Paper Citation Record · LEDGER

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective

As of 13 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 1 inbound Pith citation observation for arXiv:2412.12276.

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

pith.paper-citation-record.v1
2412.12276 v3

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:20:23.832163Z

measured 69 of 69 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:49:37.474320Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T00:49:40.740002Z

Reference resolution

68 of 68 outbound references displayed

verified exact1
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External citation measurements

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Outbound references

Observation 1843ef40-2b3b-41a4-bf13-f087d3d41467 · outbound

This paper cites write newline.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective write newline

Reference 1

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Observation 6f5ce1e6-2362-41e9-baa3-346a31e385b9 · outbound

This paper cites Transformers learn to implement preconditioned gradient descent for in-context learning.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Transformers learn to implement preconditioned gradient descent for in-context learning

Reference 2

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Observation 8352b623-d509-46f9-8ede-a5f3b3406b15 · outbound

This paper cites Compositional Foundation Models for Hierarchical Planning.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Compositional Foundation Models for Hierarchical Planning

Reference 3

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Observation b2e41798-5349-4670-bf80-9508cd864d61 · outbound

This paper cites In-Context Language Learning: Architectures and Algorithms.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective In-Context Language Learning: Architectures and Algorithms

Reference 4

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Observation 6e0e83c8-487a-42e0-aa99-2748ad46e4c2 · outbound

This paper cites Understanding intermediate layers using linear classifier probes.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Understanding intermediate layers using linear classifier probes

Reference 5

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Observation c1ec4d5b-d780-4f50-b8cf-69d0bc8a4577 · outbound

This paper cites Transformers as statisticians: Provable in-context learning with in-context algorithm selection.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Transformers as statisticians: Provable in-context learning with in-context algorithm selection

Reference 6

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Observation e8b23984-262b-4bab-9d24-3ef8af68c49b · outbound

This paper cites and Moore, R.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective and Moore, R

Reference 7

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Observation 789b99bc-0c0e-48c8-a70f-06f7db6830de · outbound

This paper cites Prompting Language Models for Linguistic Structure.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Prompting Language Models for Linguistic Structure

Reference 8

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Observation e5f8f8e2-50b2-4f80-9b1b-a6ea3e1c126d · outbound

This paper cites Emergence of sparse representations from noise.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Emergence of sparse representations from noise

Reference 9

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Observation 3ecf42b0-3029-4c49-bfa4-b0560c11b1f3 · outbound

This paper cites Language Models are Few-Shot Learners.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Language Models are Few-Shot Learners

Reference 10

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Observation 58e24df1-393b-4c50-bafa-00f84f108776 · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 11

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Observation 967a51ce-6d57-436d-8ac3-15bccb9c1f1a · outbound

This paper cites Truth is Universal: Robust Detection of Lies in LLMs.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Truth is Universal: Robust Detection of Lies in LLMs

Reference 12

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Observation 7a626177-4f91-484c-906f-dbad86508d1d · outbound

This paper cites R., Muti, H.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective R., Muti, H

Reference 13

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Observation e5791223-b2b6-4060-b1bd-0f1bd961e5c8 · outbound

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

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 14

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Observation c1424f37-f9e1-408e-affd-44fa7ab1b715 · outbound

This paper cites Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 15

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Observation 804fdf38-2340-445c-8583-1b2a192b8439 · outbound

This paper cites Discovering Latent Concepts Learned in BERT.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Discovering Latent Concepts Learned in BERT

Reference 16

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Observation ef26be57-87c3-4a40-b51b-51f188ecd554 · outbound

This paper cites Faith and Fate: Limits of Transformers on Compositionality.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Faith and Fate: Limits of Transformers on Compositionality

Reference 17

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Observation cf465b21-e766-40db-8b25-3906a042abb9 · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 18

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Observation a3ecdc4c-0b71-495b-86fb-1a18ba809d5a · outbound

This paper cites S., and Valiant, G.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective S., and Valiant, G

Reference 19

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Observation 8736e315-6d2a-4c61-8066-c2a4c5e847a6 · outbound

This paper cites Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation

Reference 20

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Observation 8d718f05-e15d-427b-8776-7f14d385b177 · outbound

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

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Gemma 2: Improving Open Language Models at a Practical Size

Reference 21

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Observation 4f4b3e2b-58ee-4a61-a525-5e654a250462 · outbound

This paper cites Is mamba capable of in-context learning?, 2024.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Is mamba capable of in-context learning?, 2024

Reference 22

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Observation cbed37b3-2fb4-494c-9b20-758f3bfee578 · outbound

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

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective OLMo: Accelerating the Science of Language Models

Reference 23

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Observation 86c45169-a28b-4f40-a1d6-35e7f0669677 · outbound

This paper cites and Dao, T.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective and Dao, T

Reference 24

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Observation 514e9903-df9a-462e-8d92-88c2681d8d18 · outbound

This paper cites Language Models Represent Space and Time.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Language Models Represent Space and Time

Reference 25

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Observation 28d2cee9-c5cc-4f25-a1ef-db8916fa8979 · outbound

This paper cites Value Augmented Sampling for Language Model Alignment and Personalization.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Value Augmented Sampling for Language Model Alignment and Personalization

Reference 26

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This paper cites Learning to grok: Emergence of in-context learning and skill composition in modular arithmetic tasks.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Learning to grok: Emergence of in-context learning and skill composition in modular arithmetic tasks

Reference 27

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective How to use and interpret activation patching

Reference 28

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This paper cites In-Context Learning Creates Task Vectors.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective In-Context Learning Creates Task Vectors

Reference 29

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Observation 6449fbd5-622d-4696-ad15-9f2f3c92a8dc · outbound

This paper cites Abstraction-of-Thought Makes Language Models Better Reasoners.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Abstraction-of-Thought Makes Language Models Better Reasoners

Reference 30

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Observation c2452abc-0f07-460e-bcbb-8b22c878c49e · outbound

This paper cites Not All LLM Reasoners Are Created Equal.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Not All LLM Reasoners Are Created Equal

Reference 31

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective LoRA: Low-Rank Adaptation of Large Language Models

Reference 32

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective The Platonic Representation Hypothesis

Reference 33

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 34

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Adam: A Method for Stochastic Optimization

Reference 35

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Comparing Abstraction in Humans and Large Language Models Using Multimodal Serial Reproduction

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Why does in-context learning fail sometimes? Evaluating in-context learning on open and closed questions

Reference 38

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective E., Papailiopoulos, D., and Oymak, S

Reference 39

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Prompting Frameworks for Large Language Models: A Survey

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective A., MacIntyre, R., Bies, A., Ferguson, M., Katz, K., and Schasberger, B

Reference 41

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective and Tegmark, M

Reference 42

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Refusal in LLMs is an Affine Function

Reference 43

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Language Models Implement Simple Word2Vec-style Vector Arithmetic

Reference 44

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Circuit Component Reuse Across Tasks in Transformer Language Models

Reference 45

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This paper cites Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Reference 46

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This paper cites Does learning the right latent variables necessarily improve in-context learning?.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Does learning the right latent variables necessarily improve in-context learning?

Reference 47

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Steering Llama 2 via Contrastive Activation Addition

Reference 48

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Space

Reference 49

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement

Reference 50

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Language models are unsupervised multitask learners

Reference 51

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Pretraining task diversity and the emergence of non-bayesian in-context learning for regression

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:20:24.827343Z

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This paper cites Impact of Pretraining Term Frequencies on Few-Shot Reasoning.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Impact of Pretraining Term Frequencies on Few-Shot Reasoning

Reference 53

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This paper cites On convergence of nearest neighbor classifiers over feature transformations.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective On convergence of nearest neighbor classifiers over feature transformations

Reference 54

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This paper cites Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research

Reference 55

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Function Vectors in Large Language Models

Reference 56

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This paper cites Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias

Reference 57

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

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Transformers learn in-context by gradient descent

Reference 58

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Unresolved cited work

Reference 59

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Transformers: State-of-the-art natural language processing

Reference 60

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective ReFT: Representation Finetuning for Language Models

Reference 61

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This paper cites An Explanation of In-context Learning as Implicit Bayesian Inference.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective An Explanation of In-context Learning as Implicit Bayesian Inference

Reference 62

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Exchangeable Sequence Models Quantify Uncertainty Over Latent Concepts

Reference 63

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

Reference 64

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This paper cites Learning by discovering concept hierarchies.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Learning by discovering concept hierarchies

Reference 65

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This paper cites @esa (Ref.

Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective @esa (Ref

Reference 66

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective Unresolved cited work

Reference 67

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Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective common structure

Reference 68

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Pith citing papers

Observation bfb181bb-915d-4989-8840-f4066b212da2 · inbound

Surprise Calibration for Better In-Context Learning cites this paper.

Surprise Calibration for Better In-Context Learning Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective

Reference 11

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