Pith. sign in

Paper Citation Record · LEDGER

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution

As of 19 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2506.12728.

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

pith.paper-citation-record.v1
2506.12728 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:49:36.194123Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T06:13:01.236121Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

42 of 42 outbound references displayed

  • verified exact1
  • verified fuzzy13
  • unresolved28
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a3f5e1f2-07c5-413f-b514-b58b3ffa8cfe · outbound

This paper cites an unresolved cited work.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Unresolved cited work

Reference 1

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:49:39.362261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T00:49:32.074316Z digest=sha256:5f1afb9f4bc358847756cbeba59a62d2bd9cbd3d6e435cd33c9d998b484282ca

Observation b04d7347-8b3a-4f69-b149-a0198ae338cf · outbound

This paper cites Large language models for software engineering: A systematic literature review[J].

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Large language models for software engineering: A systematic literature review[J]

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:49:39.214121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T00:49:32.183137Z digest=sha256:ea9662e8c554c61df438c9460ab4e112cc30789313e63735807b48b30147dca4

Observation 727d9d79-577b-4f21-bc1e-c5a7dda46d82 · outbound

This paper cites Software testing with large language models: Survey, landscape, and vision[J].

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Software testing with large language models: Survey, landscape, and vision[J]

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:49:39.050603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T00:49:32.293255Z digest=sha256:6c8f0cada8652f14074723b40e26f68cca96ad2770598d0bc86104379352a82b

Observation 2e364cb1-de29-4d49-84f4-1b7c54b1b34f · outbound

This paper cites DeepSeek-V3 Technical Report.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution DeepSeek-V3 Technical Report

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:32.443911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:32.443911Z digest=sha256:eeec80ef68f2ac159ab886445d06dac0ee6f7d344d8faefc81913e1aa1683881

Observation 084e6028-bf51-4543-bc4a-d2bacf6b507b · outbound

This paper cites DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:32.598281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:32.598281Z digest=sha256:a5581ce4186c9e13440636ab13813c3d55133bcbdbf83fadfb043e11bbb2ea37

Observation 11bae016-b600-4909-8608-773efd5b1f5b · outbound

This paper cites Code Llama: Open Foundation Models for Code.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Code Llama: Open Foundation Models for Code

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:32.634419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:32.634419Z digest=sha256:297c9c8cb427c3e158a4992a2ba78a26f6bddd6e904942aef446dbe73138fb9a

Observation 439e8265-1dc0-4352-8220-e8b6908b13c6 · outbound

This paper cites An Empirical Study on the Code Refactoring Capability of Large Language Models.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution An Empirical Study on the Code Refactoring Capability of Large Language Models

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:32.723197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:32.723197Z digest=sha256:2ec8b0c52b5a3df4300529c9793a73486daecd64e600825593b3f4035da5c434

Observation c2876b8f-598f-4e32-991b-2172931ebfb8 · outbound

This paper cites an unresolved cited work.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Unresolved cited work

Reference 8

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:49:38.905020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T00:49:32.848263Z digest=sha256:d62e4d227416c1c2d6fd42cd6c17d0203b61466266c276f6d332f727f7a0a91a

Observation 4bd08387-6570-41de-ae70-0879edb369f2 · outbound

This paper cites an unresolved cited work.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Unresolved cited work

Reference 9

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:49:38.795769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T00:49:32.974363Z digest=sha256:445178bc17cd1ff3e0e1d67164dc70e475ed2874e958ba349d85cc2594c03866

Observation 3b46656a-b300-4914-99ed-0374aa3172bf · outbound

This paper cites The Llama 3 Herd of Models.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution The Llama 3 Herd of Models

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:33.035023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:33.035023Z digest=sha256:f9837c172cb2221c5a0bc085c535c4937f336c9500ad7462697f9bbce802bcf0

Observation ab4c1e4f-4cec-4e60-bf96-970011da2db6 · outbound

This paper cites SWE-bench: Can Language Models Resolve Real-World GitHub Issues?.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:33.128640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:33.128640Z digest=sha256:3b2006773d44422185af0a90d55bce3be10ea4a300afd9168972546a92cf33c3

Observation 7af1aebc-6709-4dd3-a8ed-bbfd388d3f02 · outbound

This paper cites Chain-of-thought in neural code gener- ation: From and for lightweight language models[J].

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Chain-of-thought in neural code gener- ation: From and for lightweight language models[J]

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:49:38.653880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T00:49:33.228431Z digest=sha256:0999c3236a76ab574ddf15e979f4e341f423a49484e6f3f58aaaa383f0db22bb

Observation 1b640a4b-4d54-4cf8-8ee1-ae2b14d108d5 · outbound

This paper cites Structured chain-of-thought prompting for code generation[J].

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Structured chain-of-thought prompting for code generation[J]

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:49:38.491408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T00:49:33.351378Z digest=sha256:7983830c8d08030f8467a827c77d6008e42d1a57429b3d373169ea098a7740d4

Observation c3601886-f1bd-4533-bb5e-a39f3a68cfa0 · outbound

This paper cites Recursive introspection: Teaching language model agents how to self-improve[J].

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Recursive introspection: Teaching language model agents how to self-improve[J]

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:49:38.361017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T00:49:33.434439Z digest=sha256:92afff8f6623c8b47dff3c5cd76385a219d799f9cd1bbc2e8566381b3ec667b4

Observation 42f418bb-9359-4ca8-a4dc-db0587dc668e · outbound

This paper cites RealCritic: Towards Effectiveness-Driven Evaluation of Language Model Critiques.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution RealCritic: Towards Effectiveness-Driven Evaluation of Language Model Critiques

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:33.542633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:33.542633Z digest=sha256:f9386534addd7671c2fa5ce85b795edc46939c90131d022c056ba0884a1d70de

Observation a8dc5062-e58a-487f-b9fb-ed3066c4ee83 · outbound

This paper cites Instruction tuning for large language models: A survey[J].

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Instruction tuning for large language models: A survey[J]

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:33.594874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:33.594874Z digest=sha256:67b11dcab7191f51ea1cc9045c7f3e25c296888952f24b335a1a99a7ad3a6a12

Observation 7010fb60-e203-4cb0-9de3-d2149442c714 · outbound

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

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:33.689845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:33.689845Z digest=sha256:5cb98dded466f577ade090c82617e45af3040f9f892599b28db4e5252d87097d

Observation 68767acb-d613-48dc-8040-06c001592b38 · outbound

This paper cites Rest-mcts*: Llm self-training via process reward guided tree search[J].

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Rest-mcts*: Llm self-training via process reward guided tree search[J]

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:49:38.191705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T00:49:33.817122Z digest=sha256:0766b47b9831153badfb3c89a1d093cc3ee8681dd153a7689dab21ecdbfbbc3f

Observation 59d93812-8bd4-4b1c-8421-7c8c5550685c · outbound

This paper cites rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:34.039982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:34.039982Z digest=sha256:d211b9f29aa577af25d3fac129b54758d82d1fcb2b1409c90f91992b3fc738f7

Observation ceb9ecb3-8dde-45d3-940b-67aae2af0558 · outbound

This paper cites Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:34.130943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:34.130943Z digest=sha256:e1a2546cd828f5b53ea7144fe0acc24ecbc8a38457d09856850f1b4039deba93

Observation 36a102e8-b41a-4e80-a14f-5d60920718b1 · outbound

This paper cites Qwen2.5-Coder Technical Report.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Qwen2.5-Coder Technical Report

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:34.246060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:34.246060Z digest=sha256:64d9becd3192d0e04c4aea0420b7d90320ab720bc07fcb1dc3778a01f6514efa

Observation 446c9324-f0d0-4177-bda7-6bb2aeb93d18 · outbound

This paper cites Lingma SWE-GPT: An open development- process-centric language model for automated software improvement.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Lingma SWE-GPT: An open development- process-centric language model for automated software improvement

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:49:38.053833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T00:49:34.356267Z digest=sha256:b482b8bb723a8efeb68bd673a8f0bf1291f8f599d78aa69afecd873356250211

Observation 086b29ee-4ca9-48e9-8217-9e66e806fdc8 · outbound

This paper cites Swe-agent: Agent-computer interfaces enable automated software engineering[J].

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Swe-agent: Agent-computer interfaces enable automated software engineering[J]

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:49:37.888583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T00:49:34.427920Z digest=sha256:d618713b06b44598a99a70d37ddd72d9f6d6fb1546adc05009aded522d89f0b6

Observation 7dbd0a19-0c5f-46b6-b891-e4bb2b8f03e5 · outbound

This paper cites SoRFT: Issue Resolving with Subtask-oriented Reinforced Fine-Tuning.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution SoRFT: Issue Resolving with Subtask-oriented Reinforced Fine-Tuning

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:34.508796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:34.508796Z digest=sha256:5c18ced4901a83edb3be3cd09ccccc4fb004d5d22f130ecbc66e6c0fc7003362

Observation fd39c5a9-98ce-4c11-80fc-d0080fab6a6b · outbound

This paper cites SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:34.669625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:34.669625Z digest=sha256:ff7c8452782c3e95db6f572d787ea915b1336fcd8d93ed503320e181afc40e92

Observation f8c7c5cd-7f9d-4b7d-93b6-5aefafe61eb3 · outbound

This paper cites Training Software Engineering Agents and Verifiers with SWE-Gym.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Training Software Engineering Agents and Verifiers with SWE-Gym

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:34.793471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:34.793471Z digest=sha256:63c86c8df39bb3bd0b234951986da08a54514d12a9d8e0edb5cc67267a69367e

Observation 304004a8-5abd-4589-9031-288696c97741 · outbound

This paper cites SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:34.867693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:34.867693Z digest=sha256:bf0cbe117f53d872fdbde661890d115d8a3d60122f6108f398dd01d81260ff1d

Observation c7396434-deae-47ec-a471-9e04d28fe1ee · outbound

This paper cites RethinkMCTS: Refining Erroneous Thoughts in Monte Carlo Tree Search for Code Generation[J].

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution RethinkMCTS: Refining Erroneous Thoughts in Monte Carlo Tree Search for Code Generation[J]

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:34.988070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:34.988070Z digest=sha256:cf2b00fc694eca8a04df53018c1d901ffbbc800f85e5e95fd60148774afc6d0b

Observation f60fe8d7-79ea-4af3-b293-db136d1d1a8c · outbound

This paper cites Agentless: Demystifying LLM-based Software Engineering Agents.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Agentless: Demystifying LLM-based Software Engineering Agents

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:35.090793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:35.090793Z digest=sha256:b5bb812e3b89daf53b228982c890db918b2baa8d17350d66c3fa06d890c4b5ca

Observation 6e07fb15-f3f7-45e2-b4e6-a840ac9ab688 · outbound

This paper cites Rest-mcts: Llm self-training via process reward guided tree search[J].

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Rest-mcts: Llm self-training via process reward guided tree search[J]

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:49:37.702231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T00:49:35.200240Z digest=sha256:558dfaca2de5de80984bd5137cd321ac459b7ff98588ef6bf9ced5ac38c7407e

Observation 31f1aaaa-86d0-4b54-ab5f-612c82a80906 · outbound

This paper cites Swe-agent: Agent-computer interfaces enable automated software engineering.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Swe-agent: Agent-computer interfaces enable automated software engineering

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:49:37.501435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T00:49:35.273464Z digest=sha256:260fa11b2e65463a9733f080568bb9e43ce7a1db173ee013468a376c1d4d79fb

Observation 17f7435c-e95b-405b-970e-ee60d809295d · outbound

This paper cites Openhands: An open platform for ai software developers as generalist agents.//The Thirteenth International Conference on Learning Representations.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Openhands: An open platform for ai software developers as generalist agents.//The Thirteenth International Conference on Learning Representations

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:49:37.347064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T00:49:35.386546Z digest=sha256:52ae4f34662cadaf2657e7f28ef5d1db21d7234d28b76a2ba2988eab07c738f5

Observation cb77cc7e-f70f-44cb-92d3-20305b43d89f · outbound

This paper cites Repository Structure-Aware Training Makes SLMs Better Issue Resolver.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Repository Structure-Aware Training Makes SLMs Better Issue Resolver

Reference 34

Resolution
verified exact
local_arxiv, observed 2026-08-07T00:49:36.476965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T00:49:35.472160Z digest=sha256:857db5c1aa3eceb36cace7e3b1bf7c1d5d8233004ef5d3e7b99902324bbf211a

Observation 43e97a3c-ab94-43da-959b-312333852b63 · outbound

This paper cites Bridging Bug Localization and Issue Fixing: A Hierarchical Localization Framework Leveraging Large Language Models.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Bridging Bug Localization and Issue Fixing: A Hierarchical Localization Framework Leveraging Large Language Models

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:35.536554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:35.536554Z digest=sha256:143b714ac3d943100ceb3aa2992a3687277da4ac68b1678e5f1a33a3393e87e3

Observation 0b40f663-46a7-429a-ae41-d907f88edb21 · outbound

This paper cites OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:35.646586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:35.646586Z digest=sha256:c2fc4d2be01906c9c162568b1f95e7d9fd7a2c829666ea3f680ea3c07f35954c

Observation 02e4352e-6e95-453e-bf01-72a906a47bcc · outbound

This paper cites Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:35.734975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:35.734975Z digest=sha256:6e21b0ecb6b7cf6393b9da50d318500591e0d6522a188dbe52bce5d01d478140

Observation 59767ec8-0ea4-49d7-934b-629c4656807e · outbound

This paper cites A Survey on Data Synthesis and Augmentation for Large Language Models.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution A Survey on Data Synthesis and Augmentation for Large Language Models

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:35.791398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:35.791398Z digest=sha256:d998de01cf8c97688333e7e667ab3e2de4001ed4c727d074a1de6f0c9f9bf881

Observation 788d0b7e-50ec-4b3d-abd9-57c5b5f5784c · outbound

This paper cites Impact of code language models on automated program repair[C]//2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE).

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Impact of code language models on automated program repair[C]//2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:35.857930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:35.857930Z digest=sha256:6d9ae9ff88c7c6011732e2c5b71ef4cbb4f66a139f5cca7a766e58ff332af20c

Observation 2180bd36-4cf5-4b4a-9bdb-32ba3fe129a3 · outbound

This paper cites An empirical evaluation of using large language models for automated unit test generation[J].

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution An empirical evaluation of using large language models for automated unit test generation[J]

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:49:37.210981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T00:49:35.962619Z digest=sha256:985c02fad4c8f956c042061c2218ea1810ca0e97f5930d8023508a5c6f2676d2

Observation c38d15e0-97a0-419d-9bf3-8c5af6c82729 · outbound

This paper cites LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:36.040614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:36.040614Z digest=sha256:d7a0f7bc88ea671c09b5c49e9e15d1d7552c48540507bd5045655a4f501e5915

Observation a14f16df-bf3d-4550-b9f9-0bb4a12efbb7 · outbound

This paper cites Lora: Low-rank adaptation of large language models[J].

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution Lora: Low-rank adaptation of large language models[J]

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:49:37.083461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T00:49:36.115764Z digest=sha256:c8658f5839edb391c19cda58fe79d510398b25f7c58aa7040964d2fa17b3c304

Observation 79f16ac7-51a7-491b-a681-9f29f3eba136 · outbound

This paper cites A Comparative Study between Full-Parameter and LoRA-based Fine-Tuning on Chinese Instruction Data for Instruction Following Large Language Model.

MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution A Comparative Study between Full-Parameter and LoRA-based Fine-Tuning on Chinese Instruction Data for Instruction Following Large Language Model

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-07T00:49:36.194123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:36.194123Z digest=sha256:605cd1ac01251077096f94a84d0ac8154595c838637d2a8493a047b271d4d1f6

Pith citing papers

Observation e9c529d4-39c3-40e0-88fe-4dfdd2219e79 · inbound

From Historical Patches to Repair Plans: Outcome-Conditioned Reasoning for Repository-Level Program Repair cites this paper.

From Historical Patches to Repair Plans: Outcome-Conditioned Reasoning for Repository-Level Program Repair MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-03T06:13:01.236121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T06:13:01.236121Z digest=sha256:16e3a0854ce12c4d637f5136e88cda9676446584b5f037b7fa9edca020a6f6d0