Infusion: Methods & Applications

• Infusion is a targeted integration approach that incorporates external knowledge, styles, or treatments into systems while preserving core functions.
• In machine learning, infusion techniques use contextual prompts and refined training strategies to boost performance metrics, as seen in QA and relation extraction tasks.
• In medical and imaging systems, infusion enables precise IV therapy control and high-fidelity reconstructions using diffusion models and advanced feedback architectures.

Infusion denotes a set of methodologies and frameworks across diverse domains—machine learning, medical systems, computer vision, and generative modeling—centered on the concept of introducing, augmenting, or administering targeted content, knowledge, or substances into an existing system. Infusion may refer to the process of incorporating domain-specific knowledge into language or vision models, administering controlled medical treatments through intravenous means, shaping model behavior by editing training data, or transferring novel styles or concepts into pre-trained generative architectures. Across these applications, infusion mechanisms are designed to be efficient, precise, and minimally disruptive to existing systems, providing significant improvements in performance, adaptability, or safety.

1. Infusion in Neural Model Customization and Knowledge Augmentation

Infusion in the context of neural models, particularly large language and vision models, refers to the technique of enhancing or adapting a pre-trained model by introducing targeted external content or knowledge. This process enables models to achieve higher performance in domain-specific or low-resource scenarios without necessitating full pre-training or architectural modifications.

Contextual prompt-based knowledge infusion, as introduced by Vasisht et al. (Vasisht et al., 2024), involves augmenting each example in a supervised training set with retrieved contextual facts from a relevant document corpus. For each instance, named entities are detected, and for each entity, top-K context blocks are extracted from a prebuilt index. The enriched prompt is constructed as $p'_i = s \oplus x_i \oplus C_i$, where $s$ is an instructional prefix, $x_i$ the serialized instance, and $C_i$ the concatenated context. The model is then fine-tuned over these context-enriched prompts via standard cross-entropy, with all parameters updated, yet the backbone (e.g., Flan-T5) architecture remaining unchanged. During inference, only the original (non-contextual) prompt is provided.

Empirical results indicate substantial gains on relation extraction and QA datasets. For example, Hits@1 for KELM-TEKGEN jumps from 0.514 (baseline FT) to 0.825 (FT + context), and Approximate Edit Distance drops from 83.25 to 0.75. Notably, this protocol enables the model to internalize knowledge relevant to rare or non-KG entities and obviates reliance on knowledge graphs or prompt-tuning modules (Vasisht et al., 2024).

Other forms of knowledge infusion include collaborative infusion strategies for stance detection, which align and fuse knowledge snippets from heterogeneous sources (e.g., Wikipedia, Web search) based on semantic similarity using sentence encoders. Lightweight, layered adapters (LoRA, prefix-tuning, attentive fusion) and staged optimizations yield SOTA performance, particularly in data-starved regimes (Yan et al., 2024).

Few-shot task settings leverage sequential fine-tuning: initial pre-fine-tuning on curated reasoning corpora (without modifying network structure) enables models to generalize to novel domains, with demonstrated gains in hate speech detection (AlKhamissi et al., 2022). In open-vocabulary scene graph generation, interaction-centric knowledge infusion employs bidirectional prompts capturing subject–object relations, resulting in more robust pseudo-supervision and improved open-vocabulary alignment, as quantified by R@100 gains (Li et al., 8 Nov 2025).

2. Infusion Training and Denoising Markov Models

Infusion training, in the generative modeling context, is a procedure for learning transition operators that progressively denoise a noise vector into a high-fidelity data sample via a Markov chain. The key innovation, as established by Bordes et al. (Bordes et al., 2017), is the replacement of the training chain with an “infusion chain,” which interpolates between pure model transitions and target sample "injections" at each step:

$$q_i^{(t)}(\tilde z_i^{(t)} | \tilde z^{(t-1)}, x) = (1-\alpha_t) p_i^{(t)}(\tilde z_i^{(t)}|\tilde z^{(t-1)}) + \alpha_t \delta_{x_i}(\tilde z_i^{(t)})$$

with an increasing infusion rate schedule $\alpha_t$ (from $0$ to $1$ over steps $t$), guiding the denoising process towards the data manifold.

The model is trained to maximize an ELBO on $\log p(x)$ using the infusion chain as a proposal, and at deployment, samples are generated by iterating the transition operator starting from noise. This Markov denoising approach yields competitive log-likelihoods and inception scores on vision benchmarks (Bordes et al., 2017), with efficient sample generation achieved in tens of steps (as opposed to thousands in standard diffusion models).

3. Infusion in Data Poisoning and Influence Functions

The Infusion framework in the context of data poisoning leverages scalable influence-function approximations to systematically craft imperceptible perturbations to a small fraction of the training data so as to induce precise behavioral changes in the trained model (Rosser et al., 10 Feb 2026). For a measurement $s$0 (e.g., target class log-probability), the first-order prediction of the effect of data edits is:

$s$1

where $s$2 is the Gauss-Newton Hessian. By optimizing tiny perturbations $s$3 (constrained in norm), Infusion demonstrates that altering as little as 0.2% of the training corpus can match or exceed the behavioral shift produced by explicit probe insertions. These effects can partially transfer across architectures (e.g., ResNet to CNN), highlighting the profound implications for adversarial attacks and interpretability of training data (Rosser et al., 10 Feb 2026).

4. Infusion in Medical Systems: Controlled Administration and Monitoring

In clinical practice, infusion generally refers to the administration of fluids, medication, or nutrients directly into a body compartment (often intravenously), demanding precise control and monitoring for patient safety.

For intravenous (IV) therapy, infusion monitoring using deep learning-based computer vision has been proposed, where a camera captures the drip chamber and a neural network classifies frames as “drop forming” or “drop formed.” State transitions are used for drop counting, from which real-time flow-rate is computed (Giaquinto et al., 2020). The system achieves 100% drop counting accuracy and $s$4 gtt/min error on test videos, is non-invasive, and can be integrated with hospital information systems for remote monitoring.

In critical care, optimization of fluid infusion (e.g., in sepsis) can be formulated as a human-in-the-loop, inverse-classification problem, where an outcome model $s$5 predicts mortality risk, and the infusion vector $s$6 is optimized (subject to deviation budget and safety constraints) to minimize predicted risk. Projected gradient descent is employed, and interpretable dosing guidelines can be extracted, aligning empirical results with clinical intuition (Gupta et al., 2020).

For anesthesia control, a model-based feedback architecture incorporates patient-specific pharmacokinetics (PK) and pharmacodynamics (PD), regulating infusion rate via observed EEG-derived bispectral index (BIS), and applying disturbance rejection through double filtering, innovation-driven correction, and hard constraints on actuation (Liu, 2021). This yields fast, overshoot-free induction and stable maintenance of anesthesia within safety margins.

5. Infusion in Generative Models: Customization and Overfitting Mitigation

Infusion also denotes parameter-efficient customization strategies in diffusion-based generative models. In text-to-image (T2I) diffusion, as in "Infusion: Preventing Customized Text-to-Image Diffusion from Overfitting" (Zeng et al., 2024), the process aims to incorporate user-provided concepts into pre-trained models while preventing both concept-agnostic and concept-specific overfitting. Formally, this is achieved by freezing the cross-attention “where” pathways (attention maps) and learning only residuals in the “what” pathways (value embeddings) for custom tokens:

$s$7

with only $s$8 replaced by $s$9.

Quantitative measures such as the Fisher divergence (for non-customized concept drift) and 2-Wasserstein distance (for mode-collapse in customized concepts) validate that this protocol preserves diversity and pre-trained knowledge with a storage budget of only $x_i$011KB per concept. This approach outperforms DreamBooth, LoRA, and other baselines in both quantitative metrics (CLIP-T, CLIP-I, DINO-I) and user-preference studies (Zeng et al., 2024).

6. Infusion in 3D and Medical Imaging Reconstruction

The concept extends to inpainting and regularization in vision and medical imaging. In 3D Gaussian inpainting, InFusion utilizes a latent diffusion-based depth completion model to guide the initialization of new 3D Gaussians in masked regions, enabling harmonized completion with accurate geometry and texture (Liu et al., 2024). The process includes removal of masked Gaussians, color and depth inpainting, point-cloud unprojection, and fine-tuning of the enhanced Gaussian set, achieving significant improvements in LPIPS, FID, and inference time over prior approaches.

In accelerated MRI reconstruction, INFusion integrates learned diffusion priors as regularizers within implicit neural representation (INR) optimization, solving:

$x_i$1

where $x_i$2 is derived from diffusion-model score matching or denoised perceptual distances (Arefeen et al., 2024). This enables high-fidelity 2D and 3D reconstructions from undersampled measurements, outperforming wavelet and classical INR baselines on NRMSE, PSNR, and SSIM.

7. Implications, Limitations, and Future Directions

Infusion strategies demonstrate substantial benefits for knowledge, style, or concept adaptation while maintaining efficiency and minimal impact on foundational model capacity. In machine learning, their capacity for finely targeted, minimally invasive adjustments raises both security and interpretability considerations; detection and mitigation of stealthy data manipulations remain an open area (Rosser et al., 10 Feb 2026). For generative modeling, decoupling architectural pathways is key to balancing retention of learned priors with targeted customization.

Limitations include potential degradation with unseen or out-of-distribution entities in knowledge-infused LLMs (Vasisht et al., 2024), the need for careful selection and alignment of external knowledge sources (Yan et al., 2024), and challenges in extending parameter-efficient techniques to larger LLMs or discrete token spaces (Zeng et al., 2024, Rosser et al., 10 Feb 2026). In clinical and imaging contexts, reliability, real-time constraints, and adaptability to diverse hardware or patient physiology require continued methodological advances.

Broadly, "infusion" defines a paradigm—a systematic, targeted, minimally disruptive incorporation of new knowledge, behavior, or functionality into complex data-driven or physical systems, with applications spanning knowledge-rich NLP, medical robotics, robust generative modeling, vision inpainting, and high-fidelity biomedical imaging.