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Paper Citation Record · LEDGER

Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2406.11176.

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

pith.paper-citation-record.v1
2406.11176 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:18:49.043336Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T13:59:51.871553Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
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  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 51193056-39d4-4b0c-8ed2-3b0bb6ff4a87 · inbound

Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation cites this paper.

Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement

Reference 91

Resolution
unresolved
no resolver link, observed 2026-08-11T11:40:13.881355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:40:13.881355Z digest=sha256:2ff84b18dd42aa59861ef4b733664fa4f03b90413809c95168cc2c586b7518ea

Observation 46d9a94c-906b-4259-a89a-c05b469818a9 · inbound

AgentRefine: Enhancing Agent Generalization through Refinement Tuning cites this paper.

AgentRefine: Enhancing Agent Generalization through Refinement Tuning Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-10T22:24:43.132207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:24:43.132207Z digest=sha256:bac04837b005a2884232f494fe8645cee73d103fbf0ce35d1fd26aee4a8fc477

Observation 592ad90b-a4d2-4c0a-a807-e590420720b9 · inbound

Exploring Expert Failures Improves LLM Agent Tuning cites this paper.

Exploring Expert Failures Improves LLM Agent Tuning Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-16T12:18:49.043336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:18:49.043336Z digest=sha256:3d4004003bdc9a6b4e7222b22c328793574e6d93c4303b8db27d002166f03ea3

Observation 9eb5afc0-64c0-48da-b24b-8ccaca91f770 · inbound

RRO: LLM Agent Optimization Through Rising Reward Trajectories cites this paper.

RRO: LLM Agent Optimization Through Rising Reward Trajectories Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-07T13:51:42.816272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:51:42.816272Z digest=sha256:0b4dcc1fc5a133845080234b54137dfaaf75aad904d793ca65811c7b112cbacc

Observation b0282fc0-9feb-4ac3-a30a-0f6c441ec3e6 · inbound

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

ARIA: Training Language Agents with Intention-Driven Reward Aggregation Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-07T12:09:00.382912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:09:00.382912Z digest=sha256:97b6d4394e8c58237874b9c4d4700b5897482304259a2da4e1465d6da6192c21

Observation b1f80aea-97f8-48a6-95d1-188bcd44a41f · inbound

LLaPipe: LLM-Guided Reinforcement Learning for Automated Data Preparation Pipeline Construction cites this paper.

LLaPipe: LLM-Guided Reinforcement Learning for Automated Data Preparation Pipeline Construction Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-06T16:23:32.671182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:23:32.671182Z digest=sha256:c9046787e8a328c18d41580c26310596ce16b6d4cd210a99fb008d2389269b39

Observation d41d0aa5-2fdd-42f2-b98b-92a9fc215029 · inbound

Source Component Shift Adaptation via Offline Decomposition and Online Mixing Approach cites this paper.

Source Component Shift Adaptation via Offline Decomposition and Online Mixing Approach Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-05T20:35:47.501357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:35:47.501357Z digest=sha256:c8f89d7921f3fa712af5677821c7b04afe821ddd04102cf0a5cb36285a3f4702

Observation b8a50294-3d51-4ab8-b6ae-ac7158af8ec7 · inbound

Leveraging OS-Level Primitives for Robotic Action Management cites this paper.

Leveraging OS-Level Primitives for Robotic Action Management Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement

Reference 69

Resolution
unresolved
no resolver link, observed 2026-08-05T20:38:44.914638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:38:44.914638Z digest=sha256:7fa22604d3676187d53b40a9b1d8d67e0d2ee3ab95a5368186ac80fa1701d799

Observation e141f2e5-c99d-43d4-acb4-8e21cc1a7426 · inbound

MENTOR: Reinforcement Learning via Flexible Teacher-Optimized Rewards for Tool-Use Distillation cites this paper.

MENTOR: Reinforcement Learning via Flexible Teacher-Optimized Rewards for Tool-Use Distillation Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-04T08:55:58.792516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T08:55:58.792516Z digest=sha256:39a3b5d8e207f9ee3aae969eec6731a3bedb73f0037af93c774595c13d86291a

Observation d03d49af-80a8-499d-b8ca-7a88a1d5fd69 · 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 Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-11T20:46:10.629997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-08T08:10:36.579810Z digest=sha256:9f83431efca308d51046e4c36f261437b89767e26b264a5b92e705912c4279b7

Observation 17c7d3ff-d50a-45d5-868b-bfe81a49f6fa · inbound

Rewarding Beliefs, Not Actions: Consistency-Guided Credit Assignment for Long-Horizon Agents cites this paper.

Rewarding Beliefs, Not Actions: Consistency-Guided Credit Assignment for Long-Horizon Agents Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement

Reference 40

Resolution
verified exact
arxiv_id, observed 2026-05-20T05:38:05.519281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-20T05:35:45.084011Z digest=sha256:dcfc86d8b2e560fd082fe755a478c38406d34bc1a419afa56b41cbb84c2fa439

Observation e7d12215-7382-44ad-8576-043a4b10013d · inbound

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

MetaPS: Adaptive Programmatic Strategy Selection for Market Agents Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement

Reference 74

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T08:39:42.661633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-06-26T11:06:28.690956Z digest=sha256:0b9e4d4400aa0f4653c9abbb8842c6fb522b8e9ac5c42cf55655f9f021aa2261

Observation 25f690f8-8941-470c-999a-6c17f4848234 · inbound

Reinforcement Learning without Ground-Truth Solutions can Improve LLMs cites this paper.

Reinforcement Learning without Ground-Truth Solutions can Improve LLMs Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement

Reference 24

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T13:59:51.873179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-06-26T04:47:47.691913Z digest=sha256:32a22d29aaa034490b130103a635816eb7c0e36025c3e6051d8e1819b2510111

Observation b00c4d8e-0e9d-4b43-a145-7500acd36a99 · inbound

Fishing Out Free Riders: Shapley-Based Reward Attribution for Parallel Reasoning via Reinforcement Learning cites this paper.

Fishing Out Free Riders: Shapley-Based Reward Attribution for Parallel Reasoning via Reinforcement Learning Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-01T13:54:21.160424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T13:54:21.160424Z digest=sha256:98cdfda9c60bc27c84ea478a4d7e4a25a120c8b0df2e510bdde0fae2a42e02dd

Observation 4c53a9ff-d3fa-4631-bbf8-a4207098375a · 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 Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement

Reference 64

Resolution
unresolved
no resolver link, observed 2026-07-31T00:46:12.901360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T00:46:12.901360Z digest=sha256:9af3e965bcf9ea060b123cc4d05394936331a431960d6b6442692e423fa8b767