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Paper Citation Record · LEDGER

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

As of 15 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 2026-08-15T06:32:42.880941+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=sha256:9e25b09c2734e18fe3ad4086d0e4b1ac81f1af279650be01fcb454edd7ba4f58

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=sha256:35e8ee3353f7a9275cf1c7c03cd5199548fcd55257ffa0cc1ff3180a0e39a661

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=sha256:42328a09805458b4502ba7570470fe8350f374732acea8f8996f314232abeabb

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:982c0986e060ead3b9b9f45fe8dd0ff679e1a8bcbe5d12a0a0aacd55414038a2

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=sha256:39559cac35d66052db197d6fa0f4bf1d9be7868a65fab218d44d4aaf67632a4d

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:928639dad11fc0674db83bfcd593cbb62774ddcc5b435dbbe21ab03df477c6fa

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:97380b87fecb0915b92f5a31723497b941661e74143ea5f34ecddd55e40a1149

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:8a1df72f2f345568dc3dca4de1453560db1d3d0248efb433b6518c8b8ad9f746

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:8e6054d688f0b0a9e55902fd0c16a6eca983340534aa65477286be2ca4d1fca1

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=sha256:2ccdaa962e8322f8474e2b7ee920c8405227f255c5bf2fbde6291b47dbeba8fe

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:fa694cc72a36373cc6e5d61574aa2197675c8f9fc68454691b25153ec550ef9f

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=sha256:7c878270be431dc4891d637af4c5e53df8a20a652ca241a73be9ee381b0d60da

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:cc92484ebfe231d0c2588cbf5f67dcfaa06fb189aee83938ed346ea1535ab923

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=sha256:842b020f1ba1599effc54cd038068f4886994efd75878ac360c9b674519cda50

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=sha256:059f1b89c13f50958139c76c4c98a38227df8abbde8a59a7063af84755bb947a

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:ab01b15615da655e447814b64a91b0d2b6340e8c2601ff1ca5ba9f41dc8ba1de

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:ef1a4a5547b98fe2a0aee877822133c7b28bd9ada8dd34aa41ac7aecec70cf3b

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:d1efe410517ccbf762c8a22bff7f09e158e40faa718c5d61b15abb50fe39a7ec

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:0f53308e07097ea91203de91d648a3c4bd5860047be3ddf00138cd8bc78ec3dd

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=sha256:78ef10364a555963a369c192357c5c3a5ea0bb9d41fca65084854f9483eb7a90

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=sha256:83a6d148589dc52a8ccde62b118b6f9ae8c2ff6b6d6eb64bf9f9cc9aa9607aee

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=sha256:5ec5ac2b1b8803e039a09383484f4a21bc72c53a5d71bfb9d22e296df19bcab8

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:f58a2f7e61fe01e2a4ccf0e0b9e76eb99d372b2d099d171137d46050d0746761

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=sha256:9dac4ab5f4ed049eb2ca40c1e0a1a16ab25b9670abc98aa188c71e01fe41ffdc

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:55fd23e3fa961e0d930b8066434469955ed38b46371d96c2be76150953fcf3cb

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:d6ddd7ad14f623372eb909a9fe0fdf89945659f13189d97192405d2d17aad759

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=sha256:8bbabb35a3a42b4322fb22404135634867fe90c6f44d19b0363a7663a68f291a

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=sha256:78976a8de2af6e3ed08f1fa4dc2d923c537dc547704751c253bbd80b77f0a7be

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=sha256:544b4f03a37c915b91dd9a8b35ae818b777f6d768049cf7c5caff68f0f1d2ed2

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=sha256:60327bc40eb5fc90021f1abe4d2d62e2e2f4f82dcd390a5ff4938fdc2d99751a

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:14d243418bbbb3863eb8aedadfa94b6976ff65dd94c7fc9e7278791bdb2ebd91

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:5ddaf50f05a109de2a7e51bca9d06b348b974f981d28cebf26e22d210bd88e0a

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=sha256:52eabafa794ebf2e1cb258952ab35a5d41b60ba33cbaf429501a4d18d0758b59

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:a23e1f4fa4dbe56c23e4cdc395a0f04746a899384240e439d0b404ce8f46d7db

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.

source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:80b6d98a905376760a35a31b80fafc3a3a9e71ac8e923a7d4a9a027badf6118c

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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no resolver link, observed 2026-06-26T08:04:16.155374Z

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:1a2319cb1a5703c7e204a07825c8a3ebf5c7911ad154fbe5dde416e9a4cfc2ed

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

Resolution
unresolved
no resolver link, observed 2026-06-26T08:04:16.155374Z

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:b00a74b339c96d57aa0c5d2c1de36454e7d48d18bb62eab9c87fad781e233b78

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

Resolution
unresolved
no resolver link, observed 2026-06-26T08:04:16.155374Z

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source=pdf_text observed=2026-06-26T08:04:16.155374Z digest=sha256:b70ce3e12a32e3ed4b908239d827a8613b7ab9a52a16ebd131d27dae37b5d299

Pith citing papers

No inbound Pith citation observations are available.