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Paper Citation Record · LEDGER

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models

As of 17 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2506.01919.

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

pith.paper-citation-record.v1
2506.01919 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:40:59.125143Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T10:12:29.063618Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

50 of 50 outbound references displayed

  • verified exact4
  • verified fuzzy5
  • unresolved40
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b37cdf96-cab3-43d4-9850-c902584a7c54 · outbound

This paper cites GPT-4 Technical Report.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models GPT-4 Technical Report

Reference 1

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unresolved
no resolver link, observed 2026-08-07T11:40:12.667810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:40:12.667810Z digest=sha256:ddb918d3c43689884fadd4e38b171671ad6487eda2859b52d9f003c4c1e225b9

Observation 2776fbb9-3174-4dd5-a944-291bb90654cf · outbound

This paper cites an unresolved cited work.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Unresolved cited work

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:40:23.082855Z digest=sha256:ad6dce197b93fa2828ed8d55c7bb43f6a48ba1ee4599dc9dd0abe6001e683a85

Observation 9e33608f-18f7-45d9-9f4e-1fa306a3e8bc · outbound

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

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models What learning algorithm is in-context learning? Investigations with linear models

Reference 3

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no resolver link, observed 2026-08-07T11:40:23.398864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:40:23.398864Z digest=sha256:686978adae026511ace64c390f315a79522a5fe61393d3c82d457791a2218861

Observation de0af1fd-b6c7-47bf-80d8-0f98468a9cb7 · outbound

This paper cites an unresolved cited work.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Unresolved cited work

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:40:23.658854Z digest=sha256:505945807a9efdeb2ca74112b8c3de8209c3d07a66653fd641971c8569230de8

Observation bb355573-f35e-4bbf-9f23-d86137c98bd3 · outbound

This paper cites an unresolved cited work.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Unresolved cited work

Reference 5

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raw_fallback, observed 2026-08-07T11:41:14.394357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:40:24.130942Z digest=sha256:62497cca2b497915a949e538dd98e6cacdef21a8426a1af16f5de052c2ed2a46

Observation 5c47feb1-a624-4b5a-8b6a-7963b64d22c2 · outbound

This paper cites an unresolved cited work.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Unresolved cited work

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:40:31.436082Z digest=sha256:6e03164dfd6cb665546f0a33ae6f1d785562c69d2bdafe8bd6b14b311de4cbc8

Observation 055ae442-6429-4ada-b088-70b6f7c76574 · outbound

This paper cites D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:40:32.879319Z digest=sha256:624ef2b05e676543b760c935d7f767f262f40c8b20bc344da8ff0c0d5f3edd68

Observation 693a0ccf-5081-41c1-97cc-3ab3f968d44d · outbound

This paper cites an unresolved cited work.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Unresolved cited work

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:40:34.930815Z digest=sha256:92ccc1a066800829283112e66fbff922a9393e123eb8cd4e7e2ce46d452db30a

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

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

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:40:35.806691Z digest=sha256:d8004e8e2db77767db4afdab9fb2053066166d3069cfaae528b00d5fabf9bcde

Observation 16078ba0-f8ee-4248-88a5-e724faf3065b · outbound

This paper cites Learning higher-order sequential structure with cloned HMMs.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Learning higher-order sequential structure with cloned HMMs

Reference 10

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verified exact
local_arxiv, observed 2026-08-07T11:40:59.940388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:40:35.917029Z digest=sha256:99494a49fcd3e003ec418e49ad1fde88ebdc90ade3f3b07c8dbdd31b25c3553e

Observation 6639830b-7776-4d2c-ab1b-dd86daaf804a · outbound

This paper cites A Survey on In-context Learning.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models A Survey on In-context Learning

Reference 11

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no resolver link, observed 2026-08-07T11:40:35.988583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:40:35.988583Z digest=sha256:4a1ad015b62271676db57578e55e33ac25430beb4494054dc235799f09187976

Observation 856b3693-b88d-428b-8f3b-c6f6e6f6f7f8 · outbound

This paper cites The Llama 3 Herd of Models.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models The Llama 3 Herd of Models

Reference 12

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no resolver link, observed 2026-08-07T11:40:36.043974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:40:36.043974Z digest=sha256:5d3a9593ca405ebcb86fc8c27894fc173f88f8fbb27349c8bfdf3371c409e19d

Observation 85af8bd4-a4d9-4667-8b5c-42a18f74b032 · outbound

This paper cites Provably Efficient High-Dimensional Bandit Learning with Batched Feedbacks.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Provably Efficient High-Dimensional Bandit Learning with Batched Feedbacks

Reference 13

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verified exact
local_arxiv, observed 2026-08-07T11:40:59.778041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:40:36.102914Z digest=sha256:db7e215bf48188c3fc2e2dd1c97fc9c3374861135b920775bb5fa1e0ecda8c2a

Observation dc45da97-be6b-4d0e-ad80-1602fce61a2a · outbound

This paper cites an unresolved cited work.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Unresolved cited work

Reference 14

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raw_fallback, observed 2026-08-07T11:41:13.864748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:40:46.567180Z digest=sha256:de398b2f4c8019b64368d0637cc6d61e4054c925fca4e834c8adf5941253d12b

Observation ce7fccea-3bfa-412f-9872-5f705a1749dc · outbound

This paper cites S., and Valiant, G.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models S., and Valiant, G

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:40:53.229870Z digest=sha256:ab2c6a0bbe4a7daf87b5f3ab78fbdd208073226382bf03e6748aae9328550ae0

Observation 748cf294-8c43-46f6-b1b0-115609ca86c8 · outbound

This paper cites an unresolved cited work.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Unresolved cited work

Reference 16

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raw_fallback, observed 2026-08-07T11:41:13.682483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:40:54.881823Z digest=sha256:9dcc54d9e8bc1d67c01a2bd222a211466c6c37f9494f71a78ae5046167e1433b

Observation 371d5abd-36dd-454a-a5e3-c3930026e809 · outbound

This paper cites How Do Transformers Learn In-Context Beyond Simple Functions? A Case Study on Learning with Representations.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models How Do Transformers Learn In-Context Beyond Simple Functions? A Case Study on Learning with Representations

Reference 17

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no resolver link, observed 2026-08-07T11:40:55.541675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:40:55.541675Z digest=sha256:c02f3c46ac9ae5bf59d5f2d317e6926b21c4c1d2980a2a1d8dfae2ff8598cccd

Observation e6110633-2700-40ad-8141-d7ab93d2bfb0 · outbound

This paper cites L., Fard, M.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models L., Fard, M

Reference 18

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verified fuzzy
raw_fallback, observed 2026-08-07T11:41:13.576491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:40:55.869665Z digest=sha256:4ea5a4923045a8aef8a19767c2813dabda3dd2bc3a211dcf55be315e78f85dbb

Observation c6a0104b-e2a7-4746-a357-8b40f9b81dc6 · outbound

This paper cites M., and Zhang, T.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models M., and Zhang, T

Reference 19

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verified fuzzy
raw_fallback, observed 2026-08-07T11:41:13.334479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:40:55.913905Z digest=sha256:b9057e54cc730dbb013dd821132ce99fdb908edbdc35a19177df25072021281d

Observation 04cfe5c6-48d7-47bf-9ad2-f2da8a8e07b9 · outbound

This paper cites A Latent Space Theory for Emergent Abilities in Large Language Models.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models A Latent Space Theory for Emergent Abilities in Large Language Models

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:40:55.929001Z digest=sha256:5235d0ce23421801354bab87fee0f00355e8018e42d78d27ae75872c3cfa0c1d

Observation 9667b24a-9dc6-4a70-8616-4d08a9a512a1 · outbound

This paper cites an unresolved cited work.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Unresolved cited work

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:40:55.994873Z digest=sha256:15ada54ad074bcb91b75fec9c17299b2b7682ac9314324b42cefed99e38f158a

Observation 50c28daa-a967-4087-85c5-2fc111ff7716 · outbound

This paper cites Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining

Reference 22

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source=arxiv_source observed=2026-08-07T11:40:56.046409Z digest=sha256:bb5a47f0374cc6f9c976ab616005bcacf6256b733acaaa845583898b73a97b9c

Observation 890197f7-e19f-432c-91dc-9beb7e6903c1 · outbound

This paper cites DeepSeek-V3 Technical Report.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models DeepSeek-V3 Technical Report

Reference 23

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source=arxiv_source observed=2026-08-07T11:40:57.515045Z digest=sha256:bd4875dbfb8675e408d52ee40518e150bbf3bf7aa72738cbf8faf9e0ad278f9d

Observation 5bae81ac-d125-41e9-bf4f-39ce966d6004 · outbound

This paper cites Transformers Learn Shortcuts to Automata.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Transformers Learn Shortcuts to Automata

Reference 24

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:40:57.702995Z digest=sha256:f8feb5965771df5d80fbc97fd39238f31c9171eb73673914770e8234976af764

Observation 06255387-f9d0-473d-b2c2-6b5e540c3c44 · outbound

This paper cites an unresolved cited work.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Unresolved cited work

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:40:57.754927Z digest=sha256:b5d037ba1d833c5e62f07320721803e9f66c474516ffecfcd6cbe3164aa0c48f

Observation fe0c9757-5b8e-414f-9857-a3e00522e994 · outbound

This paper cites One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention

Reference 26

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:40:57.792547Z digest=sha256:ee6e97c681c2b98a119298802896f9781a4b0dde97da788c0a8dafc27b43ad36

Observation ed888521-334d-46be-b910-a37d407f457f · outbound

This paper cites an unresolved cited work.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Unresolved cited work

Reference 27

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raw_fallback, observed 2026-08-07T11:41:12.710778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:40:57.829080Z digest=sha256:fd458b676756cac4e06806dce76d5c5d10471ef4f125a5e186c0048a82d65814

Observation ff75ee10-68d8-4aa0-8153-f544be1c01b3 · outbound

This paper cites How Transformers Learn Causal Structure with Gradient Descent.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models How Transformers Learn Causal Structure with Gradient Descent

Reference 28

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no resolver link, observed 2026-08-07T11:40:57.895555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:40:57.895555Z digest=sha256:05286c1b0cfe3289a1036e63fda0f6a20170ceb601aed184c7dbe855b0502ad3

Observation 326c245e-c853-4bdb-b297-3cf83230767f · outbound

This paper cites an unresolved cited work.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Unresolved cited work

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:40:57.908032Z digest=sha256:3c1e3659aacd865b6ec45e618618271a7ead5e801318dcabb93790d20eabb35d

Observation 1bbec8c5-d780-4efd-b101-c71eff7e0608 · outbound

This paper cites How do Transformers perform In-Context Autoregressive Learning?.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models How do Transformers perform In-Context Autoregressive Learning?

Reference 30

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metadata mismatch
local_arxiv, observed 2026-08-07T11:40:59.562424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:40:57.962510Z digest=sha256:8990cd8ff02edba0379082adfd14b72180160917ce73f4fe0332b8006b80b2a0

Observation bbe4fb67-cc2c-4df4-8f49-8e65cf0b7eb6 · outbound

This paper cites an unresolved cited work.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Unresolved cited work

Reference 31

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:40:58.052249Z digest=sha256:d70df613861f21a6f50ebd20eb1802abec394e67b74c37bf8b8da25d3a58787a

Observation d925f7b2-3ae8-4090-8b14-6240d68b8245 · outbound

This paper cites an unresolved cited work.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Unresolved cited work

Reference 32

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raw_fallback, observed 2026-08-07T11:41:11.710017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:40:58.072860Z digest=sha256:207e063e87963e58e68858c6d6820fb62ea4156d67c25203767e5911d53dcc5c

Observation 779e0284-b2c4-450c-8704-da4d773c4b42 · outbound

This paper cites M., Gordon, G.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models M., Gordon, G

Reference 33

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raw_fallback, observed 2026-08-07T11:41:11.279696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:40:58.089009Z digest=sha256:eacd7c63efdb99feea86e7d5901c1f4c1ceb4d48ec53bc8099bbad3cddc6ec89

Observation cf229b8a-0ba3-499e-80ec-c0e29e1ddead · outbound

This paper cites an unresolved cited work.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Unresolved cited work

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:40:58.094976Z digest=sha256:edee99f89c98c916798a4c62e8e105891d2f39c698da247e48228b7b55d4adcf

Observation 3cdea488-9094-47f5-a437-92a721f7c29b · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Gemini: A Family of Highly Capable Multimodal Models

Reference 35

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no resolver link, observed 2026-08-07T11:40:58.150549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:40:58.150549Z digest=sha256:9af71a88f50d4838ad643d8afac02bbea1cad473ac9dc36123262cb8f9c240b8

Observation d1d4c934-f4a0-4538-9fc2-4ddb244e4398 · outbound

This paper cites an unresolved cited work.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Unresolved cited work

Reference 36

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raw_fallback, observed 2026-08-07T11:41:10.501658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:40:58.217100Z digest=sha256:f0e3568f412be38a99d298e5e450c150c6ab5f0bdbc503e80115e3c3de971266

Observation f2c721da-71f6-4cf5-b227-b7e4ecb95e08 · outbound

This paper cites D., Kallus, N., and Sun, W.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models D., Kallus, N., and Sun, W

Reference 37

Resolution
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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 8ec3ccfb-8c15-4498-bf95-836c4064540a · outbound

This paper cites Representation Learning for Online and Offline RL in Low-rank MDPs.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Representation Learning for Online and Offline RL in Low-rank MDPs

Reference 38

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation d11c2078-b3e0-4003-85bc-00a0f8735915 · outbound

This paper cites and De Moor, B.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models and De Moor, B

Reference 39

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation b144fd76-23a1-4a73-8fea-c564226b56d4 · outbound

This paper cites an unresolved cited work.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Unresolved cited work

Reference 40

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 7e694279-6e89-4837-8944-32f5895c4042 · outbound

This paper cites Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency

Reference 41

Resolution
verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 31ef48d3-c761-481b-8322-7b40f84d5bf2 · outbound

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

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

Reference 42

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 61bfc8df-6e29-498f-a4bb-b80796adfafa · outbound

This paper cites Emergent Abilities of Large Language Models.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Emergent Abilities of Large Language Models

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-07T11:40:58.654553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:40:58.654553Z digest=sha256:0109aa7283cfdd14c0df5590bf8c0150a40945f484545e1860ec81cb185cfc40

Observation 731d3c29-9af1-406b-9439-20e3501f0d56 · outbound

This paper cites Transformers and Their Roles as Time Series Foundation Models.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Transformers and Their Roles as Time Series Foundation Models

Reference 44

Resolution
verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:40:58.659411Z digest=sha256:0b6c61bdb3e6c1a762540c557b501c8cdb1ef9c1bbd3e57ecad6a72d8c6efb4e

Observation 9d9de378-adb2-4292-99b9-f01158159a3d · outbound

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

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models An Explanation of In-context Learning as Implicit Bayesian Inference

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T11:40:58.725453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:40:58.725453Z digest=sha256:1c0f9bf13ff6d51401323c8bc917e05e5cae89925a158751e064f86447e0b16c

Observation 7ddbca05-3e2f-4a35-a360-4dbf9c978d31 · outbound

This paper cites an unresolved cited work.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Unresolved cited work

Reference 46

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:41:09.531435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 2da23092-c1ae-4d83-8bc9-f31ae897ad70 · outbound

This paper cites an unresolved cited work.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Unresolved cited work

Reference 47

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:41:09.288353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation a4394478-de6c-47ae-8fce-b8b756c13a87 · outbound

This paper cites PAC Reinforcement Learning for Predictive State Representations.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models PAC Reinforcement Learning for Predictive State Representations

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-07T11:40:58.992054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:40:58.992054Z digest=sha256:14d375b0ce21bf36d60b10450780aadb428ab2f6860acac3c621d9525eda623a

Observation 921f6718-d955-4964-b11a-baad02b271c8 · outbound

This paper cites an unresolved cited work.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Unresolved cited work

Reference 49

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:41:08.735863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:40:59.082190Z digest=sha256:0ea1e038d3f7e5775e07cfd316b707cdd033e6704e3a62c252375b864777a6ea

Observation cd2b7cde-9753-4264-84e6-239d9620c0e8 · outbound

This paper cites GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-07T11:40:59.125143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:40:59.125143Z digest=sha256:af246ea7cb6587a4c0420fbc10c9c050b60cbc2e4997805d82f0ff6cc80aec1d

Pith citing papers

Observation ed003c3e-4a09-4780-9dd9-fc76dc6516a7 · inbound

Extracting Algorithms in Pre-trained LLMs: A Case on Hidden Markov Models cites this paper.

Extracting Algorithms in Pre-trained LLMs: A Case on Hidden Markov Models Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models

Reference 20

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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