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Paper Citation Record · LEDGER

HALO: Hybrid Adaptive Latent Reasoning for Language Models

As of 8 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 2026-08-07T06:34:17.273281+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=sha256:66f7ca5ab28735b3dc1d322006c366f59f687208ec5495db061f2691806b2b15

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=sha256:8480d1f4cce302d03309c2e2e248ac18599e57b0c594c12df9e28f2fa0dc5fdd

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=sha256:6b3e57afc7fc90ba5fbb3aa9f8393468db949ffb04c72a30fa7ef1f6be251a16

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=sha256:4701597066186d026a9b6a8783bfb30d1325f40e5b10befa03f1063be5de2258

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:ee15a37be434abc9af62afc063052bb434dfae567b9dc637c6c8dd003188cd6f

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:d2afb907b1a3a92aecf804eb737606893ab4407ec90bb2f13ac57b60d546e051

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=sha256:8ca26f6fa30f356783bcc430cab4fdf49ffe39422d2f52e34a8b3005069235a6

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:b663a9bf8ea63c0949ba032e23579cb8963ecaef56801c2bccc25207c1eb1ad5

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=sha256:7692132c658294b787f8407cea9792d0e70e2b290b0a062d28e4b18a68d7bf75

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:e263acee806e0261a88545a6a97d9a29dce02f5f8f2c70eaaaf93d63ff993081

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:ad6053e67baf3020e4c7c254733ee4e464696969438218546ab73d8ac064630b

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=sha256:25ba2e77078adec63ad8dff08aa99769633a0307c7f0701fec167ab6e4112d32

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:f01f009baec98647bdfa76cd66d1345e89118c14034a07aad84c491909bc7550

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=sha256:78940d2f199aaad3fe1da7a3fe284f183cad6c586b1d145f6f6c90e0d7911667

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=sha256:58930f2158ab98cd55071511127fd256a2b47b992e80bc2618f8e5fc95eb7f1f

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:b2823c5be521267dc5ce2a2d75eeb45428b5ae57b795757c27301fc754af84ef

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=sha256:9e57875eae0b2a345d84a5b91aa75ce3736616e4ca172060900f2d0cfa955f51

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=sha256:55844f0644c45074e8398a7c92979fce08453c70a9e4c201522fd29eaa37093d

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:b10d73be0c1c63840da77c3ea0c6ea62d2588749150fb5c52ca5a298f8bfd9b7

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=sha256:379236c694b99cc0c54621bddd2362b60758d0ceae184b0ef5e7cbe695198aa8

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=sha256:45af6114e587ca3bc45f1b508af8ff33e3c56e639a2afbbc1e1cfd12b29d3ca5

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:b706f16018eb7eb8a9c3a38e6f3aea009762ee489bcb9ea1ccfaf9cba4a138d1

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:a71891824bc1c3fb2ae1be9d8455e5b69ec1abd17ce3348101d6b1759f9985c6

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=sha256:22ff6204fdc23fa7d91f316b46cfde9ba6edc60e88daa82776775b634cc8ec31

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=sha256:1477bc6d6051dc507375785ed34a1fb01cdc56879ad484aff4fa7d5b721d096c

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:ed6930dcd9dc9584b48dd2b072a6510c6e2f8b4b155108ecf3e4b049c83ade9d

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:e73cff7b673326801f7d54b9b0fca8ef2309dec7bcf774fd01a131461e83739e

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=sha256:54688d6d944407333d5c4fe0f121c8bcc902406c96e13305a1777aba2ba39ea9

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=sha256:638a028497a4d57a1eddb7e0c63f6aff96d2dccbf54b91b597260f024d29a3ee

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=sha256:27363e0e167a84fbd42deb538ed0a3db16d937af9eb040039768b95bc0b9a4cb

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:b2a72b0e6bd2be9214e2d7160486d3afdb16f633b8e58441e82237a33f9c8e5a

Pith citing papers

No inbound Pith citation observations are available.