Paper Citation Record · LEDGER

LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 34 inbound Pith citation observations for arXiv:2310.01469.

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

pith.paper-citation-record.v1
2310.01469 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 34 of 34 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 34 of 34 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T12:26:08.293870Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

60
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 5daf9ec1-4e5d-41a5-b001-c834d8950947 · inbound

MolReFlect: Towards In-Context Fine-grained Alignments between Molecules and Texts cites this paper.

MolReFlect: Towards In-Context Fine-grained Alignments between Molecules and Texts LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-23T16:45:42.522287Z

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.

Observation bf942f0d-bccd-4299-9793-c9bd8554422c · inbound

Evaluating the Effectiveness of LLMs in Fixing Maintainability Issues in Real-World Projects cites this paper.

Evaluating the Effectiveness of LLMs in Fixing Maintainability Issues in Real-World Projects LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-09T12:26:08.293870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5044e11-c43b-4ec1-a3ca-2eefa0ef74d6 · inbound

Ensemble based approach to quantifying uncertainty of LLM based classifications cites this paper.

Ensemble based approach to quantifying uncertainty of LLM based classifications LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-08T00:04:41.228134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a840ba81-bf82-4d7c-b18c-51085516dd1a · inbound

Unleashing the Power of Large Language Model for Denoising Recommendation cites this paper.

Unleashing the Power of Large Language Model for Denoising Recommendation LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-07T22:50:53.858414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8deb8a6-5f5b-4f94-b1b5-869887b043d6 · inbound

Thinking beyond the anthropomorphic paradigm benefits LLM research cites this paper.

Thinking beyond the anthropomorphic paradigm benefits LLM research LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-07T22:21:52.730334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf046fe1-345e-4a0d-a01d-d68f87373dde · inbound

Trust at Your Own Peril: A Mixed Methods Exploration of the Ability of Large Language Models to Generate Expert-Like Systems Engineering Artifacts and a Characterization of Failure Modes cites this paper.

Trust at Your Own Peril: A Mixed Methods Exploration of the Ability of Large Language Models to Generate Expert-Like Systems Engineering Artifacts and a Characterization of Failure Modes LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 121

Resolution: unresolved; no resolver link, observed 2026-08-07T21:21:31.718839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef2784aa-85c2-4fe6-bcaa-a310ed94b91b · inbound

Pierce the Mists, Greet the Sky: Decipher Knowledge Overshadowing via Knowledge Circuit Analysis cites this paper.

Pierce the Mists, Greet the Sky: Decipher Knowledge Overshadowing via Knowledge Circuit Analysis LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:21.074482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 565d526e-015d-4df0-911e-e27ace91c9c7 · inbound

Concept Incongruence: An Exploration of Time and Death in Role Playing cites this paper.

Concept Incongruence: An Exploration of Time and Death in Role Playing LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T15:31:31.683255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fde98d1-3178-4553-82be-f66021aa2fbc · inbound

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models cites this paper.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:36.391398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82fa0039-8dee-4f1b-b434-678f037104a7 · inbound

Efficient Black-Box Fault Localization for System-Level Test Code Using Large Language Models cites this paper.

Efficient Black-Box Fault Localization for System-Level Test Code Using Large Language Models LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-05-19T07:32:08.475590Z

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.

Observation b941c1e2-2f5d-4dd5-a024-b6b17658da2c · inbound

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora cites this paper.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T22:44:00.555656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b35e0fd-17c3-411e-b3df-fd2bb3f4f467 · inbound

LLM-Assisted Question-Answering on Technical Documents Using Structured Data-Aware Retrieval Augmented Generation cites this paper.

LLM-Assisted Question-Answering on Technical Documents Using Structured Data-Aware Retrieval Augmented Generation LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T21:55:10.183657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba4cb56d-21ca-45f1-8e65-deda30a7ecb4 · inbound

Bridging the Gap: Leveraging Retrieval-Augmented Generation to Better Understand Public Concerns about Vaccines cites this paper.

Bridging the Gap: Leveraging Retrieval-Augmented Generation to Better Understand Public Concerns about Vaccines LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T16:41:45.778288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 554a97c0-2d24-44d9-9699-165d396c9e6d · inbound

Butterfly Effects in Toolchains: A Comprehensive Analysis of Failed Parameter Filling in LLM Tool-Agent Systems cites this paper.

Butterfly Effects in Toolchains: A Comprehensive Analysis of Failed Parameter Filling in LLM Tool-Agent Systems LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T15:39:58.795247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7db7cf92-ec2f-42a2-96a0-cd342419cf3c · inbound

Large Language Model Agent for Structural Drawing Generation Using ReAct Prompt Engineering and Retrieval Augmented Generation cites this paper.

Large Language Model Agent for Structural Drawing Generation Using ReAct Prompt Engineering and Retrieval Augmented Generation LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T14:07:28.281329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ccf30041-62f4-40fc-91d2-ef4da288f6a6 · inbound

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming cites this paper.

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:41.102125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09794e4f-6f05-46dc-a889-66a884f0093f · inbound

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting cites this paper.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 124

Resolution: unresolved; no resolver link, observed 2026-08-05T17:39:28.430990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 997361c4-9f8f-48c8-b51b-b727509ae87d · inbound

Principled Detection of Hallucinations in Large Language Models via Multiple Testing cites this paper.

Principled Detection of Hallucinations in Large Language Models via Multiple Testing LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:46:51.564855Z

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.

Observation 731ddbac-8d8f-4a25-b8c1-b3807d23a5d8 · inbound

Charting the Future of Scholarly Knowledge with AI: A Community Perspective cites this paper.

Charting the Future of Scholarly Knowledge with AI: A Community Perspective LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 119

Resolution: unresolved; no resolver link, observed 2026-08-05T15:32:34.296244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0848b3bf-217d-4a88-a68c-a14b3f7ecd1b · inbound

Breaking to Build: A Threat Model of Prompt-Based Attacks for Securing LLMs cites this paper.

Breaking to Build: A Threat Model of Prompt-Based Attacks for Securing LLMs LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-05T05:59:28.745638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82dcc78f-23a4-4c8a-a9df-c0c2f75bfebb · inbound

Parasites in the Toolchain: A Large-Scale Analysis of Attacks on the MCP Ecosystem cites this paper.

Parasites in the Toolchain: A Large-Scale Analysis of Attacks on the MCP Ecosystem LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-18T18:46:45.287895Z

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.

Observation a5edd221-edc6-4ec2-bba9-cd6770c2b83e · inbound

Investigating Symbolic Triggers of Hallucination in Gemma Models Across HaluEval and TruthfulQA cites this paper.

Investigating Symbolic Triggers of Hallucination in Gemma Models Across HaluEval and TruthfulQA LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T22:18:43.973456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 464466a8-92ad-415b-9dda-b497ad7a14b9 · inbound

When Search Goes Wrong: Red-Teaming Web-Augmented Large Language Models cites this paper.

When Search Goes Wrong: Red-Teaming Web-Augmented Large Language Models LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:31:11.634636Z

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.

Observation f125f028-a402-43c8-9884-ed83307e4699 · inbound

LLM-EDT: Large Language Model Enhanced Cross-domain Sequential Recommendation with Dual-phase Training cites this paper.

LLM-EDT: Large Language Model Enhanced Cross-domain Sequential Recommendation with Dual-phase Training LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-21T19:00:30.512878Z

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.

Observation 4a46f46e-e50d-4c87-a425-7fd0e300b43d · inbound

SelfGrader: LLM Jailbreak Detection via Anchored Token-Level Logits cites this paper.

SelfGrader: LLM Jailbreak Detection via Anchored Token-Level Logits LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T21:48:19.369935Z

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.

Observation 0ef58a00-9470-4e3c-ab9c-3055722640cb · inbound

Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit cites this paper.

Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:16:00.980184Z

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.

Observation 8d9cfaa5-d9de-476a-8155-1571a88e6f50 · inbound

Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit cites this paper.

Reference 54

Resolution: unresolved; no resolver link, observed 2026-07-12T22:57:13.837709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07963e1c-d824-48e5-8af3-07c60e346115 · inbound

REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations cites this paper.

REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 183

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T20:17:56.542903Z

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.

Observation 1a744953-4a9f-4e65-9474-3429da54854d · inbound

Dive into Ambiguity: A*-Inspired Multi-Agents Commonsense Obfuscation Attack on LLM Prompts cites this paper.

Dive into Ambiguity: A*-Inspired Multi-Agents Commonsense Obfuscation Attack on LLM Prompts LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-07-01T21:26:16.413696Z

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.

Observation 3f96b5c7-1c18-488a-b4b4-074f56cc4be0 · inbound

Hybrid Adversarial Defence for Natural Language Understanding Tasks cites this paper.

Hybrid Adversarial Defence for Natural Language Understanding Tasks LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 48

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T07:26:46.306694Z

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.

Observation 1816875d-35aa-4024-89ac-6c55ad4afd11 · inbound

Efficient Retrieval-Augmented Generation via Token Co-occurrence Graphs cites this paper.

Efficient Retrieval-Augmented Generation via Token Co-occurrence Graphs LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:14:18.902855Z

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.

Observation d222690d-2e51-4425-b6df-b17441f817d5 · inbound

SeeMe: Mitigating Hallucinations in Large Vision-Language Models through Effective Visual Token Engineering cites this paper.

SeeMe: Mitigating Hallucinations in Large Vision-Language Models through Effective Visual Token Engineering LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-11T21:15:25.010442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1afd918-d576-4d97-b909-ecaba04672d1 · inbound

How Jailbreak Attacks Inform Safety Alignment: A Defender-Centric, Shapley-Based Evaluation of Jailbreak Contributions cites this paper.

How Jailbreak Attacks Inform Safety Alignment: A Defender-Centric, Shapley-Based Evaluation of Jailbreak Contributions LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T18:54:52.804286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a217f3e-185d-470b-b788-ed18225b3a77 · inbound

DICA: Dual-Indicator Guided Contrastive Alignment in Multimodal Large Language Models cites this paper.

DICA: Dual-Indicator Guided Contrastive Alignment in Multimodal Large Language Models LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 50

Resolution: unresolved; no resolver link, observed 2026-07-31T23:32:09.115033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.