Paper Citation Record · LEDGER

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2412.15838.

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

pith.paper-citation-record.v1
2412.15838 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:34:55.554751Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T01:32:22.464378Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 68979fad-f720-4082-b6ee-53bd0998d496 · inbound

From System 1 to System 2: A Survey of Reasoning Large Language Models cites this paper.

From System 1 to System 2: A Survey of Reasoning Large Language Models Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback

Reference 284

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:36:24.497327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5960097a-5d40-4e62-b534-0e45e1f6e7d5 · inbound

SafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Constrained Learning cites this paper.

SafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Constrained Learning Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:32:22.467784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e4fbbf22-fdf0-409d-b2e1-5dc8c0824026 · inbound

Generative RLHF-V: Learning Principles from Multi-modal Human Preference cites this paper.

Generative RLHF-V: Learning Principles from Multi-modal Human Preference Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T14:34:55.554751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41965ca6-c991-4de9-88d5-55ec184c603d · inbound

SUDER: Self-Improving Unified Large Multimodal Models for Understanding and Generation with Dual Self-Rewards cites this paper.

SUDER: Self-Improving Unified Large Multimodal Models for Understanding and Generation with Dual Self-Rewards Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T05:30:27.009844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 235f0a51-cc3f-4255-85f3-c40e292c7bf1 · inbound

From Multimodal Perception to Strategic Reasoning: A Survey on AI-Generated Game Commentary cites this paper.

From Multimodal Perception to Strategic Reasoning: A Survey on AI-Generated Game Commentary Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T00:23:22.423350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3313bbb2-a990-4a28-ab9c-bcf0eb042741 · inbound

HKGAI-V1: Towards Regional Sovereign Large Language Model for Hong Kong cites this paper.

HKGAI-V1: Towards Regional Sovereign Large Language Model for Hong Kong Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T17:38:16.405895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98bc5119-0e18-4399-87e6-325f9abdd670 · inbound

A Survey on Training-free Alignment of Large Language Models cites this paper.

A Survey on Training-free Alignment of Large Language Models Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-05T21:18:42.053686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a5b4f03-2e3d-46e9-b251-80b90ffcab23 · inbound

Identifying Topological Invariants of Non-Hermitian Systems via Domain-Adaptive Multimodal Model for Mathematics cites this paper.

Identifying Topological Invariants of Non-Hermitian Systems via Domain-Adaptive Multimodal Model for Mathematics Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:10:55.128834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fc88937f-7680-458d-8545-f77f7945806f · inbound

Identifying Topological Invariants of Non-Hermitian Systems via Domain-Adaptive Multimodal Model for Mathematics cites this paper.

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-21T10:20:00.192149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1ab7cce1-dd4d-47eb-b7be-dc48274b0e9f · inbound

Step-Level Preference Learning for Generative Agents in Social Simulations cites this paper.

Step-Level Preference Learning for Generative Agents in Social Simulations Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T02:00:25.588376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebb96359-7d81-4f7c-a9ed-a2e1029b10b3 · inbound

AgentHPOBench: A Benchmark For Evaluating LLM Agents as Sequential Hyperparameter Optimizers cites this paper.

AgentHPOBench: A Benchmark For Evaluating LLM Agents as Sequential Hyperparameter Optimizers Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-03T03:15:14.295305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.