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Paper Citation Record · LEDGER

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback

As of 13 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 1 inbound Pith citation observation for arXiv:2506.02298.

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

pith.paper-citation-record.v1
2506.02298 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:30:57.048733Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:45:59.767986Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T16:46:03.964361Z

Reference resolution

30 of 30 outbound references displayed

  • verified exact0
  • verified fuzzy1
  • unresolved29
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 784323fb-bcbd-4a17-83bc-97e9da8aa4fc · outbound

This paper cites GPT-4 Technical Report.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback GPT-4 Technical Report

Reference 1

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no resolver link, observed 2026-08-07T11:30:54.759555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:54.759555Z digest=sha256:c4501eb5f2a1451f4ff60baca99a4dcbe5fc11531c53abd6e4f86979eb3ea4db

Observation 3856173b-3022-4920-b467-67c7b4a97525 · outbound

This paper cites an unresolved cited work.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback Unresolved cited work

Reference 2

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no resolver link, observed 2026-08-07T11:30:54.810276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:54.810276Z digest=sha256:9abb7b92494778e37051c2af844a67a80c7a01ba53693a09c9d6e548dd469b32

Observation 7722ca1e-4be8-49b1-b8d0-65aaa2cb1e06 · outbound

This paper cites FireAct: Toward Language Agent Fine-tuning.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback FireAct: Toward Language Agent Fine-tuning

Reference 3

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:54.902423Z digest=sha256:8f1c9f4417eabaf6dc2f8874d5440e44e2fd1d83a6888b4568408873af8a862f

Observation 5424dcfb-a1fc-4f34-8475-3f14546a082b · outbound

This paper cites The Llama 3 Herd of Models.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback The Llama 3 Herd of Models

Reference 4

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no resolver link, observed 2026-08-07T11:30:54.978221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:54.978221Z digest=sha256:c6fcd9a56866f0bf9c92c2b5e9eda12954bd920d68db040a82b9c84006e26ca5

Observation b124154d-4a45-4a0c-99f7-8696387b869d · outbound

This paper cites ToolTalk: Evaluating Tool-Usage in a Conversational Setting.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback ToolTalk: Evaluating Tool-Usage in a Conversational Setting

Reference 5

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no resolver link, observed 2026-08-07T11:30:55.058714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:55.058714Z digest=sha256:ac12f3a694e1faecf551723dce9828dbf85c882b5e9618f744fb957c7697ea79

Observation 67f1e5c9-5bc6-452e-b189-7713f76606e3 · outbound

This paper cites an unresolved cited work.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback Unresolved cited work

Reference 6

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raw_fallback, observed 2026-08-07T11:30:58.475493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-07T11:30:55.152532Z digest=sha256:d333154e131ae55528bb3c73c5705077a2d0cb90dc8140900902503a9fcbe5fd

Observation 23cb4f08-1b04-48a3-9c2d-2262cb79a598 · outbound

This paper cites Mixtral of Experts.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback Mixtral of Experts

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:55.239784Z digest=sha256:a8e9f8124bf8bd597026cb42a412821afc924d6df493fd54768243b19a90182b

Observation 9da97f14-6dec-49f7-8e6b-b925afd3ce4a · outbound

This paper cites ToolScan: A Benchmark for Characterizing Errors in Tool-Use LLMs.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback ToolScan: A Benchmark for Characterizing Errors in Tool-Use LLMs

Reference 8

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no resolver link, observed 2026-08-07T11:30:55.307859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:55.307859Z digest=sha256:a2d6350845153632db5d548a23ea7455e0abf56d5b1ffc35697ab4688b6e72ba

Observation f72839fa-1d8f-4c98-b8b6-028e3ec1722f · outbound

This paper cites AgentBench: Evaluating LLMs as Agents.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback AgentBench: Evaluating LLMs as Agents

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:55.404623Z digest=sha256:1632df52c52385a8c85eb19a52ef635ae3c212f81562ee67844e9d8917cd6719

Observation 57c884b9-c144-4f9b-965c-25e88c647e55 · outbound

This paper cites AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:55.494458Z digest=sha256:97525911d01aa78f5440c93701ac3623b6305c09eb615ae2e2003d4da72b2b68

Observation 0033256e-4c6a-44d5-84b0-8f3f55f264d8 · outbound

This paper cites an unresolved cited work.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback Unresolved cited work

Reference 11

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raw_fallback, observed 2026-08-07T11:30:58.236604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-07T11:30:55.576480Z digest=sha256:c01d92870b7757dba9d4fe43ebe1e553915b86276e4619fe387910134b82facd

Observation 07df1bbd-0184-437d-ac61-5330f2269e25 · outbound

This paper cites AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents

Reference 12

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unresolved
no resolver link, observed 2026-08-07T11:30:55.654047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:55.654047Z digest=sha256:7bff1e03ebe0da805853dfc681b28e1fd03d274379a925cf58246c668fb263a8

Observation a6b2997e-2852-4a30-b082-e3494ea93b59 · outbound

This paper cites an unresolved cited work.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback Unresolved cited work

Reference 13

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no resolver link, observed 2026-08-07T11:30:55.726969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:55.726969Z digest=sha256:373ba0c41a699649d2c4129baa71b6dfb6c6b838f7b5dad834f941040ce9a259

Observation e1509e81-cb5b-4906-b6aa-ad0eb7cb1fd3 · outbound

This paper cites an unresolved cited work.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback Unresolved cited work

Reference 14

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unresolved
no resolver link, observed 2026-08-07T11:30:55.820959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:55.820959Z digest=sha256:6a14302f6741446fa40a47e35d864509bbd268b94c6b386686281c2f182df810

Observation 53a71b66-ade2-484c-a333-c54d6e689ae1 · outbound

This paper cites ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:55.882646Z digest=sha256:6027fab79d656e3b369f66f14087518b18f395c087ddfc9f428045c15522d329

Observation 7be69043-74e1-4d4b-9984-ba1366878922 · outbound

This paper cites an unresolved cited work.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback Unresolved cited work

Reference 16

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unresolved
no resolver link, observed 2026-08-07T11:30:55.960177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:55.960177Z digest=sha256:da3c97ee4a67592405c6a25ae0fe0cda4d9fad948fb058c9433fd3b41785d270

Observation 19867723-796a-4262-b848-43cf24b3e5e8 · outbound

This paper cites Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:56.040940Z digest=sha256:8ee7f15b44e67f51d4880139a14748e3efc066d5755cda984cb22c86cc587364

Observation 82ce7764-72c6-4eba-b8f9-e75bc4f81d44 · outbound

This paper cites Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments

Reference 18

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no resolver link, observed 2026-08-07T11:30:56.118004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:56.118004Z digest=sha256:64aa1f137060e546e70869f79fcf4b8da6fc9452d816939ba4450971055a99fd

Observation 6aa6d5e7-af23-4734-a2bf-b0c769644d38 · outbound

This paper cites an unresolved cited work.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback Unresolved cited work

Reference 19

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no resolver link, observed 2026-08-07T11:30:56.192550Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:56.192550Z digest=sha256:d916f0a4726ad4024757fae8e8b1aed999a275006cf5953ee6b6a0d3233ae938

Observation ba39a1fd-be63-4687-abeb-baa85f06085d · outbound

This paper cites OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments

Reference 20

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no resolver link, observed 2026-08-07T11:30:56.287971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:56.287971Z digest=sha256:b3ea7ae3aa83093d002d8104f2a1dfae1c06e2be377e9a2f61180bca7c15772c

Observation e63821cd-ced6-402c-89ee-9737b051f880 · outbound

This paper cites an unresolved cited work.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback Unresolved cited work

Reference 21

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:30:57.896383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-07T11:30:56.369516Z digest=sha256:89c7fed297d19abb31287c909292bbfca4886e1677aa25c7c33f04b9bcd51be9

Observation 5bb1e9e3-be8d-4c99-b788-d47dc6d59e0a · outbound

This paper cites Patil, Ion Stoica, and Joseph E.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback Patil, Ion Stoica, and Joseph E

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:30:57.750331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-07T11:30:56.474114Z digest=sha256:9dfe8b8ae2aa0ad4b6210acf38946292fce51475082ba52e7f43aa82a02e473d

Observation f24f8c81-bc38-4130-9d76-e8d7303ecdda · outbound

This paper cites $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains

Reference 23

Resolution
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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:56.554440Z digest=sha256:6ba14bc693c42d67a0b22df839d2df118d25782809ce82561dba188c3b7bb571

Observation 17537d2f-6127-43fb-9c65-0315d93d996b · outbound

This paper cites an unresolved cited work.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback Unresolved cited work

Reference 24

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unresolved
no resolver link, observed 2026-08-07T11:30:56.611814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:56.611814Z digest=sha256:66a7bd3e7571317c1ad81e543b122e55888d36e50094534c9162dbafa9da8d12

Observation 17d6f05d-44d4-4250-8ca1-a25226fcd2c9 · outbound

This paper cites ActionStudio: A Lightweight Framework for Data and Training of Large Action Models.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback ActionStudio: A Lightweight Framework for Data and Training of Large Action Models

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-07T11:30:56.700112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:56.700112Z digest=sha256:572c6e451e43598d92e7427ec9c428fd5730fa3a69172d9a912dc10021bb38b2

Observation 8b37857e-8851-413d-87d0-9aa357bf5ecf · outbound

This paper cites AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-07T11:30:56.761441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:56.761441Z digest=sha256:8f204b968491bccd0d055ff64e399bef67fa2a94d762f53a3020c58c24c4c3e9

Observation 903d018c-e11c-4446-9e6b-614dd46ea320 · outbound

This paper cites xLAM: A Family of Large Action Models to Empower AI Agent Systems.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback xLAM: A Family of Large Action Models to Empower AI Agent Systems

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-07T11:30:56.817155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:56.817155Z digest=sha256:1a9a02384b8f90382a2c9917abfa0cf7133789dd95cb33c02e492ed7ce3620c2

Observation f80f2062-b766-4401-9e76-ef9e5998702d · outbound

This paper cites WebArena: A Realistic Web Environment for Building Autonomous Agents.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback WebArena: A Realistic Web Environment for Building Autonomous Agents

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-07T11:30:56.886944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:56.886944Z digest=sha256:656936d766853063c9555dd5322f53b37257743795b2278371ce2bfc1a0e2720

Observation a894b1dc-9508-4ff3-b46f-74929a17e9c9 · outbound

This paper cites online" 'onlinestring :=.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback online" 'onlinestring :=

Reference 29

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unresolved
no resolver link, observed 2026-08-07T11:30:56.979211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:56.979211Z digest=sha256:b783556494abcf4baa6adf5c5a0c0e02ed4342f6e7d53a3002b3b5224dbd86da

Observation 89235b0e-808b-44b9-99b6-4ddb72295c6a · outbound

This paper cites write newline.

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback write newline

Reference 30

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unresolved
no resolver link, observed 2026-08-07T11:30:57.048733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:57.048733Z digest=sha256:2be99169702e3aec87b3b99c2137d5c311e68e58f46d85da946167daffbffc51

Pith citing papers

Observation a05bf665-2307-4f98-84fe-e0c110d1fff4 · inbound

MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models cites this paper.

MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback

Reference 12

Resolution
verified exact
local_arxiv, observed 2026-08-06T16:46:04.002763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-06T16:45:59.767986Z digest=sha256:c9cc584d968984f4c00e09cfed8d379d91b64abf5b2838ae192d6d5cdca1260a