Paper Citation Record · LEDGER

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning

As of 24 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2607.09693.

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

pith.paper-citation-record.v1
2607.09693 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T17:39:27.292075Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

30 of 30 outbound references displayed

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unresolved30
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External citation measurements

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Outbound references

Observation 9484e85d-f0a9-46dd-a901-ea47b808b074 · outbound

This paper cites Power-SMC: Low-latency sequence-level power sampling for training-free LLM reasoning.arXiv preprint arXiv:2602.10273,.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Power-SMC: Low-latency sequence-level power sampling for training-free LLM reasoning.arXiv preprint arXiv:2602.10273,

Reference 1

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Observation 97a9fa99-fa4d-4a2d-a71e-2c7c1145eb3c · outbound

This paper cites Eliciting Latent Predictions from Transformers with the Tuned Lens.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Eliciting Latent Predictions from Transformers with the Tuned Lens

Reference 2

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Observation bc01ed4c-353c-4174-9ab0-b0c7f61bd423 · outbound

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

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 3

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Observation 1d77668d-5f65-43d3-8851-637039ed181c · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Evaluating Large Language Models Trained on Code

Reference 4

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Observation fe9b09d1-48da-4972-9e12-76686ff96b29 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Training Verifiers to Solve Math Word Problems

Reference 5

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Observation f10c1ce1-5256-4b7f-8848-fd081d68639a · outbound

This paper cites The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models

Reference 6

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Observation b5d4126f-58ce-4963-b2e3-ff60eae41dde · outbound

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

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-14T17:39:27.292075Z

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Observation 77bffb0b-7d68-4730-81a3-1ab726e8f2a6 · outbound

This paper cites Rewarding the unlikely: Lifting GRPO beyond dis- tribution sharpening.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Rewarding the unlikely: Lifting GRPO beyond dis- tribution sharpening

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-14T17:39:27.292075Z

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Observation e0b2c7e8-2b7a-4a12-800d-d954a3cc34ca · outbound

This paper cites Scalable power sampling: Unlocking efficient, training-free reasoning for LLMs via distribution sharpening.arXiv preprint arXiv:2601.21590,.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Scalable power sampling: Unlocking efficient, training-free reasoning for LLMs via distribution sharpening.arXiv preprint arXiv:2601.21590,

Reference 9

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Observation 7a0676f3-1760-4b49-8a1b-c32d4f982b3a · outbound

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

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Language Models (Mostly) Know What They Know

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-14T17:39:27.292075Z

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Observation 1de329e7-0f01-429f-9a16-fdfb1cf0e331 · outbound

This paper cites Reasoning with Sampling: Your Base Model is Smarter Than You Think.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Reasoning with Sampling: Your Base Model is Smarter Than You Think

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-14T17:39:27.292075Z

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Observation 4d830e88-ed03-42b2-b5d8-5dddeb094b3c · outbound

This paper cites Tulu 3: Pushing Frontiers in Open Language Model Post-Training.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-14T17:39:27.292075Z

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Observation 73ed15f6-2509-4765-b00a-b2d1e0f77bbb · outbound

This paper cites Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-14T17:39:27.292075Z

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Observation 2e5fd948-b31b-40a0-938e-1b1a73e752fd · outbound

This paper cites Maximizing Confidence Alone Improves Reasoning.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Maximizing Confidence Alone Improves Reasoning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-14T17:39:27.292075Z

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Observation 9b1907bc-fcc8-4c9f-8d4f-550f376330df · outbound

This paper cites Qwen2.5 Technical Report.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Qwen2.5 Technical Report

Reference 15

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Observation bca546a2-83e2-4000-9de8-91ceef1dd4c8 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Proximal Policy Optimization Algorithms

Reference 16

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Observation a7aa20c8-69e4-4189-9fff-24b2378356b6 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-14T17:39:27.292075Z

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Observation 35fffbe0-93cb-4d5a-8829-f4f83000f389 · outbound

This paper cites Spurious Rewards: Rethinking Training Signals in RLVR.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Spurious Rewards: Rethinking Training Signals in RLVR

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-14T17:39:27.292075Z

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Observation ae3d3cd9-66b3-4ddd-a9c4-7bd03ae5743c · outbound

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

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-14T17:39:27.292075Z

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Observation 9ae9caf7-aae5-4d94-965c-b29c28373d27 · outbound

This paper cites Outcome-based Exploration for LLM Reasoning.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Outcome-based Exploration for LLM Reasoning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-14T17:39:27.292075Z

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Observation 273c4e87-3b4e-4594-896c-cb6a122faba4 · outbound

This paper cites Bottom-up Policy Optimization: Your Language Model Policy Secretly Contains Internal Policies.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Bottom-up Policy Optimization: Your Language Model Policy Secretly Contains Internal Policies

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-14T17:39:27.292075Z

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Observation ee4cb7f7-cf70-4820-857f-9af9eeb2fb34 · outbound

This paper cites Solving math word problems with process- and outcome-based feedback.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Solving math word problems with process- and outcome-based feedback

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-14T17:39:27.292075Z

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Observation a8ab791d-e680-45d3-8d12-76d0ca05e8d5 · outbound

This paper cites Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-14T17:39:27.292075Z

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Observation 7082292b-65cd-4224-995d-bdb78a582362 · outbound

This paper cites Learning to Reason without External Rewards.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Learning to Reason without External Rewards

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-14T17:39:27.292075Z

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Observation 623a6443-048d-415b-8415-5f136b5b3393 · outbound

This paper cites Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-14T17:39:27.292075Z

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Observation 47eecfc7-f682-4f39-b2c1-5b9e86c1544c · outbound

This paper cites The layer-wise entropy is Hl(t) =− P v pl(v|x ≤t) logp l(v|x ≤t).

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning The layer-wise entropy is Hl(t) =− P v pl(v|x ≤t) logp l(v|x ≤t)

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-14T17:39:27.292075Z

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Observation 4035f865-2e85-48c7-8176-74fc6a12356c · outbound

This paper cites GRPO reference.Following Karan and Du (2025), our GRPO references are produced rather than quoted: we posttrain each base model with GRPO (Shao et al.,.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning GRPO reference.Following Karan and Du (2025), our GRPO references are produced rather than quoted: we posttrain each base model with GRPO (Shao et al.,

Reference 27

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Observation 61a1473d-b056-455a-9737-acfd73b8534f · outbound

This paper cites The training setup mirrors theirs—we adopt the GRPO implementation of Shao et al.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning The training setup mirrors theirs—we adopt the GRPO implementation of Shao et al

Reference 28

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Observation b7d8daf2-a4c5-4c22-82d8-7c934b72dc85 · outbound

This paper cites F.1 NORMALIZABILITY OF THEDEGSTARGET Proposition 1(Normalizability).Let the support S={x:p(x)>0} be finite (e.g.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning F.1 NORMALIZABILITY OF THEDEGSTARGET Proposition 1(Normalizability).Let the support S={x:p(x)>0} be finite (e.g

Reference 29

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Observation 8dcdb4c3-c59f-48d5-9f2c-3437fdaf6506 · outbound

This paper cites from a pro- posal g with g(x)>0 on S.

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning from a pro- posal g with g(x)>0 on S

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-14T17:39:27.292075Z

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Pith citing papers

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