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Paper Citation Record · LEDGER

ICLR: In-Context Learning of Representations

As of 18 August 2026, this Paper Citation Record lists 78 of 78 outbound references and 13 inbound Pith citation observations for arXiv:2501.00070.

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

pith.paper-citation-record.v1
2501.00070 v2

Coverage vector

measured 78 of 78 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:24:01.701539Z

measured 91 of 91 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:06:05.137301Z

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

78 of 78 outbound references displayed

  • verified exact0
  • verified fuzzy24
  • unresolved54
  • parse uncertain0
  • malformed identifier0
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External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation a61acc38-7580-4c34-9485-5a2964d8e8a1 · outbound

This paper cites Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color.

ICLR: In-Context Learning of Representations Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color

Reference 1

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source=arxiv_source observed=2026-08-10T23:24:01.378299Z digest=sha256:f2f8d1abbf87fa8fa966eb140f4659942dd84096ef6a315b26e7c1aca513f2a7

Observation 041dacfa-210f-4600-a508-91f1a26afddb · outbound

This paper cites Many-Shot In-Context Learning.

ICLR: In-Context Learning of Representations Many-Shot In-Context Learning

Reference 2

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source=arxiv_source observed=2026-08-10T23:24:01.383674Z digest=sha256:77282d8ef3d2a3cd58fdb88b060938b71b3e69216d9423dfcbc80e5ed4c59b65

Observation 5b2ce1a6-f22f-49c4-81eb-dd9eedbc0930 · outbound

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

ICLR: In-Context Learning of Representations What learning algorithm is in-context learning? Investigations with linear models

Reference 3

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source=arxiv_source observed=2026-08-10T23:24:01.387784Z digest=sha256:ed8fa87cf0280da99ee15ea6bf6d2211aec4f5d454dfc2359310ecb4912c3b03

Observation 986467b8-8129-401c-9454-e4f05ff4933e · outbound

This paper cites Physics of Language Models: Part 3.1, Knowledge Storage and Extraction.

ICLR: In-Context Learning of Representations Physics of Language Models: Part 3.1, Knowledge Storage and Extraction

Reference 4

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source=arxiv_source observed=2026-08-10T23:24:01.392192Z digest=sha256:138dc030addb033c4b4210bfe6111fd5b01c7caccd798e4e42c0fef301ed1a52

Observation 1cf46890-2a53-4aca-ac59-b197752c6abf · outbound

This paper cites Physics of Language Models: Part 1, Learning Hierarchical Language Structures.

ICLR: In-Context Learning of Representations Physics of Language Models: Part 1, Learning Hierarchical Language Structures

Reference 5

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source=arxiv_source observed=2026-08-10T23:24:01.396438Z digest=sha256:4e078e140a7fc86ff42e37c6c2f5afd5f1f1c237cead1822c13295f4b29f6c41

Observation 194a94c7-ae30-40d6-8aec-33a4bee8358f · outbound

This paper cites Many-shot jailbreaking.

ICLR: In-Context Learning of Representations Many-shot jailbreaking

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-10T23:24:01.401133Z digest=sha256:67841f0f4dfdeeb3f87352762bff7aa79bc1922e7da8c81d0144036277856205

Observation bc7c5116-8375-439c-bad3-d69239c2b966 · outbound

This paper cites Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet, 2024.

ICLR: In-Context Learning of Representations Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet, 2024

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-10T23:24:01.405906Z digest=sha256:a1eccd3970c96c631e7888a8e7923cb67876f881cf023233e18152cd2360f80c

Observation 09d2549d-ebf6-4c6d-8f95-e90db1e1628a · outbound

This paper cites Refusal in Language Models Is Mediated by a Single Direction.

ICLR: In-Context Learning of Representations Refusal in Language Models Is Mediated by a Single Direction

Reference 8

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source=arxiv_source observed=2026-08-10T23:24:01.409601Z digest=sha256:ba03436b3d0e13890614bf7467e332226588da0a1d834e6586bdffb85e6fec97

Observation e9f69897-4df2-4976-abc0-7e21fe6cdbb9 · outbound

This paper cites In-Context Learning Dynamics with Random Binary Sequences.

ICLR: In-Context Learning of Representations In-Context Learning Dynamics with Random Binary Sequences

Reference 9

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Observation c7987213-9000-40b7-8010-c9a60c01eec4 · outbound

This paper cites Semantics, conceptual role.

ICLR: In-Context Learning of Representations Semantics, conceptual role

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation fa776a47-cc3f-47d4-bfd9-450e7d399e12 · outbound

This paper cites Compression in visual working memory: using statistical regularities to form more efficient memory representations.

ICLR: In-Context Learning of Representations Compression in visual working memory: using statistical regularities to form more efficient memory representations

Reference 11

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source=arxiv_source observed=2026-08-10T23:24:01.421749Z digest=sha256:3db7d21a0d52dcf711b92472e907a34cd7e53571cd03b3e6ccfa712d2483c832

Observation f5b88c37-71b9-4964-8cdb-d27122b5c549 · outbound

This paper cites Language models are few-shot learners.

ICLR: In-Context Learning of Representations Language models are few-shot learners

Reference 12

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source=arxiv_source observed=2026-08-10T23:24:01.425803Z digest=sha256:d193206a2b28cc8837074f908b1950708fc2b92b2768beff46532ad52deaa71e

Observation bf7b8024-154c-4095-8aa7-02c740577e1a · outbound

This paper cites Discovering Latent Knowledge in Language Models Without Supervision.

ICLR: In-Context Learning of Representations Discovering Latent Knowledge in Language Models Without Supervision

Reference 13

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Observation 25edca5c-9dd1-435d-bbb7-afeee542aa90 · outbound

This paper cites Recurrent Neural Networks Learn to Store and Generate Sequences using Non-Linear Representations.

ICLR: In-Context Learning of Representations Recurrent Neural Networks Learn to Store and Generate Sequences using Non-Linear Representations

Reference 14

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Observation 149d1d57-ee48-4685-a794-592a24de9e36 · outbound

This paper cites The Llama 3 Herd of Models.

ICLR: In-Context Learning of Representations The Llama 3 Herd of Models

Reference 15

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source=arxiv_source observed=2026-08-10T23:24:01.437730Z digest=sha256:53567c9cc4d580a83df383d7874fa0b9028e20d4852da872328a52fbb67724d7

Observation a8055c1c-0c4d-4676-b528-4b47258fb230 · outbound

This paper cites Not All Language Model Features Are One-Dimensionally Linear.

ICLR: In-Context Learning of Representations Not All Language Model Features Are One-Dimensionally Linear

Reference 16

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source=arxiv_source observed=2026-08-10T23:24:01.441554Z digest=sha256:274fbb0c6ac013da05e0ea8e41ad4208622b7f2651d1a5f1ff3e035a53260b21

Observation ba12801a-3bba-4408-88fe-0e36bc454074 · outbound

This paper cites On a theorem of weyl concerning eigenvalues of linear transformations i.

ICLR: In-Context Learning of Representations On a theorem of weyl concerning eigenvalues of linear transformations i

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-10T23:24:01.445345Z digest=sha256:fe4c854ee5b1c35c7ab3a408491640bcca6560a8e79e9a3b35e2f9d6bf677651

Observation aa120fcf-7586-4618-ad2f-3244df00b3b3 · outbound

This paper cites NNsight and NDIF: Democratizing Access to Open-Weight Foundation Model Internals.

ICLR: In-Context Learning of Representations NNsight and NDIF: Democratizing Access to Open-Weight Foundation Model Internals

Reference 18

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source=arxiv_source observed=2026-08-10T23:24:01.448947Z digest=sha256:d500df165d897a21f945d9af20da824c886eef37b5e37bd6f5ee16e14fc65dcb

Observation 6f14a47b-d205-41f3-8f57-70e97f3b1491 · outbound

This paper cites What Can Transformers Learn In-Context? A Case Study of Simple Function Classes.

ICLR: In-Context Learning of Representations What Can Transformers Learn In-Context? A Case Study of Simple Function Classes

Reference 19

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Observation a6c983ca-3945-482d-8b6f-ae56e9c57e2a · outbound

This paper cites A map of abstract relational knowledge in the human hippocampal--entorhinal cortex.

ICLR: In-Context Learning of Representations A map of abstract relational knowledge in the human hippocampal--entorhinal cortex

Reference 20

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raw_fallback, observed 2026-08-10T23:24:02.878460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation ad727de8-6240-4260-b768-cecf5a44534c · outbound

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

ICLR: In-Context Learning of Representations Gemma 2: Improving Open Language Models at a Practical Size

Reference 21

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Observation 24970903-99df-4cfb-9b29-5c32c1ca0b61 · outbound

This paper cites Abrupt Learning in Transformers: A Case Study on Matrix Completion.

ICLR: In-Context Learning of Representations Abrupt Learning in Transformers: A Case Study on Matrix Completion

Reference 22

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source=arxiv_source observed=2026-08-10T23:24:01.463739Z digest=sha256:8339e9217ba2a3f6a89b8448682935ea9121d022468a829e84bbf6248574e27a

Observation ec760f06-c1b1-4b13-bfa2-1bcd874bf5a7 · outbound

This paper cites Language Models Represent Space and Time.

ICLR: In-Context Learning of Representations Language Models Represent Space and Time

Reference 23

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source=arxiv_source observed=2026-08-10T23:24:01.467479Z digest=sha256:6445afe47e9d17c23b02eb98a30422d7a57c86b24ae8f68b3cfa1f9e2b9345c2

Observation ac1f9785-4695-452a-9390-22a7bd1d80f9 · outbound

This paper cites World Models.

ICLR: In-Context Learning of Representations World Models

Reference 24

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Observation 3ab41d5b-6e1a-4860-bcc7-2ce1d6a06ddd · outbound

This paper cites Conceptual role semantics.

ICLR: In-Context Learning of Representations Conceptual role semantics

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-10T23:24:01.475507Z digest=sha256:9620d6a78de298d81cebceca7fe5a737c3672874b3f165673040f3fc3b7188fb

Observation 4951190f-6eb4-469e-b8ee-4feac1c2f77b · outbound

This paper cites In-context learning creates task vectors.

ICLR: In-Context Learning of Representations In-context learning creates task vectors

Reference 26

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Observation 760cd474-697e-47ab-a26e-236f863e1867 · outbound

This paper cites Hooyberghs, B.

ICLR: In-Context Learning of Representations Hooyberghs, B

Reference 27

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 14c943d6-00c6-4c7f-b1d7-5c59e01f7a74 · outbound

This paper cites Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks.

ICLR: In-Context Learning of Representations Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks

Reference 28

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Observation 7aa066f0-da58-49f4-a2d2-5259547a0168 · outbound

This paper cites Evidence of Learned Look-Ahead in a Chess-Playing Neural Network.

ICLR: In-Context Learning of Representations Evidence of Learned Look-Ahead in a Chess-Playing Neural Network

Reference 29

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Observation 9a436826-2237-46fe-b591-df1f6fc041f7 · outbound

This paper cites Linear Connectivity Reveals Generalization Strategies.

ICLR: In-Context Learning of Representations Linear Connectivity Reveals Generalization Strategies

Reference 30

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Observation 0f90dcfb-6c6a-4007-a9f4-dd183fe6d718 · outbound

This paper cites Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model.

ICLR: In-Context Learning of Representations Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model

Reference 31

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Observation cbc9cd22-26e0-4557-8573-83a709d07658 · outbound

This paper cites In-context Reinforcement Learning with Algorithm Distillation.

ICLR: In-Context Learning of Representations In-context Reinforcement Learning with Algorithm Distillation

Reference 32

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Observation 943ab309-e513-4d18-a5dd-eac081c05e0f · outbound

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

ICLR: In-Context Learning of Representations A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicity

Reference 33

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source=arxiv_source observed=2026-08-10T23:24:01.513222Z digest=sha256:f2012f93a36747938de523233afb0af18f2676a4f4a48d581b31458e2d1064b0

Observation c53ed356-5273-4351-8af3-d9c6402866c9 · outbound

This paper cites Supervised pretraining can learn in-context reinforcement learning.

ICLR: In-Context Learning of Representations Supervised pretraining can learn in-context reinforcement learning

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-10T23:24:01.517310Z digest=sha256:a8837d2d00ab12b4bac98d2dea22b451e9e0a49543729e9a7fc36384ef32b92e

Observation c66b1053-f746-49db-a85f-be5c297fae9d · outbound

This paper cites Implicit Representations of Meaning in Neural Language Models.

ICLR: In-Context Learning of Representations Implicit Representations of Meaning in Neural Language Models

Reference 35

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Observation ddd9931e-7124-4171-9b76-abbb1066cbf7 · outbound

This paper cites Emergent world representations: Exploring a sequence model trained on a synthetic task.

ICLR: In-Context Learning of Representations Emergent world representations: Exploring a sequence model trained on a synthetic task

Reference 36

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-10T23:24:01.525501Z digest=sha256:afade60eb6e185af5a61512ef2051793015195685734b76504c3d38d29274b62

Observation dd8d414e-7691-4dad-898d-bc9c1c2965a3 · outbound

This paper cites Emergent world representations: Exploring a sequence model trained on a synthetic task.

ICLR: In-Context Learning of Representations Emergent world representations: Exploring a sequence model trained on a synthetic task

Reference 37

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Observation 498f728d-cdfa-4f00-80e3-03141e9b32e0 · outbound

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

ICLR: In-Context Learning of Representations Inference-time intervention: Eliciting truthful answers from a language model

Reference 38

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.533582Z digest=sha256:1c392891f4dcbedcec6046462d7e72d2df748a7cc6fb96579e76f263cd1395c3

Observation a2bf6d93-532f-481f-8258-3e94ef94436c · outbound

This paper cites In-Context Learning with Many Demonstration Examples.

ICLR: In-Context Learning of Representations In-Context Learning with Many Demonstration Examples

Reference 39

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no resolver link, observed 2026-08-10T23:24:01.538433Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-10T23:24:01.538433Z digest=sha256:0148d84ea277bfa81cd1d4a91d5035ce3823c40657cf3888eb34b1a93b6db7a5

Observation 6d58a7dd-489c-4d65-9e50-19c8cf1607bf · outbound

This paper cites Transformers Learn Shortcuts to Automata.

ICLR: In-Context Learning of Representations Transformers Learn Shortcuts to Automata

Reference 40

Resolution
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no resolver link, observed 2026-08-10T23:24:01.543001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.543001Z digest=sha256:c3992912a5401c797b2f92fdf725ad2968cee31151f5a29e0119e22a9286e61a

Observation dc5b4726-7e73-4949-856b-2aaabd30880f · outbound

This paper cites Towards understanding grokking: An effective theory of representation learning.

ICLR: In-Context Learning of Representations Towards understanding grokking: An effective theory of representation learning

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:24:02.781927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-10T23:24:01.547033Z digest=sha256:289a3241ce7dac9a7a0cea6e6a803614a61e7d76550139204fa0c7a25e341895

Observation 90469e54-1b0b-43c3-9507-e8af4c175e45 · outbound

This paper cites Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity.

ICLR: In-Context Learning of Representations Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity

Reference 42

Resolution
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no resolver link, observed 2026-08-10T23:24:01.550983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.550983Z digest=sha256:80dfa126c5eb927bf3454cd070fd8c771114fe3b5b9acbfdb24e976f704947ac

Observation c8d309ed-12df-4c86-aab6-c5f794b39d37 · outbound

This paper cites Mechanistic mode connectivity.

ICLR: In-Context Learning of Representations Mechanistic mode connectivity

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:24:02.768836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-10T23:24:01.554843Z digest=sha256:1cb2b6d4555b28df03ce71f5acd12f7b7663c2a9861875899b90e9a0f2a1df1d

Observation 03a0ae8c-d464-4755-a367-d8cfa3d4dda7 · outbound

This paper cites A Percolation Model of Emergence: Analyzing Transformers Trained on a Formal Language.

ICLR: In-Context Learning of Representations A Percolation Model of Emergence: Analyzing Transformers Trained on a Formal Language

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:01.558461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.558461Z digest=sha256:a3f2873b5815544ada915890881fc5d80b4b6f4f3337fc26490cd1343b47f92c

Observation 2104c868-97cf-43c4-9972-7a33cec56a89 · outbound

This paper cites Transferring structural knowledge across cognitive maps in humans and models.

ICLR: In-Context Learning of Representations Transferring structural knowledge across cognitive maps in humans and models

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:24:02.756610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-10T23:24:01.562740Z digest=sha256:4e966c336d3f5444b10b4fdf988b17ce0f6dbe2f60cd1c871c674238fb5bf43e

Observation 15581811-b04f-40f5-98a4-7f0affca2d26 · outbound

This paper cites Flexible neural representations of abstract structural knowledge in the human entorhinal cortex.

ICLR: In-Context Learning of Representations Flexible neural representations of abstract structural knowledge in the human entorhinal cortex

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:24:02.745367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-10T23:24:01.566096Z digest=sha256:745fcbc04e5a7f716fd90e24479fdf7fbea128e4e2a3ea32848518fc78af719e

Observation baca784e-295a-4b61-9e21-3380fd913d0c · outbound

This paper cites The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets.

ICLR: In-Context Learning of Representations The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:01.570732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.570732Z digest=sha256:3d20915ff74ead7f2384d57a9f64ee24c8931162d06b971e17378ebc556df6f1

Observation 327763a6-7811-4699-8fab-c23aff650686 · outbound

This paper cites Efficient Estimation of Word Representations in Vector Space.

ICLR: In-Context Learning of Representations Efficient Estimation of Word Representations in Vector Space

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:01.575663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.575663Z digest=sha256:a4da6a39b5c09d85df7c56da3eff1b52e1bb63679deb80adc98b1ff8ba43d255

Observation 694b52da-2449-4a49-85d1-611c2155d860 · outbound

This paper cites Emergent Linear Representations in World Models of Self-Supervised Sequence Models.

ICLR: In-Context Learning of Representations Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:01.580066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.580066Z digest=sha256:6a0c41a5f55e486dec576f74aff5aed513ec1c9111a8df10dad45be68854b12c

Observation baf6e8ba-f03a-4fcd-bd5b-e91d904c6358 · outbound

This paper cites an unresolved cited work.

ICLR: In-Context Learning of Representations Unresolved cited work

Reference 50

Resolution
unresolved
raw_fallback, observed 2026-08-10T23:24:02.732342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-10T23:24:01.584013Z digest=sha256:d91614da03ae6a1c6175ba1e6aeafee0ceda65d7b267d18e86ae1c9a4b8fdd3a

Observation 9c765ef4-7b8c-4180-a474-37a0ee1ee9ac · outbound

This paper cites Representation Shattering in Transformers: A Synthetic Study with Knowledge Editing.

ICLR: In-Context Learning of Representations Representation Shattering in Transformers: A Synthetic Study with Knowledge Editing

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:01.587939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.587939Z digest=sha256:8345e83912d11d39f5c8b5a15477a536c5d22b7abea06f5ecb47c4588a2412a1

Observation b4660bde-2f18-4977-b38a-c5857dbb9571 · outbound

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

ICLR: In-Context Learning of Representations Competition Dynamics Shape Algorithmic Phases of In-Context Learning

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:01.591962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.591962Z digest=sha256:12a2e228959a11851a3148ce1dce208f9d2cfcbb67b528948345710eb611e402

Observation f4ae1e6c-7ed6-44cc-a883-3e9191d5b35c · outbound

This paper cites Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Space.

ICLR: In-Context Learning of Representations Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Space

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:01.595778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.595778Z digest=sha256:7767fc806338ba24fe04ca9d99d6e78cb3c9d28b49efb864663f309e7505f2b3

Observation 9903204b-44bb-4b4e-b1cf-fefdbdb333fa · outbound

This paper cites The Geometry of Categorical and Hierarchical Concepts in Large Language Models.

ICLR: In-Context Learning of Representations The Geometry of Categorical and Hierarchical Concepts in Large Language Models

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:01.599539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.599539Z digest=sha256:4740199a12d9ba717d4188d61f5671f91d5d6ab0c3c51b1d9b99011af12ee597

Observation 52423ccb-e417-4de4-915c-1aaa5bcdb45f · outbound

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

ICLR: In-Context Learning of Representations The Linear Representation Hypothesis and the Geometry of Large Language Models

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:01.604482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.604482Z digest=sha256:a939634d7dc3da251e67ef34522b20af78f28992af4e7618728734483535dc54

Observation 54d5339a-be2c-4d0e-8381-f4d07873f2ca · outbound

This paper cites Mapping language models to grounded conceptual spaces.

ICLR: In-Context Learning of Representations Mapping language models to grounded conceptual spaces

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:24:02.718909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-10T23:24:01.608473Z digest=sha256:a16e347bc1d049e73acc5bd6311208b2c3c76c349efd7012bf50c828907d7aeb

Observation edb3a078-0e03-469c-9731-796d37115518 · outbound

This paper cites Glove: Global vectors for word representation.

ICLR: In-Context Learning of Representations Glove: Global vectors for word representation

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:24:02.702408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-10T23:24:01.612007Z digest=sha256:480efcde5d63c54257b7f0f9b925556a42d16017f1600a07e810c253034b09f0

Observation ac9d7667-5d63-465a-b286-07d0ed023f6c · outbound

This paper cites Why think step by step? reasoning emerges from the locality of experience.

ICLR: In-Context Learning of Representations Why think step by step? reasoning emerges from the locality of experience

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:24:02.688819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-10T23:24:01.616060Z digest=sha256:f0c1c24098b31fc27ae5d42b5f328f4ae9c23264433822557387b2855c5cbe4b

Observation af160166-814f-4c74-9ede-453aa5b877e6 · outbound

This paper cites Sometimes i am a tree: Data drives unstable hierarchical generalization.

ICLR: In-Context Learning of Representations Sometimes i am a tree: Data drives unstable hierarchical generalization

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:01.622154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.622154Z digest=sha256:58e8a52d0a27075b2afb714931a1c68bdb37f6b8bc025601c3dd581c09019935

Observation 09b08f8a-1e59-4cdb-8b2a-202d25e6c9c8 · outbound

This paper cites Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks.

ICLR: In-Context Learning of Representations Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:01.625863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.625863Z digest=sha256:637b5328bb8c4291856bee50ed7cdc1465ed419fcb81ccbd287a8bb3e68cb80d

Observation ab4ae75b-2feb-426a-b2e3-e8f28eebf36f · outbound

This paper cites Steering llama 2 via contrastive activation addition.

ICLR: In-Context Learning of Representations Steering llama 2 via contrastive activation addition

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:01.630158Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.630158Z digest=sha256:2fe10fe1d30b2e2741ab021ebd806ded2e4bce846111acfafc4b7d4574cadf0b

Observation cce515d6-fe17-43d5-b813-c203f0e91a31 · outbound

This paper cites Transformers represent belief state geometry in their residual stream.

ICLR: In-Context Learning of Representations Transformers represent belief state geometry in their residual stream

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:01.634831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.634831Z digest=sha256:2ac2124d3a182a1442bf1f6c3cb4fc3a7468444ebcf465814163ffae832aff0e

Observation 0e948ccd-847e-4730-8604-f3d8e9fc0903 · outbound

This paper cites Spectral and algebraic graph theory.

ICLR: In-Context Learning of Representations Spectral and algebraic graph theory

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:24:02.674005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-10T23:24:01.639459Z digest=sha256:49be6dc30c41a5b87b639d857f8ef01f6e6a53ef472d2d08e2c40a3226727328

Observation ab3416d7-3f5d-4845-b3c2-c7cf5f826e0a · outbound

This paper cites Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.

ICLR: In-Context Learning of Representations Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:01.643483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.643483Z digest=sha256:e2e791eff6cdd7633031e55ff068ce89f9f87e5be72c127081beba311167448c

Observation ff2fc2c7-4335-49f0-9f1e-b826c0cd9530 · outbound

This paper cites Function Vectors in Large Language Models.

ICLR: In-Context Learning of Representations Function Vectors in Large Language Models

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:01.647563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.647563Z digest=sha256:b31e2952e29d0e120631800998278f1eb18b792d71f407afa46a7f80871da151

Observation e77a0b56-3d9d-4d34-9524-1a15d297fe0c · outbound

This paper cites Can neural networks learn implicit logic from physical reasoning? In The eleventh international conference on learning representations, 2022.

ICLR: In-Context Learning of Representations Can neural networks learn implicit logic from physical reasoning? In The eleventh international conference on learning representations, 2022

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:24:02.661566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-10T23:24:01.651404Z digest=sha256:13686f57e35bb22ddc0180be347cf8b8ddaafee34c3918c551237ea41d95b0a5

Observation cb9929fc-033c-42e0-9dde-e5d1a78ce585 · outbound

This paper cites How to draw a graph.

ICLR: In-Context Learning of Representations How to draw a graph

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:24:02.646878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-10T23:24:01.655425Z digest=sha256:a6fc8741a427bfef02616078aa3841f587a7b6d27a1a7f79685337d08d7443cb

Observation 1610c978-03b5-4469-843d-a59d72847b7e · outbound

This paper cites Evaluating the World Model Implicit in a Generative Model.

ICLR: In-Context Learning of Representations Evaluating the World Model Implicit in a Generative Model

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:01.659540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.659540Z digest=sha256:855b5ac2857ecf852ec94baef7788afe0c2a50a32284b6cae249719622c65b9c

Observation d4d06f4b-2b51-4944-9035-6a9a81ac8914 · outbound

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

ICLR: In-Context Learning of Representations Transformers learn in-context by gradient descent

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:24:02.633811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-10T23:24:01.663453Z digest=sha256:f2fe754288f1062e5425961dee4f41d7d829cc707f28e381292cf9fd97c32c12

Observation 7968d552-b9cb-4bf2-bb96-45e1e6e77504 · outbound

This paper cites Uncovering mesa-optimization algorithms in Transformers.

ICLR: In-Context Learning of Representations Uncovering mesa-optimization algorithms in Transformers

Reference 70

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:01.667776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.667776Z digest=sha256:40ab82cd774cf142cd96ee9ab5afa9cec3419d043470c6709a204237b375a1c6

Observation 543dd19f-5e16-47bd-b216-4abc2d532da5 · outbound

This paper cites Emergent Abilities of Large Language Models.

ICLR: In-Context Learning of Representations Emergent Abilities of Large Language Models

Reference 71

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:01.672477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.672477Z digest=sha256:37aecb798c96dcb056b12fb41cb0fccca764077260e52c8991981b97009d461e

Observation 6b048d21-5333-41fb-b7ef-0d0b90a77af8 · outbound

This paper cites Transformers are uninterpretable with myopic methods: a case study with bounded dyck grammars.

ICLR: In-Context Learning of Representations Transformers are uninterpretable with myopic methods: a case study with bounded dyck grammars

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:24:02.621667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-10T23:24:01.676159Z digest=sha256:a2c95a4e17628da6aac07b802cf386d19e835f020c370b4aa74f267c1449fe61

Observation a21f3a35-9eee-483f-bcdf-b82588d23b28 · outbound

This paper cites The tolman-eichenbaum machine: unifying space and relational memory through generalization in the hippocampal formation.

ICLR: In-Context Learning of Representations The tolman-eichenbaum machine: unifying space and relational memory through generalization in the hippocampal formation

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:24:02.608333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-10T23:24:01.679920Z digest=sha256:d4415aa50df99d46b64684b581df07c778306c2db5e983ab6994dccbdaad52dd

Observation ad3ab82a-4e86-49aa-af09-9633a3ca7a16 · outbound

This paper cites Transformers from an optimization perspective.

ICLR: In-Context Learning of Representations Transformers from an optimization perspective

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:24:02.595009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-10T23:24:01.683628Z digest=sha256:e16d7faf39c8863c6d96a53674737fb2f22222c978a8503ea839f837f2aecd01

Observation 3d599de6-ce4b-4234-8735-d37525f184ff · outbound

This paper cites write newline.

ICLR: In-Context Learning of Representations write newline

Reference 75

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:01.687218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.687218Z digest=sha256:909ed4b13f789455076739bf1cbd8532f5bb1c6806ea5140686b98545a8c609a

Observation 33905bfe-3208-46bb-a715-79376da362c0 · outbound

This paper cites @esa (Ref.

ICLR: In-Context Learning of Representations @esa (Ref

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:01.692126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:24:01.692126Z digest=sha256:bfa7a40561edf97e6fd9a93006a186bd3a16a0b20390bba422e3b67bcbc63f2e

Observation 182b45f7-a037-4048-b21b-e2ccecf3f64b · outbound

This paper cites an unresolved cited work.

ICLR: In-Context Learning of Representations Unresolved cited work

Reference 77

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:01.697079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation ab2c5af7-a986-44ce-ad64-38c6bb073fa5 · outbound

This paper cites an unresolved cited work.

ICLR: In-Context Learning of Representations Unresolved cited work

Reference 78

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Unavailable: canonical work link unavailable.

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Pith citing papers

Observation b53388a2-3ccf-4462-b05b-63196849e882 · inbound

Harmonic Loss Trains Interpretable AI Models cites this paper.

Harmonic Loss Trains Interpretable AI Models ICLR: In-Context Learning of Representations

Reference 19

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no resolver link, observed 2026-08-09T14:52:24.247836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation fee1d4d4-a77b-4914-9591-779585ae5ef3 · inbound

Utility Engineering: Analyzing and Controlling Emergent Value Systems in AIs cites this paper.

Utility Engineering: Analyzing and Controlling Emergent Value Systems in AIs ICLR: In-Context Learning of Representations

Reference 44

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no resolver link, observed 2026-08-08T00:04:57.375757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation fdb4c33e-d39a-4ca7-875c-f4926456ee38 · inbound

A Survey of Scaling in Large Language Model Reasoning cites this paper.

A Survey of Scaling in Large Language Model Reasoning ICLR: In-Context Learning of Representations

Reference 152

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 8fb2db42-a670-4540-a911-2925557aaf4e · inbound

Scaling sparse feature circuit finding for in-context learning cites this paper.

Scaling sparse feature circuit finding for in-context learning ICLR: In-Context Learning of Representations

Reference 48

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Source-reported events for the cited work

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Observation d3d6ecd3-ceff-424c-88bd-5cb7f759430a · inbound

On Entity Identification in Language Models cites this paper.

On Entity Identification in Language Models ICLR: In-Context Learning of Representations

Reference 2013

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation cbb59033-0b0e-47da-a17c-3453b7beacaf · inbound

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

Provable Low-Frequency Bias of In-Context Learning of Representations ICLR: In-Context Learning of Representations

Reference 13

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unresolved
no resolver link, observed 2026-08-06T16:37:03.260977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e0ac701d-b4a0-4f9b-a380-65175412effb · inbound

A Markov Categorical Framework for Language Modeling cites this paper.

A Markov Categorical Framework for Language Modeling ICLR: In-Context Learning of Representations

Reference 23

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verified exact
arxiv_id, observed 2026-05-19T02:36:59.925121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 0934223d-ea0f-42ea-b9f9-43dd36198e80 · inbound

ALAS: Adaptive Long-Horizon Action Synthesis via Async-pathway Stream Disentanglement cites this paper.

ALAS: Adaptive Long-Horizon Action Synthesis via Async-pathway Stream Disentanglement ICLR: In-Context Learning of Representations

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-11T14:06:05.002199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 67a62d91-c067-4665-97eb-002fe41e0563 · inbound

A framework for analyzing concept representations in neural models cites this paper.

A framework for analyzing concept representations in neural models ICLR: In-Context Learning of Representations

Reference 172

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arxiv_id, observed 2026-05-09T22:18:59.141723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-05-09T14:49:22.776209Z digest=sha256:c7e9d70d31fc062543ee0e8c0530cf20e4c7b8c42c9a5bde633aac2311cea040

Observation 689aa3bb-4778-4f13-9d7f-a1543783fed9 · inbound

Uncovering and Shaping the Latent Representation of 3D Scene Topology in Vision-Language Models cites this paper.

Uncovering and Shaping the Latent Representation of 3D Scene Topology in Vision-Language Models ICLR: In-Context Learning of Representations

Reference 46

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verified exact
arxiv_id, observed 2026-05-11T05:00:55.026979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-11T00:56:06.243890Z digest=sha256:d5ed86947d1ffd54b41c98ca1d45e1164438ef170ac7ca20d3c6ffb926794113

Observation 1ff63002-bf84-41ac-b4ce-1920cc4cf796 · inbound

Belief or Circuitry? Causal Evidence for In-Context Graph Learning cites this paper.

Belief or Circuitry? Causal Evidence for In-Context Graph Learning ICLR: In-Context Learning of Representations

Reference 9

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verified exact
arxiv_id, observed 2026-05-12T08:41:23.966886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-12T00:52:07.392800Z digest=sha256:d4fd13edeab58ba3f9e3c095e21bbded9c7d1d8f4fb92c691fca946a1d5c00f6

Observation 893d285e-9c96-4949-853d-b2c56c178cc3 · inbound

Yang-Mills-Higgs: A Geometric Theory of Binary Labels on Non-Contractible Spaces cites this paper.

Yang-Mills-Higgs: A Geometric Theory of Binary Labels on Non-Contractible Spaces ICLR: In-Context Learning of Representations

Reference 15

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verified exact
arxiv_id, observed 2026-07-02T06:06:40.546568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-07-02T06:04:57.007005Z digest=sha256:7981bffb12cf4e48add77c72a803733a583fdaaf2c9ddac07a79256b89374ffb

Observation 7d93e54b-1075-4f79-b895-bd09716d3342 · inbound

Context Is King: How In-Context Specification Shapes the Geometry of Concepts cites this paper.

Context Is King: How In-Context Specification Shapes the Geometry of Concepts ICLR: In-Context Learning of Representations

Reference 15

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unresolved
no resolver link, observed 2026-07-31T15:17:59.813238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-31T15:17:59.813238Z digest=sha256:86000f360bc1c560fd4d213e1ef40d32d49047e09ec431ca8616dba694ef80b1