Pith. sign in

Paper Citation Record · LEDGER

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling

As of 9 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2605.28835.

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

pith.paper-citation-record.v1
2605.28835 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-12T23:19:32.665793Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

18 of 18 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved18
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6cd7149e-1b7d-4f2d-a042-69fa82b894f0 · outbound

This paper cites ACM Computing Surveys55(12), 1–38 (2023).

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling ACM Computing Surveys55(12), 1–38 (2023)

Reference 1

Resolution
unresolved
no resolver link, observed 2026-07-12T23:19:32.665793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:19:32.665793Z digest=sha256:0fd40a4f5c6b7cbd285653f011ab32d84842897ea9d1fc5a04bfb216cf4a6070

Observation dd76276f-3176-4654-abc7-356b8f72fd19 · outbound

This paper cites Re-Ex: Revising after Explanation Reduces the Factual Errors in LLM Responses.

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling Re-Ex: Revising after Explanation Reduces the Factual Errors in LLM Responses

Reference 2

Resolution
unresolved
no resolver link, observed 2026-07-12T23:19:32.665793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:19:32.665793Z digest=sha256:583c8b8a4cd2376050c01069ad6cde845e7070fbdf5488cec5d1f76a456fa0c8

Observation c79cf549-dfc2-4ac8-9d8a-a66cdf934110 · outbound

This paper cites Auto-Encoding Variational Bayes.

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling Auto-Encoding Variational Bayes

Reference 3

Resolution
unresolved
no resolver link, observed 2026-07-12T23:19:32.665793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:19:32.665793Z digest=sha256:c11e30da4192552bf02bb76b1307873e2b1f1eda1da7ba33c971ff1544827a65

Observation be67078d-ed4e-44d0-a17e-1d86f7e9a973 · outbound

This paper cites Advances in neural information processing systems 33, 9459–9474 (2020).

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling Advances in neural information processing systems 33, 9459–9474 (2020)

Reference 4

Resolution
unresolved
no resolver link, observed 2026-07-12T23:19:32.665793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:19:32.665793Z digest=sha256:15d3cafa8ea3d2a96b307385016af0fc1f6012ed9db42ee30e6fd1eaa31304b0

Observation d8b8f8d7-fedd-49fc-b0a9-3a03ca3b0481 · outbound

This paper cites In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL).

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL)

Reference 5

Resolution
unresolved
no resolver link, observed 2026-07-12T23:19:32.665793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:19:32.665793Z digest=sha256:22e0ed5eb09cd6b23e6f724553ad33d3faf56ab6c1fe93bd8b5ca3cd0a99449c

Observation 7b7cafc2-1e94-4d31-b5f6-aabb5b427161 · outbound

This paper cites In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL).

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL)

Reference 6

Resolution
unresolved
no resolver link, observed 2026-07-12T23:19:32.665793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:19:32.665793Z digest=sha256:692b1dea0c8b5eaf9bbe40ae535facc589bf7da7c57264928fd41c17cfde0a59

Observation 155c84ba-ce62-4ba8-9381-b362322935f8 · outbound

This paper cites In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP).

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Reference 7

Resolution
unresolved
no resolver link, observed 2026-07-12T23:19:32.665793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:19:32.665793Z digest=sha256:96d6454f5910476ce941ffd35b021b9a14d6dfca505f24ab209af8b90c5a762a

Observation d941d23e-43fa-44ff-9b50-8cd69b5fe928 · outbound

This paper cites Morgan Kaufmann Publishers (1988).

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling Morgan Kaufmann Publishers (1988)

Reference 8

Resolution
unresolved
no resolver link, observed 2026-07-12T23:19:32.665793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:19:32.665793Z digest=sha256:15526e6c757ed79ec8875cd9dcdb8794ab3a42271b7b2736109fb2615859e989

Observation 11c1e448-4d29-4d74-a93f-d48830b50acd · outbound

This paper cites Semantic Communications: Principles and Challenges.

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling Semantic Communications: Principles and Challenges

Reference 9

Resolution
unresolved
no resolver link, observed 2026-07-12T23:19:32.665793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:19:32.665793Z digest=sha256:ab35d298689e2f0913aaf8701907817ef9b08c664609cc897c6a10871e3bfdf8

Observation 0073eb99-03b6-4e89-abb5-e014170d04f5 · outbound

This paper cites The Bell System Techni- cal Journal27(3), 379–423 (1948).

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling The Bell System Techni- cal Journal27(3), 379–423 (1948)

Reference 10

Resolution
unresolved
no resolver link, observed 2026-07-12T23:19:32.665793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:19:32.665793Z digest=sha256:a116c708b4ccf0507da79f0af1eba77e5121afa52bd76d9be45b99651464a539

Observation 00b4ff90-9514-4b5e-8a3f-16d00ac80661 · outbound

This paper cites IEEE Transactions on Information Theory27(5), 533–547 (1981).

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling IEEE Transactions on Information Theory27(5), 533–547 (1981)

Reference 11

Resolution
unresolved
no resolver link, observed 2026-07-12T23:19:32.665793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:19:32.665793Z digest=sha256:5e5a805e35a51c0f6f6813c74dbdd776e2a39d4be53f89cc741d146658cbf423

Observation 0da81f6c-d815-4903-b2da-22133e708882 · outbound

This paper cites LLMs Know What They Need: Leveraging a Missing Information Guided Framework to Empower Retrieval-Augmented Generation.

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling LLMs Know What They Need: Leveraging a Missing Information Guided Framework to Empower Retrieval-Augmented Generation

Reference 12

Resolution
unresolved
no resolver link, observed 2026-07-12T23:19:32.665793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:19:32.665793Z digest=sha256:e6b1f5a27e4689f530edf5a5bfc5c791a494d2433792cbec8f8fad1c8f28f7ea

Observation e54bfd78-9ff0-408a-845b-69519a59aafb · outbound

This paper cites Transactions on Machine Learning Research (2022).

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling Transactions on Machine Learning Research (2022)

Reference 13

Resolution
unresolved
no resolver link, observed 2026-07-12T23:19:32.665793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:19:32.665793Z digest=sha256:410fcc704369c52b18f429bb467584cf9fde0ccceba88ae16a915a0c280abf62

Observation c03c99f1-632e-40ae-b652-248e7240e7cf · outbound

This paper cites Ethical and social risks of harm from Language Models.

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling Ethical and social risks of harm from Language Models

Reference 14

Resolution
unresolved
no resolver link, observed 2026-07-12T23:19:32.665793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:19:32.665793Z digest=sha256:8b9245a5fd9010cce4145b0d350b7f9065f5b512f4a86ca59958cfc3c42afd38

Observation 45f05006-142d-4eea-bc35-f13371cec1a6 · outbound

This paper cites IEEE transactions on signal processing69, 2663–2675 (2021).

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling IEEE transactions on signal processing69, 2663–2675 (2021)

Reference 15

Resolution
unresolved
no resolver link, observed 2026-07-12T23:19:32.665793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:19:32.665793Z digest=sha256:d55f835818173ccf6f917538fae98a321e098c2f50ed54708f41bad016ad1a7a

Observation a10f3d3a-efd1-45d3-b449-e19ac9e7171c · outbound

This paper cites Corrective Retrieval Augmented Generation.

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling Corrective Retrieval Augmented Generation

Reference 16

Resolution
unresolved
no resolver link, observed 2026-07-12T23:19:32.665793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:19:32.665793Z digest=sha256:ed00955a09145290e364b2f2253fd9bb1e67fda387f469dec4f1ecbf58dda96d

Observation 0c4c76f2-7f4a-45f3-939c-668991f4b638 · outbound

This paper cites In: International Conference on Learning Representations (ICLR) (2024).

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling In: International Conference on Learning Representations (ICLR) (2024)

Reference 17

Resolution
unresolved
no resolver link, observed 2026-07-12T23:19:32.665793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:19:32.665793Z digest=sha256:dd21ef89dbc61aedfe3fbd7e082281d00b28fb02cef30134575746f7ce469743

Observation 078773e7-2a3e-4984-8afd-67380afb1db9 · outbound

This paper cites In: Advances in Neural Information Processing Systems (NeurIPS) (2024).

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling In: Advances in Neural Information Processing Systems (NeurIPS) (2024)

Reference 18

Resolution
unresolved
no resolver link, observed 2026-07-12T23:19:32.665793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:19:32.665793Z digest=sha256:6836d877f6a2d5a04e2654a11177b697dc5a48fd1aba603d21d936956f73eff7

Pith citing papers

No inbound Pith citation observations are available.