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Paper Citation Record · LEDGER

FormaRL: Enhancing Autoformalization with no Labeled Data

As of 23 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 3 inbound Pith citation observations for arXiv:2508.18914.

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

pith.paper-citation-record.v1
2508.18914 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:11:23.289082Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T11:31:37.801454Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T00:05:31.606007Z

Reference resolution

46 of 46 outbound references displayed

  • verified exact1
  • verified fuzzy5
  • unresolved39
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7c9a5e06-b68f-4f0a-aee2-997be33faaba · outbound

This paper cites write newline.

FormaRL: Enhancing Autoformalization with no Labeled Data write newline

Reference 1

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source=arxiv_source observed=2026-08-05T16:11:23.097128Z digest=sha256:acf7f08e0fddeea7c66d4d4ae28c7b888898e40d71e92ae92882278ab9dac9f4

Observation aa2a384d-4646-4433-9de9-f0dc1a5f339e · outbound

This paper cites @esa (Ref.

FormaRL: Enhancing Autoformalization with no Labeled Data @esa (Ref

Reference 2

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source=arxiv_source observed=2026-08-05T16:11:23.102063Z digest=sha256:7e1ac3ab7055df855caf7ca391a72937b4856de542d57782fb8ec7be97f67b39

Observation f269ac43-dfe8-4b9a-9259-1cd30b25c04d · outbound

This paper cites an unresolved cited work.

FormaRL: Enhancing Autoformalization with no Labeled Data Unresolved cited work

Reference 3

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source=arxiv_source observed=2026-08-05T16:11:23.106634Z digest=sha256:60f2b2c0686b56078807bf621f702db273470488e076b68a9cede2bb95b5e077

Observation b72506bc-161b-47a6-9349-98fcf2f99960 · outbound

This paper cites an unresolved cited work.

FormaRL: Enhancing Autoformalization with no Labeled Data Unresolved cited work

Reference 4

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-05T16:11:23.110523Z digest=sha256:4fef5accd9a9d87ebb8da583b818d975eb0e4745192f656c7925a2778c3de5d7

Observation ccbf8285-d16a-4d7d-86bc-2d72467a8620 · outbound

This paper cites ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics.

FormaRL: Enhancing Autoformalization with no Labeled Data ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics

Reference 5

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source=arxiv_source observed=2026-08-05T16:11:23.114520Z digest=sha256:b406ae40a8fa11d046d98b402eb4ef8f6bfd9365dcc87b220623e77716c50a58

Observation 88bd4f5a-a330-4566-9222-7716c4687490 · outbound

This paper cites Deepmind hits milestone in solving maths problems—AI’s next grand challenge.

FormaRL: Enhancing Autoformalization with no Labeled Data Deepmind hits milestone in solving maths problems—AI’s next grand challenge

Reference 6

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verified fuzzy
raw_fallback, observed 2026-08-05T16:11:23.852059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-05T16:11:23.118799Z digest=sha256:cd36d8b5bf75b73233f5b939e9888755f7bc1578c5aaa12699e234874bb6d5ef

Observation 7b032b84-ea0a-47f0-9b18-4d89a6f03b0d · outbound

This paper cites Towards Autoformalization of Mathematics and Code Correctness: Experiments with Elementary Proofs.

FormaRL: Enhancing Autoformalization with no Labeled Data Towards Autoformalization of Mathematics and Code Correctness: Experiments with Elementary Proofs

Reference 7

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metadata mismatch
local_arxiv, observed 2026-08-05T16:11:23.785223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-05T16:11:23.122670Z digest=sha256:4abc637784a85f26b473f3254aadbed997909da10722c3c588e2294aad099f46

Observation cb23e3f2-df29-408f-95a1-a58bb5c32015 · outbound

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

FormaRL: Enhancing Autoformalization with no Labeled Data DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 8

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source=arxiv_source observed=2026-08-05T16:11:23.127841Z digest=sha256:f32413e9e1014d1e6f2b7e2aa8be741d1f88a76375775fd3a3685a1d31c17e6f

Observation c475bef1-ca8c-4381-996e-42e391cb50d4 · outbound

This paper cites DeepSeek-V3 Technical Report.

FormaRL: Enhancing Autoformalization with no Labeled Data DeepSeek-V3 Technical Report

Reference 9

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source=arxiv_source observed=2026-08-05T16:11:23.132275Z digest=sha256:6e882841b3124c0b05b2ddc5bbc29eed479aceaf696338897fb952e299ef09ae

Observation 09d6afd5-29cf-4f76-a7ea-8d5b00ed40ff · outbound

This paper cites Qwen2.5-Coder Technical Report.

FormaRL: Enhancing Autoformalization with no Labeled Data Qwen2.5-Coder Technical Report

Reference 10

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source=arxiv_source observed=2026-08-05T16:11:23.137179Z digest=sha256:77eea1e7f2fd6f69470ca8332c17b0940dc6ec6137eb4708d990b1e7d83647e9

Observation 90a7a43e-baaa-4df7-b2b2-1931dd034a66 · outbound

This paper cites Multilingual Mathematical Autoformalization.

FormaRL: Enhancing Autoformalization with no Labeled Data Multilingual Mathematical Autoformalization

Reference 11

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source=arxiv_source observed=2026-08-05T16:11:23.142195Z digest=sha256:ce20ea3117cfff27a3f976a69d7965d732ed1b55ae1debd229350b9161b12c26

Observation 1b4f71fc-f388-4227-9c42-bc58764a76a3 · outbound

This paper cites Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs.

FormaRL: Enhancing Autoformalization with no Labeled Data Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs

Reference 12

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source=arxiv_source observed=2026-08-05T16:11:23.146879Z digest=sha256:203614366a2d604595b91edf50551a0915b4416ed127cc929170e517627fcb04

Observation 9b49024a-7409-4cfe-968d-bb5055889cf4 · outbound

This paper cites LIMR: Less is More for RL Scaling.

FormaRL: Enhancing Autoformalization with no Labeled Data LIMR: Less is More for RL Scaling

Reference 13

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source=arxiv_source observed=2026-08-05T16:11:23.150929Z digest=sha256:3cc1b4c87d96f273c085d6e2c3873e8ff80567243f4177ea6a2e504e3e491002

Observation ff858c86-95a5-4b72-9c2a-588cbfa8cea1 · outbound

This paper cites HunyuanProver: A Scalable Data Synthesis Framework and Guided Tree Search for Automated Theorem Proving.

FormaRL: Enhancing Autoformalization with no Labeled Data HunyuanProver: A Scalable Data Synthesis Framework and Guided Tree Search for Automated Theorem Proving

Reference 14

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source=arxiv_source observed=2026-08-05T16:11:23.155247Z digest=sha256:098da9118d5b73581e18e44aa8f2da1b77a48f62f567b9fda4c4cefed91a27e2

Observation 5eef2327-a35a-4aca-9d21-b3f1438a3d87 · outbound

This paper cites A Survey on Deep Learning for Theorem Proving.

FormaRL: Enhancing Autoformalization with no Labeled Data A Survey on Deep Learning for Theorem Proving

Reference 15

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source=arxiv_source observed=2026-08-05T16:11:23.159080Z digest=sha256:bde290ca9124cb48722247c075a4a420f000dfb113c401104908cbfb74b72491

Observation 256f5555-7d0e-46c3-9f7a-1ab23821b4ed · outbound

This paper cites Let's Verify Step by Step.

FormaRL: Enhancing Autoformalization with no Labeled Data Let's Verify Step by Step

Reference 16

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source=arxiv_source observed=2026-08-05T16:11:23.163914Z digest=sha256:43545cb3cc5a1720adc343d3f3793fb7556072c58f5dc352a8af1aa7bedea0a8

Observation 3e24d30f-1829-4291-b97d-14e40d41ce9f · outbound

This paper cites Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving.

FormaRL: Enhancing Autoformalization with no Labeled Data Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving

Reference 17

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source=arxiv_source observed=2026-08-05T16:11:23.168166Z digest=sha256:672f123e7dbd77350c602116e94ac28ea7b9a8f27328bc86aaee919b9d699a18

Observation c05c82d8-511e-4593-9806-c29a29e24984 · outbound

This paper cites Rethinking and improving autoformalization: towards a faithful metric and a dependency retrieval-based approach.

FormaRL: Enhancing Autoformalization with no Labeled Data Rethinking and improving autoformalization: towards a faithful metric and a dependency retrieval-based approach

Reference 18

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verified fuzzy
raw_fallback, observed 2026-08-05T16:11:23.842971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-05T16:11:23.176569Z digest=sha256:2e4966711f520b6ae8e8eec6022119fad8714a84fa9b7056888f12d0320483db

Observation 423725fe-c6b9-420e-8742-329ebdf318c6 · outbound

This paper cites SimPO: Simple Preference Optimization with a Reference-Free Reward.

FormaRL: Enhancing Autoformalization with no Labeled Data SimPO: Simple Preference Optimization with a Reference-Free Reward

Reference 19

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source=arxiv_source observed=2026-08-05T16:11:23.181357Z digest=sha256:7ba9efd16ce5930bea2f967efdaa24b82354efb69c45be273b3b55d8022b2daf

Observation c81d8d9e-4b9a-4706-a5e1-fa98f92cde8f · outbound

This paper cites The lean 4 theorem prover and programming language.

FormaRL: Enhancing Autoformalization with no Labeled Data The lean 4 theorem prover and programming language

Reference 20

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verified fuzzy
raw_fallback, observed 2026-08-05T16:11:23.834014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-05T16:11:23.185328Z digest=sha256:53f82e9b1e225c7bbc46d4f27e8987b3b98f6c3ce1273257ec60ec808e6a78c1

Observation a35fecee-d9b9-4086-80a4-36c8451ff5c6 · outbound

This paper cites Autoformalizing Euclidean Geometry.

FormaRL: Enhancing Autoformalization with no Labeled Data Autoformalizing Euclidean Geometry

Reference 21

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source=arxiv_source observed=2026-08-05T16:11:23.190126Z digest=sha256:b2db0c9c5fb6adb2ae74652239da15945aa0f877554b5b882724c993322ee9f4

Observation bbc7d3d2-5f7b-4dd7-b325-a428b80faf20 · outbound

This paper cites GPT-4o System Card.

FormaRL: Enhancing Autoformalization with no Labeled Data GPT-4o System Card

Reference 22

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source=arxiv_source observed=2026-08-05T16:11:23.195566Z digest=sha256:db8fe87a9165f2ab1a0447fade52bdc249891b112d3ff46aff65450bfd0fc918

Observation e138f994-3b02-4258-896b-b2571952ceeb · outbound

This paper cites OpenAI o1 System Card.

FormaRL: Enhancing Autoformalization with no Labeled Data OpenAI o1 System Card

Reference 23

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source=arxiv_source observed=2026-08-05T16:11:23.200047Z digest=sha256:bddba33c2e5736cbeb847d92f612d60f332bc174780f1853ee0799383ba05a47

Observation 9b08fa05-172e-47be-9aec-561d8d618f5b · outbound

This paper cites Training language models to follow instructions with human feedback.

FormaRL: Enhancing Autoformalization with no Labeled Data Training language models to follow instructions with human feedback

Reference 24

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source=arxiv_source observed=2026-08-05T16:11:23.204611Z digest=sha256:e4c6741ba3c9a01b7d47e87806dc50f9a4cb333972b275c758f2e6c6b2a19761

Observation f75b48d4-9f55-4e60-a038-045a328c4e75 · outbound

This paper cites A New Approach Towards Autoformalization.

FormaRL: Enhancing Autoformalization with no Labeled Data A New Approach Towards Autoformalization

Reference 25

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local_arxiv, observed 2026-08-05T16:11:23.647118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-05T16:11:23.208664Z digest=sha256:6f1643dc40cd6bc7cde8dbceff36362911acad954595605aed19248ea895a038

Observation c24737b9-367e-4472-b074-c6fd5ae7f6d3 · outbound

This paper cites Improving autoformalization using type checking, 2024.

FormaRL: Enhancing Autoformalization with no Labeled Data Improving autoformalization using type checking, 2024

Reference 26

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source=arxiv_source observed=2026-08-05T16:11:23.213982Z digest=sha256:ee32e7d6dc0805e4cc32a03e16bad00a3990e3a187048fbbb156b3f4badb50f1

Observation 1507ff20-393a-40d0-a0ad-63167d6e6f9b · outbound

This paper cites Qwen2.5 Technical Report.

FormaRL: Enhancing Autoformalization with no Labeled Data Qwen2.5 Technical Report

Reference 27

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source=arxiv_source observed=2026-08-05T16:11:23.218244Z digest=sha256:0910d87a4a3905161c495b186f16da38307027c95e1cdb3df2683098e3413a68

Observation 3ad1bddd-0f55-4e18-98d1-2550d91bafbc · outbound

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

FormaRL: Enhancing Autoformalization with no Labeled Data DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 28

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no resolver link, observed 2026-08-05T16:11:23.222319Z

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source=arxiv_source observed=2026-08-05T16:11:23.222319Z digest=sha256:d6c83b69c8e5c59ec7a60a301c7c148f3fe945239fb61ccf8714175cd2c5127b

Observation 08fdd5d5-0e78-42df-8506-52558346f085 · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

FormaRL: Enhancing Autoformalization with no Labeled Data Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 29

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source=arxiv_source observed=2026-08-05T16:11:23.227793Z digest=sha256:566302f1f8a7f9f9810ca71739860d2c446f17b31a2b4aae99ca7feb227e3cb4

Observation 52b0ffd0-ff00-4145-afa8-c9b50f287377 · outbound

This paper cites Paulson Tobias Nipkow, Markus Wenzel.

FormaRL: Enhancing Autoformalization with no Labeled Data Paulson Tobias Nipkow, Markus Wenzel

Reference 30

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verified fuzzy
raw_fallback, observed 2026-08-05T16:11:23.824803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-05T16:11:23.231891Z digest=sha256:838f10f212c7a60444dcb3137346cfb2f53a226771437369dbbba9f3b9841c9e

Observation 11e6884d-4aaf-48f7-9ed1-06b09837e0c0 · outbound

This paper cites Trl: Transformer reinforcement learning.

FormaRL: Enhancing Autoformalization with no Labeled Data Trl: Transformer reinforcement learning

Reference 31

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source=arxiv_source observed=2026-08-05T16:11:23.235631Z digest=sha256:32e674473034fff02b9e74bae08f9cfb611f68ffbd202932345307cfeb49d4b8

Observation 93a1edd2-e2a6-4bf8-88c7-75d62821539c · outbound

This paper cites NaturalProofs: Mathematical Theorem Proving in Natural Language.

FormaRL: Enhancing Autoformalization with no Labeled Data NaturalProofs: Mathematical Theorem Proving in Natural Language

Reference 32

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no resolver link, observed 2026-08-05T16:11:23.239508Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:11:23.239508Z digest=sha256:041141b931966292b68c4fcf4a96423be6fd813c51abcf7f895cb4fb94567f8f

Observation 4124be23-6479-4402-8f03-a57667e26312 · outbound

This paper cites Autoformalization with Large Language Models.

FormaRL: Enhancing Autoformalization with no Labeled Data Autoformalization with Large Language Models

Reference 33

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no resolver link, observed 2026-08-05T16:11:23.244019Z

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source=arxiv_source observed=2026-08-05T16:11:23.244019Z digest=sha256:4821c1e0b4152b100abfde449e51837d629f3a80209633548dc35ddb249f5979

Observation 72823dd0-1932-4c16-8666-bdf96e370de9 · outbound

This paper cites InternLM 2.5- StepProver : Advancing automated theorem proving via expert iteration on large-scale LEAN problems, 2024.

FormaRL: Enhancing Autoformalization with no Labeled Data InternLM 2.5- StepProver : Advancing automated theorem proving via expert iteration on large-scale LEAN problems, 2024

Reference 34

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source=arxiv_source observed=2026-08-05T16:11:23.248720Z digest=sha256:528f92cfbd2cb153de65c23ceaf85382a13c84289978a026817a8fb7e7082d98

Observation 6bda71c3-1de0-44b5-966d-cdb31ff6760c · outbound

This paper cites DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data.

FormaRL: Enhancing Autoformalization with no Labeled Data DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data

Reference 35

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:11:23.252729Z digest=sha256:0d8a52bcddd528dd8aa6a874ae97355a0ab1b88103a91661e9908f37ad75e147

Observation e2641853-d88e-4123-92cc-f015eff25bff · outbound

This paper cites DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search.

FormaRL: Enhancing Autoformalization with no Labeled Data DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search

Reference 36

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no resolver link, observed 2026-08-05T16:11:23.256524Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:11:23.256524Z digest=sha256:1bcf15ecf9dc6c028cb1623d2b2079e816a5a70cc2947791d107cf36e9ad81ea

Observation 856d68b1-ee68-45d4-83cd-61fb19b36890 · outbound

This paper cites BFS -prover: Scalable best-first tree search for LLM -based automatic theorem proving, 2025.

FormaRL: Enhancing Autoformalization with no Labeled Data BFS -prover: Scalable best-first tree search for LLM -based automatic theorem proving, 2025

Reference 37

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no resolver link, observed 2026-08-05T16:11:23.260022Z

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source=arxiv_source observed=2026-08-05T16:11:23.260022Z digest=sha256:82229672f81c979f92f79f47b4cef021d1670ee0fe8850b2608ffa31a18afbf6

Observation bbdfa00b-e9e8-4f32-809c-773adb360c7d · outbound

This paper cites Qwen2 Technical Report.

FormaRL: Enhancing Autoformalization with no Labeled Data Qwen2 Technical Report

Reference 38

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no resolver link, observed 2026-08-05T16:11:23.262918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:11:23.262918Z digest=sha256:b45b1c95d451fe18c36cc659a13db8e7dc7e7b96c66eb58d98ac0948380e60fb

Observation d208237d-31a9-44e0-a98f-3467aca6df50 · outbound

This paper cites Formal Mathematical Reasoning: A New Frontier in AI.

FormaRL: Enhancing Autoformalization with no Labeled Data Formal Mathematical Reasoning: A New Frontier in AI

Reference 39

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no resolver link, observed 2026-08-05T16:11:23.266209Z

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source=arxiv_source observed=2026-08-05T16:11:23.266209Z digest=sha256:b3655b0caf2da1ad4c95c0290a1ad6b8f1f4cb69118a21bafad507c25007bb26

Observation 505c370d-42dd-499c-bfe1-47886082f2f8 · outbound

This paper cites Lean Workbook: A large-scale Lean problem set formalized from natural language math problems.

FormaRL: Enhancing Autoformalization with no Labeled Data Lean Workbook: A large-scale Lean problem set formalized from natural language math problems

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-05T16:11:23.269420Z

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source=arxiv_source observed=2026-08-05T16:11:23.269420Z digest=sha256:a1e37b43903d9582714f0c006f8e8c7dcbc639881a801751fc51fdf6265e4133

Observation b99e079f-30c5-422b-b5c4-0c8aff2b2635 · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

FormaRL: Enhancing Autoformalization with no Labeled Data DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 41

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no resolver link, observed 2026-08-05T16:11:23.273126Z

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source=arxiv_source observed=2026-08-05T16:11:23.273126Z digest=sha256:e4e46ab7561a826a87235db0d0b9727b40139da74247ebfc4bf1b59a5a9b49b5

Observation c670aed7-3ef8-4ff4-ad8f-fb62af544d80 · outbound

This paper cites Interactive theorem proving and program development.

FormaRL: Enhancing Autoformalization with no Labeled Data Interactive theorem proving and program development

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T16:11:23.810788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-05T16:11:23.276263Z digest=sha256:f4bbdd465f4cf23b1f67f62bd0511de7fb7643beb4e4f34f2f0289579301b846

Observation 395c8afb-ae67-4504-aa3e-5bd4f93197b5 · outbound

This paper cites 7b model and 8k examples: Emerging reasoning with reinforcement learning is both effective and efficient.

FormaRL: Enhancing Autoformalization with no Labeled Data 7b model and 8k examples: Emerging reasoning with reinforcement learning is both effective and efficient

Reference 43

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no resolver link, observed 2026-08-05T16:11:23.279356Z

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source=arxiv_source observed=2026-08-05T16:11:23.279356Z digest=sha256:c6520004a4221ca5e7bc3ceeac52d1f83848c9a59cc0c0928082e637d08e44a3

Observation a4bb4ad3-cd92-4155-8927-362760e4f1a6 · outbound

This paper cites Decomposing the Enigma: Subgoal-based Demonstration Learning for Formal Theorem Proving.

FormaRL: Enhancing Autoformalization with no Labeled Data Decomposing the Enigma: Subgoal-based Demonstration Learning for Formal Theorem Proving

Reference 44

Resolution
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no resolver link, observed 2026-08-05T16:11:23.282888Z

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source=arxiv_source observed=2026-08-05T16:11:23.282888Z digest=sha256:77988c093e9f5266e288a8f496180364d3d64cee6aba5295250815f1c3dbd9c9

Observation ccd7e14b-2ba3-49f8-adb3-739ba6e80cf4 · outbound

This paper cites MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics.

FormaRL: Enhancing Autoformalization with no Labeled Data MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-05T16:11:23.286063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:11:23.286063Z digest=sha256:5752783930c62025e076f01a23dbee4c33a8f829cb8da2f12438a9aa0e064176

Observation 35f961b5-5d53-4bf0-bea4-d8de5a4f5883 · outbound

This paper cites Fine-Tuning Language Models from Human Preferences.

FormaRL: Enhancing Autoformalization with no Labeled Data Fine-Tuning Language Models from Human Preferences

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-05T16:11:23.289082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:11:23.289082Z digest=sha256:6de3bcbb5a00f0329bce4d580f3d3adbf8b8cc17297eb083b98657186c6cb0f7

Pith citing papers

Observation 223c5477-2a2c-464e-a8d2-371114ac0efb · inbound

A Survey of Reinforcement Learning for Large Reasoning Models cites this paper.

A Survey of Reinforcement Learning for Large Reasoning Models FormaRL: Enhancing Autoformalization with no Labeled Data

Reference 213

Resolution
verified exact
arxiv_id, observed 2026-05-18T00:05:31.608352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-18T00:02:24.352947Z digest=sha256:685790ffa44c2bda3f39e4b2efa831e49d0615564bc5c5122e7ae3a44720fef2

Observation 71ca18f5-fcb2-4325-ac55-0a6ac68debf8 · inbound

Aria: An Agent For Retrieval and Iterative Auto-Formalization via Dependency Graph cites this paper.

Aria: An Agent For Retrieval and Iterative Auto-Formalization via Dependency Graph FormaRL: Enhancing Autoformalization with no Labeled Data

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-04T11:31:37.801454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:31:37.801454Z digest=sha256:5ba1f99c6579dcc254130d977c77edbffcf73aa153b8366f80436b906990304e

Observation a8611d10-463f-49ed-aff8-11a4eeb128d1 · inbound

SFT-GRPO Data Overlap as a Post-Training Hyperparameter for Autoformalization cites this paper.

SFT-GRPO Data Overlap as a Post-Training Hyperparameter for Autoformalization FormaRL: Enhancing Autoformalization with no Labeled Data

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-10T13:25:26.431285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-10T13:21:52.225115Z digest=sha256:9eacf5bea27326b34ab7a1bb5faf135db12fb97639e26f1bb263cead4b190f45