Paper Citation Record · LEDGER

Adaptive Task Vectors for Large Language Models

As of 8 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 1 inbound Pith citation observation for arXiv:2506.03426.

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

pith.paper-citation-record.v1
2506.03426 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:13:02.532066Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T05:32:01.059706Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T05:33:58.583382Z

Reference resolution

52 of 52 outbound references displayed

verified exact0
verified fuzzy18
unresolved34
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 38da544c-8c97-43c0-8615-d222dc1df4e2 · outbound

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

Adaptive Task Vectors for Large Language Models Language models are few-shot learners,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T11:12:58.778015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 819fb1e3-c901-4271-a6d4-87290c127cb3 · outbound

This paper cites A Survey on In-context Learning.

Adaptive Task Vectors for Large Language Models A Survey on In-context Learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T11:12:58.888797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9195e073-f686-422c-9b7d-378da5004a1e · outbound

This paper cites What Makes Good In-Context Examples for GPT-$3$?.

Adaptive Task Vectors for Large Language Models What Makes Good In-Context Examples for GPT-$3$?

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T11:12:59.012993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a551a0d-f5f1-47c3-a57e-82a5d5ca1957 · outbound

This paper cites Revisiting Demonstration Selection Strategies in In-Context Learning.

Adaptive Task Vectors for Large Language Models Revisiting Demonstration Selection Strategies in In-Context Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T11:12:59.151780Z

Source-reported events for the cited work

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Observation 20bf3ef1-b7d0-4a42-b34a-69bac13ae6d5 · outbound

This paper cites Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning.

Adaptive Task Vectors for Large Language Models Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T11:12:59.307070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee8ef9b7-c48d-404d-8c54-9e52813fd1d9 · outbound

This paper cites Long-context LLMs Struggle with Long In-context Learning.

Adaptive Task Vectors for Large Language Models Long-context LLMs Struggle with Long In-context Learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T11:12:59.410752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 096863bb-d6f2-4e92-8a8e-d0684c9c8f62 · outbound

This paper cites Babilong: Testing the limits of llms with long context reasoning-in-a-haystack,.

Adaptive Task Vectors for Large Language Models Babilong: Testing the limits of llms with long context reasoning-in-a-haystack,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:13:06.169909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d0516e09-fbf7-4ffe-b76d-50a3b401c244 · outbound

This paper cites In-context learning creates task vectors,.

Adaptive Task Vectors for Large Language Models In-context learning creates task vectors,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:13:06.155628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 952f458a-8a26-4823-9db7-5efd733b08f1 · outbound

This paper cites Editing models with task arithmetic,.

Adaptive Task Vectors for Large Language Models Editing models with task arithmetic,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:13:05.995015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1ee2963a-a376-42f3-811f-738b07d7b17a · outbound

This paper cites In-context vectors: making in context learning more effective and controllable through latent space steering,.

Adaptive Task Vectors for Large Language Models In-context vectors: making in context learning more effective and controllable through latent space steering,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:13:05.774495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ca4dc751-e105-43d9-9c6a-5d9a8d361259 · outbound

This paper cites Implicit in-context learning,.

Adaptive Task Vectors for Large Language Models Implicit in-context learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:13:05.612115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d5eb0e16-23f1-41cf-8112-c355d6b989c5 · outbound

This paper cites ELICIT: LLM augmentation via external in-context capability,.

Adaptive Task Vectors for Large Language Models ELICIT: LLM augmentation via external in-context capability,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:13:05.409486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c4497d80-102c-46a5-8124-e43cdbf1b82f · outbound

This paper cites Task Vectors in In-Context Learning: Emergence, Formation, and Benefit.

Adaptive Task Vectors for Large Language Models Task Vectors in In-Context Learning: Emergence, Formation, and Benefit

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T11:12:59.912293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27c0e2bc-40e2-4461-af4e-24b02d849e5e · outbound

This paper cites Multimodal task vectors enable many-shot multimodal in-context learning,.

Adaptive Task Vectors for Large Language Models Multimodal task vectors enable many-shot multimodal in-context learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:13:05.193032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f41412e9-06e1-4b92-a849-ac5906b0b39b · outbound

This paper cites Calibrate before use: Improving few-shot per- formance of language models,.

Adaptive Task Vectors for Large Language Models Calibrate before use: Improving few-shot per- formance of language models,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:13:04.969946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3c80c705-bae6-4d13-92c7-4f5a9196cdfa · outbound

This paper cites Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity.

Adaptive Task Vectors for Large Language Models Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:00.135436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3c21e7b-a1a2-47b0-b4cd-72952f9182e0 · outbound

This paper cites Batch-ICL: Effective, Efficient, and Order-Agnostic In-Context Learning.

Adaptive Task Vectors for Large Language Models Batch-ICL: Effective, Efficient, and Order-Agnostic In-Context Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:00.203912Z

Source-reported events for the cited work

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Observation 8c6e98ea-542b-4ac2-af5e-c4e583bef43a · outbound

This paper cites When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers.

Adaptive Task Vectors for Large Language Models When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:00.284594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5ecef76-6764-43f0-b751-2e2fd2868c99 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

Adaptive Task Vectors for Large Language Models Chain-of-thought prompting elicits reasoning in large language models,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:00.363796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32fac6aa-a469-40e5-ac82-552be42b2f57 · outbound

This paper cites Large language models are zero-shot reasoners,.

Adaptive Task Vectors for Large Language Models Large language models are zero-shot reasoners,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:00.427070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65b60eaf-0b1c-44fe-9b9b-8e16e46870fb · outbound

This paper cites Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

Adaptive Task Vectors for Large Language Models Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:00.493569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a220138-0b02-4377-b9ca-ee5d79b7e14f · outbound

This paper cites Rethinking the role of demonstrations: What makes in-context learning work?.

Adaptive Task Vectors for Large Language Models Rethinking the role of demonstrations: What makes in-context learning work?

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:13:04.764162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e785dca7-014a-432a-8397-2faac73e6898 · outbound

This paper cites Learning to retrieve prompts for in-context learning,.

Adaptive Task Vectors for Large Language Models Learning to retrieve prompts for in-context learning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:13:04.467054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b15fcaca-27c5-44e7-a27b-30cd2da825b9 · outbound

This paper cites Attention is all you need,.

Adaptive Task Vectors for Large Language Models Attention is all you need,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:00.682152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e191d50-6cae-44ee-a48f-3417cfa905c5 · outbound

This paper cites Language models are unsupervised multitask learners,.

Adaptive Task Vectors for Large Language Models Language models are unsupervised multitask learners,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:00.735101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0493f50-eea2-4303-b1f7-ed7797f95962 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

Adaptive Task Vectors for Large Language Models Lora: Low-rank adaptation of large language models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:00.834646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02dcddf0-9aec-4a74-8202-63523cf2c525 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Adaptive Task Vectors for Large Language Models Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:00.870552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3cea4c2-1328-4005-bd43-bd54952659da · outbound

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

Adaptive Task Vectors for Large Language Models Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:00.986510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b12e7881-c29f-4255-8c19-7c82092b3967 · outbound

This paper cites Towards a unified view of parameter-efficient transfer learning,.

Adaptive Task Vectors for Large Language Models Towards a unified view of parameter-efficient transfer learning,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:13:04.212827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b93ba10c-84ea-4b82-a058-b3f079d4d0b9 · outbound

This paper cites The Llama 3 Herd of Models.

Adaptive Task Vectors for Large Language Models The Llama 3 Herd of Models

Reference 30

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be870b53-86e7-4899-a2cb-9543c15c7b2e · outbound

This paper cites Mistral 7B.

Adaptive Task Vectors for Large Language Models Mistral 7B

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:01.175650Z

Source-reported events for the cited work

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Observation a0733e3b-288a-467a-a850-6776666e762a · outbound

This paper cites The probabilistic relevance framework: Bm25 and beyond,.

Adaptive Task Vectors for Large Language Models The probabilistic relevance framework: Bm25 and beyond,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:13:03.970289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 13a7a218-3334-4230-9b01-30f83e16e073 · outbound

This paper cites A primer in bertology: What we know about how bert works,.

Adaptive Task Vectors for Large Language Models A primer in bertology: What we know about how bert works,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:13:03.759215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 81ca89b0-5b9d-41d4-b232-a704866a992c · outbound

This paper cites BERT Rediscovers the Classical NLP Pipeline.

Adaptive Task Vectors for Large Language Models BERT Rediscovers the Classical NLP Pipeline

Reference 34

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Source-reported events for the cited work

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Observation 968dc86a-d13d-4b88-b26e-6abe8ae82d74 · outbound

This paper cites A mathematical framework for transformer circuits,.

Adaptive Task Vectors for Large Language Models A mathematical framework for transformer circuits,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:13:03.620714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dc79206a-c45a-484d-a9da-927e50ee0fb8 · outbound

This paper cites CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge.

Adaptive Task Vectors for Large Language Models CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:01.509144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d4219f0-d7ce-435a-b88e-f5bf5884a9bb · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

Adaptive Task Vectors for Large Language Models Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:01.572751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbd1ad22-5623-4460-8765-532819ff3992 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Adaptive Task Vectors for Large Language Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:01.655117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 661617bc-6654-40d1-ad9c-25e845c47fce · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

Adaptive Task Vectors for Large Language Models BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 39

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Source-reported events for the cited work

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Observation bc114cea-38e4-4b2a-a6f9-6d4311b6fa26 · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

Adaptive Task Vectors for Large Language Models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:01.715513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e40b5a3-6292-43bc-ae6c-ddf80e2d7bb6 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Adaptive Task Vectors for Large Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:01.813781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d6dcc4f-9533-4d0d-a14a-9b9723210fd0 · outbound

This paper cites MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms.

Adaptive Task Vectors for Large Language Models MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:01.866493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b561a723-f924-47e7-a5f0-a19124e52cc9 · outbound

This paper cites Mmlu- pro: A more robust and challenging multi-task language understanding benchmark,.

Adaptive Task Vectors for Large Language Models Mmlu- pro: A more robust and challenging multi-task language understanding benchmark,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:13:03.512415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 805eee57-73d7-44a2-873d-e2ec3c41b2cd · outbound

This paper cites CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models.

Adaptive Task Vectors for Large Language Models CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:02.006492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33bddff3-8d45-4133-b6bc-273c7b42b143 · outbound

This paper cites BBQ: A Hand-Built Bias Benchmark for Question Answering.

Adaptive Task Vectors for Large Language Models BBQ: A Hand-Built Bias Benchmark for Question Answering

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:02.053050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9f69b43-c8af-4198-bea6-db3b9973a233 · outbound

This paper cites Pointer Sentinel Mixture Models.

Adaptive Task Vectors for Large Language Models Pointer Sentinel Mixture Models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:02.140061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 454011cf-02b1-467e-afe8-5b330de3beed · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

Adaptive Task Vectors for Large Language Models GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:02.203627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3f990dc-24bc-4510-84aa-80fb118a02d3 · outbound

This paper cites Super- glue: A stickier benchmark for general-purpose language understanding systems,.

Adaptive Task Vectors for Large Language Models Super- glue: A stickier benchmark for general-purpose language understanding systems,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:13:03.383113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f15bcf8f-20b4-428e-bda1-71ef257517b5 · outbound

This paper cites Analysing Mathematical Reasoning Abilities of Neural Models.

Adaptive Task Vectors for Large Language Models Analysing Mathematical Reasoning Abilities of Neural Models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:02.328424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c6b56cf-804c-41ec-bb2d-13ba17da6d53 · outbound

This paper cites Eleutherai/lm-evaluation-harness: Major refactor,.

Adaptive Task Vectors for Large Language Models Eleutherai/lm-evaluation-harness: Major refactor,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:02.402442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7473b941-50e9-493c-ae29-6553a58c9adf · outbound

This paper cites For every pair (hℓ, vsmall) there exist static LoRA factors (Wdown, Wup) and a scale s, all independent of the runtime query, such that ˜hℓ = ˆhℓ for all inputs.

Adaptive Task Vectors for Large Language Models For every pair (hℓ, vsmall) there exist static LoRA factors (Wdown, Wup) and a scale s, all independent of the runtime query, such that ˜hℓ = ˆhℓ for all inputs

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:13:03.235997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6cf3a833-9960-4cf4-ae91-b416058904f7 · outbound

This paper cites ATV implies LoRA.

Adaptive Task Vectors for Large Language Models ATV implies LoRA

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:13:03.109619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation bf395aeb-aaa9-432b-a507-1d1e993ae462 · inbound

Distributional Alignment as a Criterion for Designing Task Vectors in In-Context Learning cites this paper.

Distributional Alignment as a Criterion for Designing Task Vectors in In-Context Learning Adaptive Task Vectors for Large Language Models

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-21T05:33:58.585658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.