Paper Citation Record · LEDGER

When Bad Data Leads to Good Models

As of 16 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 2 inbound Pith citation observations for arXiv:2505.04741.

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

pith.paper-citation-record.v1
2505.04741 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:25:00.352149Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T14:53:35.432279Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T16:07:08.941549Z

Reference resolution

51 of 51 outbound references displayed

verified exact0
verified fuzzy14
unresolved36
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b2f95a0d-c763-4d66-8f47-141e27d29ba2 · outbound

This paper cites write newline.

When Bad Data Leads to Good Models write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.094915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bdaa413-5218-4268-b5ff-2d0900576d25 · outbound

This paper cites Understanding intermediate layers using linear classifier probes.

When Bad Data Leads to Good Models Understanding intermediate layers using linear classifier probes

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.101334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2100cedd-d90d-4a1f-b140-cbeec05e5e12 · outbound

This paper cites Toxicity of the Commons: Curating Open-Source Pre-Training Data.

When Bad Data Leads to Good Models Toxicity of the Commons: Curating Open-Source Pre-Training Data

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T23:25:00.868858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f767017b-facc-44fd-86f7-3a7948583d22 · outbound

This paper cites Linear algebraic structure of word senses, with applications to polysemy.

When Bad Data Leads to Good Models Linear algebraic structure of word senses, with applications to polysemy

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:25:01.175216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fb30460a-59c5-40c3-afbe-9aa3508beb6c · outbound

This paper cites Probing classifiers: Promises, shortcomings, and advances.

When Bad Data Leads to Good Models Probing classifiers: Promises, shortcomings, and advances

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:25:01.159363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9c253f6a-d6c7-409c-b366-ed71761c567f · outbound

This paper cites Eliciting Latent Predictions from Transformers with the Tuned Lens.

When Bad Data Leads to Good Models Eliciting Latent Predictions from Transformers with the Tuned Lens

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.122413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b6646a1-61e8-4c26-881e-672a32d40197 · outbound

This paper cites The Accuracy Paradox in RLHF: When Better Reward Models Don't Yield Better Language Models.

When Bad Data Leads to Good Models The Accuracy Paradox in RLHF: When Better Reward Models Don't Yield Better Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.127552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2caa96ed-3b17-4a01-ac18-57aa299bb531 · outbound

This paper cites UltraFeedback: Boosting Language Models with Scaled AI Feedback.

When Bad Data Leads to Good Models UltraFeedback: Boosting Language Models with Scaled AI Feedback

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.133124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2a48bba-7454-4da2-9525-77d95eb0b4ec · outbound

This paper cites Sparse Autoencoders Find Highly Interpretable Features in Language Models.

When Bad Data Leads to Good Models Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.138738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa169dbb-ccc3-4820-964d-67a11a660243 · outbound

This paper cites Plug and Play Language Models: A Simple Approach to Controlled Text Generation.

When Bad Data Leads to Good Models Plug and Play Language Models: A Simple Approach to Controlled Text Generation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.143735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e032bdb-b0ca-41d5-bf95-6951b59003c9 · outbound

This paper cites Toy Models of Superposition.

When Bad Data Leads to Good Models Toy Models of Superposition

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.150012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54f300f0-9d0a-4c71-a5cb-a87b170d8f63 · outbound

This paper cites an unresolved cited work.

When Bad Data Leads to Good Models Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.154504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6961c34a-9882-4e07-b1d2-36dfe273bbc1 · outbound

This paper cites Openwebtext corpus.

When Bad Data Leads to Good Models Openwebtext corpus

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.159644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9afb00c9-3290-4a60-8934-0424c6d12ea2 · outbound

This paper cites OLMo: Accelerating the Science of Language Models.

When Bad Data Leads to Good Models OLMo: Accelerating the Science of Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.164201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac0b6d1e-efde-43e6-9c69-4ecd9902cf7c · outbound

This paper cites Don't Stop Pretraining: Adapt Language Models to Domains and Tasks.

When Bad Data Leads to Good Models Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.169716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8547cafb-0b5f-4792-8aff-180dfcbc10d8 · outbound

This paper cites T oxi G en: A large-scale machine-generated dataset for adversarial and implicit hate speech detection.

When Bad Data Leads to Good Models T oxi G en: A large-scale machine-generated dataset for adversarial and implicit hate speech detection

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.175329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2e46bb3-c397-40f9-9707-44d402f05bdb · outbound

This paper cites Training Compute-Optimal Large Language Models.

When Bad Data Leads to Good Models Training Compute-Optimal Large Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.180719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b50b71e-24ee-41c8-8840-109b107aec9f · outbound

This paper cites Toxic comment classification challenge, 2018.

When Bad Data Leads to Good Models Toxic comment classification challenge, 2018

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:25:01.131842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bba9cde2-5b94-4b39-a4af-73f468a71afc · outbound

This paper cites CTRL: A Conditional Transformer Language Model for Controllable Generation.

When Bad Data Leads to Good Models CTRL: A Conditional Transformer Language Model for Controllable Generation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.191369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70ac03cd-a255-48bc-80fe-72e4d5369f55 · outbound

This paper cites Understanding the Effects of RLHF on LLM Generalisation and Diversity.

When Bad Data Leads to Good Models Understanding the Effects of RLHF on LLM Generalisation and Diversity

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.196576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e469645b-796e-4199-9f17-d2c0fa4f5858 · outbound

This paper cites GeDi: Generative Discriminator Guided Sequence Generation.

When Bad Data Leads to Good Models GeDi: Generative Discriminator Guided Sequence Generation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.201730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c6085e7-949a-426e-a80e-86e932d435e9 · outbound

This paper cites A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicity.

When Bad Data Leads to Good Models A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicity

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.206648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 368755d9-9af7-4597-a1c6-31321658284f · outbound

This paper cites Inference-time intervention: Eliciting truthful answers from a language model.

When Bad Data Leads to Good Models Inference-time intervention: Eliciting truthful answers from a language model

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:25:01.115476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation abf2596f-f907-4d30-bece-f075c645a25a · outbound

This paper cites Contrastive Decoding: Open-ended Text Generation as Optimization.

When Bad Data Leads to Good Models Contrastive Decoding: Open-ended Text Generation as Optimization

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.216849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b41d7ef-1f55-44ad-be48-c5cb2954f73f · outbound

This paper cites Disentangling transformer language models as superposed topic models.

When Bad Data Leads to Good Models Disentangling transformer language models as superposed topic models

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:25:01.099144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 97e1c1e6-4f9b-4150-bf1d-582a8b7ebae1 · outbound

This paper cites Mitigating the Alignment Tax of RLHF.

When Bad Data Leads to Good Models Mitigating the Alignment Tax of RLHF

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.228033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c6791db-f08a-4e44-825b-e5ef10f89236 · outbound

This paper cites DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts.

When Bad Data Leads to Good Models DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.233785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5157de0-8165-48ab-a585-33bab035de6d · outbound

This paper cites Amd-olmo: A series of 1b language models trained from scratch by amd on amd instinct™ mi250 gpus., October 2024.

When Bad Data Leads to Good Models Amd-olmo: A series of 1b language models trained from scratch by amd on amd instinct™ mi250 gpus., October 2024

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:25:01.082439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c76c5b64-8033-4efa-ade9-0d5460a3e08b · outbound

This paper cites A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity.

When Bad Data Leads to Good Models A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.244162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53856e84-5eda-4652-8888-4b64f1baa7d7 · outbound

This paper cites On linear representations and pretraining data frequency in language models.

When Bad Data Leads to Good Models On linear representations and pretraining data frequency in language models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:25:01.066997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8224ebc5-700f-4a65-9cc8-343bae33333d · outbound

This paper cites Linguistic regularities in continuous space word representations.

When Bad Data Leads to Good Models Linguistic regularities in continuous space word representations

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.254124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44aa07b2-1ef2-42b2-af50-a93b04dabbb5 · outbound

This paper cites Interpreting gpt: The logit lens.

When Bad Data Leads to Good Models Interpreting gpt: The logit lens

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:25:01.039903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 613c008c-b05a-4f10-b2ad-5de148c0d92b · outbound

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

When Bad Data Leads to Good Models Training language models to follow instructions with human feedback

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.264005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1188968-aaab-47db-9d52-c217652598ea · outbound

This paper cites Raiders of the lost kek: 3.5 years of augmented 4chan posts from the politically incorrect board.

When Bad Data Leads to Good Models Raiders of the lost kek: 3.5 years of augmented 4chan posts from the politically incorrect board

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:25:01.014366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 913bea3b-fdc1-4e18-ab57-ed3e00371e55 · outbound

This paper cites Generative agents: Interactive simulacra of human behavior.

When Bad Data Leads to Good Models Generative agents: Interactive simulacra of human behavior

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.273439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 854dec26-189e-492c-ab36-e59efe142155 · outbound

This paper cites The Linear Representation Hypothesis and the Geometry of Large Language Models.

When Bad Data Leads to Good Models The Linear Representation Hypothesis and the Geometry of Large Language Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.278006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 175f1564-f108-4308-8292-d22005c637e2 · outbound

This paper cites Perspective | developers, 2024.

When Bad Data Leads to Good Models Perspective | developers, 2024

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:25:00.987592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bce706af-0f82-4858-ac05-332473f85003 · outbound

This paper cites Adding Instructions during Pretraining: Effective Way of Controlling Toxicity in Language Models.

When Bad Data Leads to Good Models Adding Instructions during Pretraining: Effective Way of Controlling Toxicity in Language Models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.288445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91e7ccab-ec8a-4cab-8642-5dc0d9ae08ff · outbound

This paper cites Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!.

When Bad Data Leads to Good Models Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.293454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 916a22c0-021b-4236-bbf6-d9106ec40964 · outbound

This paper cites Scaling Language Models: Methods, Analysis & Insights from Training Gopher.

When Bad Data Leads to Good Models Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.298525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cae6ff8d-8afd-4467-8cf0-e156b2b961c5 · outbound

This paper cites Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

When Bad Data Leads to Good Models Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.303571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee09727c-4ae3-466c-9222-be2923d71879 · outbound

This paper cites an unresolved cited work.

When Bad Data Leads to Good Models Unresolved cited work

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.308538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac047372-dd4c-4781-aff0-ebee555c821e · outbound

This paper cites Self-diagnosis and self-debiasing: A proposal for reducing corpus-based bias in nlp.

When Bad Data Leads to Good Models Self-diagnosis and self-debiasing: A proposal for reducing corpus-based bias in nlp

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.313458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a8d70f4-356e-4be9-9c0c-1799478091f9 · outbound

This paper cites Process for adapting language models to society (palms) with values-targeted datasets.

When Bad Data Leads to Good Models Process for adapting language models to society (palms) with values-targeted datasets

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:25:00.952087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3e9390c6-1218-43ac-8444-0c8c4253a16d · outbound

This paper cites BERT Rediscovers the Classical NLP Pipeline.

When Bad Data Leads to Good Models BERT Rediscovers the Classical NLP Pipeline

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.322982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9dd612bc-acd7-4ed1-b92a-2794c86064c5 · outbound

This paper cites LaMDA: Language Models for Dialog Applications.

When Bad Data Leads to Good Models LaMDA: Language Models for Dialog Applications

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.327735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c966ef15-db07-43ee-bc29-deb4faecc115 · outbound

This paper cites Activation addition: Steering language models without optimization.

When Bad Data Leads to Good Models Activation addition: Steering language models without optimization

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:25:00.936145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e6672175-a16e-4f4e-a20f-4891429806aa · outbound

This paper cites Exploring the limits of domain-adaptive training for detoxifying large-scale language models.

When Bad Data Leads to Good Models Exploring the limits of domain-adaptive training for detoxifying large-scale language models

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:25:00.919423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0ecbe4bc-f605-430b-9d7a-5a799b4d55b0 · outbound

This paper cites RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models.

When Bad Data Leads to Good Models RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.342165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e445eca-790a-49e6-87db-cbb605393df2 · outbound

This paper cites Lower bounds on the maximum cross correlation of signals (corresp.).

When Bad Data Leads to Good Models Lower bounds on the maximum cross correlation of signals (corresp.)

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:25:00.902623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6c75294e-4f85-42b7-9b59-344afc331f98 · outbound

This paper cites Representation Engineering: A Top-Down Approach to AI Transparency.

When Bad Data Leads to Good Models Representation Engineering: A Top-Down Approach to AI Transparency

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:00.352149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 10b518e5-d95a-4060-9b57-3d13b862019b · inbound

Making the Most of Limited Data: Score-Aware Training for Text-to-Music Generation cites this paper.

Making the Most of Limited Data: Score-Aware Training for Text-to-Music Generation When Bad Data Leads to Good Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:07:08.943080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e0b15acf-3aab-4fff-a4fe-63fb8b27140c · inbound

Mark, Don't Erase: Token Inoculation for Dual-Use Knowledge in LLMs cites this paper.

Mark, Don't Erase: Token Inoculation for Dual-Use Knowledge in LLMs When Bad Data Leads to Good Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T14:53:35.432279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.