Paper Citation Record · LEDGER

Echo: Learning from Experience Data via User-Driven Refinement

As of 6 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2605.21984.

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

pith.paper-citation-record.v1
2605.21984 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-22T06:37:34.840129Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+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

22 of 22 outbound references displayed

verified exact20
verified fuzzy1
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fe2cd991-c4b3-4371-9fe2-83b7b9328721 · outbound

This paper cites Amershi, D.

Echo: Learning from Experience Data via User-Driven Refinement Amershi, D

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T06:46:12.487277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b1d00ca7-94a1-4783-bcf6-8931ed68a7ae · outbound

This paper cites Experience with GitHub Copilot for Developer Productivity at Zoominfo.

Echo: Learning from Experience Data via User-Driven Refinement Experience with GitHub Copilot for Developer Productivity at Zoominfo

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-22T06:41:10.777816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b58f2b7f-6a1a-4a85-be89-8210b6ee9528 · outbound

This paper cites Efficient Training of Language Models to Fill in the Middle.

Echo: Learning from Experience Data via User-Driven Refinement Efficient Training of Language Models to Fill in the Middle

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-22T06:41:10.801115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e0bccba7-a27d-4e04-8a15-84edc45fbe7c · outbound

This paper cites Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision.

Echo: Learning from Experience Data via User-Driven Refinement Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-22T06:41:10.797126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 85dbb226-d4be-4a22-8545-5ed867158fa0 · outbound

This paper cites Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback.

Echo: Learning from Experience Data via User-Driven Refinement Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-22T06:41:10.792145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a88346b7-d73a-4c27-9ff8-14081bf257b4 · outbound

This paper cites Scaling agent learning via experience synthesis.

Echo: Learning from Experience Data via User-Driven Refinement Scaling agent learning via experience synthesis

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-22T06:41:10.787332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cb86744f-254a-491f-b6f6-05b45c2464ac · outbound

This paper cites Challenges of Real-World Reinforcement Learning.

Echo: Learning from Experience Data via User-Driven Refinement Challenges of Real-World Reinforcement Learning

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-22T06:41:10.782219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2fce3314-36b4-4e47-a22a-e24a1a2bad7f · outbound

This paper cites InCoder: A Generative Model for Code Infilling and Synthesis.

Echo: Learning from Experience Data via User-Driven Refinement InCoder: A Generative Model for Code Infilling and Synthesis

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-22T06:41:10.772913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a26e147f-85b6-4689-84c3-6f2260ba1973 · outbound

This paper cites Textbooks Are All You Need.

Echo: Learning from Experience Data via User-Driven Refinement Textbooks Are All You Need

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-22T06:41:10.768553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 56254546-81d1-48ef-b006-16a0f1e8cdb0 · outbound

This paper cites DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence.

Echo: Learning from Experience Data via User-Driven Refinement DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-05-22T06:41:10.764164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 18d646ff-63ba-4ec5-97a3-a691ee81490d · outbound

This paper cites Don't Stop Pretraining: Adapt Language Models to Domains and Tasks.

Echo: Learning from Experience Data via User-Driven Refinement Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-22T06:41:10.759241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation be025eea-7c21-41ee-bd3e-2b19cfe6c51b · outbound

This paper cites Reinforcement Learning from User Feedback.

Echo: Learning from Experience Data via User-Driven Refinement Reinforcement Learning from User Feedback

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-22T06:41:10.732700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 865bf3de-d5f4-4e28-a103-e8f70b141bbf · outbound

This paper cites Training Compute-Optimal Large Language Models.

Echo: Learning from Experience Data via User-Driven Refinement Training Compute-Optimal Large Language Models

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-22T06:41:10.736913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e0e1dd31-d37e-46ac-a712-c893c4bc90a6 · outbound

This paper cites Scaling Laws for Neural Language Models.

Echo: Learning from Experience Data via User-Driven Refinement Scaling Laws for Neural Language Models

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-22T06:41:10.740988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 607e726f-ec26-4c5c-89e8-a9825a0dd7cf · outbound

This paper cites an unresolved cited work.

Echo: Learning from Experience Data via User-Driven Refinement Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-05-22T06:46:12.483020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9fa53fc0-cc1f-444b-847d-caf1d37b253a · outbound

This paper cites AI-assisted Code Authoring at Scale: Fine-tuning, deploying, and mixed methods evaluation.

Echo: Learning from Experience Data via User-Driven Refinement AI-assisted Code Authoring at Scale: Fine-tuning, deploying, and mixed methods evaluation

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-22T06:41:10.754897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c3bec708-e189-4f44-b25d-bad7a0bf9a9b · outbound

This paper cites Measuring Agents in Production.

Echo: Learning from Experience Data via User-Driven Refinement Measuring Agents in Production

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-06-08T02:03:47.777191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 49dc7d42-1589-413a-8a81-6913141f277c · outbound

This paper cites CoEdIT: Text Editing by Task-Specific Instruction Tuning.

Echo: Learning from Experience Data via User-Driven Refinement CoEdIT: Text Editing by Task-Specific Instruction Tuning

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-22T06:41:10.722739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 753e4b9b-a004-4b50-aa38-a956b7989e00 · outbound

This paper cites CodeBLEU: a Method for Automatic Evaluation of Code Synthesis.

Echo: Learning from Experience Data via User-Driven Refinement CodeBLEU: a Method for Automatic Evaluation of Code Synthesis

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-05-22T06:41:10.727928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 750a3829-d2ae-408b-a863-880325f82d3e · outbound

This paper cites ALFWorld: Aligning Text and Embodied Environments for Interactive Learning.

Echo: Learning from Experience Data via User-Driven Refinement ALFWorld: Aligning Text and Embodied Environments for Interactive Learning

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-22T06:41:10.717779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d6ce63a6-6829-420a-840f-6e9858894bbe · outbound

This paper cites Online Experiential Learning for Language Models.

Echo: Learning from Experience Data via User-Driven Refinement Online Experiential Learning for Language Models

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-06-30T03:17:28.982221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 279c5188-7d12-4d1c-8ec6-32364f4b2214 · outbound

This paper cites The Landscape of Agentic Reinforcement Learning for LLMs: A Survey.

Echo: Learning from Experience Data via User-Driven Refinement The Landscape of Agentic Reinforcement Learning for LLMs: A Survey

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-22T06:41:10.745267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Pith citing papers

No inbound Pith citation observations are available.