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Paper Citation Record · LEDGER

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning

As of 7 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 11 inbound Pith citation observations for arXiv:2506.11672.

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

pith.paper-citation-record.v1
2506.11672 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:09:31.323766Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:40:20.613277Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T23:26:23.175930Z

Reference resolution

29 of 29 outbound references displayed

  • verified exact1
  • verified fuzzy9
  • unresolved19
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e7deeb41-7e8f-4733-b52e-5c013f3821f3 · outbound

This paper cites MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 6

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:09:29.465777Z digest=sha256:79e7915497e24432d87e4f724b299e909ade2feb27ac2442f31347f344614653

Observation 927f035b-2bee-4582-9dcb-55fb6ccceaf5 · outbound

This paper cites Higher Layers Need More LoRA Experts.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning Higher Layers Need More LoRA Experts

Reference 7

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no resolver link, observed 2026-08-07T04:09:29.607588Z

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source=pdf_text observed=2026-08-07T04:09:29.607588Z digest=sha256:6e0303cd134efa64091e0ca576d72f2d65b5dea8a65ae2d7b2c644e72101eaea

Observation d121950c-74a0-4677-b63c-8878a46a361e · outbound

This paper cites an unresolved cited work.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning Unresolved cited work

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 8a19a04d-3c44-432e-894f-9a99e5135f38 · outbound

This paper cites Llaca: Multimodal large language continual assistant.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning Llaca: Multimodal large language continual assistant

Reference 11

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source=pdf_text observed=2026-08-07T04:09:29.983904Z digest=sha256:4db349d8e4b5d142cc7ce6f01dbe8ae03b59ff08f82af57ca96b028237ef4f43

Observation e8070477-22a8-4078-9e16-777e1355326e · outbound

This paper cites Alphalora: Assigning lora experts based on layer training quality.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning Alphalora: Assigning lora experts based on layer training quality

Reference 12

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raw_fallback, observed 2026-08-07T04:09:33.555302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 5fb42835-dcd5-4050-ba1c-2ce4760570f3 · outbound

This paper cites an unresolved cited work.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning Unresolved cited work

Reference 13

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raw_fallback, observed 2026-08-07T04:09:33.372968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 1c889673-1bb4-41c0-8cde-b067de7deca9 · outbound

This paper cites ConPET: Continual Parameter-Efficient Tuning for Large Language Models.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning ConPET: Continual Parameter-Efficient Tuning for Large Language Models

Reference 16

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source=pdf_text observed=2026-08-07T04:09:30.379735Z digest=sha256:8cecdd9a01a1620397bd4a54c10d67e979d0e0e86ff13dfa5b5f1c65b7023ed8

Observation d75023ed-f923-46e5-a9c8-6d56d4c6dcf6 · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 17

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T04:09:30.458721Z digest=sha256:19345590f67d6806193368a8c925a06075be4e794906079e5a6567ae8c77db04

Observation cced0ed5-a749-4d15-8551-4987389ecbdc · outbound

This paper cites Orthogonal subspace learning for language model continual learning.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning Orthogonal subspace learning for language model continual learning

Reference 18

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T04:09:30.535440Z digest=sha256:db396965deafb04e374b20f8ba664ebe704e924f3974e6809dd64f0314f8da39

Observation 6e7dc6d7-574c-4760-bc1e-89969a74e542 · outbound

This paper cites SMoLoRA: Exploring and Defying Dual Catastrophic Forgetting in Continual Visual Instruction Tuning.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning SMoLoRA: Exploring and Defying Dual Catastrophic Forgetting in Continual Visual Instruction Tuning

Reference 19

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source=pdf_text observed=2026-08-07T04:09:30.636693Z digest=sha256:51f9fbdbec9b314c84ac8dd92d540cb26de380fa18b87d92678cf921ab2f9657

Observation 83585115-ece6-457b-a812-ab0a24d95450 · outbound

This paper cites Beyond Anti-Forgetting: Multimodal Continual Instruction Tuning with Positive Forward Transfer.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning Beyond Anti-Forgetting: Multimodal Continual Instruction Tuning with Positive Forward Transfer

Reference 20

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no resolver link, observed 2026-08-07T04:09:30.693896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:09:30.693896Z digest=sha256:992ad9dda4118b82296efb80c49270661ef4f70b0dcc3e325e1acea6e6c2ea3d

Observation 2193e504-0396-4d27-8390-f69c30c0412f · outbound

This paper cites InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Reference 21

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no resolver link, observed 2026-08-07T04:09:30.755791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:09:30.755791Z digest=sha256:5d4e578b136dafa5cd7acef7980d5b106067ff51d83095c4d60d9d9a613702c9

Observation 177289e8-494c-4046-b319-752a30d0124e · outbound

This paper cites By combining LLMs with multimodal encoders, they support tasks such as image captioning and visual question answering.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning By combining LLMs with multimodal encoders, they support tasks such as image captioning and visual question answering

Reference 24

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verified fuzzy
raw_fallback, observed 2026-08-07T04:09:32.826530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 1626102d-d880-42fc-950f-e5e22c476ccf · outbound

This paper cites Model expansion increases capacity to handle new tasks while preserving prior information.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning Model expansion increases capacity to handle new tasks while preserving prior information

Reference 25

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verified fuzzy
raw_fallback, observed 2026-08-07T04:09:32.667867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:09:31.001568Z digest=sha256:94352161d821200a99f78d5ee4d52d26b8d9ea208c11b69cbfbe6379d43b18c4

Observation e5d4cb06-093d-446e-b7ed-41d1b198ed25 · outbound

This paper cites Recent research has extended the Mixture of Experts (MoE) framework (Jacobs et al., 1991; Shazeer et al.,.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning Recent research has extended the Mixture of Experts (MoE) framework (Jacobs et al., 1991; Shazeer et al.,

Reference 26

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verified fuzzy
raw_fallback, observed 2026-08-07T04:09:32.512818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 25e0306e-ecb4-41c8-9ea2-40608c224f17 · outbound

This paper cites These models are called Mixture of LoRA Experts (MoLE).

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning These models are called Mixture of LoRA Experts (MoLE)

Reference 27

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verified fuzzy
raw_fallback, observed 2026-08-07T04:09:32.377985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:09:31.172018Z digest=sha256:ae1b0c8afb41c1791293b4c6d1c62e75512f68ac363f42241d04a81583a28a65

Observation 7f46e54b-5a69-4de2-aa27-be8b47dcba01 · outbound

This paper cites Recently, curriculum learning is also widely adopted in LLMs’ pretraining process, e.g., Kimi K1.5 (Team et al., 2025), DeepSeek-Prover-V2 (Ren et al., 2025), Seed-Coder.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning Recently, curriculum learning is also widely adopted in LLMs’ pretraining process, e.g., Kimi K1.5 (Team et al., 2025), DeepSeek-Prover-V2 (Ren et al., 2025), Seed-Coder

Reference 28

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation fa9b2bf0-68be-4265-b477-0dabdce1c0c0 · outbound

This paper cites an unresolved cited work.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning Unresolved cited work

Reference 128

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation ac58083a-d904-4748-a9b7-e61c4db7b964 · outbound

This paper cites PMoE: Progressive Mixture of Experts with Asymmetric Transformer for Continual Learning.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning PMoE: Progressive Mixture of Experts with Asymmetric Transformer for Continual Learning

Reference 1991

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source=pdf_text observed=2026-08-07T04:09:29.805992Z digest=sha256:563e816c3b55e85a279013bbbbee64871449143fff402fe33e6fd7fc8e071dbe

Observation f6282684-f70b-4a05-9150-df55278f0b47 · outbound

This paper cites adaptability and its capacity to maintain performance on previous tasks while learning new ones.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning adaptability and its capacity to maintain performance on previous tasks while learning new ones

Reference 2014

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raw_fallback, observed 2026-08-07T04:09:31.660983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:09:31.323766Z digest=sha256:829d0edc35287b25a2c49d05cac8775ba6e9eb14889a73b6ddf970643f9f3445

Observation 83566a4e-e947-4855-b2e7-067d96b1a93f · outbound

This paper cites DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition

Reference 2017

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Observation 4c35ef6f-fd57-458f-a913-65de4f2c18d9 · outbound

This paper cites Qwen2.5-VL Technical Report.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning Qwen2.5-VL Technical Report

Reference 2018

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Observation 7b61919e-e07b-46fa-95d6-ddb0cac35d78 · outbound

This paper cites Progressive Neural Networks.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning Progressive Neural Networks

Reference 2019

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Observation fbbd6491-6ad2-4997-b957-51fb5f8e4fab · outbound

This paper cites Continual Instruction Tuning for Large Multimodal Models.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning Continual Instruction Tuning for Large Multimodal Models

Reference 2020

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Observation c172d1c2-8a7b-4d46-9e93-fe69f94abed5 · outbound

This paper cites X., and Wen, J.-R.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning X., and Wen, J.-R

Reference 2021

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verified fuzzy
raw_fallback, observed 2026-08-07T04:09:33.714485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:09:29.880662Z digest=sha256:7c8a836880fd883969f27c78dc861657866e93489f86d4bde3d8961b49cba9fc

Observation 50355626-aa5a-4faf-a6bd-96aecdeefd26 · outbound

This paper cites Continual LLaVA: Continual Instruction Tuning in Large Vision-Language Models.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning Continual LLaVA: Continual Instruction Tuning in Large Vision-Language Models

Reference 2022

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation b79c439d-6923-4cc1-91c7-8ce6369cbcdf · outbound

This paper cites Loramoe: Alleviating world knowledge forgetting in large language models via moe-style plugin.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning Loramoe: Alleviating world knowledge forgetting in large language models via moe-style plugin

Reference 2023

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verified fuzzy
raw_fallback, observed 2026-08-07T04:09:33.876278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:09:29.109997Z digest=sha256:3358f7ad0321ff9650365a223c3cc3be7f34291c5a52abac6b73b55d7049f7e4

Observation aa43eaf6-836a-4db9-adae-2533a8bb54bf · outbound

This paper cites Improving Multi-Modal Learning with Uni-Modal Teachers.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning Improving Multi-Modal Learning with Uni-Modal Teachers

Reference 2024

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no resolver link, observed 2026-08-07T04:09:29.309827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:09:29.309827Z digest=sha256:d58c21617a5bbf54f51070877bdb918bf5b88d7cb8dc1ef1cc98480ee6bd8ccf

Observation 71c6daac-25de-4194-8bc4-e686916df6a1 · outbound

This paper cites Multimodal continual graph learning with neural architecture search.

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning Multimodal continual graph learning with neural architecture search

Reference 2025

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verified fuzzy
raw_fallback, observed 2026-08-07T04:09:34.020074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:09:28.696572Z digest=sha256:868150c2f2d501efce183967f82882b7aa05f482f6175e36801c7eace1a7d695

Pith citing papers

Observation 96d8380d-dc7a-4d0d-83b4-111658ec0acc · inbound

Continual Learning for Generative AI: From LLMs to MLLMs and Beyond cites this paper.

Continual Learning for Generative AI: From LLMs to MLLMs and Beyond Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning

Reference 57

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:40:20.613277Z digest=sha256:dbb6747ee42a964919588553ae1fc845c264bbf418bedf2f3a0b85df79d1f9f7

Observation 971d14c7-de6a-4142-bf7b-6283eb85a2bc · inbound

PASs-MoE: Mitigating Misaligned Co-drift among Router and Experts via Pathway Activation Subspaces for Continual Learning cites this paper.

PASs-MoE: Mitigating Misaligned Co-drift among Router and Experts via Pathway Activation Subspaces for Continual Learning Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning

Reference 2025

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no resolver link, observed 2026-08-03T09:44:13.374266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:44:13.374266Z digest=sha256:2e4bf37a6b8de9a3da74eba8dbac4b6710e005f6c31d567240e6e1ff481ae800

Observation 54e375dc-27cc-42f8-af73-c99c6c1b6e2b · inbound

CRAFT: Forgetting-Aware Intervention-Based Adaptation for Continual Learning cites this paper.

CRAFT: Forgetting-Aware Intervention-Based Adaptation for Continual Learning Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-11T18:36:09.042884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-08T14:57:29.592305Z digest=sha256:c58f5841ce2a7ebe3d5f828115df6a22a7eba158e4d6e9a026e6dd7597ddaaf8

Observation df8d1a75-16c8-49b9-9b46-9adea920b554 · inbound

CRAFT: Forgetting-Aware Intervention-Based Adaptation for Continual Learning cites this paper.

CRAFT: Forgetting-Aware Intervention-Based Adaptation for Continual Learning Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning

Reference 5

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verified exact
arxiv_id, observed 2026-05-11T05:05:57.181381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-11T00:52:39.062539Z digest=sha256:2590d4fdd319e07da3b5dce7915ffbff252fdf19dac1c829f39f2d842680983d

Observation 1c806293-3537-4128-8293-0431fb11c850 · inbound

Hystar: Hypernetwork-driven Style-adaptive Retrieval via Dynamic SVD Modulation cites this paper.

Hystar: Hypernetwork-driven Style-adaptive Retrieval via Dynamic SVD Modulation Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-12T07:06:35.281948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-12T03:45:05.990528Z digest=sha256:41e3104f7efde82e0f7e3b3d125cf357c7beff67fcf1bb6e688edc669093b5ad

Observation 48b126a2-e8e8-466c-aa10-ea07180495b4 · inbound

Dynamic Cross-Modal Prompt Generation for Multimodal Continual Instruction Tuning cites this paper.

Dynamic Cross-Modal Prompt Generation for Multimodal Continual Instruction Tuning Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-12T07:21:25.225128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-12T03:27:45.784361Z digest=sha256:b1637b14fcadaf1393ee3e94a98af2232002f9c7b9f524e292f2485d35ba3e53

Observation ef3cc7d0-93ad-4226-bea9-fbffb6f64d77 · inbound

PEAM: Parametric Embodied Agent Memory through Contrastive Internalization of Experience in Minecraft cites this paper.

PEAM: Parametric Embodied Agent Memory through Contrastive Internalization of Experience in Minecraft Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning

Reference 1

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T16:43:39.979877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-29T16:42:13.713749Z digest=sha256:35c2412eeae3a3ecc7f62f83f520abdbe9c72f04b25a5c7442a5379f6fcd8316

Observation 753c9023-14f2-4186-8f1f-3d64cbe04325 · inbound

CRAM: Centroid-Routing and Adaptive MoE for Multimodal Continual Instruction Tuning cites this paper.

CRAM: Centroid-Routing and Adaptive MoE for Multimodal Continual Instruction Tuning Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning

Reference 73

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T23:26:23.177973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-06-28T14:18:11.452378Z digest=sha256:71302aa1d527944110684013cddaae9854c049cf6cfa718df81ff5992e9c159c

Observation 13048f2e-c9f0-43cf-9f29-d3120394c49e · inbound

ProtoAda: Prototype-Guided Adaptive Adapter Expansion and Geometric Consolidation for Multimodal Continual Instruction Tuning cites this paper.

ProtoAda: Prototype-Guided Adaptive Adapter Expansion and Geometric Consolidation for Multimodal Continual Instruction Tuning Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning

Reference 73

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T22:36:17.949192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-06-28T15:09:00.320198Z digest=sha256:06bee27193616a5f47c5e1992ddc1fca7573daf9772499ba8f207077024b9729

Observation cd576287-5c4c-4e2a-aad9-7f730f077f24 · inbound

Towards Continual Motion-Language Agents: LoRA Variants for Incremental Motion Understanding and Generation cites this paper.

Towards Continual Motion-Language Agents: LoRA Variants for Incremental Motion Understanding and Generation Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-06-30T08:04:28.950688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-30T07:36:50.034451Z digest=sha256:eb0190558e8220c8f3e9576eeaca7faf85080d895250db4aa9c5f2ee39039acc

Observation 94eb0d07-fab4-4489-b1f9-ea37fd3b5c32 · inbound

CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detection cites this paper.

CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detection Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning

Reference 19

Resolution
unresolved
no resolver link, observed 2026-07-12T06:01:58.407747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T06:01:58.407747Z digest=sha256:4743166a970935b72d7144f2da0566a0be727b9b578db56e74d0b77d4e9e731a