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Paper Citation Record · LEDGER

HALO: Hybrid Adaptive Latent Reasoning for Language Models

As of 10 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2607.08775.

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

pith.paper-citation-record.v1
2607.08775 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-13T08:04:15.548790Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

31 of 31 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f6f38938-9495-43fd-8935-f1c76a2ee59b · outbound

This paper cites Adaptive Computation Time for Recurrent Neural Networks.

HALO: Hybrid Adaptive Latent Reasoning for Language Models Adaptive Computation Time for Recurrent Neural Networks

Reference 1

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:38fe222c75fa8be3c8b3f9e5da783782a21704bb9852f031f14525f34c16334b

Observation c03f5e73-51e6-4fda-83ee-1a579cb8011f · outbound

This paper cites International Conference on Learning Representations , year=.

HALO: Hybrid Adaptive Latent Reasoning for Language Models International Conference on Learning Representations , year=

Reference 2

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:9b704f663da010566d78d41c766020abd194d3f0ac3d1b4a3cfe73d5402837bf

Observation 850997c2-9fe8-4636-bebb-9277cdc93b2c · outbound

This paper cites International Conference on Learning Representations , year=.

HALO: Hybrid Adaptive Latent Reasoning for Language Models International Conference on Learning Representations , year=

Reference 3

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:e62b5f77d19b2c4f7e7f5da5a948ba4cee0fbcbf387a4d7f2ba0c7c851b5f581

Observation f2befab4-f163-4ed2-a8a7-83f4ab5011e8 · outbound

This paper cites Mixture-of-Depths: Dynamically allocating compute in transformer-based language models.

HALO: Hybrid Adaptive Latent Reasoning for Language Models Mixture-of-Depths: Dynamically allocating compute in transformer-based language models

Reference 4

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:a2110351cfc4f9e099f0213a57b666c8672a45db9a0d2ee2f730c44db74c450a

Observation 33da7345-a60d-4b74-831f-c78b1d4d620d · outbound

This paper cites Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics , year=.

HALO: Hybrid Adaptive Latent Reasoning for Language Models Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics , year=

Reference 5

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:60d3aeacba077ba21568d1280d43dfc3e78cbf2458b7b387b0b1914d64111fb4

Observation 593fe009-ff17-431e-967f-94d4b445790f · outbound

This paper cites Early Exit Is a Natural Capability in Transformer-based Models: An Empirical Study on Early Exit without Joint Optimization.

HALO: Hybrid Adaptive Latent Reasoning for Language Models Early Exit Is a Natural Capability in Transformer-based Models: An Empirical Study on Early Exit without Joint Optimization

Reference 6

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:d5c9411de9e8844a37d3a9c23967ac60ed9a6e1f250283b1d5c5176390487a9d

Observation 78842441-c4bc-4e1a-b663-eaf9792e9398 · outbound

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

HALO: Hybrid Adaptive Latent Reasoning for Language Models Training Large Language Models to Reason in a Continuous Latent Space

Reference 7

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:ffeec0bd1dce1f01315f47d68512bd264a4ca5efc7d9de020cbafa96de2b3f15

Observation 44e9ebab-b43f-41f0-b3ab-5e7ab27946b0 · outbound

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

HALO: Hybrid Adaptive Latent Reasoning for Language Models Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach

Reference 8

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:8d514677c7daee66df0040ad1a898e5d633e60dc82c8d1a20a515fe18787668e

Observation f080ee9e-ff10-427d-b3ae-4f535125ffe6 · outbound

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

HALO: Hybrid Adaptive Latent Reasoning for Language Models Reasoning with Latent Thoughts: On the Power of Looped Transformers

Reference 9

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:6e2211bbdc35a2e00fd81d7bd96d8d6d563e438df46647442713fd002307127b

Observation 458f52c7-d69b-4a38-bdec-d1799ef788db · outbound

This paper cites arXiv preprint arXiv:2505.20674 , year=.

HALO: Hybrid Adaptive Latent Reasoning for Language Models arXiv preprint arXiv:2505.20674 , year=

Reference 10

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:916550f7d89faa05b0456a90b800379e04f71e4c1f004c1fc88382038ea58198

Observation 3ccd0674-f357-4fbe-a3fd-e1f23ba3c5ea · outbound

This paper cites arXiv preprint arXiv:2603.01914 , year=.

HALO: Hybrid Adaptive Latent Reasoning for Language Models arXiv preprint arXiv:2603.01914 , year=

Reference 11

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:bee9d8356eeb3db0cb2de4ae4cb84db1bd220cdd92b38a7a04f462b7173b11b7

Observation ba8dcab3-fafe-454b-8654-d31af62a757c · outbound

This paper cites arXiv preprint arXiv:2602.08864 , year=.

HALO: Hybrid Adaptive Latent Reasoning for Language Models arXiv preprint arXiv:2602.08864 , year=

Reference 12

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:3b13c41562f98dd2d96c74e74ced44d0c138b39ef4ec5ddb89a1fa3a10080332

Observation 61de596c-06ed-4a76-a435-836276e90bd9 · outbound

This paper cites Less is More: Recursive Reasoning with Tiny Networks.

HALO: Hybrid Adaptive Latent Reasoning for Language Models Less is More: Recursive Reasoning with Tiny Networks

Reference 13

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:b2a09eb1758b47fc174f2cb28eaee875d836f1410e20c4b4fdd0da7fd8e581ee

Observation a2c4bf35-6056-4c1a-8342-6b7a57c34a00 · outbound

This paper cites Advances in Neural Information Processing Systems , year=.

HALO: Hybrid Adaptive Latent Reasoning for Language Models Advances in Neural Information Processing Systems , year=

Reference 14

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:67d2fdbbbcec8ad4273071d1f9e67e2cfeef13528a7864041f9ecc84805ad1e8

Observation eb67a045-97be-45f5-9c96-8367b971cf9b · outbound

This paper cites Lessons from the Trenches on Reproducible Evaluation of Language Models.

HALO: Hybrid Adaptive Latent Reasoning for Language Models Lessons from the Trenches on Reproducible Evaluation of Language Models

Reference 15

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:2f42ec178d52f0f3efcb54a8eb0ba76f1b1df447f807308d5be9c33a9a80c955

Observation 578b5d1e-d8d7-47f6-8770-b1cc04f0f023 · outbound

This paper cites Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs.

HALO: Hybrid Adaptive Latent Reasoning for Language Models Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs

Reference 16

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:c95a9ab3d4d2db5e58fb6a86136716b838a8cc812dbc68dd6b9431fb503dd0ce

Observation a05b2858-6ec6-4d8d-8e24-796cc2e04459 · outbound

This paper cites Phi-4 Technical Report.

HALO: Hybrid Adaptive Latent Reasoning for Language Models Phi-4 Technical Report

Reference 17

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:11f3ee86b2cd6171bdb3fd21d3fcbf0eea80c5a9baefe2e72d819269f2694b1e

Observation 0e9808fc-55b9-4753-bb13-d9e6274a5f30 · outbound

This paper cites MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark.

HALO: Hybrid Adaptive Latent Reasoning for Language Models MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

Reference 18

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:ef187032e937709137594a22dd579915f9d1f831e9b07ce7f476f6024b0b5be5

Observation 9ccee3a2-0909-455a-993f-3b1c7a38aee1 · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

HALO: Hybrid Adaptive Latent Reasoning for Language Models GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 19

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:4ae0f27dd03a18ee56ccf16dea85b882d7eeaa79df0289d8e0c3116c7503ee0a

Observation 51e392ef-1a9f-4a44-9ac3-11afe9da6b3e · outbound

This paper cites International Conference on Learning Representations , year=.

HALO: Hybrid Adaptive Latent Reasoning for Language Models International Conference on Learning Representations , year=

Reference 20

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:bfba7f1a248c15d86230757721f69947f0a8d6a199b4b0d8c0a83c5d719969cc

Observation 4dc58edf-621c-4dd9-84d4-76932a93e286 · outbound

This paper cites Advances in Neural Information Processing Systems , year=.

HALO: Hybrid Adaptive Latent Reasoning for Language Models Advances in Neural Information Processing Systems , year=

Reference 21

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:c7d791d25bd65c5e00c37689efc50318e3eb6b897396331ef975281de4569e11

Observation 2af33980-ed5d-4560-98c5-e81b5118642d · outbound

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

HALO: Hybrid Adaptive Latent Reasoning for Language Models Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 22

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:0937ebea7113ef8b7f3c400268c68cbde79cc02dedcf680c8a4d7402738686db

Observation 1bc90999-fd13-4159-a84a-dad5438e6e3f · outbound

This paper cites and Schnell, Eric and Barkai, Edna , title=.

HALO: Hybrid Adaptive Latent Reasoning for Language Models and Schnell, Eric and Barkai, Edna , title=

Reference 23

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:d295d597670def6c6bddacb46fc0c6ed10756240b911c71cbd630ae015f78ee2

Observation 7d02fbc4-be96-4efa-8a32-adcc046593f5 · outbound

This paper cites Think before you speak: Training Language Models With Pause Tokens.

HALO: Hybrid Adaptive Latent Reasoning for Language Models Think before you speak: Training Language Models With Pause Tokens

Reference 24

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:515cc3688a664ff846ce6420854645051041fc88bd0613510ebc7b538ae69f59

Observation df759e49-697e-4c62-9e09-cbc861c0f71f · outbound

This paper cites arXiv preprint arXiv:2507.10524 , year=.

HALO: Hybrid Adaptive Latent Reasoning for Language Models arXiv preprint arXiv:2507.10524 , year=

Reference 25

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:f9c17415a8e989e493d29560ce19c59f4f30d2547d5e5ac2a9a6de90e8e7a8b6

Observation b0e978b7-3ea4-495a-ba59-7850560438a3 · outbound

This paper cites arXiv preprint arXiv:2602.11451 , year=.

HALO: Hybrid Adaptive Latent Reasoning for Language Models arXiv preprint arXiv:2602.11451 , year=

Reference 26

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:4cb47cca2b30be8bde1b6577afdf1bf167dd4e82c80ec69e22897e2a0827b0af

Observation 11678794-60c1-4edb-82b2-650780ba28bc · outbound

This paper cites arXiv preprint arXiv:2603.08082 , year=.

HALO: Hybrid Adaptive Latent Reasoning for Language Models arXiv preprint arXiv:2603.08082 , year=

Reference 27

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:5867913b90e82412cb9add82c5d7f8e2b0c0f1d0cef0192290f6b511ccde8e8d

Observation 6a710062-44a1-4bce-b506-fa19bf8243b6 · outbound

This paper cites arXiv preprint arXiv:2603.15051 , year=.

HALO: Hybrid Adaptive Latent Reasoning for Language Models arXiv preprint arXiv:2603.15051 , year=

Reference 28

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:c7eed4b10031a77216587ceb12088f3ab7365d7ebeb6c3024bae4dc53266c15d

Observation eadcfa8f-6540-4294-8e7c-55ae2b6d9b66 · outbound

This paper cites arXiv preprint arXiv:2602.08220 , year=.

HALO: Hybrid Adaptive Latent Reasoning for Language Models arXiv preprint arXiv:2602.08220 , year=

Reference 29

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:49bf11e313b007ece2c1c28c62bbd12ecf9d06e4a18a565fb8326619c4f8d281

Observation e3c69a08-02c0-4bab-95de-747035237b85 · outbound

This paper cites Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking.

HALO: Hybrid Adaptive Latent Reasoning for Language Models Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking

Reference 30

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:259920598b1c3652a326840c923a33c21ef1e525b2b2e426ec197fb42cd4faab

Observation db674671-77ca-4c30-bb98-8d82f62f3bd7 · outbound

This paper cites Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , year=.

HALO: Hybrid Adaptive Latent Reasoning for Language Models Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , year=

Reference 31

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source=arxiv_source observed=2026-07-13T08:04:15.548790Z digest=sha256:7c1e0f08daa345bb0db479e74f69cd304b3936f204c596768989cf925a58fa95

Pith citing papers

No inbound Pith citation observations are available.