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Paper Citation Record · LEDGER

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning

As of 21 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2603.09221.

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

pith.paper-citation-record.v1
2603.09221 v2

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-15T12:09:16.468055Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

53 of 53 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8e4324de-148f-4028-aa05-ea8189e15752 · outbound

This paper cites xLSTM: Extended Long Short-Term Memory.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning xLSTM: Extended Long Short-Term Memory

Reference 1

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:5dddb1a4a9686bad007ec4a23ad9df2bc639e3fd6f7e11de4b59d07438818f67

Observation b212fa0f-809e-417f-9151-aaeb59022787 · outbound

This paper cites Titans: Learning to Memorize at Test Time.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Titans: Learning to Memorize at Test Time

Reference 2

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:d9f59fb078dfed1940d055340667bfa7bd9bef7d848503403495ca46ce4852d7

Observation 1c34e7f2-9bc9-4425-a69e-3ee15492f910 · outbound

This paper cites ATLAS: Learning to Optimally Memorize the Context at Test Time.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning ATLAS: Learning to Optimally Memorize the Context at Test Time

Reference 3

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:d1117031136cda91476c903a1d79ae7e6ad119eca480502cc4889e962dd4c91d

Observation dbe85ece-f7d8-4c00-aa56-6ce08f17feaa · outbound

This paper cites The riccati equation(book).Berlin and New York, Springer-Verlag, 1991, 347,.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning The riccati equation(book).Berlin and New York, Springer-Verlag, 1991, 347,

Reference 4

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:a265a00c5e8c34a117430091810a12e91d3561bb00df7ab9f5fe7177dd7f24d3

Observation 04952d78-9fe0-4662-a288-842eb0d9893f · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 5

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:17b19a494744c2c0720d32e10adeb8881672debe57eba77e15a252d45073a54c

Observation 358b04d7-f3e2-43c2-b2d7-2eae303134e9 · outbound

This paper cites Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 6

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:fa96aa3d83a6bf5e82476aef21d90521252da71c3e311edfc46af5d6ccfec11d

Observation 751c406e-0ba2-4d13-9622-f63b155ca20c · outbound

This paper cites Simulation of Graph Algorithms with Looped Transformers.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Simulation of Graph Algorithms with Looped Transformers

Reference 7

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:55d598ca3dfd9ee3d69021736683d458447b888ac2bf47fd531581673ac8169c

Observation 19deb135-22eb-4e42-a387-9f0ed445e441 · outbound

This paper cites Reinforcement Pre-Training.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Reinforcement Pre-Training

Reference 8

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:b1aed5b7ff549a7617792e3ab9948f1d57489943bc45971b06e302b92749baa4

Observation 00fb7d85-b6dd-487d-9948-49c2878c645e · outbound

This paper cites Hymba: A Hybrid-head Architecture for Small Language Models.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Hymba: A Hybrid-head Architecture for Small Language Models

Reference 9

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:e337238d04a8f6a155842b71f61f8c6113d50de96984ec12e9ad3de5dcd32bbc

Observation 7207a865-88dc-43a1-95a8-1e04cb37f2d8 · outbound

This paper cites Mom: Linear sequence modeling with mixture-of-memories.arXiv preprint arXiv:2502.13685,.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Mom: Linear sequence modeling with mixture-of-memories.arXiv preprint arXiv:2502.13685,

Reference 10

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:4e0eabe0256d6d5754b366da38cab0278c0c07a46725a8081270943fc75ba1ae

Observation fc6b477b-fcd2-409b-850d-b8bb70353cfe · outbound

This paper cites Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach

Reference 11

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:5c33467ec034645987b5d06daba148a8dd62cefe54f65f3cd595fc17c5f16f93

Observation 9f28ca49-85c9-44ed-9ee6-a1bb4747e12f · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 12

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:95f94f643290d4b247cd05a853652a8df1b1180360790f2fc48c05f808b72fdb

Observation 9afe5823-cf32-4513-a03a-6fa30ae30dcd · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Efficiently Modeling Long Sequences with Structured State Spaces

Reference 13

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:256e986b3a68dea85cb07d1d82ed8524c345346b6ec1bf7392c1e3227241ee1b

Observation faa9a1f6-7fd8-45d5-a18f-9df796efc293 · outbound

This paper cites OpenThoughts: Data Recipes for Reasoning Models.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning OpenThoughts: Data Recipes for Reasoning Models

Reference 14

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:db223e98103756416a928f8ec686305c7374963bac6c1a7e82ea12e7be387d08

Observation d1baa6d2-0c18-4035-8eeb-6cba0953d19a · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 15

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:b966e70d742fc74e0fd134330b31eadcdbf9386cb82204edf7ccdb8375d643f1

Observation c627677a-69f1-4e92-981c-4c332157d653 · outbound

This paper cites World Models.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning World Models

Reference 16

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:50382cf4561ece5dac64317d22fa01d193a1bb3bcff0834cd6c7272550011ca2

Observation bbfac5d4-0d98-49e7-8428-c5e9af73f459 · outbound

This paper cites Dream to Control: Learning Behaviors by Latent Imagination.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Dream to Control: Learning Behaviors by Latent Imagination

Reference 17

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:bb91cef67b274e15dd2052061b384ccccc123414f45c5d57cc6e1149ce25183f

Observation eafb0b50-f16f-42ca-8105-9781c824625a · outbound

This paper cites Temporal Difference Learning for Model Predictive Control.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Temporal Difference Learning for Model Predictive Control

Reference 18

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:1df979fcfda95c893c79a70f362e89cf29216798b6663be45c2eb13553377a73

Observation 1b0a595f-bb9c-445f-b29c-d4334d1bfea6 · outbound

This paper cites TD-MPC2: Scalable, Robust World Models for Continuous Control.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning TD-MPC2: Scalable, Robust World Models for Continuous Control

Reference 19

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:ff3dddd756fc8be23aeb1f16e5e70fd9605b4d1c0a5c6548ff372b4ab4d1bf1b

Observation 94914fa7-8ee6-445b-924a-b01d0229cb85 · outbound

This paper cites Reasoning with Language Model is Planning with World Model.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Reasoning with Language Model is Planning with World Model

Reference 20

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:aca717f34590aac937fcc44cc23de4cd476d1cae927f635a4dbd7d8d336b6660

Observation 5d24e057-23f1-4c70-9b46-9ea51e271968 · outbound

This paper cites Training Large Language Models to Reason in a Continuous Latent Space.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Training Large Language Models to Reason in a Continuous Latent Space

Reference 21

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:b51e27db7384076bece2c1b0ff3325dba5e871adf368fe0fa58e4bfc2f62e95f

Observation 00c30892-adf2-4c53-abdb-936f5002d76a · outbound

This paper cites N., Prabhumoye, S., Kautz, J., Patwary, M., Shoeybi, M., Catanzaro, B., and Choi, Y.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning N., Prabhumoye, S., Kautz, J., Patwary, M., Shoeybi, M., Catanzaro, B., and Choi, Y

Reference 22

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:82c43cef414f06fd3af09d34004a79ecf4603e89ee308cb30f021f289f496509

Observation 50de6cee-1b9a-4b70-8905-73db18cbd01f · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Measuring Mathematical Problem Solving With the MATH Dataset

Reference 23

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Observation 87456be6-6f4a-4214-80fa-5ce1baea5fcd · outbound

This paper cites A., et al.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning A., et al

Reference 24

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:811fcb81bb7794e5fd3bb3e377915391c428d772dbea272c8cff8686c2deda9f

Observation d5aff8de-b336-4d22-96cf-19c30755bb05 · outbound

This paper cites Longhorn: State Space Models are Amortized Online Learners.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Longhorn: State Space Models are Amortized Online Learners

Reference 25

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:3bc3caf4998029446e60786a6619f11cc8f8dd217099b56dc1f703f6b87558e4

Observation d698be93-ffc9-42dc-a6dc-575cd643e950 · outbound

This paper cites Large Language Model Guided Tree-of-Thought.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Large Language Model Guided Tree-of-Thought

Reference 26

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:350f77aeb9d1f1c2b2c6f94028fce63acf589b55b9968a164ab7c8d46532b985

Observation 742dde78-69a3-492c-b41d-7d8a374bd9df · outbound

This paper cites RWKV-7 "Goose" with Expressive Dynamic State Evolution.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning RWKV-7 "Goose" with Expressive Dynamic State Evolution

Reference 27

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:12803dcd2d37bdf663d610ff8aeb5b94a0afba3043149350d0ee93d277f9e2c3

Observation 6dbd1192-92f5-4530-b7c5-08b399574af6 · outbound

This paper cites Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling

Reference 28

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:796417d118e167551cd5f650026cbe163610b0cbd625a0bd3e556cd3e824b19d

Observation ef2b899b-92e5-4a83-8c7b-793a18f7a1d5 · outbound

This paper cites Reasoning with Latent Thoughts: On the Power of Looped Transformers.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Reasoning with Latent Thoughts: On the Power of Looped Transformers

Reference 29

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:e45bba5c15e77034b0870886f756beeef8de14fbd1638083f7e6cf3369906d61

Observation 6245b61a-0462-4dee-a1a9-b3ec27dccd05 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 30

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:f9ea5e33e568524df958366e43d4be995f5b603617811ee9e8f8c5ceec26d96a

Observation 943f47e7-105d-4da0-9618-0f3943d6f598 · outbound

This paper cites The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity

Reference 31

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:0f6ff6763f7a164142f004df796baa43975ff4c2faaab9b341fe21eaf3bf6942

Observation 9d934d02-be7a-4042-aad9-da871b7efabc · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 32

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:770ab07e5e241f8b5f4b9771426b4918bb885300d761441c2196d10418ad7ab9

Observation 84c34f5d-6aa9-4958-9be5-7adfe7b31779 · outbound

This paper cites Retentive Network: A Successor to Transformer for Large Language Models.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Retentive Network: A Successor to Transformer for Large Language Models

Reference 33

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:5cc296e448633256821d10cc0591faa81ea398bc6194b8e70e1f3a510c0085d8

Observation cc9994e7-763a-49ae-adef-958d476c6513 · outbound

This paper cites Learning to (Learn at Test Time): RNNs with Expressive Hidden States.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Learning to (Learn at Test Time): RNNs with Expressive Hidden States

Reference 34

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:a4b0514ebfec50b9ad954a24e82b04bdde85178824dbc90dd8d44ea38e784704

Observation 24028d9a-c31c-4211-8aef-291fa9ff5eff · outbound

This paper cites End-to-end test-time training for long context.arXiv preprint arXiv:2512.23675,.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning End-to-end test-time training for long context.arXiv preprint arXiv:2512.23675,

Reference 35

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:e1ff0498db301859b957b651916194d48b98ce5f1d1691c751a9360de769d652

Observation 197bb3a4-9682-4dd5-bc3c-dd00706898e0 · outbound

This paper cites Uncovering mesa-optimization algorithms in Transformers.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Uncovering mesa-optimization algorithms in Transformers

Reference 36

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:1b12f9dd16781377e2fe94415eb53721ce89269ecb701db865da8c423e7bcb6f

Observation 4204e8bc-b8dd-4d18-b7b7-d1592ec0ad47 · outbound

This paper cites MesaNet: Sequence Modeling by Locally Optimal Test-Time Training.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning MesaNet: Sequence Modeling by Locally Optimal Test-Time Training

Reference 37

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:92a72a3c3dc721959e8d9d761b4b560f1f9e3e30f4be6f1e4872dabb292d3d57

Observation 082420cc-d84d-4390-8bf1-6ee580290e00 · outbound

This paper cites Hierarchical Reasoning Model.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Hierarchical Reasoning Model

Reference 38

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:10e9252b7374e4dd011b4eae0e0727f693a0fd87b966679e1e2e1bd687ccb624

Observation 91f52f97-81b6-4e7c-a2da-0596f92752a3 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 39

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:951ad97040fd1cb983a1d5fcdf3dd87a0c3cceb0f8f2678b93faf6d61b5fd3b0

Observation 52f26761-85b8-4931-b7c4-f7990128c0c8 · outbound

This paper cites Looped Transformers are Better at Learning Learning Algorithms.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Looped Transformers are Better at Learning Learning Algorithms

Reference 40

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:42782d9a4dbc3433191ded3af07f67160959b46184fb3cd0b18302e7f7572ddb

Observation d6c5eceb-afb5-409d-9933-064bcbef1c3a · outbound

This paper cites Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Reference 41

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:7d86836352307cd7570c05454f47723153ebd6132116c59f382b3e5a99347c49

Observation 559408aa-c6e0-4bd3-9eb9-1ae0a39d89bd · outbound

This paper cites Learning to Discover at Test Time.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Learning to Discover at Test Time

Reference 42

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:6b25090ff015a549b9158d2f2fc78c2b6ddfce360817e4e2112427e0b8828705

Observation d97c9438-8049-4dc2-bcb2-df68ae252969 · outbound

This paper cites Pretraining language models to ponder in continuous space.arXiv preprint arXiv:2505.20674,.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Pretraining language models to ponder in continuous space.arXiv preprint arXiv:2505.20674,

Reference 43

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:2564cda996ceb2a3b7b939c731dcf613937c39046bdd45883f6bbd21a7aaf91c

Observation ca9f0afb-e7c0-40f6-a8e6-fee7cbe2acde · outbound

This paper cites Test-Time Training Done Right.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Test-Time Training Done Right

Reference 44

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:0a1195836ef370b0dfba604de87f2499a1073818d3a5d914bcd1cb03f4d9a0c1

Observation f57d357d-fa63-4595-b8b3-3c85d7fd2e20 · outbound

This paper cites Emergence of superposition: Unveiling the training dynamics of chain of continuous thought.arXiv preprint arXiv:2509.23365, 2025a.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Emergence of superposition: Unveiling the training dynamics of chain of continuous thought.arXiv preprint arXiv:2509.23365, 2025a

Reference 45

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:654fa6ec640372d80586ec4fffc4f2e109b224a8bd5a2421f620ab83c1bac52e

Observation 115557d2-d4d5-4dce-a765-2dcbd8cdffa6 · outbound

This paper cites In the main text, we use𝒉 0 in place of𝒉 𝑖𝑛𝑖𝑡 without separate notations when no confusion arises.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning In the main text, we use𝒉 0 in place of𝒉 𝑖𝑛𝑖𝑡 without separate notations when no confusion arises

Reference 46

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:869358944ead14765dd7e697dde9e8863d4759448432104d87017bf9895602ce

Observation 15fac4d8-9830-4f8e-8652-9b7a391031fc · outbound

This paper cites 𝜕 𝜕𝜃 𝒙 𝜕 𝜕𝜃 𝝃 # =.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning 𝜕 𝜕𝜃 𝒙 𝜕 𝜕𝜃 𝝃 # =

Reference 47

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:45ac68ae65b6723c23abb2c07515f4ac9f6243d8827ade13453b05a8533ed5d2

Observation 6672788d-cf77-4ed1-be99-8c4e4851847f · outbound

This paper cites an unresolved cited work.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Unresolved cited work

Reference 48

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:66e661374c0a26eb49f0219c7611d41f9930db4542c9c07aea105b26dac924d7

Observation f6526f8e-1cab-4587-8d71-d42b84f83463 · outbound

This paper cites Although [𝒀 1,𝒀 2] are partitioned row-wise and distributed across CUDA blocks, this normalization can be performed independently within each kernel without synchronization.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Although [𝒀 1,𝒀 2] are partitioned row-wise and distributed across CUDA blocks, this normalization can be performed independently within each kernel without synchronization

Reference 49

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:119196e6c8343672265f3fa55b56411dc381aecba73b1d654b5eefc1caeb30a1

Observation f1946c24-7def-4b14-b619-765a55c17872 · outbound

This paper cites To the best of our knowledge, it is the only other approach that explores online RL–based adaptation at test time.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning To the best of our knowledge, it is the only other approach that explores online RL–based adaptation at test time

Reference 50

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:b4ac89f4a6c11270d9421d7c037463dc8ccb38fec262cf5b2dfaa314dab49ea7

Observation 8c36b370-ca67-4b7e-b825-f80a0f053e03 · outbound

This paper cites world”, upon which it performs planning and decision-making. All parameters of this “world.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning world”, upon which it performs planning and decision-making. All parameters of this “world

Reference 51

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:84cc470ed9fc46befc2ad2b98c5c2670b7e84d0a38839439a0bb7442aef583d6

Observation dc6b4faa-d194-4989-b81d-f07f4323d6c7 · outbound

This paper cites thinking.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning thinking

Reference 52

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:fe46e57b99d0c9a58c981b77801970e2e9c877c3949609e51886625c23c0dfc1

Observation 01d2426b-e098-4846-bf7c-98b54ec604b6 · outbound

This paper cites Memory footprint is evaluated separately by measuring peak GPU memory usage during execution, reported in GB.

Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning Memory footprint is evaluated separately by measuring peak GPU memory usage during execution, reported in GB

Reference 53

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source=pdf_text observed=2026-07-15T12:09:16.468055Z digest=sha256:75685e16811476c1f1070938d86698eafdbaa26b82e1f06561b142460bcf4546

Pith citing papers

No inbound Pith citation observations are available.