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Paper Citation Record · LEDGER

Measuring and Detecting Harmful AI Sycophancy

As of 9 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2608.05624.

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

pith.paper-citation-record.v1
2608.05624 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T05:19:12.817298Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

28 of 28 outbound references displayed

  • verified exact3
  • verified fuzzy2
  • unresolved23
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f4b4648f-8d2f-4152-9c6b-428646796de1 · outbound

This paper cites Dissociating the Internal Representations of Sycophancy in LLMs.

Measuring and Detecting Harmful AI Sycophancy Dissociating the Internal Representations of Sycophancy in LLMs

Reference 1

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verified exact
local_arxiv, observed 2026-08-08T05:19:13.444833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T05:19:12.723182Z digest=sha256:b542e09f382145730fe013dae1db00b4eb1bc7c37793b1708f5f83ac181c510b

Observation d18a469d-71bf-4795-b231-87a0f6539778 · outbound

This paper cites SWAY: A Counterfactual Computational Linguistic Approach to Measuring and Mitigating Sycophancy.

Measuring and Detecting Harmful AI Sycophancy SWAY: A Counterfactual Computational Linguistic Approach to Measuring and Mitigating Sycophancy

Reference 3

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:19:12.730844Z digest=sha256:44c0e456fe4898606b0bc9c9913e91ae9c80d3c98d663a54891a080fbb08f19d

Observation 8cda36d8-eb37-41a4-a970-04869b8c1cdd · outbound

This paper cites Detecting and Controlling Sycophancy with Cascading Linear Features.

Measuring and Detecting Harmful AI Sycophancy Detecting and Controlling Sycophancy with Cascading Linear Features

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-08-08T05:19:13.267374Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T05:19:12.734557Z digest=sha256:bb5fba629458416155236f68a446a82c1ff72a165592e1370cccac522523b0c2

Observation ae16b80b-d727-451c-89ce-a4e132d92aaf · outbound

This paper cites Dual-Stance Evaluation of Sycophancy: The Structure of Agreement and the Limits of Intervention.

Measuring and Detecting Harmful AI Sycophancy Dual-Stance Evaluation of Sycophancy: The Structure of Agreement and the Limits of Intervention

Reference 5

Resolution
verified exact
local_arxiv, observed 2026-08-08T05:19:13.252409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T05:19:12.739331Z digest=sha256:7254fb67e36bf0e98021b428b43fd6fb8e8eddf707d9db1d6f4b25fcfdb4fd86

Observation 81fc91c9-b1b5-4767-be6b-0234b580d664 · outbound

This paper cites ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators.

Measuring and Detecting Harmful AI Sycophancy ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

Reference 7

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-08T05:19:12.746627Z digest=sha256:6eb163335910f95a53abf1cbfa2ffc2f04aae81307990344e271af77c0779f58

Observation 1acac7d0-6cdf-495c-a53d-6951b281b955 · outbound

This paper cites DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing.

Measuring and Detecting Harmful AI Sycophancy DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing

Reference 9

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source=pdf_text observed=2026-08-08T05:19:12.752226Z digest=sha256:8a6def88d42da1a71b374ef223cf869005c60473613bd30bc7ccd16d2f02866b

Observation 51cbe12c-e93c-436d-905a-1fcd3dcd7e45 · outbound

This paper cites Sycophantic AI makes human interaction feel more effortful and less satisfying over time.

Measuring and Detecting Harmful AI Sycophancy Sycophantic AI makes human interaction feel more effortful and less satisfying over time

Reference 11

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source=pdf_text observed=2026-08-08T05:19:12.757881Z digest=sha256:aabb2faf52d334b4abcc364e48723e9bd4b3130c91f849df086762eff41b5cda

Observation 286809f5-c565-412e-8b5b-f4782532b914 · outbound

This paper cites an unresolved cited work.

Measuring and Detecting Harmful AI Sycophancy Unresolved cited work

Reference 14

Resolution
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raw_fallback, observed 2026-08-08T05:19:13.472187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T05:19:12.768761Z digest=sha256:3639290f440462b69971082304a8ac5184d1634f82a0f4c78a7d775f21080792

Observation 664bf98b-3d9d-4cde-93f4-1c8a60998f08 · outbound

This paper cites TRUTH DECAY: Quantifying Multi-Turn Sycophancy in Language Models.

Measuring and Detecting Harmful AI Sycophancy TRUTH DECAY: Quantifying Multi-Turn Sycophancy in Language Models

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:19:12.771978Z digest=sha256:955c37e58eddaf7292d60f0de1b814aed09f2948bf705b11a5b288c8a14f5d22

Observation 80a74254-58d1-4841-a673-841510d862e6 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Measuring and Detecting Harmful AI Sycophancy RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 16

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source=pdf_text observed=2026-08-08T05:19:12.775389Z digest=sha256:bb6156c588649b1089322da7ed1dbfbb2991705ed65332c355e20d508dd28297

Observation f5594707-a557-43eb-a7e3-9b2adc876ff0 · outbound

This paper cites Linear Probe Penalties Reduce LLM Sycophancy.

Measuring and Detecting Harmful AI Sycophancy Linear Probe Penalties Reduce LLM Sycophancy

Reference 18

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source=pdf_text observed=2026-08-08T05:19:12.781624Z digest=sha256:06e4e1ba6fda98b0b538e9a19dbdf0dd27b1445b19b1993e3f2ae0a2c5133ab7

Observation 81adafea-efc1-4d99-bb27-617c24cee093 · outbound

This paper cites Perez, S.

Measuring and Detecting Harmful AI Sycophancy Perez, S

Reference 19

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T05:19:12.785088Z digest=sha256:28ffe82318e7ae3e10e882f1dde65709044f03c1f977bf2cd6b444c1dabe2d8b

Observation 3913b1f1-9025-401a-89a2-0e98170b408f · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Measuring and Detecting Harmful AI Sycophancy DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 21

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no resolver link, observed 2026-08-08T05:19:12.793355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:19:12.793355Z digest=sha256:5326a775290f02b596f6b95b9b8ad0a0e27425e3ed531b18a66cdeeb19011030

Observation 48118e48-91c0-4c5d-bbad-f57acd1ece1c · outbound

This paper cites Sharma, M.

Measuring and Detecting Harmful AI Sycophancy Sharma, M

Reference 22

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T05:19:12.796964Z digest=sha256:9356272e49441bbccf42c0400e4ee7965a8fe75d4f2aff9c69501eac5c9638f8

Observation c4db1b5f-3e96-489f-8bfb-44905b32aa65 · outbound

This paper cites OpenAI GPT-5 System Card.

Measuring and Detecting Harmful AI Sycophancy OpenAI GPT-5 System Card

Reference 23

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:19:12.799910Z digest=sha256:ea218c2d1139a09d49a81bd26c7ef717acffa9d4b283a2ce05d68bb32b3b89e6

Observation f1d09b0c-00a2-438c-83bc-d9124c34c764 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Measuring and Detecting Harmful AI Sycophancy Gemma 2: Improving Open Language Models at a Practical Size

Reference 25

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source=pdf_text observed=2026-08-08T05:19:12.806761Z digest=sha256:2024551a0c529ca1d5cd157eed9f28351b1ebdf26db981df279daa59a688227f

Observation d3db0df4-76d9-4337-af07-61e1f0d74f9e · outbound

This paper cites Simple synthetic data reduces sycophancy in large language models.

Measuring and Detecting Harmful AI Sycophancy Simple synthetic data reduces sycophancy in large language models

Reference 26

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:19:12.810394Z digest=sha256:6630e9dd3bc18a624c646657165c766152dee9d08054861bbda62075e0df747e

Observation 486483ae-a3de-4aa9-8a3d-7469d7bd0bc1 · outbound

This paper cites What Counts as AI Sycophancy? A Taxonomy and Expert Survey of a Fragmented Construct.

Measuring and Detecting Harmful AI Sycophancy What Counts as AI Sycophancy? A Taxonomy and Expert Survey of a Fragmented Construct

Reference 27

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source=pdf_text observed=2026-08-08T05:19:12.813640Z digest=sha256:77dedadc72737ef94db5e4263de620eb77cccd1dc59ecb627b0db31d8598c77c

Observation 3dcebf2f-9845-40f1-b59b-490be23f8f09 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Measuring and Detecting Harmful AI Sycophancy mixup: Beyond Empirical Risk Minimization

Reference 28

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source=pdf_text observed=2026-08-08T05:19:12.817298Z digest=sha256:abd4c54fe53a58d0492438fca81ca6aebcd092839f5b12a35312ab21740122e4

Observation b3e9428c-142e-4495-a7fd-e2d79cd87fb5 · outbound

This paper cites Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization.

Measuring and Detecting Harmful AI Sycophancy Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

Reference 2003

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source=pdf_text observed=2026-08-08T05:19:12.789769Z digest=sha256:78fa17f5f06219d980a3f29f56b456590512a9411d72b1ee5e65bb274c05948e

Observation 044e988b-199b-4e09-8031-7fa348ca620c · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Measuring and Detecting Harmful AI Sycophancy Gemini: A Family of Highly Capable Multimodal Models

Reference 2016

Resolution
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no resolver link, observed 2026-08-08T05:19:12.803198Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:19:12.803198Z digest=sha256:67e5d3fe92af6a919b0b6bc400cd219972f651547a1e4d2173b1c26af72176f5

Observation 7066b3d6-f50f-43cd-a28e-78821e30052f · outbound

This paper cites User Detection and Response Patterns of Sycophantic Behavior in Conversational AI.

Measuring and Detecting Harmful AI Sycophancy User Detection and Response Patterns of Sycophantic Behavior in Conversational AI

Reference 2019

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source=pdf_text observed=2026-08-08T05:19:12.778415Z digest=sha256:3ff7e280e8f8fa7b8491934a329363ce7479ef25f93c42f1cfce4591fb2c4371

Observation c118e299-1a59-4ba2-9496-025dda2233f3 · outbound

This paper cites Devlin, M.-W.

Measuring and Detecting Harmful AI Sycophancy Devlin, M.-W

Reference 2020

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source=pdf_text observed=2026-08-08T05:19:12.749552Z digest=sha256:142328c9ce16478726d5e0b1913ed58457e19707c74182a97033499309026f51

Observation 93815809-28e7-4728-9f91-74866ec7ac8f · outbound

This paper cites an unresolved cited work.

Measuring and Detecting Harmful AI Sycophancy Unresolved cited work

Reference 2021

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:19:12.755215Z digest=sha256:cb06a462c195f0ebf5b67f0a94fa35b9bd81b271d66a2decbf73920186ef28e2

Observation 99f128a5-61c0-4c15-bb0b-dce5dcaa97c5 · outbound

This paper cites Mistral 7B.

Measuring and Detecting Harmful AI Sycophancy Mistral 7B

Reference 2023

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:19:12.762125Z digest=sha256:14a6af94620d5e5eca065f83b4f9a48ac3f296cf54ea7c93d6f9638715e2be7f

Observation 3f0d03f3-6023-423c-986f-50d05fc924bf · outbound

This paper cites an unresolved cited work.

Measuring and Detecting Harmful AI Sycophancy Unresolved cited work

Reference 2024

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T05:19:12.765826Z digest=sha256:3e042eb39bc351ba8e1106e3fd6d68f9d54c0492cfd5c2fe9126598b44618d66

Observation aa42ab40-5eb1-486d-833c-94798fb3be2a · outbound

This paper cites ELEPHANT: Measuring and understanding social sycophancy in LLMs.

Measuring and Detecting Harmful AI Sycophancy ELEPHANT: Measuring and understanding social sycophancy in LLMs

Reference 2025

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source=pdf_text observed=2026-08-08T05:19:12.743000Z digest=sha256:4a40c7afb7ad41a73d02f6cc3f83f3027f5285b4d89f31029583fb5c7828a12c

Observation ca6c2d72-40e3-4bd7-a6b7-50f06bcf50fc · outbound

This paper cites an unresolved cited work.

Measuring and Detecting Harmful AI Sycophancy Unresolved cited work

Reference 2026

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source=pdf_text observed=2026-08-08T05:19:12.727391Z digest=sha256:b7249a4ff3d13d1a75d92fa4067fbb970c34eaacbf93abfb36064ea35201c207

Pith citing papers

No inbound Pith citation observations are available.