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Paper Citation Record · LEDGER

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases

As of 8 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2606.22906.

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

pith.paper-citation-record.v1
2606.22906 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T08:04:16.155374Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

38 of 38 outbound references displayed

  • verified exact11
  • verified fuzzy0
  • unresolved27
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4db8ee82-876b-48fe-83e9-080c09679d16 · outbound

This paper cites Source code summarization in the era of large language models,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Source code summarization in the era of large language models,

Reference 1

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:e9ba91cdc8d8bbd5fe557db3ff6b60c777033b662ed64d7c96737abf0db28f06

Observation c28da664-a4d2-4ef1-8a37-518c35dc5123 · outbound

This paper cites Automatic code generation techniques: A systematic literature review,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Automatic code generation techniques: A systematic literature review,

Reference 2

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:67ec91c3f4edab66d0e590728ff99f58549933ab457b85cec7002d0755a693b7

Observation dc0143ae-ef5d-40e0-aacb-bfa3d7f080ff · outbound

This paper cites A systematic literature review on large language models for auto- mated program repair,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases A systematic literature review on large language models for auto- mated program repair,

Reference 3

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:31adbc6ec6ec7d70d90c3be18f1fc1c93322d6e770e31c21e0fe3d2504594915

Observation 9d47cc5e-50f8-4686-ae58-0b5e4541c725 · outbound

This paper cites A Survey of Large Language Model Agents for Question Answering.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases A Survey of Large Language Model Agents for Question Answering

Reference 4

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arxiv_id, observed 2026-07-04T11:19:50.120181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:8fe051672c9bc781ca69eeab6b08c28328755f2087890c47f275fa7460aa35fb

Observation d3ddb43f-b4a3-48c7-85e5-18b6b905a65c · outbound

This paper cites A review on edge large language models: Design, execution, and applications,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases A review on edge large language models: Design, execution, and applications,

Reference 5

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:06677f6245c840eca037cb25a64ad0147f582169e32487b07ac66e36935c3e92

Observation 8913d9dd-f92a-45c8-95da-dc15ba356e65 · outbound

This paper cites Beyond Code Snippets: Benchmarking LLMs on Repository-Level Question Answering.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Beyond Code Snippets: Benchmarking LLMs on Repository-Level Question Answering

Reference 6

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verified exact
local_arxiv, observed 2026-07-04T11:19:50.123072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:389f8166b9f1a23f9f1a16f548ff90bf3712860333ebd20dda764b0b60712186

Observation f117c29e-9bb8-4672-85c3-feec7c10f8e0 · outbound

This paper cites Archagent: Scalable legacy soft- ware architecture recovery with llms,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Archagent: Scalable legacy soft- ware architecture recovery with llms,

Reference 7

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arxiv_id, observed 2026-07-04T11:19:50.125954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:c71ef0cccfb2232e06a03e8084164e2b227ef41923aa490c080b8ad2c21e17c0

Observation fed4f005-3a4d-4912-aecc-3ec6b6040de6 · outbound

This paper cites Locobench-agent: An interactive benchmark for LLM agents in long-context software engineering.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Locobench-agent: An interactive benchmark for LLM agents in long-context software engineering

Reference 8

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arxiv_id, observed 2026-07-04T11:19:50.110068Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:ef16a65d75180444465cb5c1e15452003aeb5669d17f4b3a379548f73674e7c5

Observation fa3c826a-51b0-41ce-bcfb-befea5fbbec2 · outbound

This paper cites Logicscan: An llm-driven framework for detecting business logic vulnerabilities in smart contracts,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Logicscan: An llm-driven framework for detecting business logic vulnerabilities in smart contracts,

Reference 9

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arxiv_id, observed 2026-07-04T11:19:50.112775Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:8b385b5a3a7feb0d3bb27a736987d2dd13a4563dd066aab8538ff0e63a760c47

Observation 853f89c1-500b-48d8-8406-1d3f48c0cef6 · outbound

This paper cites Missconf: Llm-enhanced reproduction of configuration- triggered bugs,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Missconf: Llm-enhanced reproduction of configuration- triggered bugs,

Reference 10

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:4dd07290c9b0a7185725bc7aa416e3f48b1f9128132cd4a0196a5ef56e518d82

Observation dc2d4f09-f3cc-42e3-9365-0ff141204efd · outbound

This paper cites Make llm a testing expert: Bringing human-like interaction to mobile gui testing via functionality-aware decisions,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Make llm a testing expert: Bringing human-like interaction to mobile gui testing via functionality-aware decisions,

Reference 11

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:770f6a1f2b0b8e7af3c32b51c868b2e26b2ce6a44c161c4b8dbb4dcf4ae09886

Observation a19e600d-d02d-420a-870b-7c11dc2b82c8 · outbound

This paper cites Loogle v2: Are llms ready for real world long dependency challenges?.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Loogle v2: Are llms ready for real world long dependency challenges?

Reference 12

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:04a2f2104aa28d0acbeb52a6b912f5475ad821e89680742850316a14795a6cdf

Observation b2e5e913-9fc1-4380-92e2-cc1267504764 · outbound

This paper cites Depen- deval: Benchmarking llms for repository dependency understanding,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Depen- deval: Benchmarking llms for repository dependency understanding,

Reference 13

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:accbf9552dbcfe458b1f7b524843852a83a0e8f1c27c6840b851fa0dc031d2c4

Observation 8553e1d5-5023-4b6d-b7bd-cc127872b075 · outbound

This paper cites Repomaster: Autonomous exploration and understanding of github repositories for complex task solving,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Repomaster: Autonomous exploration and understanding of github repositories for complex task solving,

Reference 14

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:e519e18369b0000d0fd746df05ab36a99491bf1aeab452d3d67f0cf99205b3a9

Observation 0c247296-3b0a-49c3-bb99-20632827f678 · outbound

This paper cites Dependency matters: Enhancing llm reasoning with explicit knowledge grounding,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Dependency matters: Enhancing llm reasoning with explicit knowledge grounding,

Reference 15

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:7d5e7516376f161851517649819ce13f8c60dddd5c47cd49fcb786f8e307de36

Observation a5e0198a-8fdd-4170-9e50-04b31ff3885c · outbound

This paper cites Gfm-rag: graph foundation model for retrieval augmented generation,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Gfm-rag: graph foundation model for retrieval augmented generation,

Reference 16

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:92e709ca5ad38f41dac0a2afbfdb88486bebbec416a3a754392af18b7d56da02

Observation 0c74675d-45b6-464a-8eec-90218ce5590b · outbound

This paper cites Vector graph-based repository understand- ing for issue-driven file retrieval,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Vector graph-based repository understand- ing for issue-driven file retrieval,

Reference 17

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arxiv_id, observed 2026-07-04T11:19:50.092794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:47280aedb2492ba81a01c88599a2bf62d56112b74a318e6d01b90e6bfd549721

Observation aa63d19a-524c-486b-927f-00535dc6cf37 · outbound

This paper cites Alibaba lingmaagent: Improving automated issue resolution via comprehensive repository exploration,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Alibaba lingmaagent: Improving automated issue resolution via comprehensive repository exploration,

Reference 18

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:b11782313048d817598c74ff796496f2ee663ee314d6c218361ead00db2b6db5

Observation fa01adf0-0c8d-45ee-ac35-713a802f694b · outbound

This paper cites Graphcodeagent: Dual graph-guided llm agent for retrieval-augmented repo-level code generation.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Graphcodeagent: Dual graph-guided llm agent for retrieval-augmented repo-level code generation

Reference 19

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arxiv_id, observed 2026-07-04T11:19:50.096124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:0aab73759ab895c1db76c40cecbba2bc1321053c439b10d29238e395b2594b80

Observation 8203b2c3-b433-4a00-94c7-984fc6bec02f · outbound

This paper cites Multi-agent llms for autonomous workflow orchestration,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Multi-agent llms for autonomous workflow orchestration,

Reference 20

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:9e8b06e9c6b38b39754fdd7d3891cd19f636b75e1646402991f379fada74fe0e

Observation 76c8087c-1daf-4840-8528-3faec1869c02 · outbound

This paper cites Acebench: A comprehensive evaluation of llm tool usage,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Acebench: A comprehensive evaluation of llm tool usage,

Reference 21

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:a8c504a8284bc0b52b5bd592833a81725a7654086243310c5ef44256a2c29594

Observation 25efe286-3f06-414c-a64b-cd22596f21c4 · outbound

This paper cites Advancing llm reasoning generalists with pref- erence trees,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Advancing llm reasoning generalists with pref- erence trees,

Reference 22

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:985e2418986831cd735615ab914cafe8be867734b8b923752b04a61d7a8427a2

Observation b02a5cf4-2482-4cdb-9728-ec4fc699f610 · outbound

This paper cites Agentinit: Initializing llm-based multi-agent systems via diversity and expertise orchestration for effective and efficient collaboration,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Agentinit: Initializing llm-based multi-agent systems via diversity and expertise orchestration for effective and efficient collaboration,

Reference 23

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:961fb683f1cbedae3e2ff4fba155d7dcf5ce8112df9e27260001051da809ca75

Observation 954bcddb-0698-4885-8bb9-081048128a74 · outbound

This paper cites Swe-bench: Can language models resolve real-world github issues?.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Swe-bench: Can language models resolve real-world github issues?

Reference 24

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:adc66d529978e783f011e8950c3b69a528159be3b879e2f023549d83526e4dd6

Observation beba9d5e-a0c4-401b-a650-677383e72b2e · outbound

This paper cites Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

Reference 25

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local_arxiv, observed 2026-07-04T11:19:50.099646Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:5d8943e4011c25148bed4d3a05e78f46800e7d4d5b703d5629f83574b4b524cd

Observation bece1cf9-b43a-40fd-a825-f33c42a64581 · outbound

This paper cites ProcCtrlBench: Evaluating Process-Level Defects and Control Preservation in LLM Coding Agents.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases ProcCtrlBench: Evaluating Process-Level Defects and Control Preservation in LLM Coding Agents

Reference 26

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local_arxiv, observed 2026-07-04T11:19:50.102327Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:c08af2951843fb5f4d947e6f7acdbfa8f7aed7ea5a8bacba8e74e5b25f94a224

Observation c7e6d7b9-f15c-46fe-aca5-edf1e17bef46 · outbound

This paper cites CodeAgent: Enhancing code generation with tool-integrated agent systems for real-world repo-level coding challenges,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases CodeAgent: Enhancing code generation with tool-integrated agent systems for real-world repo-level coding challenges,

Reference 27

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:3c3b43a93b50c5ef1a77bcf0da2c5afa646f5f227827437eca9258fde104a2e8

Observation 72488b0b-5e66-4d0c-a731-967cc770bf82 · outbound

This paper cites An empirical study of retrieval-augmented code generation: Challenges and opportunities,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases An empirical study of retrieval-augmented code generation: Challenges and opportunities,

Reference 28

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:202185f8ff190f96c165a3f0a3ef91afb353f2bc60c17f85190161d98d72749b

Observation ce373daf-6198-4f91-a368-57f0434fa611 · outbound

This paper cites Vul-rag: Enhancing llm-based vulnerability detection via knowledge-level rag,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Vul-rag: Enhancing llm-based vulnerability detection via knowledge-level rag,

Reference 29

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:f0126b174addc9c2452d28422df0f9bed0226a51876dec32919e8d594ead6225

Observation d06426dc-b5f8-4417-9497-00b8cad81893 · outbound

This paper cites Empower- ing graphrag with knowledge filtering and integration,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Empower- ing graphrag with knowledge filtering and integration,

Reference 30

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:bc7a2c4bb3be940b87dd86a5cb79b8695a1565f58e7d55657819c53a836586c2

Observation ffab3ab4-2d73-4aa9-b0f5-4feb9ed2a490 · outbound

This paper cites From Local to Global: A Graph RAG Approach to Query-Focused Summarization.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases From Local to Global: A Graph RAG Approach to Query-Focused Summarization

Reference 31

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local_arxiv, observed 2026-07-04T11:19:50.106200Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:079226012cdcbfd521919ec48d31e4fd4a06282bf09b36f1e7e1dcaead6e7168

Observation fb3225fb-4559-4908-a383-37164da8794d · outbound

This paper cites Reliable graph-rag for codebases: Ast- derived graphs vs llm-extracted knowledge graphs,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Reliable graph-rag for codebases: Ast- derived graphs vs llm-extracted knowledge graphs,

Reference 32

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arxiv_id, observed 2026-07-04T11:19:50.116338Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:441f775fe67afd99f703b999556ef6c3047c359b45b7e21acefa0885c53d1927

Observation 19c7b5fe-3256-4531-b853-84f796773ed9 · outbound

This paper cites Retrievalattention: Accelerating long-context llm inference via vector retrieval,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Retrievalattention: Accelerating long-context llm inference via vector retrieval,

Reference 33

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:cc4e8ce544592a995bf31fd5729f6df2af64c2f5a009299455d18a185c1f2c96

Observation fed31a89-749c-46cb-961f-44780edcfb62 · outbound

This paper cites Burstgpt: A real-world workload dataset to optimize llm serving systems,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Burstgpt: A real-world workload dataset to optimize llm serving systems,

Reference 34

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:b4481e2b1a8c268553ee954408ea9f4fab22581e7fd25c6e6ed96e5428d462a8

Observation d836280a-45de-4b2a-ba93-638fc9589a1b · outbound

This paper cites Ds-mhp: Improving chain-of-thought through dynamic subgraph-guided multi- hop path,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Ds-mhp: Improving chain-of-thought through dynamic subgraph-guided multi- hop path,

Reference 35

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation b2b25693-1245-4ebe-bbef-a825ed50ab45 · outbound

This paper cites Smooth reading: Bridging the gap of recurrent llm to self-attention llm on long- context understanding,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Smooth reading: Bridging the gap of recurrent llm to self-attention llm on long- context understanding,

Reference 36

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Observation c05bb520-18b1-4d62-a67b-bd8a13acf7e0 · outbound

This paper cites Easytool: Enhancing llm-based agents with concise tool instruction,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Easytool: Enhancing llm-based agents with concise tool instruction,

Reference 37

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Observation 3468cb74-8c94-4704-acca-f8fe54b5e34d · outbound

This paper cites Retrieve-plan-generation: An iterative planning and answering frame- work for knowledge-intensive llm generation,.

From Fragments to Paths: Task-Level Context Recovery for Large Industrial Codebases Retrieve-plan-generation: An iterative planning and answering frame- work for knowledge-intensive llm generation,

Reference 38

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