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Paper Citation Record · LEDGER

Measuring and Detecting Harmful AI Sycophancy

As of 8 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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:19:12.723182Z digest=sha256:b45dc05db49fd7d7ff7af4829202dbc7641a85e1a1ed9e8de6816a291f1980cf

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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no resolver link, observed 2026-08-08T05:19:12.730844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:19:12.730844Z digest=sha256:54a236d479412b90c02715990c9832881682273b1909d7be12e6046bde2b4165

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:19:12.734557Z digest=sha256:39987efc34a6c3368a2861ed6f7bb27a6c8dd798cf31b6f7ed914f8b504452af

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:19:12.739331Z digest=sha256:1dc213ebc6491988086bd41c41273d7a76948d9b4bce3954455ddb568ff32691

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=sha256:2fb5f83761a852bb746728ae588c28cd253a2cca1ba9cf271445874b4712aefe

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=sha256:0825cb7333ec143aed0317391015ea31bf5dc3e394928836e2523b24abe692c9

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=sha256:3dde78ad3b2b1e4ed53be4f29d4e0c1c5d9abb8ecaccce708dca46a28c06c878

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:19:12.768761Z digest=sha256:31a304283137430cf43891e4e77238d9be188aeefea080c333bd9502e8e6c174

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=sha256:60f18a16a1ce0bd5e225dd2b2ade2ced6c5d6ceb11bdd36b3cae04110d1b42e8

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=sha256:093bec074e0f7328988e3c48210cd4a7e7a7720a8b8280a5cc9a36a70951423a

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:d1d209fe5aba1b92859edd7ec253f355bc882223e10a243775638dab6fd02d65

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:19:12.785088Z digest=sha256:b55aee683863c589bc272855c23e9bb6deff600ab88dcb5987e7de7518f7e6b6

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:d18bca0d227b73619a5d4e342bfc8adb5c8c5770add81240494aa104769697f2

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:19:12.796964Z digest=sha256:2290493b54a58c263f3d3e159fb46cefc2c6c1caed6672636c3e40b6922b1b72

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=sha256:3affe78acc8304eec6232e060f547586d27fd9bf34aec2c872421a652019f289

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=sha256:5c83219d2aa6fe9a374e1ace190f7a036116b1905f4a97827d9dc85a062ee317

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:19:12.810394Z digest=sha256:484eee57edb0731cf6cfa259569d633babcc3103a95750ef3d51d1c24292c4ad

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=sha256:9c01d01db35eda01824ba2447e3ec442c2b7fe57b214920e08db40f0429d180e

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:be41c81727b06fc89f07c2d007838cc82053d1c0b055e91747c3c875a8bf47bb

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:b190e275974ed066d36afd8c58be2e4e2d0177dd3ba2b08e12bee2e74847b75f

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

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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=sha256:565349a7bfbadc987c193f1c9d57be0246a3b2b4e2a0ce9579eb3223cd2d804d

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:e5051dc79f314b54561f7fabe854058f93ac1641eba6f510bef702988a843f6c

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:b00b85039fa8d719bf68d17dadf33feeb81ac97dc0934e1070ebc81277d0ab21

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:bd3771cc672831e32642dcc7f71c85f0ca11074aa3fef1fde0bfc608cbb16da7

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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source=pdf_text observed=2026-08-08T05:19:12.762125Z digest=sha256:3483fec6c2361ee6c29a41b914a3c0d5e5626fefbbef8920c7830946c8378df4

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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raw_fallback, observed 2026-08-08T05:19:13.481185Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T05:19:12.765826Z digest=sha256:9c676dd1fbbe5515f01a5fe527c27b084920b7bae17d46065852204c41cd5272

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=sha256:8f925816292cca652d30b8e5b73d6e2d898ab9048374fff7f1d86b3e7312804d

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=sha256:9570e43bf2eee13b9d491bad9e15c383278d87501d9c2f8f0dacc15c32d7b31e

Pith citing papers

No inbound Pith citation observations are available.