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Paper Citation Record · LEDGER

Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability

As of 8 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 1 inbound Pith citation observation for arXiv:2506.15629.

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

pith.paper-citation-record.v1
2506.15629 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:57:40.694596Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T04:30:43.189580Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T04:30:48.754585Z

Reference resolution

14 of 14 outbound references displayed

  • verified exact0
  • verified fuzzy3
  • unresolved9
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0076b84d-9800-459d-bfb0-e891237f7221 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T23:57:40.032102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:57:40.032102Z digest=sha256:f145f3eb0eabdfdfacefb6520e865c36149be2a03c736a52967354a58564f695

Observation 69c3db94-30a5-4a9c-b9b2-b7832dff1810 · outbound

This paper cites The Flan Collection: Designing Data and Methods for Effective Instruction Tuning.

Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability The Flan Collection: Designing Data and Methods for Effective Instruction Tuning

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T23:57:40.162587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:57:40.162587Z digest=sha256:52f2eaaf84c35da885a6a2bb5f5f501d81409556a6b568ddf04a2e3d951558be

Observation 76f9fbe1-bc22-419c-87c7-f008cc97567d · outbound

This paper cites Qwen2.5 Technical Report.

Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability Qwen2.5 Technical Report

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T23:57:40.298421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:57:40.298421Z digest=sha256:1f35cdd5e8d9a84651b6786322be02daea1da89bb02c9c2fd81d37414725eecc

Observation 5812209e-114e-4228-8f7f-ae839b172c2d · outbound

This paper cites In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 294– 305, Abu Dhabi, UAE.

Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 294– 305, Abu Dhabi, UAE

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:57:41.843620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:57:40.359164Z digest=sha256:c7f0c557d3238dac3d93cd4703b3a2626ea24c7d035b310f18ec61f002616c9a

Observation bbcb41cf-a78a-41b6-8a4f-bab3058c77d5 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T23:57:40.424363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:57:40.424363Z digest=sha256:2e0dd5a526a050fd3d0b7366c7babd97103d5f34795ce0d8b4eeb8ccb5e3ea6e

Observation 7f78eb45-bea9-4bee-8e5f-e7f0d1fcce30 · outbound

This paper cites an unresolved cited work.

Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability Unresolved cited work

Reference 11

Resolution
unresolved
raw_fallback, observed 2026-08-06T23:57:41.627954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:57:40.466926Z digest=sha256:5d7990d3330f9be5cb27a0931ea5655452d9e3c8f9ceffaf0858ccd4b978157b

Observation e84dfed7-6421-47f1-b79a-88ffa776968d · outbound

This paper cites Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions.

Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T23:57:40.532308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:57:40.532308Z digest=sha256:e47a1d46cd1cf611c3270a5df301a1608b73df442d31d43d2ace17f85c460625

Observation 78c0ee75-25f8-41ed-934a-03fa27939abd · outbound

This paper cites Due to variations in CoT reasoning, unified evaluation is challenging; therefore, we evaluated using reasoning models.

Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability Due to variations in CoT reasoning, unified evaluation is challenging; therefore, we evaluated using reasoning models

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:57:41.346694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:57:40.694596Z digest=sha256:10b86b9bbe004a028fe74afef0fd35dc025ce400f308e17fcd44d6b1d5616fc9

Observation 4c753744-d9a9-4c67-a2f4-bcc20dfbfd54 · outbound

This paper cites in the specified order.

Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability in the specified order

Reference 2004

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:57:41.496800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:57:40.601860Z digest=sha256:c1a08673592086e55eb1a9250c7ebc63eb76aebfc4837f6d7e10e936a363dbef

Observation 31bd9282-8065-42c7-bf52-79b39fbe1247 · outbound

This paper cites an unresolved cited work.

Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability Unresolved cited work

Reference 2021

Resolution
unresolved
raw_fallback, observed 2026-08-06T23:57:42.071807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:57:39.969355Z digest=sha256:c5301a9d8f89d28ae1c4fddee69631f7d6810f7d5b198a3ede2bb318c88aa466

Observation 6ba05634-7b53-4ab3-a68a-26b8a9270f49 · outbound

This paper cites 2 OLMo 2 Furious.

Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability 2 OLMo 2 Furious

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-06T23:57:40.233595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:57:40.233595Z digest=sha256:fcb0bfc76fedfc8e49a450813fadeabedf6267602511a4313453978b61dc7055

Observation c6cd6dcf-85fe-4af9-a139-7f7494b74107 · outbound

This paper cites Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks.

Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-06T23:57:40.101494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:57:40.101494Z digest=sha256:aab9ce094423973679b9952fb86ce90b894515bb277d775c0e4f8a9dbd871ea1

Observation 4c97b84d-a993-49bd-a8c5-25085914a22a · outbound

This paper cites Interpreting token compositionality in LLMs: A robustness analysis.

Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability Interpreting token compositionality in LLMs: A robustness analysis

Reference 2024

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T23:57:41.174227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:57:39.850564Z digest=sha256:e20941c67883a8cd0976ad7e806cea0c7102ca6ea9c7bf71fe0de6fb286b12c0

Observation fa9898c5-f805-4ab0-923e-25cf1f84c653 · outbound

This paper cites CARMA: Enhanced Compositionality in LLMs via Advanced Regularisation and Mutual Information Alignment.

Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability CARMA: Enhanced Compositionality in LLMs via Advanced Regularisation and Mutual Information Alignment

Reference 2025

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T23:57:41.005243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:57:39.906396Z digest=sha256:3fcb79c14f3e906a6721505beddcb22e2b2ea78b0aee44a8fdf920a51429043e

Pith citing papers

Observation 10a0be47-6ecc-48d0-9f7c-af1da1abf9c6 · inbound

Toward Skill-Native LLMs: Skill Entropy for Benchmarking and Training Long-Horizon Reasoning cites this paper.

Toward Skill-Native LLMs: Skill Entropy for Benchmarking and Training Long-Horizon Reasoning Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability

Reference 42

Resolution
verified exact
local_arxiv, observed 2026-08-06T04:30:48.758172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T04:30:43.189580Z digest=sha256:43bdf3badc290615d888d96eecd2f8e2a7cb3c67d05de91cc1ab14d9a9582f68