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Paper Citation Record · LEDGER

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling

As of 7 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2506.16043.

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

pith.paper-citation-record.v1
2506.16043 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:48:24.288710Z

measured 17 of 17 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

17 of 17 outbound references displayed

  • verified exact1
  • verified fuzzy2
  • unresolved13
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3d47c2c8-f0d3-470a-91d3-6b778fa7cdc3 · outbound

This paper cites Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals.

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T23:48:23.226273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:48:23.226273Z digest=sha256:6b4787203a94d29d9bca6a60642f79376160fa92dc4dbc4cf67a6d212fa1498a

Observation 0e540efa-2fb3-4634-bd08-f39e197350a0 · outbound

This paper cites Sets: Leveraging self-verification and self-correction for improved test-time scaling.arXiv preprint arXiv:2501.19306,.

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling Sets: Leveraging self-verification and self-correction for improved test-time scaling.arXiv preprint arXiv:2501.19306,

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-06T23:48:23.396435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:48:23.396435Z digest=sha256:79e30d0993b21c6d638fcf27581a745fa76c173e9aa4bb4efd8603c83e4b0a97

Observation d75020ad-6eee-4253-a63f-a5dadfac1594 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling Training Verifiers to Solve Math Word Problems

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T23:48:23.495664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:48:23.495664Z digest=sha256:5a68788b9c54c32f9d111905b236b335424e6961bc027e441188c099120b49b9

Observation e1dffc48-5b3e-485f-844b-22ccc0ae2bac · outbound

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

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T23:48:23.585240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:48:23.585240Z digest=sha256:e6b88e22cb3856adf0bde74e63c119656c83624f8417d129d3d5154aa098dc28

Observation 28fef33b-21e1-4d66-8a7b-351a7232b1c6 · outbound

This paper cites Scaling LLM Inference with Optimized Sample Compute Allocation.

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling Scaling LLM Inference with Optimized Sample Compute Allocation

Reference 9

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T23:48:24.675376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:48:23.760554Z digest=sha256:4a924fb781059aa071835ce61be136f40b04afc24b6c0221ec1481903082487f

Observation d405f04f-a2fc-45f9-a601-187236bd3f5f · outbound

This paper cites Disc: Dynamic decomposition improves llm inference scaling.arXiv preprint arXiv:2502.16706,.

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling Disc: Dynamic decomposition improves llm inference scaling.arXiv preprint arXiv:2502.16706,

Reference 10

Resolution
verified exact
raw_fallback, observed 2026-08-06T23:48:24.533166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:48:23.820233Z digest=sha256:87df97b8c3bd2ef9d4d22107b88551e7dce4106530de5b08436a999572481129

Observation b268ae39-a8ca-455c-ac26-18d7aada2edd · outbound

This paper cites MetaScale: Test-Time Scaling with Evolving Meta-Thoughts.

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling MetaScale: Test-Time Scaling with Evolving Meta-Thoughts

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-06T23:48:23.885449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:48:23.885449Z digest=sha256:9e5d896139e65b6c7ddd894319e25451c8dc66ec4545bbf4a9efbb397fa7001c

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

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

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:48:23.944186Z digest=sha256:51874344810538eb5b1965dffde8f459c7b802111f9db5a092f6645d9e368bbe

Observation 4ce299b7-cfcd-4273-989a-806561b59ec2 · outbound

This paper cites Think Smarter not Harder: Adaptive Reasoning with Inference Aware Optimization.

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling Think Smarter not Harder: Adaptive Reasoning with Inference Aware Optimization

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T23:48:24.009626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:48:24.009626Z digest=sha256:f13c8ec62b4eca0db222af69291208a0a6070988c0d771cb6a32bef39e42440b

Observation 4534c48b-06d5-4f36-a5ef-a54052e1f8de · outbound

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

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-06T23:48:24.063354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:48:24.063354Z digest=sha256:63204af3c2dd1fdeface396acc4bdae97be402ead1d6c2f3a2054398dfe3939f

Observation 1f7d8b0a-e87d-43d6-87ca-94b4f407da40 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T23:48:24.139966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:48:24.139966Z digest=sha256:8986291e614dc57d0ff4757d02d57552a7e036b690fc0b34e532f0fc4469a02e

Observation d4c7a105-c154-4107-85d9-e06e514fdb68 · outbound

This paper cites s1: Simple test-time scaling.

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling s1: Simple test-time scaling

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-06T23:48:24.205684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:48:24.205684Z digest=sha256:f286cfde08b58457f0bc6400ad32c171a1369e604462227425339fbcb9c2dd31

Observation c53ca326-8215-4a84-b17d-63bbe1d95642 · outbound

This paper cites When hindsight is not 20/20: Testing limits on reflective thinking in large language models.

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling When hindsight is not 20/20: Testing limits on reflective thinking in large language models

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:48:24.949839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:48:24.288710Z digest=sha256:2f395b766500bf953b37ff89d6c304ab4f2b726175feb8b04398d5cfe260746f

Observation 4fe582ac-d6bd-4b48-95ff-05d66416fec0 · outbound

This paper cites Alphazero-like tree-search can guide large language model decoding and training.

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling Alphazero-like tree-search can guide large language model decoding and training

Reference 2002

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:48:25.123890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:48:23.667462Z digest=sha256:07e3679e06696ccc1da99cb04031f91c496e330bde02662849a178774eed0d1e

Observation a30c1c74-b806-4531-aeba-dff77327aa7d · outbound

This paper cites Theoretical guarantees on the best-of-n alignment policy.

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling Theoretical guarantees on the best-of-n alignment policy

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-06T23:48:23.000632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:48:23.000632Z digest=sha256:9350ed4dd3bbf82c804fb7a85e0b5633ab37f3266e3165efb989b861763f9b89

Observation 48c87aef-8562-4b42-ac5a-2ba211690cd4 · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-06T23:48:23.089767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:48:23.089767Z digest=sha256:52bb10b7d64acbff9abceee5171983f18b577ffbd63bcc731d2819301f1c798e

Observation 85ae93d2-d9c2-4abf-b286-f50101a78a02 · outbound

This paper cites Scalable best-of-n selection for large language models via self-certainty.

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling Scalable best-of-n selection for large language models via self-certainty

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-06T23:48:23.323303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:48:23.323303Z digest=sha256:6aa0cf78cad0a01413c513ebe073b80883a9fd32264f4a696384e3fb1e277908

Pith citing papers

No inbound Pith citation observations are available.