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Paper Citation Record · LEDGER

Federated In-Context LLM Agent Learning

As of 13 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 1 inbound Pith citation observation for arXiv:2412.08054.

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

pith.paper-citation-record.v1
2412.08054 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:20:55.849430Z

measured 13 of 13 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 2026-08-07T05:42:38.760856Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T05:42:38.860234Z

Reference resolution

12 of 12 outbound references displayed

  • verified exact0
  • verified fuzzy3
  • unresolved9
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6aecb592-5ef2-4213-ad46-45b5c9a348e5 · outbound

This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.

Federated In-Context LLM Agent Learning DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-11T18:20:55.793098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:20:55.793098Z digest=sha256:74984338eea88fe0e98841abfa27801fbde64c468f33c88f274813a34ee19eba

Observation 627c7a63-6173-413d-a356-d7099962f4e6 · outbound

This paper cites The Llama 3 Herd of Models.

Federated In-Context LLM Agent Learning The Llama 3 Herd of Models

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-11T18:20:55.799112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:20:55.799112Z digest=sha256:2e30938cc7edfc1cb1b47c3d698d50ec1d56457b3fdb66ac86041c137fda759c

Observation acd873ee-43c0-4a33-8ce5-42fc9e0704b8 · outbound

This paper cites FedPFT: Federated Proxy Fine-Tuning of Foundation Models.

Federated In-Context LLM Agent Learning FedPFT: Federated Proxy Fine-Tuning of Foundation Models

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-11T18:20:55.816993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:20:55.816993Z digest=sha256:bd42891aff8d3f7b01fcd8e6934304bd78a15ebf3a95dbb73c754e8426b8b216

Observation c3415445-0dcb-413d-84d9-9e0fb3747e89 · outbound

This paper cites Counting-Stars: A Multi-evidence, Position-aware, and Scalable Benchmark for Evaluating Long-Context Large Language Models.

Federated In-Context LLM Agent Learning Counting-Stars: A Multi-evidence, Position-aware, and Scalable Benchmark for Evaluating Long-Context Large Language Models

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-11T18:20:55.822964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:20:55.822964Z digest=sha256:44d4afe2bae8b1293446426dac7d53b6da9c37d09e98d29054e6bee288b055ad

Observation 7133fb2f-2567-4950-a6d8-2199b3312123 · outbound

This paper cites ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases.

Federated In-Context LLM Agent Learning ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T18:20:55.829172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:20:55.829172Z digest=sha256:2674535907c11d8a6b4e2be5d3b239dd340254e8c8c2ceaf0a48459eb023a2f5

Observation 8471c832-5d7b-493e-b592-2ac15abf923a · outbound

This paper cites In Bouamor, H.; Pino, J.; and Bali, K., eds., Pro- ceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , 968–979.

Federated In-Context LLM Agent Learning In Bouamor, H.; Pino, J.; and Bali, K., eds., Pro- ceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , 968–979

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:20:56.085029Z

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=pdf_text observed=2026-08-11T18:20:55.834030Z digest=sha256:7fb73c5658d8f4c557fa69102385cc6b61eb2abfe75b53802397cfbafbf582ab

Observation 72537b6f-e5bc-4d66-af24-1ab6f8980d26 · outbound

This paper cites C-Pack: Packed Resources For General Chinese Embeddings.

Federated In-Context LLM Agent Learning C-Pack: Packed Resources For General Chinese Embeddings

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-11T18:20:55.839437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:20:55.839437Z digest=sha256:a6e6603da1186d12ac6f82b1e2d812592751c820873044a244bb346d30e7b31f

Observation c1c79bb2-e898-4f81-9828-aeb03d29210d · outbound

This paper cites In Annual Meeting of the Association of Computational Linguistics 2023, 9963–9977.

Federated In-Context LLM Agent Learning In Annual Meeting of the Association of Computational Linguistics 2023, 9963–9977

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:20:56.064781Z

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=pdf_text observed=2026-08-11T18:20:55.844502Z digest=sha256:873c865da3aa97c5cb16a4124e17fe035cb737dbf9506bbd303b76275e61b9bc

Observation 8b3eb11c-f02a-4ae2-a354-c5f39ea3545e · outbound

This paper cites Federated Learning with Non-IID Data.

Federated In-Context LLM Agent Learning Federated Learning with Non-IID Data

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-11T18:20:55.849430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:20:55.849430Z digest=sha256:83f56ac4eddfc7021ca7dc00d9aea1f1926a452f1e5ffdf34e9cc4e6b60ee38e

Observation 26b6eff9-a2d4-42df-90c0-51bc18130d46 · outbound

This paper cites Dublin, Ireland and Online: Association for Computational Linguistics.

Federated In-Context LLM Agent Learning Dublin, Ireland and Online: Association for Computational Linguistics

Reference 2022

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:20:56.618877Z

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=pdf_text observed=2026-08-11T18:20:55.811398Z digest=sha256:113051074dbb0e672c7c3933fc3311a44817d86dabca86be8697baf86c081773

Observation 18007888-9169-4827-b7a4-62f67b14dc5c · outbound

This paper cites Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models.

Federated In-Context LLM Agent Learning Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-11T18:20:55.805074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:20:55.805074Z digest=sha256:01c9a14940a5dcbc1431fd18a5d6d75ea1c1c4d4b38e90f5ac1ff456b8225bbe

Observation 98035dd5-0125-4f69-a14d-cc0004c189f0 · outbound

This paper cites RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents.

Federated In-Context LLM Agent Learning RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-11T18:20:55.786633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:20:55.786633Z digest=sha256:e823a1349ef69d276927e1a151bf8251aa882660b61bd3745feeb27474c05ff8

Pith citing papers

Observation 4bc713b8-ab39-4e32-b1a0-ee44190aebc3 · inbound

Federated In-Context Learning: Iterative Refinement for Improved Answer Quality cites this paper.

Federated In-Context Learning: Iterative Refinement for Improved Answer Quality Federated In-Context LLM Agent Learning

Reference 33

Resolution
metadata mismatch
local_arxiv, observed 2026-08-07T05:42:38.866255Z

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=pdf_text observed=2026-08-07T05:42:38.760856Z digest=sha256:0b86dc1b79a9a835459e84b0b2b438ff93762812944cdf2a30bac5c3d0e65b13