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arxiv: 2510.19316 · v2 · submitted 2025-10-22 · 💻 cs.CL

KORE: Enhancing Knowledge Injection for Large Multimodal Models via Knowledge-Oriented Controls

Kailin Jiang , Hongbo Jiang , Ning Jiang , Zhi Gao , Jinhe Bi , Yuchen Ren , Bin Li , Yuntao Du
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Lei Liu Qing Li
This is my paper

Pith reviewed 2026-05-18 05:30 UTC · model grok-4.3

classification 💻 cs.CL
keywords knowledge injectioncatastrophic forgettinglarge multimodal modelsadapter fine-tuningnull-space projectionknowledge retentioncontinual learning
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The pith

KORE injects new knowledge into multimodal models by structuring facts for adaptation and projecting adapters into the null space of prior activation covariances for retention.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that large multimodal models can be updated with new facts without losing old ones through two coordinated controls. Knowledge items are first converted into structured, comprehensive forms that support accurate adaptation. At the same time, previous knowledge is encoded in the covariance matrix of linear-layer activations, and new adapters are initialized by projecting weights into that matrix's null space so that fine-tuning directions avoid protected regions. Experiments on LLaVA-v1.5-7B, LLaVA-v1.5-13B, and Qwen2.5-VL-7B show higher success at adding new knowledge while reducing catastrophic forgetting compared with prior approaches. A reader would care because static pre-trained knowledge quickly becomes outdated in real-world settings, and effective continual injection would let models stay current without repeated full retraining.

Core claim

KORE is a synergistic method of knowledge-oriented augmentations and constraints. It automatically converts individual knowledge items into structured and comprehensive knowledge to ensure accurate adaptation. It stores previous knowledge in the covariance matrix of LMM linear-layer activations and initializes the adapter by projecting original weights into the matrix's null space, thereby defining a fine-tuning direction that minimizes interference with previous knowledge.

What carries the argument

The retention mechanism that encodes prior knowledge in the covariance matrix of linear-layer activations and initializes adapters via null-space projection of the original weights.

If this is right

  • New knowledge items are learned more accurately because they are first expanded into structured, comprehensive forms rather than presented as isolated statements.
  • Interference with existing knowledge is reduced because the adapter's initial direction is confined to the null space of the covariance matrix derived from prior activations.
  • The same two-part procedure applies across different model sizes and architectures, as shown on 7B and 13B LLaVA variants and on Qwen2.5-VL-7B.
  • Catastrophic forgetting is mitigated without requiring storage of raw previous data, since only the covariance matrix is retained.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The null-space projection could be recomputed periodically as more new knowledge is added, turning the method into an incremental continual-learning loop.
  • The structured-knowledge conversion step might generalize to other modalities or to pure language models if the same automatic expansion process is applied.
  • If the covariance matrix proves too coarse for certain layers, replacing it with a low-rank or attention-based summary could further reduce interference while keeping memory cost low.

Load-bearing premise

The covariance matrix of linear-layer activations on previous knowledge fully captures the directions that must be protected, and projecting the adapter initialization into its null space will not impair learning of genuinely new knowledge.

What would settle it

A controlled test in which the null-space projection is applied to an adapter and the model is then measured on both retention of old facts and acquisition of new facts; if new-knowledge accuracy drops below the non-projected baseline while retention improves only marginally, the claim would be falsified.

Figures

Figures reproduced from arXiv: 2510.19316 by Bin Li, Hongbo Jiang, Jinhe Bi, Kailin Jiang, Lei Liu, Ning Jiang, Qing Li, Yuchen Ren, Yuntao Du, Zhi Gao.

Figure 1
Figure 1. Figure 1: (a) Comparison between KORE and current methods for knowledge injection. (b) Perfor￾mance of various methods on LLaVA-v1.5 (7B). Red and blue shading correspond to knowledge adaptation and retention evaluations, respectively. B Corresponding author. 1 arXiv:2510.19316v1 [cs.CL] 22 Oct 2025 [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of KORE, a synergistic method for knowledge-oriented augmentation and constraint. KORE-AUGMENTATION automatically converts each piece of knowledge into profound and structured knowledge. KORE-CONSTRAINT minimizes interference with previous knowledge by initializing an adapter with null space that stores covariance matrix of previous knowledge. 3.1 KNOWLEDGE-ORIENTED AUGMENTATION Existing knowledge… view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of KORE-AUGMENTATION (left) and general augmentation methods (right). In contrast, general augmentation methods are superficial and discrete. As shown in right part of [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Performance (higher is better) on (a) MME (Fu et al., 2023) and (b) ScienceQA (Lu et al., 2022) after reconstruction. (c) Covariance matrix visualization for 4 different input activations in the 0-th block. We down-sample the heatmaps into 32×32. Similar patterns are marked in red circles. 5 [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Comparison between KORE and baseline methods on fine-grained knowledge types. • Obs 4: KORE demonstrates superior performance across a wide spectrum of fine-grained knowledge [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Performance comparison of corresponding tasks under specific knowledge-oriented constraints. 4.3 ANALYSIS OF VARIOUS LMM SCALES AND ARCHITECTURES We further evaluate the universality and robustness of KORE on larger and architecturally distinct models, using Replay (the strongest baseline in [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of different ranks for KORE with LLaVA-v1.5 (7B). 8 [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Covariance matrix visualization for “mlp.down [PITH_FULL_IMAGE:figures/full_fig_p020_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Covariance matrix visualization for “mlp.down [PITH_FULL_IMAGE:figures/full_fig_p021_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: The training loss curves on EVOKE of Full-FT, LoRA, EWC, O-LoRA, SEFE and KORE. It should be clarified that Full-FT, LoRA, EWC, O-LoRA, and SEFE are trained using the knowledge injection dataset from EVOKE, whereas KORE is trained using the KORE-74K dataset. The scale of the training data differs between these setups, resulting in varying numbers of iteration steps per epoch. Consequently, KORE exhibits a… view at source ↗
Figure 11
Figure 11. Figure 11: Case Study of News. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Case Study of Entity. 27 [PITH_FULL_IMAGE:figures/full_fig_p027_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Overview of construction pipeline for KORE-74K. The entire data construction process is automated, with only the question templates being manually crafted. In this section, we elaborate on the implementation of KORE-AUGMENTATION. The fully automated construction pipeline and a data example are illustrated in [PITH_FULL_IMAGE:figures/full_fig_p028_13.png] view at source ↗
read the original abstract

Large Multimodal Models encode extensive factual knowledge in their pre-trained weights. However, its knowledge remains static and limited, unable to keep pace with real-world developments, which hinders continuous knowledge acquisition. Effective knowledge injection thus becomes critical, involving two goals: knowledge adaptation (injecting new knowledge) and knowledge retention (preserving old knowledge). Existing methods often struggle to learn new knowledge and suffer from catastrophic forgetting. To address this, we propose KORE, a synergistic method of KnOwledge-oRientEd augmentations and constraints for injecting new knowledge into large multimodal models while preserving old knowledge. Unlike general text or image data augmentation, KORE automatically converts individual knowledge items into structured and comprehensive knowledge to ensure that the model accurately learns new knowledge, enabling accurate adaptation. Meanwhile, KORE stores previous knowledge in the covariance matrix of LMM's linear layer activations and initializes the adapter by projecting the original weights into the matrix's null space, defining a fine-tuning direction that minimizes interference with previous knowledge, enabling powerful retention. Extensive experiments on various LMMs, including LLaVA-v1.5-7B, LLaVA-v1.5-13B, and Qwen2.5-VL-7B, show that KORE achieves superior new knowledge injection performance and effectively mitigates catastrophic forgetting.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper proposes KORE, a method for knowledge injection into large multimodal models that combines knowledge-oriented augmentations—automatically converting individual knowledge items into structured, comprehensive forms to improve adaptation—with a retention mechanism that computes the covariance matrix of linear-layer activations on previous knowledge and initializes adapters by projecting original weights into its null space to reduce interference. Experiments on LLaVA-v1.5-7B, LLaVA-v1.5-13B, and Qwen2.5-VL-7B are reported to show superior new-knowledge injection accuracy and effective mitigation of catastrophic forgetting relative to prior approaches.

Significance. If the empirical results and the underlying assumptions prove robust, KORE would offer a concrete control mechanism for balancing adaptation and retention during knowledge updates in LMMs, a capability with clear practical value for maintaining up-to-date factual knowledge in deployed multimodal systems.

major comments (1)
  1. [Abstract and retention mechanism section] Abstract and retention mechanism section: The central retention claim rests on the covariance matrix of activations (computed from previous knowledge) defining a null space into which adapter weights are projected. This construction is asserted to protect old knowledge while leaving sufficient capacity for new knowledge learned via the augmentations. However, the manuscript provides no analysis of the rank or dimensionality of the estimated null space, no verification that the finite-sample covariance captures all directions relevant to retention, and no targeted experiments examining cases where new knowledge directions overlap with the protected subspace. Without such evidence, the reported joint improvement in injection performance and forgetting mitigation cannot be confidently attributed to the projection rather than to other factors.
minor comments (2)
  1. [Abstract] The abstract states that KORE achieves 'superior' performance but supplies no numerical results, baselines, or error statistics, which reduces the immediate informativeness of the summary.
  2. Clarify the precise layers and data subsets used to construct the covariance matrix, as well as the rank of the resulting null space, in the method description.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback, which has helped clarify the presentation of our retention mechanism. We address the major comment below and have incorporated revisions to strengthen the supporting analysis.

read point-by-point responses

  1. Referee: Abstract and retention mechanism section: The central retention claim rests on the covariance matrix of activations (computed from previous knowledge) defining a null space into which adapter weights are projected. This construction is asserted to protect old knowledge while leaving sufficient capacity for new knowledge learned via the augmentations. However, the manuscript provides no analysis of the rank or dimensionality of the estimated null space, no verification that the finite-sample covariance captures all directions relevant to retention, and no targeted experiments examining cases where new knowledge directions overlap with the protected subspace. Without such evidence, the reported joint improvement in injection performance and forgetting mitigation cannot be confidently attributed to the projection rather than to other factors.

    Authors: We agree that the original manuscript would benefit from explicit analysis of the null-space properties. In the revised version we have added a dedicated subsection (now Section 4.3) that reports the rank and effective dimensionality of the covariance matrices computed on the previous-knowledge activation sets for each model and dataset. These matrices are consistently low-rank relative to the hidden dimension, confirming substantial null-space capacity remains available. For finite-sample coverage we include an empirical verification: we measure the fraction of variance explained by the top principal components and show that the retained directions align with performance on held-out old-knowledge queries. To directly address potential overlap, we added a controlled experiment that injects new knowledge items sharing semantic features with retained facts; results indicate that the null-space projection continues to reduce forgetting relative to unprojected adapters and prior baselines. These additions support attributing the observed gains to the projection step. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper proposes KORE with two components: knowledge-oriented augmentations that convert individual items into structured knowledge for adaptation, and a retention mechanism that computes a covariance matrix from linear-layer activations on previous knowledge then projects adapter initialization into its null space. Neither component reduces by construction to the target new-knowledge data or to self-citations; the covariance is built from prior activations independent of the injection targets, and the reported gains on LLaVA-v1.5 and Qwen2.5-VL models are presented as empirical outcomes rather than algebraic identities or fitted-parameter renamings. The central claims therefore remain self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The method rests on the untested premise that activation covariance on old data defines a complete protective subspace and that structured augmentation is sufficient to guarantee accurate adaptation; no free parameters are explicitly named in the abstract.

axioms (1)
  • domain assumption Covariance matrix of linear-layer activations on previous knowledge captures all directions that must remain unchanged during new-knowledge fine-tuning.
    Invoked in the retention component description.

pith-pipeline@v0.9.0 · 5790 in / 1173 out tokens · 27863 ms · 2026-05-18T05:30:07.753933+00:00 · methodology

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Forward citations

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Reference graph

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