Paper Citation Record · LEDGER

Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2402.11651.

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

pith.paper-citation-record.v1
2402.11651 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:24:04.530447Z

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

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

2
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 ebce67f4-bfa5-497f-b4d9-cf4dd332b1c7 · inbound

AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning cites this paper.

AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-23T18:48:20.386266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1f11527f-9088-4f73-b740-6982e84933bc · inbound

Towards Adaptive Mechanism Activation in Language Agent cites this paper.

Towards Adaptive Mechanism Activation in Language Agent Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-12T05:09:47.918852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4f2e0d2-7b2f-4c36-a474-aa7acc360373 · inbound

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions cites this paper.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-08T19:07:35.479808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5b0055d-7329-42b5-89b6-d4517c227117 · 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 Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-16T12:24:04.530447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 748cf0a8-fb8c-4516-882a-c84645db6add · inbound

Divide and Conquer: Grounding LLMs as Efficient Decision-Making Agents via Offline Hierarchical Reinforcement Learning cites this paper.

Divide and Conquer: Grounding LLMs as Efficient Decision-Making Agents via Offline Hierarchical Reinforcement Learning Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T14:10:08.942221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6019eb82-e0fa-4fa5-b0a3-d7dce12e2c28 · 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 Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T14:06:56.596041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4321bbf-c7f3-42b4-90a5-8aca749254dc · inbound

PGPO: Enhancing Agent Reasoning via Pseudocode-style Planning Guided Preference Optimization cites this paper.

PGPO: Enhancing Agent Reasoning via Pseudocode-style Planning Guided Preference Optimization Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T11:47:50.812929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73074153-2e5e-44f9-81ac-73f5cfc3c85f · inbound

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

Unleashing Embodied Task Planning Ability in LLMs via Reinforcement Learning Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T21:55:04.444807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69ab1f96-ecc9-437c-a1b1-9e2e3e7828ed · inbound

LLM Priors for ERM over Programs cites this paper.

LLM Priors for ERM over Programs Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-04T09:41:00.977441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a149a689-1aa9-4f62-9159-2f6e36b7fadf · inbound

SpanVLA: Efficient Action Bridging and Learning from Negative-Recovery Samples for Vision-Language-Action Model cites this paper.

SpanVLA: Efficient Action Bridging and Learning from Negative-Recovery Samples for Vision-Language-Action Model Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents

Reference 67

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:56:11.106795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 93fa921f-3737-4600-845a-fcaa19275709 · inbound

On-Policy Self-Evolution via Failure Trajectories for Agentic Safety Alignment cites this paper.

On-Policy Self-Evolution via Failure Trajectories for Agentic Safety Alignment Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:32:24.485251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d957ded0-c6bd-472f-92bc-7d1e7d3f61e1 · inbound

Reversa: A Reverse Documentation Engineering Framework for Converting Legacy Software into Operational Specifications for AI Agents cites this paper.

Reversa: A Reverse Documentation Engineering Framework for Converting Legacy Software into Operational Specifications for AI Agents Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-20T09:03:09.726877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 64847219-317c-41b7-be34-15ef0b0cf9db · inbound

Constraint Tax in Open-Weight LLMs: An Empirical Study of Tool Calling Suppression Under Structured Output Constraints cites this paper.

Constraint Tax in Open-Weight LLMs: An Empirical Study of Tool Calling Suppression Under Structured Output Constraints Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:50:11.241838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 896cc7b9-c227-48b8-b059-40c9543f532e · inbound

Diagnosis Before Recovery: Turning Agent Failures into Selective Self-Correction cites this paper.

Diagnosis Before Recovery: Turning Agent Failures into Selective Self-Correction Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-16T00:33:33.490521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.