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Paper Citation Record · LEDGER

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment

As of 9 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 3 inbound Pith citation observations for arXiv:2502.07803.

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

pith.paper-citation-record.v1
2502.07803 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:29:05.278283Z

measured 50 of 50 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T05:05:24.659730Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T20:57:46.852989Z

Reference resolution

47 of 47 outbound references displayed

  • verified exact1
  • verified fuzzy19
  • unresolved27
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 546b5378-dbce-4bc6-9e3f-a0eaf0a2c79f · outbound

This paper cites write newline.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment write newline

Reference 1

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unresolved
no resolver link, observed 2026-08-09T10:29:05.033813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.033813Z digest=sha256:6a27fdc94165036e2f14597ef381c5933bd0744058afada93c5f8014c9e4c411

Observation f8c4e1a5-455b-40d7-a4ac-ed6ad09d44af · outbound

This paper cites Program Synthesis with Large Language Models.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Program Synthesis with Large Language Models

Reference 2

Resolution
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no resolver link, observed 2026-08-09T10:29:05.040864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.040864Z digest=sha256:7d5c632fbad36aa8b16e8b2fada9ff0398bdfa8510b6fea8e2de4eea6d55e01c

Observation e7b251da-7b3b-4775-9bd5-e5eb5ce7eb03 · outbound

This paper cites Graph of thoughts: Solving elaborate problems with large language models.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Graph of thoughts: Solving elaborate problems with large language models

Reference 3

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unresolved
no resolver link, observed 2026-08-09T10:29:05.046662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.046662Z digest=sha256:8325ba22116030fdb746c0158843e4cc16da450a342e5af26b6aefc088272496

Observation 9c53218c-887f-4688-8b3d-6877426fe8be · outbound

This paper cites Codet: Code generation with generated tests.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Codet: Code generation with generated tests

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:29:06.524615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T10:29:05.053066Z digest=sha256:d2aad8f7d6c5daae5995973fac64fdf3ed74005eed43b0584d8eae37042e43e2

Observation 8443c857-5f40-491e-a936-6d34bec86d26 · outbound

This paper cites Divide-and-Conquer Meets Consensus: Unleashing the Power of Functions in Code Generation.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Divide-and-Conquer Meets Consensus: Unleashing the Power of Functions in Code Generation

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:05.058277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.058277Z digest=sha256:2def19ba36cb371237a0501fa5c5874525b06292cc4ac50a09c65d3fef16872e

Observation e4dcc9f1-862f-43db-9936-9a65220c5cff · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Evaluating Large Language Models Trained on Code

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:05.063871Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.063871Z digest=sha256:3a0c3ebe2e02bdb725281344261e72aeaf5a5475596247de8f0ba4ca02be6048

Observation 46dad573-cdb3-4923-a018-add935895613 · outbound

This paper cites an unresolved cited work.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Unresolved cited work

Reference 7

Resolution
unresolved
raw_fallback, observed 2026-08-09T10:29:06.509207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T10:29:05.069800Z digest=sha256:44c7c0d88628aa6d6da724731f8f94b420275d69ac1846787740a59b5a7dc668

Observation 4c285c34-c410-484b-9060-470b5d386903 · outbound

This paper cites Teaching large language models to self-debug.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Teaching large language models to self-debug

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:29:06.493500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T10:29:05.075473Z digest=sha256:990cfd6ae2d6f78c5a7d64d7ce2e8e1523e25759c50dca456bfab1c0fdd56a20

Observation 2c13c0c4-67ce-4b4c-9f66-8ad954c445ec · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Training Verifiers to Solve Math Word Problems

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:05.086920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.086920Z digest=sha256:70cec4b275d170331e6b50e2642293e0f4d91bd77776043812e4d6cf4a33d871

Observation 13d73605-4ecc-4e01-a00f-3be79f6bc01e · outbound

This paper cites J., Kaiser, G.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment J., Kaiser, G

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:05.091899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.091899Z digest=sha256:f7fd4a6d98ba611f92379f3b998bf49b997393d1aa7fb4a6a56fefd523f3002c

Observation 7e9691ca-7c51-409c-aa27-d70dbe6a639b · outbound

This paper cites an unresolved cited work.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Unresolved cited work

Reference 12

Resolution
verified exact
arxiv_id_nonexistent, observed 2026-08-09T10:29:06.130853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T10:29:05.097030Z digest=sha256:64a1b5b7a28c2cc47290120f31d6708b23f271f0432a1b7147549664203d2b74

Observation 08957de5-5eaf-4ba8-a095-25b1d9cd9fc6 · outbound

This paper cites Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:05.102114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.102114Z digest=sha256:16abb7b7809c78c6d29540b08e3aee06a18b1da44d531d0a49ae36c2c23bdb7e

Observation cd16ddfb-efbb-4572-a6d5-9c2511e94b97 · outbound

This paper cites PAL: program-aided language models.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment PAL: program-aided language models

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:29:06.478263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T10:29:05.107086Z digest=sha256:ca53ddd48f4f5345dd125b041cf9552fc16c2f64f6ae81fa145d249841b685ba

Observation 53553d9f-808b-4c23-9d8c-e0f73eb4051d · outbound

This paper cites CRITIC: large language models can self-correct with tool-interactive critiquing.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment CRITIC: large language models can self-correct with tool-interactive critiquing

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:29:06.462711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T10:29:05.112118Z digest=sha256:3231928b48c0bf956831f04e63e7ad08cb44aaccb6219fe7b78bcc6264f84d6b

Observation 614d7321-f87c-454b-89c3-060cb693c5ae · outbound

This paper cites Measuring mathematical problem solving with the MATH dataset.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Measuring mathematical problem solving with the MATH dataset

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:29:06.447129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T10:29:05.117049Z digest=sha256:c248188aa0ecec20236d53d3e524684976c6a922ffeed59c9f2ac9647e6cedc9

Observation 634cebc7-abe4-478a-b59d-bea7ec6add2e · outbound

This paper cites an unresolved cited work.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Unresolved cited work

Reference 17

Resolution
unresolved
raw_fallback, observed 2026-08-09T10:29:06.431580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T10:29:05.122253Z digest=sha256:6bdbbada7febf20c67202bdf76e07b97f7430b5e7c73cc5447834f5e1a1c4fb2

Observation 014e535e-2a16-4fc1-97fe-9340b404de88 · outbound

This paper cites CodeCoT: Tackling Code Syntax Errors in CoT Reasoning for Code Generation.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment CodeCoT: Tackling Code Syntax Errors in CoT Reasoning for Code Generation

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:05.127201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.127201Z digest=sha256:36e42b715aaecaea89a8448206e59d957d47dd89d145055b1203bf2e5f189ed1

Observation 510e90b4-76a4-4954-831f-b0223688190d · outbound

This paper cites S., Yu, A.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment S., Yu, A

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:29:06.416055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T10:29:05.133023Z digest=sha256:82d31142da0df849df53f23d080c6a22fd4ffb8b8c50e0d07dd7ae71135f70be

Observation b6c54d26-b4b2-4a57-8b19-709b50005ae4 · outbound

This paper cites Language Models (Mostly) Know What They Know.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Language Models (Mostly) Know What They Know

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:05.138311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.138311Z digest=sha256:391430aff446b4dde94af521d35a12aee515925446ee886fe63e42be15740784

Observation 16015822-9748-4955-98ab-52ee9babb422 · outbound

This paper cites When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:05.143540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.143540Z digest=sha256:feba3f186eb6bf0098911645a63ef62694f6b4249df2385af30d5b1370a8822a

Observation 9db64824-aa3a-45b0-8241-10af2b7d6823 · outbound

This paper cites S., Yang, L., tse Huang, J., Zhu, Z., Zhang, L., and Lyu, M.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment S., Yang, L., tse Huang, J., Zhu, Z., Zhang, L., and Lyu, M

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:05.148721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.148721Z digest=sha256:fe642fd72da040100b88e7f8bd958ff27bdc261d8894aa64946e0fbe83919f26

Observation acdb6949-71ce-4d71-981f-c35c2c6de934 · outbound

This paper cites Getting from Generative AI to Trustworthy AI: What LLMs might learn from Cyc.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Getting from Generative AI to Trustworthy AI: What LLMs might learn from Cyc

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:05.153703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.153703Z digest=sha256:ea54ca22a13f2b8702e37987cf75764ee5479f548d357d2304e2cbbba0f269c6

Observation 246844a9-4ed8-4b8a-a996-6917b2a5b08c · outbound

This paper cites How do humans write code? large models do it the same way too.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment How do humans write code? large models do it the same way too

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:29:06.400548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T10:29:05.159065Z digest=sha256:c6e88eac569cf469c0db5c30d56dd424965b7cb7c3a2543077f81085f9806cda

Observation 04b2d777-4c84-4185-9089-c11925e05978 · outbound

This paper cites Deductive verification of chain-of-thought reasoning.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Deductive verification of chain-of-thought reasoning

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:29:06.384723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T10:29:05.163963Z digest=sha256:80695fb565ed39e29fb5ed70dfba55abbe87b8e0cadd699871cd5fa8510ba308

Observation d2bbc911-f087-4886-917e-013449c9fa5d · outbound

This paper cites Large Language Models have Intrinsic Self-Correction Ability.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Large Language Models have Intrinsic Self-Correction Ability

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:05.168527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.168527Z digest=sha256:0686cc5e5278dc6a30e48d634d132373f68f2b45e472aa4c1ca505e848392d47

Observation 3ac8284d-ce21-4762-8ab6-83c2d3f19bd0 · outbound

This paper cites Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:05.173640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.173640Z digest=sha256:1a670e989bf6ed0f659d11f200bbde7dbcc9d3df8063ce0679f0697e6f457c58

Observation aa978ee7-04bd-4a07-8f2e-25c65f47e082 · outbound

This paper cites S., Wang, Y., and Zhang, L.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment S., Wang, Y., and Zhang, L

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:29:06.368367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T10:29:05.179022Z digest=sha256:f8222892333d52d45f0cdc5000f69fc70f212b0ee53caf2b2488ac36654fc078

Observation ed2af96e-322e-458b-ab1d-2811b2a10a33 · outbound

This paper cites At which training stage does code data help llms reasoning? In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment At which training stage does code data help llms reasoning? In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:29:06.351535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T10:29:05.183962Z digest=sha256:7c9b0dc59df192c0ad30af965b8fd096edac756c050e22bcc7f9c9dcdaa65e92

Observation 6113975c-dd1c-4165-93c7-991c3395e200 · outbound

This paper cites W., and Rainforth, T.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment W., and Rainforth, T

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:29:06.335582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T10:29:05.188691Z digest=sha256:77775ad4545ce436603a26455fd989fc71c96e5f1db73427d5b8c688d2e7d5c2

Observation 0798575f-cd5d-4322-b406-dbc7a931026f · outbound

This paper cites GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:05.193607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.193607Z digest=sha256:f70a6a2960c9562c763df5321eef5d08151ac74224150ab0167b1a4d7182bbe4

Observation 495fd769-889d-484a-85a9-e2807d6b4a6c · outbound

This paper cites Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:05.198791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.198791Z digest=sha256:5be1e4071e6cc6d90ad846cb0373901ea8e11b81a9f4a22c8ac28c3abdaf7ae6

Observation cbc3e3eb-9ea6-4c4b-92bc-46414105ca7b · outbound

This paper cites I., and Lin, X.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment I., and Lin, X

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:29:06.318943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T10:29:05.203849Z digest=sha256:32151a7102aa0d90f8a57cc8fb3fd44705867d1f769682ecea19a82a1fd7f732

Observation 1e9a7cf2-a22b-4a8c-b9af-727862e60653 · outbound

This paper cites Gsm8k, 2025.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Gsm8k, 2025

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:29:06.303349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T10:29:05.208740Z digest=sha256:6c9af88e098a33f5836e17d46380a04b88004af27d6ee2fd849c8aac7a6a5407

Observation ba0f31c6-aa94-4218-96c5-3a57ad034b0f · outbound

This paper cites and Freitas, A.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment and Freitas, A

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:29:06.287603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T10:29:05.213686Z digest=sha256:5462c93e61d4fc54f08e2519d736aa9587805b39c47393faccd696183e12168b

Observation 6bf86bf7-9edf-4159-8fed-fe3702351e56 · outbound

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

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:05.218643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.218643Z digest=sha256:65820743dd5ad912f64e0163f3c5437fc15a64f4e54de51193187ae8715445f3

Observation 8115c949-cfaa-4942-9038-22dba9639f4b · outbound

This paper cites Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judges.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judges

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:05.223746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.223746Z digest=sha256:a9984efe850df176a26827dec450b4140e941945ed2dcc3857d92f01835f9acc

Observation 96a5fe5a-9189-4e90-b59e-f3d8c88903af · outbound

This paper cites V., Chi, E.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment V., Chi, E

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:05.228814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.228814Z digest=sha256:48f2d8387b2a3bbd7e22aeb8e63b9ce6ff3b50867dbf9e05dca3d3f12523a4e5

Observation 970a9825-e105-450d-9f2d-58678045473d · outbound

This paper cites H., Le, Q.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment H., Le, Q

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:29:06.260497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T10:29:05.233550Z digest=sha256:b1be3b07295959ec6a0784f914d52873e91f1fd1a426b0fcb69afdf6181bb4e8

Observation b42e9d0e-ab6b-4232-ad48-4c1d3372b8ee · outbound

This paper cites Enhancing Mathematical Reasoning in LLMs by Stepwise Correction.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Enhancing Mathematical Reasoning in LLMs by Stepwise Correction

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:05.238315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.238315Z digest=sha256:69e799ad22bcad093c5e2814eac4bf2292ad0ed3ee25f9aa3f12ee3dc46e21bc

Observation 2260a021-644a-4f99-bcc4-dd091ff0fa30 · outbound

This paper cites Keep the Conversation Going: Fixing 162 out of 337 bugs for $0.42 each using ChatGPT.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Keep the Conversation Going: Fixing 162 out of 337 bugs for $0.42 each using ChatGPT

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:05.243597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.243597Z digest=sha256:313a95017263c052e6e48ccc21f5ce1a32939a7c926b07e53e82407826e432ae

Observation e524d678-a637-4c35-a15f-16df02300661 · outbound

This paper cites Decompose, analyze and rethink: Solving intricate problems with human-like reasoning cycle.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Decompose, analyze and rethink: Solving intricate problems with human-like reasoning cycle

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:29:06.244645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T10:29:05.248972Z digest=sha256:3715b7cec2accf1487ea32e56a9995ec5583d1a1b9274faee3f4e55afe3e5019

Observation e66bd507-a5a3-4440-a442-3eb7ffc538ee · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Tree of thoughts: Deliberate problem solving with large language models

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:29:06.228494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T10:29:05.253800Z digest=sha256:5e3c5cdb1bd026fd8946b0116a85e6a25fa6176d6840434ed01d237055c7ab39

Observation 0da10a2e-e72d-4328-8b63-a267aadc27fb · outbound

This paper cites Natural language reasoning, A survey.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Natural language reasoning, A survey

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:05.258902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.258902Z digest=sha256:0eb7c4284ae7298fef2c25e64e55a21c08b71b3f97f7b00eb45c6a7a35dd0d30

Observation f7c361ab-5fcc-4d20-a9dd-a37479962e3c · outbound

This paper cites Automatic chain of thought prompting in large language models.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Automatic chain of thought prompting in large language models

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:29:06.212486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T10:29:05.263966Z digest=sha256:374d6e4ad2e14439e6262164351562b1e7e462156f18fab9709c566d9a733b58

Observation 56815825-cfc0-408d-b483-f2649955f4d3 · outbound

This paper cites Y., Fu, J., Chen, W., and Yue, X.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Y., Fu, J., Chen, W., and Yue, X

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:05.268522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.268522Z digest=sha256:8647c9616cbe5854d2c39748e405ff43cd98b45d02085363251d0c69c8f1216d

Observation 3248cc4a-974e-4a1c-a9cb-d094b8cf4760 · outbound

This paper cites an unresolved cited work.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Unresolved cited work

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:05.273505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:29:05.273505Z digest=sha256:e4db43b59dbf7e3bedac8da427ac0cde42fc789e81a27a46a7f55bb008c9e229

Observation 2cbf3484-457b-4b26-8303-afb828f04d7c · outbound

This paper cites V., and Chi, E.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment V., and Chi, E

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T10:29:06.194997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-09T10:29:05.278283Z digest=sha256:a75cd28516f95aff0e237885c4535a55e6939711b6863f969b2a9b348c2ec969

Pith citing papers

Observation 2446dc37-cdd5-45d7-8059-da7222ba9836 · inbound

Scaling over Scaling: Exploring Test-Time Scaling Plateau in Large Reasoning Models cites this paper.

Scaling over Scaling: Exploring Test-Time Scaling Plateau in Large Reasoning Models Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-07T13:58:35.602583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:58:35.602583Z digest=sha256:7e218803de0f7a89aded5583a48221623dde96191fc0df01d8c82ab0d2bcf966

Observation 8e0008be-aeaa-4c03-856d-10bb03515b6a · inbound

Reasoning Can Be Restored by Correcting a Few Decision Tokens cites this paper.

Reasoning Can Be Restored by Correcting a Few Decision Tokens Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-05-19T20:57:46.854504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-19T20:56:48.771058Z digest=sha256:8ea07c5d9526467f93bd7bd4862ff474634c84877ae79189d8e30d862def51ba

Observation 36205259-99cd-47fa-a780-7651f597f3c8 · inbound

Refining Over Resampling: Test-Time Self-Correction for LLM Reasoning cites this paper.

Refining Over Resampling: Test-Time Self-Correction for LLM Reasoning Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-08T05:05:24.659730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T05:05:24.659730Z digest=sha256:a8d4c4743ce5cedb899b5fa471c7e6f51152cc03d60238dc94a95d7a66057c2b