0936824262d4012cd4d6838b67493677ecd807789f8624772a84fbff0

Observation 517855bc-add8-4741-8a6b-641a7a3ed0ae · outbound

This paper cites Supervising strong learners by amplifying weak experts.

Lexical Hints of Accuracy in LLM Reasoning Chains Supervising strong learners by amplifying weak experts

Reference 11

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no resolver link, observed 2026-08-05T18:48:52.218451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:48:52.218451Z digest=sha256:b142326ddedf4a4db4ab86f8acc47a81c713685248008e75ae20f25f67f78019

Observation 2df94f2f-fa6a-46d1-9824-df4cd846188b · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

Lexical Hints of Accuracy in LLM Reasoning Chains Constitutional AI: Harmlessness from AI Feedback

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:48:52.361776Z digest=sha256:771e510d7303246bee38e6f7a717e32eb1898d54d983286fc34d0fc562672ab2

Observation a1ae48c1-88f8-4a02-bb1e-8c8525e6dc68 · outbound

This paper cites Training language models to follow instructions with human feedback.

Lexical Hints of Accuracy in LLM Reasoning Chains Training language models to follow instructions with human feedback

Reference 13

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no resolver link, observed 2026-08-05T18:48:52.469472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:48:52.469472Z digest=sha256:64b0f4f3a92d784a2c1965af04dfe5ceffb64163fe5afc0f4de726d88b1eabc0

Observation 1d27f6f6-0010-4c70-a380-7c5d1f095c51 · outbound

This paper cites Deep reinforcement learning from human preferences.

Lexical Hints of Accuracy in LLM Reasoning Chains Deep reinforcement learning from human preferences

Reference 14

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no resolver link, observed 2026-08-05T18:48:52.582529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:48:52.582529Z digest=sha256:87159c56609e3f001f5d5375ad2aa372acddbd462f33787aad2f9937fc999b2a

Observation 879f3845-db6c-4c66-934f-8dd57cd4e66e · outbound

This paper cites Thinking, Fast and Slow.

Lexical Hints of Accuracy in LLM Reasoning Chains Thinking, Fast and Slow

Reference 15

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no resolver link, observed 2026-08-05T18:48:52.724623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:48:52.724623Z digest=sha256:f4dc4e98dacb699aec75107867e71514a14921bdd4268a31f8348c073894297d

Observation cb0f190a-8d74-4599-9404-c84acb9f2407 · outbound

This paper cites Judgment under uncertainty: Heuristics and biases.

Lexical Hints of Accuracy in LLM Reasoning Chains Judgment under uncertainty: Heuristics and biases

Reference 16

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verified fuzzy
raw_fallback, observed 2026-08-05T18:48:59.666249Z

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.

source=pdf_text observed=2026-08-05T18:48:52.848858Z digest=sha256:3ca917a66044f59dc9a48417ea32885a9c5115ca7cc11f38f3fd757dd6207f30

Observation ac56889a-884f-4308-8961-c30920db1152 · outbound

This paper cites EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models.

Lexical Hints of Accuracy in LLM Reasoning Chains EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models

Reference 17

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no resolver link, observed 2026-08-05T18:48:52.986090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:48:52.986090Z digest=sha256:aecd973c7ed78f623f42430082b1386af8af1fbcc8fba4cd33708503874a65cf

Observation 1a0c9a77-6560-4646-af5b-272626ed5d7a · outbound

This paper cites PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models.

Lexical Hints of Accuracy in LLM Reasoning Chains PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:48:53.146867Z digest=sha256:913b5260dbe69af4d5e0a9fc8adb50cb6b9ca69d86c791c8de9f4465111f52a7

Observation 9afb61da-6c6e-4e21-9c9e-2fb3924da373 · outbound

This paper cites TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models.

Lexical Hints of Accuracy in LLM Reasoning Chains TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:48:53.336959Z digest=sha256:132072521ccb0109f096460b12b02ba361ea9e267694e4875bb59485b3fad86b

Observation f41a1c87-17d8-4fe7-8dbe-42ada4b068ab · outbound

This paper cites OWASP top 10 for large language model applications 2025.

Lexical Hints of Accuracy in LLM Reasoning Chains OWASP top 10 for large language model applications 2025

Reference 20

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raw_fallback, observed 2026-08-05T18:48:59.326415Z

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.

source=pdf_text observed=2026-08-05T18:48:53.477038Z digest=sha256:6f1968325fb499cb02cb8bf4ddd0406e20a2f96f86207cf4ddf36b831c112ad5

Observation d20cec10-060b-499b-bc34-648554ce6558 · outbound

This paper cites MITRE ATLAS (adver- sarial threat landscape for artificial-intelligence systems), 2021.

Lexical Hints of Accuracy in LLM Reasoning Chains MITRE ATLAS (adver- sarial threat landscape for artificial-intelligence systems), 2021

Reference 21

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verified fuzzy
raw_fallback, observed 2026-08-05T18:48:59.043242Z

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.

source=pdf_text observed=2026-08-05T18:48:53.635262Z digest=sha256:3c11851cfd828f488c5351321834a385a956706a623c445a3033cd8591764053

Observation b5b8127f-f1b2-4441-a567-01b7d05251d3 · outbound

This paper cites Artificial intelligence risk man- agement framework (AI RMF 1.0).

Lexical Hints of Accuracy in LLM Reasoning Chains Artificial intelligence risk man- agement framework (AI RMF 1.0)

Reference 22

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verified fuzzy
raw_fallback, observed 2026-08-05T18:48:58.719160Z

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.

source=pdf_text observed=2026-08-05T18:48:53.754057Z digest=sha256:15dedb33696cb05c30e71da982eb6d214a2d5914c14ec2d0147fa0cfb3e230a8

Observation 6661c7c7-9dc1-4c99-899e-338d35a160e9 · outbound

This paper cites Information technology—artificial intelligence— guidance on risk management, 2023.

Lexical Hints of Accuracy in LLM Reasoning Chains Information technology—artificial intelligence— guidance on risk management, 2023

Reference 23

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raw_fallback, observed 2026-08-05T18:48:58.423304Z

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.

source=pdf_text observed=2026-08-05T18:48:53.890287Z digest=sha256:6030c1c06c6de63584577aaebcb337fb5f26afee787f199bdd1a0797eb8ff49a

Observation 49ccff31-0c15-4bab-98d9-4f370195e362 · outbound

This paper cites Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned.

Lexical Hints of Accuracy in LLM Reasoning Chains Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned

Reference 24

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:48:53.995402Z digest=sha256:38e5f0bdb5aa2a202c77b1c667507d97ec6dc99da50db917320a132fa483b702

Observation 363659ad-1e53-4862-a1de-9b89b37fa061 · outbound

This paper cites Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training.

Lexical Hints of Accuracy in LLM Reasoning Chains Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training

Reference 25

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 2efe275a-1be1-4064-ba2d-490e5cb9ba6e · outbound

This paper cites an unresolved cited work.

Lexical Hints of Accuracy in LLM Reasoning Chains Unresolved cited work

Reference 26

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unresolved
raw_fallback, observed 2026-08-05T18:48:58.181584Z

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.

source=pdf_text observed=2026-08-05T18:48:54.263071Z digest=sha256:788bd995a79bdb6ffacb25bd4c6d847bea2c7b987c481dfe95312e5fcb4b75a1

Observation df091262-2b34-4612-a296-c11eea7c22ab · outbound

This paper cites De- veloping trustworthy artificial intelligence: In- sights from research on interpersonal, human- automation, and human-AI trust.

Lexical Hints of Accuracy in LLM Reasoning Chains De- veloping trustworthy artificial intelligence: In- sights from research on interpersonal, human- automation, and human-AI trust

Reference 27

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verified fuzzy
raw_fallback, observed 2026-08-05T18:48:57.905727Z

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.

source=pdf_text observed=2026-08-05T18:48:54.394167Z digest=sha256:400898396e7220a05c0b64275a0ab8e5fbaa71dcb17b8f5e5f0a65c0587119dc

Observation db113349-b86e-4fa4-8e03-037e0f9746ea · outbound

This paper cites Bartz, Karen S.

Lexical Hints of Accuracy in LLM Reasoning Chains Bartz, Karen S

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:48:57.606364Z

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.

source=pdf_text observed=2026-08-05T18:48:54.525631Z digest=sha256:5cc8164f595a66264cb70aa77961e273e79bb11a2cc6d01fd744fe34fe1e545e

Observation 057089c1-1ec1-4573-9577-a8b38f678c7a · outbound

This paper cites A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions.

Lexical Hints of Accuracy in LLM Reasoning Chains A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-05T18:48:54.676287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:48:54.676287Z digest=sha256:bbcb6df812be6a10f521be3ba59233884f472e169e337ca92ed6c010289302b1

Observation 7d73111e-a77d-4051-beac-d08c7a0af318 · outbound

This paper cites Prospect theory: An analysis of decision under risk.

Lexical Hints of Accuracy in LLM Reasoning Chains Prospect theory: An analysis of decision under risk

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:48:57.290609Z

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.

source=pdf_text observed=2026-08-05T18:48:54.860134Z digest=sha256:d5ce846043458559601a8f97b81f1fcfc11edb9d9b6b22e3cf7f3d8adf2d9688

Observation d452841b-2e53-4fde-9d34-49a2cc79b726 · outbound

This paper cites Cialdini.

Lexical Hints of Accuracy in LLM Reasoning Chains Cialdini

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:48:57.075557Z

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.

source=pdf_text observed=2026-08-05T18:48:54.972582Z digest=sha256:7de52878c153015ab52c1745b929f3dd83531a39387f051f348e8ab04761ed4e

Observation 8f6a53b5-275d-47a2-9db7-69caaa45928c · outbound

This paper cites "Think First, Verify Always": Training Humans to Face AI Risks.

Lexical Hints of Accuracy in LLM Reasoning Chains "Think First, Verify Always": Training Humans to Face AI Risks

Reference 32

Resolution
verified exact
local_arxiv, observed 2026-08-05T18:48:55.982994Z

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.

source=pdf_text observed=2026-08-05T18:48:55.133357Z digest=sha256:7886d65ad1a7f2dbc8421536dfb0267f36fa20dbe0e0f99509acd6613b7beeb8

Observation 0977b82f-fa52-49ef-83d3-80b7898c5f42 · outbound

This paper cites O’Reilly Media, 2005.

Lexical Hints of Accuracy in LLM Reasoning Chains O’Reilly Media, 2005

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:48:56.745007Z

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.

source=pdf_text observed=2026-08-05T18:48:55.255996Z digest=sha256:20721fa8ed0377ab0091115cec0e48ad80ecd499b40e46a69a371dd632f55e4c

Observation 610e1a58-b872-4d84-b057-22402d221150 · outbound

This paper cites an unresolved cited work.

Lexical Hints of Accuracy in LLM Reasoning Chains Unresolved cited work

Reference 34

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unresolved
raw_fallback, observed 2026-08-05T18:48:56.433758Z

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.

source=pdf_text observed=2026-08-05T18:48:55.352603Z digest=sha256:1f127ce5bdf53bc8b070259f2aa84fd6b72dec5350338c90b735d8a031b753de

Observation 69d219e6-fa59-48bb-93bd-65d5e1b9cb5b · outbound

This paper cites Cognitive Cybersecurity for Artificial Intelligence: Guardrail Engineering with CCS-7.

Lexical Hints of Accuracy in LLM Reasoning Chains Cognitive Cybersecurity for Artificial Intelligence: Guardrail Engineering with CCS-7

Reference 35

Resolution
verified exact
local_arxiv, observed 2026-08-05T18:48:55.775129Z

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.

source=pdf_text observed=2026-08-05T18:48:55.479301Z digest=sha256:6b27367149dbb14ca5b80fbbad5445465bd45bbbf83c649cac7f1145aa75eb79

Pith citing papers

Observation 0dbbe5fe-f866-44ac-9748-ce8c1ef9db00 · inbound

Sanity Checks for Long-Form Hallucination Detection cites this paper.

Sanity Checks for Long-Form Hallucination Detection Lexical Hints of Accuracy in LLM Reasoning Chains

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-12T08:06:28.402436Z

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.

source=pdf_text observed=2026-05-12T01:19:19.238980Z digest=sha256:e98453719052fb27709c87687ec03561a950c2a8a013e6269298edea74359cf4

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How Much Does a Reasoning Summary Reveal? An Observability Ladder for Large Language Models cites this paper.

How Much Does a Reasoning Summary Reveal? An Observability Ladder for Large Language Models Lexical Hints of Accuracy in LLM Reasoning Chains

Reference 50

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