Pith. sign in

Paper Citation Record · LEDGER

Refining Answer Distributions for Improved Large Language Model Reasoning

As of 12 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2412.13292.

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

pith.paper-citation-record.v1
2412.13292 v2

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:20:59.620313Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

27 of 27 outbound references displayed

  • verified exact0
  • verified fuzzy6
  • unresolved21
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 26499ff9-785a-4d3a-acb5-8c46eb186d7c · outbound

This paper cites Graph of Thoughts: Solving Elaborate Problems with Large Language Models.

Refining Answer Distributions for Improved Large Language Model Reasoning Graph of Thoughts: Solving Elaborate Problems with Large Language Models

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-11T13:20:59.547385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:20:59.547385Z digest=sha256:816e3048dae8effadd6d9071ff10622ee4addcfda25ca93662ea0c7dcf647062

Observation d2841a24-983a-4f2a-9475-7e3b1105b477 · outbound

This paper cites Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future.

Refining Answer Distributions for Improved Large Language Model Reasoning Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-11T13:20:59.553637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:20:59.553637Z digest=sha256:90a3f1af1a960248df07721b480da830f981d832c705c1e8894a8a95c429dc8b

Observation 80adb5e4-1072-4a87-a657-62feaa36f14b · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Refining Answer Distributions for Improved Large Language Model Reasoning Training Verifiers to Solve Math Word Problems

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-11T13:20:59.556405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:20:59.556405Z digest=sha256:2275d615579119fe9bd2b0e5f4752b805183f0e0dac1c393fadca09cce934134

Observation 603666a3-ed1e-4ff9-9181-601bdb0f911b · outbound

This paper cites an unresolved cited work.

Refining Answer Distributions for Improved Large Language Model Reasoning Unresolved cited work

Reference 6

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:20:59.772514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:20:59.615533Z digest=sha256:2eacccc3a5dca68eb0eb6a65362c55e88c917ea38d046773c2c329127f51ab89

Observation 51b60cdc-43d5-4f05-a7ac-d6c5f522cee2 · outbound

This paper cites Hint-before-Solving Prompting: Guiding LLMs to Effectively Utilize Encoded Knowledge.

Refining Answer Distributions for Improved Large Language Model Reasoning Hint-before-Solving Prompting: Guiding LLMs to Effectively Utilize Encoded Knowledge

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-11T13:20:59.561716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:20:59.561716Z digest=sha256:39ed5145ba0ce3f020d367bafdd795f78d9f497db16572ba6159fa527003c497

Observation e63615c3-663f-410a-8ab0-267dd4c2f3ba · outbound

This paper cites CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing.

Refining Answer Distributions for Improved Large Language Model Reasoning CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T13:20:59.564488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:20:59.564488Z digest=sha256:7a6185b740ded5dd5f9ed5a710ac6a342a112fcef05aa496e2059a3b186e0e3f

Observation f17c06e4-e3ca-42c3-96ee-ece2619ae96d · outbound

This paper cites The Llama 3 Herd of Models.

Refining Answer Distributions for Improved Large Language Model Reasoning The Llama 3 Herd of Models

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-11T13:20:59.567568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:20:59.567568Z digest=sha256:1cd3f017b2a93b4a432b28f37713b5c9818f43c2cf6e8e93ba3ae4a374e4b069

Observation 95f36640-66b7-4943-834a-008364c83ad9 · outbound

This paper cites GPT-4 Technical Report.

Refining Answer Distributions for Improved Large Language Model Reasoning GPT-4 Technical Report

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-11T13:20:59.573382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:20:59.573382Z digest=sha256:98e22fcd51761cb7e7fd28cd5870b4c88131f727c3996cb87ea163993ca67e04

Observation 42649661-0a10-473c-9864-6c3b581eb629 · outbound

This paper cites REFINER: Reasoning Feedback on Intermediate Representations.

Refining Answer Distributions for Improved Large Language Model Reasoning REFINER: Reasoning Feedback on Intermediate Representations

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-11T13:20:59.576627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:20:59.576627Z digest=sha256:f43f16b5f29de00c39147e82691f26b604d3dbd85df388caae63bb0d48d3e2d5

Observation 9b3039ed-0a9d-488b-9b13-3e6012c09c2d · outbound

This paper cites Srivastava, A.

Refining Answer Distributions for Improved Large Language Model Reasoning Srivastava, A

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:20:59.811909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:20:59.582804Z digest=sha256:796f11e69d91d2e65a9ff786915f6abf948aea29d1e33d3e973a3677b7679891

Observation af516c37-022e-4df5-ab92-02b73f0a1bfb · outbound

This paper cites LLMs cannot find reasoning errors, but can correct them given the error location.

Refining Answer Distributions for Improved Large Language Model Reasoning LLMs cannot find reasoning errors, but can correct them given the error location

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-11T13:20:59.586434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:20:59.586434Z digest=sha256:4ed896cacba801354cdcfa086ed6ad2a89d5586288514dde138be449f511218e

Observation 2a975cc3-a174-430c-a545-ee37b92dd798 · outbound

This paper cites Emergent Abilities of Large Language Models.

Refining Answer Distributions for Improved Large Language Model Reasoning Emergent Abilities of Large Language Models

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-11T13:20:59.589284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:20:59.589284Z digest=sha256:18538e36d73fa115fb466a962307ca6524f587a0e665828fe0560ae47f62e709

Observation 529ee426-b75b-4e80-8f9c-e7a2fa29eda2 · outbound

This paper cites Tree of Thoughts: Deliberate Problem Solving with Large Language Models.

Refining Answer Distributions for Improved Large Language Model Reasoning Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-11T13:20:59.592315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:20:59.592315Z digest=sha256:6874c4428e7e69bf0e91019fb236174b703a188b1b623fe271f3ed1c1c342a54

Observation 4b153e26-3749-4212-8f15-760e5d54fba9 · outbound

This paper cites Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization.

Refining Answer Distributions for Improved Large Language Model Reasoning Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-11T13:20:59.595233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:20:59.595233Z digest=sha256:bb509ee69d2d979da7a33fa51787b1e886dae6629d895c1febb545bc3d838e43

Observation a5e19c7b-f84e-4546-b05f-ac73e8b3a0b5 · outbound

This paper cites STaR: Bootstrapping Reasoning With Reasoning.

Refining Answer Distributions for Improved Large Language Model Reasoning STaR: Bootstrapping Reasoning With Reasoning

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-11T13:20:59.598559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:20:59.598559Z digest=sha256:899e7fa2879a5d75ca29e98a941cb43a863c22309b3a5ef81c478e305835fb96

Observation 4bc161a8-cfbf-420c-9d00-add52fcf8cc3 · outbound

This paper cites Date Understanding.

Refining Answer Distributions for Improved Large Language Model Reasoning Date Understanding

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:20:59.796804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:20:59.607199Z digest=sha256:44c37e9bf792d39960c80aaa7c07bddc6c36c605a244728bf1b1543a0817e964

Observation 5cbefa19-4c80-4dc3-9c83-42883f3e51b5 · outbound

This paper cites In order to reduce the API cost of the experiments, we restrict running the more expensive 70B model to only the three most difficult benchmarks.

Refining Answer Distributions for Improved Large Language Model Reasoning In order to reduce the API cost of the experiments, we restrict running the more expensive 70B model to only the three most difficult benchmarks

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:20:59.788493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:20:59.610112Z digest=sha256:4345163d54496deb56e2b80149820e5ddc9ab9e5e66ac5b340decb812b45a9d0

Observation a3a3a763-c9ab-4fb6-b7fa-58267861347a · outbound

This paper cites an unresolved cited work.

Refining Answer Distributions for Improved Large Language Model Reasoning Unresolved cited work

Reference 24

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:20:59.780667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:20:59.612886Z digest=sha256:66c819f925a803155e04139a963b8cb27eb3c9bd252ae6ee057f89e61f4e48cd

Observation 113404d6-4271-43ef-8f9a-051b23f30785 · outbound

This paper cites The base examples are taken from Zheng et al.

Refining Answer Distributions for Improved Large Language Model Reasoning The base examples are taken from Zheng et al

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:20:59.764868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:20:59.617844Z digest=sha256:64fc761b92547160793f0d4d14e00c6577ab0281318349a8ae958fb3805edbd0

Observation 1a5710d7-4a35-4ff6-aed7-79f7880e9655 · outbound

This paper cites For Christmas, he got two toys each from his mom and dad.

Refining Answer Distributions for Improved Large Language Model Reasoning For Christmas, he got two toys each from his mom and dad

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:20:59.756726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:20:59.620313Z digest=sha256:a51282ca8975ca535d3025a6639faeb9affd6602e9f0cb983bdd82151d25988c

Observation 14828895-3c49-459b-a03e-4ff8f9d8e925 · outbound

This paper cites Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought.

Refining Answer Distributions for Improved Large Language Model Reasoning Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought

Reference 2015

Resolution
unresolved
no resolver link, observed 2026-08-11T13:20:59.580070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:20:59.580070Z digest=sha256:7491d168b54522117d1582ba85c0913d268b7fc4300024ebf22ddf75298c8200

Observation 6ec0730a-7343-4440-921f-934de8622b1c · outbound

This paper cites Although these arithmetic problems in the previous benchmarks are relatively simple for humans, LLMs often struggle in solving these types of problems (Patel et al., 2021).

Refining Answer Distributions for Improved Large Language Model Reasoning Although these arithmetic problems in the previous benchmarks are relatively simple for humans, LLMs often struggle in solving these types of problems (Patel et al., 2021)

Reference 2017

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:20:59.804758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T13:20:59.604463Z digest=sha256:55eeb9c92b9a94ff5ccb67784c6ca98248f953273e3c341d1549c81b4de1074c

Observation 78de401d-ac1a-4a46-8690-5c8e456f2864 · outbound

This paper cites ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs.

Refining Answer Distributions for Improved Large Language Model Reasoning ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-11T13:20:59.550485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:20:59.550485Z digest=sha256:ead7dc11bbbdd9725f41655bc4ef095a7bb75405bbd4ff374bd20c4bb9f1950a

Observation 5b9a5be5-565a-4aad-a329-946b2dda3428 · outbound

This paper cites Improving Factuality and Reasoning in Language Models through Multiagent Debate.

Refining Answer Distributions for Improved Large Language Model Reasoning Improving Factuality and Reasoning in Language Models through Multiagent Debate

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-11T13:20:59.558921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:20:59.558921Z digest=sha256:e97df1513a20afbe39b11e89e6e69503670e4f82c8a6ac3c78dee9911d55c6eb

Observation 8d0ef82c-876b-48ee-8b3a-aa0550c3589b · outbound

This paper cites Self-Convinced Prompting: Few-Shot Question Answering with Repeated Introspection.

Refining Answer Distributions for Improved Large Language Model Reasoning Self-Convinced Prompting: Few-Shot Question Answering with Repeated Introspection

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-11T13:20:59.601734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:20:59.601734Z digest=sha256:8d7a75acb157887b63bfd1431df86fb9c0baa50a3c2e700246808992cd2ced69

Observation fb22d84d-125e-49fe-a389-7002506c671a · outbound

This paper cites RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs.

Refining Answer Distributions for Improved Large Language Model Reasoning RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-11T13:20:59.543487Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:20:59.543487Z digest=sha256:4f1f6c217fee483588f9a352de6b36a875c2284faf8d3ff46d775f80aea8521d

Observation 1e9c5fe6-792f-4a08-87fe-763072c2dce9 · outbound

This paper cites Deliberate then Generate: Enhanced Prompting Framework for Text Generation.

Refining Answer Distributions for Improved Large Language Model Reasoning Deliberate then Generate: Enhanced Prompting Framework for Text Generation

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-11T13:20:59.570483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:20:59.570483Z digest=sha256:cf6f95f0e60b9cc015ef026990e97e0471e381895e707d01f18fac638e018991

Pith citing papers

No inbound Pith citation observations are available.