Paper Citation Record · LEDGER

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness

As of 17 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2504.17068.

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

pith.paper-citation-record.v1
2504.17068 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

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measured 47 of 47 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation a83f8bc1-d6c1-4517-8edd-ca8146d3847f · outbound

This paper cites Evolutionary-scale prediction of atomic-level protein structure with a language model,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Evolutionary-scale prediction of atomic-level protein structure with a language model,

Reference 1

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Observation 49d57f65-8b93-4c8d-87e3-4737c59379a5 · outbound

This paper cites Dnabert-2: Efficient foundation model and benchmark for multi-species genome,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Dnabert-2: Efficient foundation model and benchmark for multi-species genome,

Reference 2

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Observation 7e2d4e48-4ce2-4e08-8063-11314df2f50d · outbound

This paper cites RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks

Reference 3

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Observation 2ad7630a-7ef6-4207-94c2-36750e6c87bf · outbound

This paper cites Transfer learning enables predictions in network biology,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Transfer learning enables predictions in network biology,

Reference 4

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Observation f3c4227a-b4cb-4776-927a-a0252481c830 · outbound

This paper cites Multi-megabase scale genome interpretation with genetic language models.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Multi-megabase scale genome interpretation with genetic language models

Reference 5

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Observation c702b97e-3e3f-4f71-a98b-3e703312fc82 · outbound

This paper cites Progen2: exploring the boundaries of protein language models,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Progen2: exploring the boundaries of protein language models,

Reference 6

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Observation 503b9085-fdd2-40e9-975d-23bab68c2f28 · outbound

This paper cites Sequence modeling and design from molecular to genome scale with evo,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Sequence modeling and design from molecular to genome scale with evo,

Reference 7

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Observation 5c537820-ff51-4f5a-ad15-fe7334b54518 · outbound

This paper cites Rna language models predict mutations that improve rna function,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Rna language models predict mutations that improve rna function,

Reference 8

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Observation 79a8a0f7-2aee-4695-aa2e-58220f20cf1a · outbound

This paper cites Designing proteins with language models,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Designing proteins with language models,

Reference 9

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Observation 49437d78-c7fc-41cf-b9f8-c5c773ab7a20 · outbound

This paper cites Proteingym: large-scale benchmarks for protein fitness prediction and design,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Proteingym: large-scale benchmarks for protein fitness prediction and design,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:53:45.095195Z

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Observation 170542f8-83c8-4b08-afa2-ef8185e1c8b4 · outbound

This paper cites Benchmarking dna sequence models for causal regulatory variant prediction in human genetics,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Benchmarking dna sequence models for causal regulatory variant prediction in human genetics,

Reference 11

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Observation a7cc7553-b4f3-448a-8fa0-d06f290c1137 · outbound

This paper cites Genome modeling and design across all domains of life with evo 2,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Genome modeling and design across all domains of life with evo 2,

Reference 12

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Observation 2f9fee4f-66cf-4c21-8a91-109f3e7eac74 · outbound

This paper cites Protein language models are biased by unequal sequence sampling across the tree of life,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Protein language models are biased by unequal sequence sampling across the tree of life,

Reference 13

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Observation d5f7a1c8-afa6-4246-8360-3acd3a22a7ea · outbound

This paper cites Removing bias in sequence models of protein fitness,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Removing bias in sequence models of protein fitness,

Reference 14

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Observation e2f586d3-fb62-40f5-8672-ff87fcbb9674 · outbound

This paper cites Protein language model fitness is a matter of preference,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Protein language model fitness is a matter of preference,

Reference 15

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Observation 96209bd8-360d-4cd0-82f3-8088a5e86823 · outbound

This paper cites Masked Language Model Scoring.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Masked Language Model Scoring

Reference 16

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Observation 25c14cb3-aff0-4cbb-acfd-8df2b8d889f0 · outbound

This paper cites Cd-search: protein domain annotations on the fly,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Cd-search: protein domain annotations on the fly,

Reference 17

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Observation 7f95dc1b-5417-4602-ad37-7698edbf962b · outbound

This paper cites Cdd/sparcle: the conserved domain database in 2020,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Cdd/sparcle: the conserved domain database in 2020,

Reference 18

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Observation 2246efb3-35a5-4ef6-8071-0c9e336213ca · outbound

This paper cites Pseudo-perplexity in one fell swoop for protein fitness estimation,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Pseudo-perplexity in one fell swoop for protein fitness estimation,

Reference 19

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Observation d78742a6-2dc1-48e5-9e8d-bc1d67309910 · outbound

This paper cites The mechanistic basis of data dependence and abrupt learning in an in-context classification task.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness The mechanistic basis of data dependence and abrupt learning in an in-context classification task

Reference 20

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Observation f8533fbc-94b6-4e95-aa6b-7cc3a8c45a19 · outbound

This paper cites Data distributional properties drive emergent in-context learning in transformers,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Data distributional properties drive emergent in-context learning in transformers,

Reference 21

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Observation 1c238229-1dd6-474e-aa8e-11ece2dcf3b3 · outbound

This paper cites The transient nature of emergent in-context learning in transformers,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness The transient nature of emergent in-context learning in transformers,

Reference 22

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This paper cites A mathematical framework for transformer circuits,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness A mathematical framework for transformer circuits,

Reference 23

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Observation 101cc215-76a4-4175-b185-a127293098a9 · outbound

This paper cites In-context Learning and Induction Heads.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness In-context Learning and Induction Heads

Reference 24

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This paper cites Language models are few-shot learners,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Language models are few-shot learners,

Reference 25

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Observation 49d14125-9806-4202-83ef-439814f38468 · outbound

This paper cites What learning algorithm is in-context learning? Investigations with linear models.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness What learning algorithm is in-context learning? Investigations with linear models

Reference 26

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This paper cites Transformers learn in-context by gradient descent,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Transformers learn in-context by gradient descent,

Reference 27

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Observation ba1a77f6-63aa-4437-afa2-e27200cdcb01 · outbound

This paper cites Competition Dynamics Shape Algorithmic Phases of In-Context Learning.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Competition Dynamics Shape Algorithmic Phases of In-Context Learning

Reference 28

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This paper cites Attention is all you need,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Attention is all you need,

Reference 29

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 30

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This paper cites Language models are unsupervised multitask learners,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Language models are unsupervised multitask learners,

Reference 31

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Observation 0b535fe9-c0c0-4520-a6e6-69c9ff40db97 · outbound

This paper cites Convolutions are competitive with transformers for protein sequence pretraining,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Convolutions are competitive with transformers for protein sequence pretraining,

Reference 32

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This paper cites Long-context protein language model,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Long-context protein language model,

Reference 33

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Observation a51fb5f1-63cb-48f7-861b-9d725b4c5541 · outbound

This paper cites Larger language models do in-context learning differently.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Larger language models do in-context learning differently

Reference 34

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Observation bae34c45-c0cf-4c11-82f6-4460a06912d5 · outbound

This paper cites Amino acid metabolism conflicts with protein diversity,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Amino acid metabolism conflicts with protein diversity,

Reference 35

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Observation 6d299b79-69ca-424d-af94-fb4230f6a1d7 · outbound

This paper cites Lost in the middle: How language models use long contexts,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Lost in the middle: How language models use long contexts,

Reference 36

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This paper cites Hairpin rna: a secondary structure of primary importance,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Hairpin rna: a secondary structure of primary importance,

Reference 37

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This paper cites Protein codes promote selective subcellular compartmentalization,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Protein codes promote selective subcellular compartmentalization,

Reference 38

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Observation 044e5c10-f726-4858-b5f7-ac880a9307dd · outbound

This paper cites Netgo 3.0: protein language model improves large-scale functional annotations,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Netgo 3.0: protein language model improves large-scale functional annotations,

Reference 39

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This paper cites Protein repeats: structures, functions, and evolution,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Protein repeats: structures, functions, and evolution,

Reference 40

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Observation f436f8ab-0113-427e-8c14-e7956115154a · outbound

This paper cites Parallel Structures in Pre-training Data Yield In-Context Learning.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Parallel Structures in Pre-training Data Yield In-Context Learning

Reference 41

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Observation c5ac879b-e5da-43bb-89b1-12e6e02d58f8 · outbound

This paper cites A practical review of mechanistic interpretability for transformer-based language models,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness A practical review of mechanistic interpretability for transformer-based language models,

Reference 42

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This paper cites Skill-Mix: a Flexible and Expandable Family of Evaluations for AI models.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Skill-Mix: a Flexible and Expandable Family of Evaluations for AI models

Reference 43

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This paper cites Can models learn skill composition from examples?,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Can models learn skill composition from examples?,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:53:44.129629Z

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This paper cites Uniprot: a hub for protein information,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Uniprot: a hub for protein information,

Reference 45

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Observation eef770fa-7367-484f-b374-6e1bb44cd924 · outbound

This paper cites Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets,.

In-Context Learning can distort the relationship between sequence likelihoods and biological fitness Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:53:44.026723Z

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Pith citing papers

Observation d5176445-c058-4dca-9223-10415180d8c3 · inbound

Induction Meets Biology: Mechanisms of Repeat Detection in Protein Language Models cites this paper.

Induction Meets Biology: Mechanisms of Repeat Detection in Protein Language Models In-Context Learning can distort the relationship between sequence likelihoods and biological fitness

Reference 2022

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