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Paper Citation Record · LEDGER

Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 28 inbound Pith citation observations for arXiv:2410.02725.

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

pith.paper-citation-record.v1
2410.02725 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

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

measured 28 of 28 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:53:40.156297Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-08T04:04:29.259300Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 99907d5d-b151-473e-b0e7-32f1c5d08195 · inbound

Teaching LLMs to Refine with Tools cites this paper.

Teaching LLMs to Refine with Tools Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 7

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no resolver link, observed 2026-08-11T06:05:31.171365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T06:05:31.171365Z digest=sha256:bd741ec572eda40091d61b8ec873d013542c43ee8cf562a25891bc0973f65086

Observation 6d18c18b-4bee-4166-80bd-08c1da95a386 · inbound

Efficiently Scaling LLM Reasoning with Certaindex cites this paper.

Efficiently Scaling LLM Reasoning with Certaindex Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 50

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no resolver link, observed 2026-08-10T23:09:44.504344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:09:44.504344Z digest=sha256:eedd3e66e2c92f95e00b341968eac074c7cc4fe978678758826a64f695096ece

Observation e4934237-0cef-47d4-8266-8f599cd14eab · inbound

Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs cites this paper.

Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 27

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verified exact
arxiv_id, observed 2026-05-13T15:51:29.352729Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-05-13T15:51:29.022336Z digest=sha256:f4fa3e11ecaea9ca6f12a0d2f50e78ef0c3f0d39ed3a707e17172b9280b0c3b3

Observation f9273b68-16a6-4870-9b06-e369653745e5 · inbound

Reasoning Language Models: A Blueprint cites this paper.

Reasoning Language Models: A Blueprint Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 106

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no resolver link, observed 2026-08-10T18:36:54.811964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T18:36:54.811964Z digest=sha256:4228c1cac57b08b6e444ed7dc36d3a5de6f32982c63ec1632f8ef3636a69be74

Observation 9e961423-758f-48a8-8a61-d77fb777ff38 · inbound

An Annotated Reading of 'The Singer of Tales' in the LLM Era cites this paper.

An Annotated Reading of 'The Singer of Tales' in the LLM Era Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 36

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no resolver link, observed 2026-08-08T20:08:32.595939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T20:08:32.595939Z digest=sha256:e26a6d278ed1e07cd01ded3e67ac0ee5c90c58e40c3216efd77e10fc4d7a579e

Observation 28dbae4a-9763-4ab2-b62d-6bb555d23956 · inbound

Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling cites this paper.

Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 32

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no resolver link, observed 2026-08-08T14:40:36.007327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T14:40:36.007327Z digest=sha256:a4b5cd82599dd558cb40c34401bd5b05495678a342366f5bbabd74255fb31768

Observation 28ba310e-d39f-456c-b61b-de74027e2517 · inbound

A Survey of Scaling in Large Language Model Reasoning cites this paper.

A Survey of Scaling in Large Language Model Reasoning Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 133

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arxiv_id, observed 2026-05-22T21:22:08.909332Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-22T21:20:07.238992Z digest=sha256:cc6774b1cb520d971eff181221000cb6bcd9e2a482761fac30d4752253a4f205

Observation 21472e94-2c9a-4cd6-904b-aca7f72a6b45 · inbound

An Empirical Study of LLM Reasoning Ability Under Strict Output Length Constraint cites this paper.

An Empirical Study of LLM Reasoning Ability Under Strict Output Length Constraint Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 28

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no resolver link, observed 2026-08-16T11:53:40.156297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T11:53:40.156297Z digest=sha256:bf04ad5667d2fb47e37d417e159bb8fbd3236a10a12ecafc790c4a7ede0386cb

Observation 2e19c568-cd48-456a-b334-b9c32ba8e09b · inbound

S-GRPO: Early Exit via Reinforcement Learning in Reasoning Models cites this paper.

S-GRPO: Early Exit via Reinforcement Learning in Reasoning Models Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 56

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no resolver link, observed 2026-08-15T22:16:38.328031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:16:38.328031Z digest=sha256:c433f4d00d1080ffe5a9c3be960f1141e4bf347888653e0fa8624cef023fbaa9

Observation e752f4b3-7b39-4de7-b8c4-3264b7c11ae7 · inbound

SLOT: Sample-specific Language Model Optimization at Test-time cites this paper.

SLOT: Sample-specific Language Model Optimization at Test-time Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 24

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no resolver link, observed 2026-08-15T20:38:16.962058Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:38:16.962058Z digest=sha256:d0e312f8b389937e68d5b87c1e4df12cb8f5abdb16296799a67a75ac9cb24296

Observation 81762ead-249f-4aca-8442-37830a99087e · inbound

TrimR: Verifier-based Training-Free Thinking Compression for Efficient Test-Time Scaling cites this paper.

TrimR: Verifier-based Training-Free Thinking Compression for Efficient Test-Time Scaling Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 15

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no resolver link, observed 2026-08-07T15:00:36.638727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:00:36.638727Z digest=sha256:e288b2caf185c3acd7ba299755ac768684174148437a52aeae02eccf0f862de1

Observation 9bc78be8-9657-4311-8b15-9ee5d12d9fcb · inbound

Route to Reason: Adaptive Routing for LLM and Reasoning Strategy Selection cites this paper.

Route to Reason: Adaptive Routing for LLM and Reasoning Strategy Selection Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 15

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no resolver link, observed 2026-08-07T14:16:54.020289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:16:54.020289Z digest=sha256:534ef8ca859bc5d99dbc30d1271bb9b55b7fd2e904cb01f0a04671f1bccf13ee

Observation d2aaa8f3-3bff-438d-87e5-5815c8ca0e76 · inbound

Temporal Sampling for Forgotten Reasoning in LLMs cites this paper.

Temporal Sampling for Forgotten Reasoning in LLMs Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 26

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no resolver link, observed 2026-08-07T14:02:23.966532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:02:23.966532Z digest=sha256:338176ce8e02924edceda76d4dd4a47c30d898d63f032b5f5215d3a93840b6fd

Observation 98034956-2a7c-447a-ab41-25cca8119c50 · inbound

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling cites this paper.

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 12

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no resolver link, observed 2026-08-06T23:48:23.944186Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:48:23.944186Z digest=sha256:0eefa45af5cabe08e552e3cd49c14d77101f68ca0e6f7fb9a0ba857ea32160d8

Observation 30fd29c9-404d-46f5-9d34-d6f2248beb88 · inbound

Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs cites this paper.

Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 25

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no resolver link, observed 2026-08-06T20:43:10.441733Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:10.441733Z digest=sha256:d8c701093340c3419f1bb83280def0ca54a46f27c1fadb99c80d37a3a63aef73

Observation e7704e66-d691-4b81-848a-b35c82a5aa27 · inbound

Energy-Based Transformers are Scalable Learners and Thinkers cites this paper.

Energy-Based Transformers are Scalable Learners and Thinkers Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 144

Resolution
unresolved
no resolver link, observed 2026-08-06T20:42:40.110205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:42:40.110205Z digest=sha256:6ceab7b9d35839bfbb34f66c3a7c1b89d6cdeaf6f68fc333cc7235ee418e72ac

Observation a2e41f12-3f29-4dfb-aa4a-277a3e5aa3a1 · inbound

TinyMusician: On-Device Music Generation with Knowledge Distillation and Mixed Precision Quantization cites this paper.

TinyMusician: On-Device Music Generation with Knowledge Distillation and Mixed Precision Quantization Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 23

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no resolver link, observed 2026-08-05T13:12:14.503038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:12:14.503038Z digest=sha256:36a142f37d8c202e4e752f2df0fe9297fb84d73028e3cd401627d21b31c527b9

Observation 93fe9e35-f1cc-4da3-94b5-bd7c185a8674 · inbound

Explicit Reasoning Makes Better Judges: A Systematic Study on Accuracy, Efficiency, and Robustness cites this paper.

Explicit Reasoning Makes Better Judges: A Systematic Study on Accuracy, Efficiency, and Robustness Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 36

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verified exact
arxiv_id, observed 2026-05-18T17:31:41.518634Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-18T17:31:28.644151Z digest=sha256:c266d979946c18f583ffed4a812e7f38dff9663c28f761b565cf22c6988ce582

Observation 148a439c-bdf6-4a70-abdf-bfae5441b0e9 · inbound

ModeX: Evaluator-Free Best-of-N Selection for Open-Ended Generation cites this paper.

ModeX: Evaluator-Free Best-of-N Selection for Open-Ended Generation Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 32

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arxiv_id, observed 2026-05-16T17:28:10.134591Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-16T17:25:27.180687Z digest=sha256:794aca372554b2c6f8405ab4ba86c9132a0b1d05ee7d32dd55bb3a208780d0f8

Observation 4c4e0fcd-30cc-4117-b030-7906676a7676 · inbound

Quantum Circuit Generation via test-time learning with large language models cites this paper.

Quantum Circuit Generation via test-time learning with large language models Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 10

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no resolver link, observed 2026-08-03T05:02:35.900921Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:02:35.900921Z digest=sha256:808ef53af3ac86bc9b9695b53bcd385ada221f7ab0a125fa2992e5964065fc8d

Observation 0ecc6883-6d92-4384-a569-db1710c99e04 · inbound

Adaptive Test-Time Compute Allocation with Evolving In-Context Demonstrations cites this paper.

Adaptive Test-Time Compute Allocation with Evolving In-Context Demonstrations Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 26

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arxiv_id, observed 2026-05-11T13:51:05.453989Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-05-09T23:55:50.606359Z digest=sha256:fa2a806957e2ca78b898a0fe5934664bba57b092e0c359debe34215071b79ee4

Observation 91fadf79-b955-4330-b141-0bbeb1c443d9 · inbound

ATLAS: Agentic Test-time Learning-to-Allocate Scaling cites this paper.

ATLAS: Agentic Test-time Learning-to-Allocate Scaling Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 38

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arxiv_id, observed 2026-07-01T22:26:17.074072Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-06-28T15:27:28.290178Z digest=sha256:0d2cf51bc7c1dda7088e5aa534e3ad64bc18f50ce85bbed2e997b258d40ae0fb

Observation 3e831cad-a8a7-4145-a1a7-e22b4c8de4be · inbound

AVIS: Adaptive Test-Time Scaling for Vision-Language Models cites this paper.

AVIS: Adaptive Test-Time Scaling for Vision-Language Models Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 31

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verified exact
arxiv_id, observed 2026-07-03T08:17:45.848719Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-06-27T10:47:41.183211Z digest=sha256:ac35ef302d620f048189175d09c653e04e0ab62b3c1b348992dba81565aa7eab

Observation bce4c447-23a7-4c53-a44f-59c1c57e0f2b · inbound

Heteroskedastic Signals in Budgeted LLM Verification: Structural Heterogeneity Limits Optimization Gains cites this paper.

Heteroskedastic Signals in Budgeted LLM Verification: Structural Heterogeneity Limits Optimization Gains Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 4

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source=pdf_text observed=2026-08-02T11:21:40.881716Z digest=sha256:513f534801c22977e68d8a3cba06f0faef916d84dc323dbaa970b9adc76816cd

Observation db3501d7-41c3-41e7-9ea6-357bf2a55238 · inbound

Hard or Just Unreached? Diagnosing the Sampling Blind Spot in Math-Reasoning Difficulty Estimation cites this paper.

Hard or Just Unreached? Diagnosing the Sampling Blind Spot in Math-Reasoning Difficulty Estimation Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 34

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metadata mismatch
arxiv_id, observed 2026-07-04T01:09:18.637930Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-06-26T20:42:53.975763Z digest=sha256:509c1267eb039107c1fda816d0c70b34276b50463f3d578c72fcaf4a8f073318

Observation d41d606d-7ba0-4050-aa3d-d9b226d5811d · inbound

Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade cites this paper.

Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 14

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metadata mismatch
local_arxiv, observed 2026-07-08T04:04:29.260568Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-07-08T03:54:37.954084Z digest=sha256:d7d9694f1f77ae51e2d49f485c78d9de741a81ddfbfecc3568c38dd043bdbe86

Observation 72f2585a-3ce4-4857-8dcb-e23485e3f759 · inbound

Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade cites this paper.

Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 18

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no resolver link, observed 2026-08-02T08:18:05.198672Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T08:18:05.198672Z digest=sha256:4033b60f76276d44d50e0ef0cb81fdc34a52919d36780a50d89aa09cc851f53a

Observation 7314de4d-bd1f-4887-a915-23c7ca31f8c5 · inbound

Accuracy Hides How Language Models Fail: Measuring Failure States Under Matched Output Budgets cites this paper.

Accuracy Hides How Language Models Fail: Measuring Failure States Under Matched Output Budgets Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 17

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:31:50.420577Z digest=sha256:4db4a663341da69726a2be12fe99a5e08fe5988a755233f86fe435d27f0eac82