Paper Citation Record · LEDGER

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

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 48 inbound Pith citation observations for arXiv:2212.10559.

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

pith.paper-citation-record.v1
2212.10559 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 48 of 48 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:25:20.413368Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
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External citation measurements

25
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 50dbbee2-35d4-437d-9146-bec7b25b55bf · inbound

Language Models can Solve Computer Tasks cites this paper.

Language Models can Solve Computer Tasks Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-17T12:17:26.697618Z

Source-reported events for the cited work

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Observation 63fd8123-6f64-4735-bd67-d11d758eb59e · inbound

A Survey of Large Language Models cites this paper.

A Survey of Large Language Models Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:46:40.660977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 15ae8f89-7997-4cf9-9e3c-183d48dbacd2 · inbound

Otter: A Multi-Modal Model with In-Context Instruction Tuning cites this paper.

Otter: A Multi-Modal Model with In-Context Instruction Tuning Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-15T02:43:47.829878Z

Source-reported events for the cited work

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Observation 5a6b0803-177a-4485-a7ac-de0eda355bb2 · inbound

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution cites this paper.

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 86

Resolution: verified exact; arxiv_id, observed 2026-05-16T08:12:31.422912Z

Source-reported events for the cited work

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Observation c806e01e-2fba-43c1-bed1-771b9d5298b4 · inbound

The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision) cites this paper.

The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision) Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-15T23:26:06.309911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1bbabdb0-3504-4554-b876-d93da813b89a · inbound

StreamAdapter: Efficient Test Time Adaptation from Contextual Streams cites this paper.

StreamAdapter: Efficient Test Time Adaptation from Contextual Streams Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 2023

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Source-reported events for the cited work

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Observation 938944bf-9a36-44c1-8fa9-5fa8c64a6387 · inbound

In-Context Deep Learning via Transformer Models cites this paper.

In-Context Deep Learning via Transformer Models Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T13:07:59.484648Z

Source-reported events for the cited work

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Observation 33fe95f2-12f7-4d3e-9378-ec8b7b035bb3 · inbound

Differential learning kinetics govern the transition from memorization to generalization during in-context learning cites this paper.

Differential learning kinetics govern the transition from memorization to generalization during in-context learning Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T10:58:18.267152Z

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Observation 6e9d58e9-90b6-4e3a-a8ef-65ed50e84451 · inbound

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data cites this paper.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T05:28:56.577266Z

Source-reported events for the cited work

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Observation 6302d483-60fa-4130-8c41-ea088a873d2a · inbound

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs cites this paper.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T23:49:02.172820Z

Source-reported events for the cited work

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Observation aa73d477-9cb0-4367-9f0a-03d69c618b2f · inbound

PromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic Languages with Example Selection from Related Example Banks cites this paper.

PromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic Languages with Example Selection from Related Example Banks Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 10

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Observation b0ff9743-1eab-4f2c-a239-4261180de51b · inbound

A Comparative Study of Learning Paradigms in Large Language Models via Intrinsic Dimension cites this paper.

A Comparative Study of Learning Paradigms in Large Language Models via Intrinsic Dimension Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 7

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Source-reported events for the cited work

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Observation 372eb0b4-0541-4972-a825-6f81ed2cc410 · inbound

Small Language Model as Data Prospector for Large Language Model cites this paper.

Small Language Model as Data Prospector for Large Language Model Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T16:33:10.445070Z

Source-reported events for the cited work

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

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

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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:20:23.592776Z

Source-reported events for the cited work

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Observation 5aaac2cf-0b3b-46fe-bcd8-cc2e69e61707 · inbound

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner cites this paper.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T05:06:15.854754Z

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Observation ecf0fd16-f61e-4f88-b71e-b968dc717523 · inbound

Hindsight Planner: A Closed-Loop Few-Shot Planner for Embodied Instruction Following cites this paper.

Hindsight Planner: A Closed-Loop Few-Shot Planner for Embodied Instruction Following Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T00:18:20.245017Z

Source-reported events for the cited work

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Observation 738ace80-b48c-41af-8936-9d6975f469f3 · inbound

Evolution and The Knightian Blindspot of Machine Learning cites this paper.

Evolution and The Knightian Blindspot of Machine Learning Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T16:30:09.539983Z

Source-reported events for the cited work

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Observation 92e23f1f-f03e-4a07-b763-62c7c1fe6948 · inbound

StaICC: Standardized Evaluation for Classification Task in In-context Learning cites this paper.

StaICC: Standardized Evaluation for Classification Task in In-context Learning Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T14:06:14.202062Z

Source-reported events for the cited work

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Observation adc83f7f-d578-48ea-a87c-63e0171acaaa · inbound

PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning cites this paper.

PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T19:22:37.897107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90265562-0c6d-4046-b329-ea28fa3ab8b5 · inbound

Mass-Editing Memory with Attention in Transformers: A cross-lingual exploration of knowledge cites this paper.

Mass-Editing Memory with Attention in Transformers: A cross-lingual exploration of knowledge Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T13:10:15.072691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13b5fa6c-41bf-43a1-8b11-d7a44164c3ca · inbound

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval cites this paper.

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-08T20:12:19.783838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3da142e9-65e9-47e2-99e6-72d879ce859f · inbound

Solving Empirical Bayes via Transformers cites this paper.

Solving Empirical Bayes via Transformers Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T20:21:58.250300Z

Source-reported events for the cited work

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Observation 1b2c741d-5e3f-4b31-8fc0-a82672e51db4 · inbound

Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model cites this paper.

Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 112

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T08:02:24.005948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f2a18f1c-ddf2-4360-96b5-16cbb6868900 · inbound

The Prompt is Mightier than the Example cites this paper.

The Prompt is Mightier than the Example Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 7

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Observation 3b5f9650-bca0-4ed6-b0b5-4582b762fb54 · inbound

Optimization-Inspired Few-Shot Adaptation for Large Language Models cites this paper.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T14:24:28.780094Z

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Observation 37303a8d-8c66-443b-b845-98f7ab01a44b · inbound

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models cites this paper.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 9

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

Adaptive Task Vectors for Large Language Models cites this paper.

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 bc3b31fb-3efc-45a2-9ef0-166de5dcd8aa · inbound

ConText: Driving In-context Learning for Text Removal and Segmentation cites this paper.

ConText: Driving In-context Learning for Text Removal and Segmentation Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T11:01:08.177043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f530a241-cb5d-45f9-98f0-db8dbcbee486 · inbound

Transformers Meet In-Context Learning: A Universal Approximation Theory cites this paper.

Transformers Meet In-Context Learning: A Universal Approximation Theory Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T10:33:36.244991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 189287f6-70ad-4f4b-a374-df3b7489b3b9 · inbound

Prompting Wireless Networks: Reinforced In-Context Learning for Power Control cites this paper.

Prompting Wireless Networks: Reinforced In-Context Learning for Power Control Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T05:59:02.033815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85552c65-25d6-4d33-ba2c-693c03187b7c · inbound

Can Gradient Descent Simulate Prompting? cites this paper.

Can Gradient Descent Simulate Prompting? Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T22:41:50.199758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e863460-63fb-42d3-b1ed-6a727aab1fb1 · inbound

Thinking About Thinking: SAGE-nano's Inverse Reasoning for Self-Aware Language Models cites this paper.

Thinking About Thinking: SAGE-nano's Inverse Reasoning for Self-Aware Language Models Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-06T21:38:16.429781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f33f08b3-2cb9-41e1-ab57-7d174a2ed617 · inbound

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning cites this paper.

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T17:52:35.454020Z

Source-reported events for the cited work

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Observation deca8069-da0c-437e-813b-8acbc46c6ab6 · inbound

Provable Low-Frequency Bias of In-Context Learning of Representations cites this paper.

Provable Low-Frequency Bias of In-Context Learning of Representations Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T16:37:02.712441Z

Source-reported events for the cited work

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Observation 4416e9c6-7f19-4c47-a553-1d75d4f02152 · inbound

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design cites this paper.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.605376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba35a10a-481a-42e2-9508-fdf5e60b80a3 · inbound

Train Once, Reuse Everywhere: Generalizable Implicit In-Context Learning by Routing Attention cites this paper.

Train Once, Reuse Everywhere: Generalizable Implicit In-Context Learning by Routing Attention Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:22.436932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 035df581-eae6-4b96-a93a-b08e5d629744 · inbound

Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding cites this paper.

Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T06:15:58.798403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 454ea40e-9d3f-4977-ae76-bf7186f1293e · inbound

Why Multimodal In-Context Learning Lags Behind? Unveiling the Inner Mechanisms and Bottlenecks cites this paper.

Why Multimodal In-Context Learning Lags Behind? Unveiling the Inner Mechanisms and Bottlenecks Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-10T13:45:28.215723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 09dcf600-7f5c-4d2d-979b-a4de53e955d8 · inbound

When Context Sticks: Studying Interference in In-Context Learning cites this paper.

When Context Sticks: Studying Interference in In-Context Learning Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:36:09.472739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9ea03faa-0a0d-4735-bbe3-5912714f6a17 · inbound

One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning cites this paper.

One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T06:56:32.066177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5d61b067-8c82-4d16-8790-406bdc1b0a09 · inbound

Towards Understanding Continual Factual Knowledge Acquisition of Language Models: From Theory to Algorithm cites this paper.

Towards Understanding Continual Factual Knowledge Acquisition of Language Models: From Theory to Algorithm Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T05:56:25.555016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 884600e5-edd9-4d87-99db-7326b94408fd · inbound

Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space cites this paper.

Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 43

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T05:27:19.289708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 52b36e7f-c9cd-4886-878b-1e1dd404afde · inbound

A Human-in-the-Loop Framework for Efficient Prompt Selection in Microscopy Vision-Language Models cites this paper.

A Human-in-the-Loop Framework for Efficient Prompt Selection in Microscopy Vision-Language Models Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-21T07:09:46.436740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e2aac111-2d4b-43a6-b390-6a6c03f2c5a9 · inbound

Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis cites this paper.

Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 45

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T17:38:43.288286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9826500b-d439-411f-a5ba-babb80ea6390 · inbound

In-context learning of closed form solution to simple linear regression task using transformer with linear self-attention cites this paper.

In-context learning of closed form solution to simple linear regression task using transformer with linear self-attention Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T22:19:53.717359Z

Source-reported events for the cited work

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Observation c62be06b-bd3c-4f73-b347-59328c7af37d · inbound

Test-Time Scaling via Error Localization cites this paper.

Test-Time Scaling via Error Localization Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 152

Resolution: unresolved; no resolver link, observed 2026-08-01T07:28:33.862109Z

Source-reported events for the cited work

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Observation 81d0ca48-4423-4063-9338-db39dbc3e5a3 · inbound

ThinkRetrieve: Retrieval-Augmented Reasoning Traces for Test-Time Scaling cites this paper.

ThinkRetrieve: Retrieval-Augmented Reasoning Traces for Test-Time Scaling Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-12T14:10:45.290195Z

Source-reported events for the cited work

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Observation aba977d2-d585-44a4-b3fc-ef3e7e08c83d · inbound

When Is a Task Vector Enough? An Empirical Theory of Implicit Multimodal ICL cites this paper.

When Is a Task Vector Enough? An Empirical Theory of Implicit Multimodal ICL Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-14T12:25:20.413368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.