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Paper Citation Record · LEDGER

Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 37 inbound Pith citation observations for arXiv:2403.02502.

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

pith.paper-citation-record.v1
2403.02502 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

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

measured 37 of 37 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:24:04.513716Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 99ba25da-7f31-497d-a26a-195632d73872 · inbound

Towards Adaptive Mechanism Activation in Language Agent cites this paper.

Towards Adaptive Mechanism Activation in Language Agent Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 42

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no resolver link, observed 2026-08-12T05:09:47.892235Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:09:47.892235Z digest=sha256:851051bc8dcb639b9a65255483ffef21db2c25bf6788aec58f44a25fad56583d

Observation 4ff3e919-ebc8-462a-8095-e3dcdfa07f0c · inbound

Aviary: training language agents on challenging scientific tasks cites this paper.

Aviary: training language agents on challenging scientific tasks Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 48

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no resolver link, observed 2026-08-10T23:07:33.642639Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:07:33.642639Z digest=sha256:b240d793961f53c8b13b01e2b0655b17d80e565bb85f3a69992b7a6644006dcf

Observation 45c31990-362d-4cfc-a117-e735ae0d5ab2 · inbound

Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models cites this paper.

Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 137

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arxiv_id, observed 2026-05-15T21:20:59.285843Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-15T21:20:59.128986Z digest=sha256:ae1bc670e89de49b5bd88d6208ba503c8d399ed8a91ac1d247b344a257ca1e49

Observation 8a6db0ca-3eeb-4964-b447-3330d691c1fd · inbound

Multi-agent KTO: Reinforcing Strategic Interactions of Large Language Model in Language Game cites this paper.

Multi-agent KTO: Reinforcing Strategic Interactions of Large Language Model in Language Game Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 8

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:20:07.192256Z digest=sha256:047b1d9bf4332368824a4f60067fb732063490c2bd55fc9462d330dafd82b8fa

Observation 89c972ec-f841-49fd-9d76-448051e020bc · inbound

Digi-Q: Learning Q-Value Functions for Training Device-Control Agents cites this paper.

Digi-Q: Learning Q-Value Functions for Training Device-Control Agents Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 31

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no resolver link, observed 2026-08-07T20:57:48.516122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T20:57:48.516122Z digest=sha256:bdf1c27588334a9bfaf8d6ae80ab3059f4f32500ffbb20139771a1999177eb0c

Observation e2214870-182c-4fdb-b567-f579675fdf5c · inbound

InstructRAG: Leveraging Retrieval-Augmented Generation on Instruction Graphs for LLM-Based Task Planning cites this paper.

InstructRAG: Leveraging Retrieval-Augmented Generation on Instruction Graphs for LLM-Based Task Planning Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 27

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no resolver link, observed 2026-08-16T12:24:04.513716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:24:04.513716Z digest=sha256:055c9581431db63dac665aa0cef840285a3a299fa725f99225914f860be5b680

Observation 90c3a263-49f4-40b1-b414-616d1062a774 · inbound

From Assistants to Adversaries: Exploring the Security Risks of Mobile LLM Agents cites this paper.

From Assistants to Adversaries: Exploring the Security Risks of Mobile LLM Agents Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 12

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no resolver link, observed 2026-08-15T20:28:15.510735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:28:15.510735Z digest=sha256:30d2e278814685a579ef60c9c8595535f75be66ab0f83e6ff32e176775892fd2

Observation b558bc67-6710-4b5a-8f0b-9704f1cbf36f · inbound

Large Language Models for Planning: A Comprehensive and Systematic Survey cites this paper.

Large Language Models for Planning: A Comprehensive and Systematic Survey Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 218

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no resolver link, observed 2026-08-07T14:12:05.686704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:12:05.686704Z digest=sha256:228be2f25e5205b4d16d0dda288ae3d2616333c67d1a8a376f40ff7e49b2ee6c

Observation 4221ee65-8241-4637-aca8-a09a0be48bff · inbound

Training LLM-Based Agents with Synthetic Self-Reflected Trajectories and Partial Masking cites this paper.

Training LLM-Based Agents with Synthetic Self-Reflected Trajectories and Partial Masking Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 26

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no resolver link, observed 2026-08-07T14:06:56.135534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:06:56.135534Z digest=sha256:fe12d69634d407dc1675a998db23a8c5e057eb6c34eab0706c02d13de7ef7424

Observation e8dd4e21-f13b-468b-b1b5-90ce35bf3098 · inbound

ManiTaskGen: A Comprehensive Task Generator for Benchmarking and Improving Vision-Language Agents on Embodied Decision-Making cites this paper.

ManiTaskGen: A Comprehensive Task Generator for Benchmarking and Improving Vision-Language Agents on Embodied Decision-Making Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 54

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no resolver link, observed 2026-08-07T13:53:59.697480Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:53:59.697480Z digest=sha256:059db7a299a64aeb802634960cf8e33651d8652e449b72ab27731f2acec727b6

Observation 5083a2e4-4759-4062-869c-bc31d46e0826 · inbound

Reinforced Reasoning for Embodied Planning cites this paper.

Reinforced Reasoning for Embodied Planning Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 43

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no resolver link, observed 2026-08-07T13:22:24.912504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:22:24.912504Z digest=sha256:168d0db318f6b5bc759be8f31a429ffee802ea49588c9273fd10580f97c9b972

Observation 4a26fead-1325-49cd-8a15-53c0823dec83 · inbound

ARIA: Training Language Agents with Intention-Driven Reward Aggregation cites this paper.

ARIA: Training Language Agents with Intention-Driven Reward Aggregation Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 12

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no resolver link, observed 2026-08-07T12:09:00.264859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:09:00.264859Z digest=sha256:b80d47c218d421e1d57b6738f123273f6471ce7071875e5a44fc348f0ce0ba25

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

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

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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no resolver link, observed 2026-08-07T11:30:56.040940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:30:56.040940Z digest=sha256:44ce425fb87ca68bb74092603b276eeae77474d59891f0462aef441d7a19af67

Observation ded4a3aa-8da1-4039-b8bd-8f75dbee4a2b · inbound

TO-GATE: Clarifying Questions and Summarizing Responses with Trajectory Optimization for Eliciting Human Preference cites this paper.

TO-GATE: Clarifying Questions and Summarizing Responses with Trajectory Optimization for Eliciting Human Preference Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 25

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no resolver link, observed 2026-08-07T11:19:15.745703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:19:15.745703Z digest=sha256:cdab5f402a0483b65d170e82449ba8a619d90e29f8493bd52d20031111a85c51

Observation b1d07fc9-dfce-45c5-b05b-89e707818a25 · inbound

CheMatAgent: Enhancing LLMs for Chemistry and Materials Science through Tree-Search Based Tool Learning cites this paper.

CheMatAgent: Enhancing LLMs for Chemistry and Materials Science through Tree-Search Based Tool Learning Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 23

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no resolver link, observed 2026-08-07T05:36:38.519062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:36:38.519062Z digest=sha256:d25a6b4d573e10edc5afdd653cdd30664d4d8326f1c8bb1f9bc6818f5668220d

Observation de0dac99-505e-4fda-9f2a-d93267561f42 · inbound

Agent-RewardBench: Towards a Unified Benchmark for Reward Modeling across Perception, Planning, and Safety in Real-World Multimodal Agents cites this paper.

Agent-RewardBench: Towards a Unified Benchmark for Reward Modeling across Perception, Planning, and Safety in Real-World Multimodal Agents Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-06T22:36:43.969795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T22:36:43.969795Z digest=sha256:dfad6c06e2cfc4b08933fe15a35d771f523f59d766dc59dea462c7efdb090e7c

Observation 30ea569b-757d-4043-b333-015c580c87eb · inbound

Unleashing Embodied Task Planning Ability in LLMs via Reinforcement Learning cites this paper.

Unleashing Embodied Task Planning Ability in LLMs via Reinforcement Learning Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 32

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no resolver link, observed 2026-08-06T21:55:03.345545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T21:55:03.345545Z digest=sha256:4981738ff6c56617d8d2f43f06ea09cd31c22fcb70354864aa9114c6776f0f3e

Observation b60306bd-6d8a-499b-a9cb-8c15f2fe02c3 · inbound

LLM Agents Are the Antidote to Walled Gardens cites this paper.

LLM Agents Are the Antidote to Walled Gardens Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 88

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arxiv_id, observed 2026-05-22T00:44:29.334526Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-22T00:41:19.750928Z digest=sha256:4b9e5155201a94d9eefce978b13b8c19a2491e973dc6a2ca491d5cf291c1af5e

Observation 957c6707-7fcb-4195-84a1-e72f0b84820a · inbound

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning cites this paper.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 56

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no resolver link, observed 2026-08-06T12:18:38.403343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T12:18:38.403343Z digest=sha256:5ccfbd87e84682e331f9bc45abf4a085bcb46d92eb13d1e541c6d6ffb4171827

Observation 2a88c7ea-1fe7-4db2-a16c-6ba094467c5d · inbound

RLVMR: Reinforcement Learning with Verifiable Meta-Reasoning Rewards for Robust Long-Horizon Agents cites this paper.

RLVMR: Reinforcement Learning with Verifiable Meta-Reasoning Rewards for Robust Long-Horizon Agents Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 22

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no resolver link, observed 2026-08-06T11:21:26.783454Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:21:26.783454Z digest=sha256:7c8bdad7c1e81837ed57427a5820eb9db396f51ba71dd52e0e3939ca602bc2d1

Observation 5f4bc58d-d935-4fc3-a9bc-4aeb5ac374f0 · inbound

Morae: Proactively Pausing UI Agents for User Choices cites this paper.

Morae: Proactively Pausing UI Agents for User Choices Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 61

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unresolved
no resolver link, observed 2026-08-05T14:22:46.685290Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:22:46.685290Z digest=sha256:96d692251722d9c4399ae07460adc8a8f40e98e3ca89a94ec52e7a41f8dc8cf7

Observation 12da300c-43b1-44f2-98bc-522a405c6a9b · inbound

C-TRAIL: A Commonsense World Framework for Trajectory Planning in Autonomous Driving cites this paper.

C-TRAIL: A Commonsense World Framework for Trajectory Planning in Autonomous Driving Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 11

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arxiv_id, observed 2026-05-13T23:28:26.147204Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-13T23:27:31.054348Z digest=sha256:d883629f82c87cc922b7168fd0a20770e8dc8bad49cb411959c58e5ed3b7ce08

Observation b8590005-2a87-4ab2-9c57-800e77e5cf8d · inbound

Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling cites this paper.

Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 4

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arxiv_id, observed 2026-05-11T07:21:00.765636Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-10T17:12:21.813803Z digest=sha256:9dd6541ea607dfad7581ff849d9136b83a5d6beb912693e59f3f164202ab89aa

Observation 0cfeddd9-b98e-4306-a053-a6e73a32ab6b · inbound

Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling cites this paper.

Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 4

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arxiv_id, observed 2026-05-12T03:01:18.099969Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-12T02:59:19.674646Z digest=sha256:20a737e93520bc7181afe1b569dc24223905fcc5128c3aee86eb8b4f97a85535

Observation d3140644-00be-4e5f-8aca-39e20086c8f1 · inbound

From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents cites this paper.

From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 19

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arxiv_id, observed 2026-05-11T20:46:10.341474Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-05-08T08:10:36.579810Z digest=sha256:33e16e82813ead76a50da6ed41d4d144d018ecb8ee22b3da416a70b64ba13428

Observation 3c960811-bc23-4655-8161-f73b79095dc4 · inbound

SkillEvolver: Skill Learning as a Meta-Skill cites this paper.

SkillEvolver: Skill Learning as a Meta-Skill Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 8

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arxiv_id, observed 2026-05-12T05:46:30.933210Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-12T04:56:14.360454Z digest=sha256:541c2f7fc5b479d08f3f472215d255a75b1cc9987020b3349604f48ba55ec77e

Observation d1c0b134-c71e-4b73-90ff-762b459da0c9 · inbound

Test-Time Deep Thinking to Explore Implicit Rules cites this paper.

Test-Time Deep Thinking to Explore Implicit Rules Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 30

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arxiv_id, observed 2026-06-30T11:54:38.217679Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-06-30T11:52:15.163893Z digest=sha256:10795cc6c1b3611e97940d3fc218c87a079dcc976257cf494c3b7c9c88383032

Observation c280ebd1-32be-4256-a36c-38317269654f · inbound

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments cites this paper.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 82

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arxiv_id, observed 2026-06-29T16:53:40.485548Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-06-29T16:51:36.524194Z digest=sha256:ddca95f4251f1b37a9cf50edbd5088172cab45a1d999c86122b2e3e7f404c210

Observation 465c0c7f-110c-41a7-8dd9-56c9b0b2ffa3 · inbound

Learn from Weaknesses: Automated Domain Specialization for Small Computer-Use Agents cites this paper.

Learn from Weaknesses: Automated Domain Specialization for Small Computer-Use Agents Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 30

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arxiv_id, observed 2026-06-29T14:23:30.930994Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-06-29T14:15:55.180284Z digest=sha256:d78643eb60df30c6daadcd2bf319b5b910dda8fd66bf051dc3c07e206eef123a

Observation 0255e5ca-c9ab-478c-997a-1620b4704b82 · inbound

Agent System Operations: Categorization, Challenges, and Future Directions cites this paper.

Agent System Operations: Categorization, Challenges, and Future Directions Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 13

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verified exact
arxiv_id, observed 2026-07-02T01:16:24.960638Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-06-28T12:14:21.322860Z digest=sha256:da698eabf544ccd7ffc2b38b7473a8964cbbe471fd115f7e10d2e7813e199a7f

Observation df35d8ce-acec-4f5e-9f04-c505f631a1b5 · inbound

HIPIF: Hierarchical Planning and Information Folding for Long-Horizon LLM Agent Learning cites this paper.

HIPIF: Hierarchical Planning and Information Folding for Long-Horizon LLM Agent Learning Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 25

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verified exact
arxiv_id, observed 2026-07-03T05:47:41.660750Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-06-27T13:04:28.758614Z digest=sha256:938cac8a6f572954c48ee7c1a2aead95d05fcb598b51f76675581b0cfae085e0

Observation e8c4f43d-36dc-4651-a696-660148e50156 · inbound

Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application cites this paper.

Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 285

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arxiv_id, observed 2026-06-27T09:50:48.540036Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-06-27T09:46:30.702256Z digest=sha256:2a3e27afa57c77c55932750afa15973cc324adaf4d2aef4b9990d151f009e43f

Observation 4b8f4369-4b4a-45bf-a075-4a9fb3033cb8 · inbound

OPD-Evolver: Cultivating Holistic Agent Evolver via On-Policy Distillation cites this paper.

OPD-Evolver: Cultivating Holistic Agent Evolver via On-Policy Distillation Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 115

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metadata mismatch
arxiv_id, observed 2026-07-03T20:48:56.469629Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-06-27T01:07:49.603969Z digest=sha256:5f8ea22a21aca1a9362770051799f82a5a1cc96903b702f7ad74339c9eee6fbd

Observation a7b3a7f0-21cc-44b3-bd62-fe69125b1956 · inbound

Training the Orchestrator: A Supervised Approach to End-to-End PDDL Planning with LLM Agents cites this paper.

Training the Orchestrator: A Supervised Approach to End-to-End PDDL Planning with LLM Agents Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 16

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metadata mismatch
arxiv_id, observed 2026-07-04T06:59:38.168063Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-06-26T13:56:51.914966Z digest=sha256:6f6a6c8fb3740b2689f6a0365a8553fb07ae0944512e7eece88ff37010095db9

Observation b3c80fb3-719f-4334-8921-b1545b440c14 · inbound

MetaPS: Adaptive Programmatic Strategy Selection for Market Agents cites this paper.

MetaPS: Adaptive Programmatic Strategy Selection for Market Agents Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 72

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T08:39:42.730106Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-06-26T11:06:28.690956Z digest=sha256:2c1160e1eeb8e6901ab22fb03efc12d147a4796098871a4311ca0b6aef59a3f6

Observation 9e11918f-c40e-4fcf-9e2c-4f56c9e8a377 · inbound

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories cites this paper.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 36

Resolution
unresolved
no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T10:33:54.851493Z digest=sha256:60b3e43729deee179f704df1ba7c1727f62368728d2245bcbbb37c37b6d46d43

Observation e4d25527-dbe6-4bf0-8e64-2cf2c4db6b9d · inbound

Leveraging Trajectory Graphs for Pre-Execution Error Diagnosis in Agentic LLM Systems cites this paper.

Leveraging Trajectory Graphs for Pre-Execution Error Diagnosis in Agentic LLM Systems Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 42

Resolution
unresolved
no resolver link, observed 2026-07-31T00:46:11.219569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T00:46:11.219569Z digest=sha256:86ec35d3bf19fbca2aec669c3a11369ca4b40a88931233d012284ee7f5d9dcb9