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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 2026-08-08T06:32:00.761636+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=sha256:23768c223c7665670fb95b193d60a570f38535ef81b5e0c30b476cac0ea8b64a

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:e4bbe572ce709810bcf2e78582898d2b071e0cb3fdffcd8226c90adc1c4ba369

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:ee188b8b94d5e629c66efc47a3ad4b45f2c071445cdc4cc7997e6ea7d0ea6c86

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=sha256:151c86477ec3b1674a0ee4af8804d6a05585603d46902f4422829f26adc24f0a

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:e5406447e698598de2f595e857af5b57151a6e5c12b5cd2f651109b23f7ffb89

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:afe271893b823dd4c925db03acdb17e2c40b59028eb12e697e5d2814476d6ea8

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=sha256:5a06e00eec5adc433bcfc426fecbae9403df1d07d98f974313b10bb98c8dc288

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:b544602caa4081b2a34b89e98f8d1ca902682379f74ae8577bb5330471b567d6

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

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source=pdf_text observed=2026-08-08T05:19:12.771978Z digest=sha256:09676301a8f118b2de65b4ed7ca7216b2b1616b3a8c2efb9f3f5c9cc9bc647fa

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=sha256:54815a961c61dd3342b58213afa44d819095fbe9b1906ed47a42de04198f58b6

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=sha256:9be4a40cfff3a75dceb334155dfc6360c773bcb9f145aac128e2e6c62f46119d

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
raw_fallback, observed 2026-08-08T05:19:13.463877Z

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=sha256:7a76a004fa16bb6c9319edbc09ffb30e011e89e433eb21eb30c2f92110ac5f69

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=sha256:36de42d59a579c9b86b75c1b5cd2a05476dca0817d4520a235233458037fa8e7

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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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:ca03fb9c50c66db3f8d6009e161773c28eb31000ce3632145de0884de4c51282

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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source=pdf_text observed=2026-08-08T05:19:12.799910Z digest=sha256:1832f7dfa7efda20d652e8052d3d71923bd0637281aaae098d0a3a7db40d5134

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:aa458c5dc03787d267376fbb37afd43224e43e5088a10bcf4de0c832c8706047

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=sha256:69dba35b592dcced9ce10226717955c40a89bb4dcc4bafd99d732027a5478cf4

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:b1a58b11dab0912a9f4975f064fac6806ea1e223d807d0739c0556de8f7f4953

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=sha256:8bc0d343047c12acf8b62d00e9e23f141a9e74169b66c95ff4eee1be8c70538d

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:d3d8adc357cf837def4da6d499e596a8ea6c371accfb3c4c3af6749e0b38bd02

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:19:12.803198Z digest=sha256:ee67644837fd60d5a2682748636967accba0fb29de075c85bd007dc6a2bcf5fe

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=sha256:5ff41064b039d971e7fa2f46743ee6fc8cb269f6e7098dc6ffed35797fdfd227

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=sha256:3f1e0bc112c8e1a6c35afb3349f4d4ec38c1d92b1f1da59fcca64200b39eeeea

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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source=pdf_text observed=2026-08-08T05:19:12.755215Z digest=sha256:b7043279240bedfbd4c6aa040fb91c685c572e9aeb0395b3bb7fb4ab558c46b7

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:cb6da818d930869ebb0579a53ef01964725d5389bc00ee71223d36aa3e886cc9

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

source=pdf_text observed=2026-08-08T05:19:12.765826Z digest=sha256:5ea645ef56f02bede799b6f04a878865aa962e897693b8aa26e31ecc798eec97

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=sha256:1dcba3a9b6b4be6e6a8c8c770dea3d69fc6e0f50b978182fba324cea3580928d

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:19:12.727391Z digest=sha256:78079629c9109141258917b6c2f2e81661a3022c8bbc9640a8c003c612c90e28

Pith citing papers

No inbound Pith citation observations are available.