Learning Capability Enhancement (LCE)

• Learning Capability Enhancement (LCE) is a set of dynamic strategies designed to improve multi-modal learning by redistributing learning signals to tackle imbalanced data and missing modalities.
• It employs global update factors and batch-level state quantification, such as gradient reweighting and Shapley value computations, to systematically balance learning across tasks.
• Empirical benchmarks on diverse datasets demonstrate that LCE enhances system performance, delivering measurable gains in domains like segmentation and emotion recognition.

Learning Capability Enhancement (LCE) refers to the set of algorithmic, architectural, and systems-level strategies designed to systematically improve the effectiveness, adaptability, and efficiency of learning processes in computational models and artificial intelligence systems. In contemporary research, LCE encompasses dynamic mechanisms for balancing learning across multiple modalities or tasks, optimizing resource allocation based on observed learning progress, and enhancing overall system robustness—particularly in scenarios with uneven data availability, task difficulty, or resource constraints.

1. Foundations and Motivation

The concept of Learning Capability Enhancement emerges from the recognition that standard learning systems often encounter bottlenecks due to imbalanced data distributions, fixed resource allocation, or insufficient adaptability. In multi-modal or dynamic environments, some modalities or tasks may be underrepresented or suffer from higher missing rates, which leads to inconsistent learning progress and possible representational degradation. Absent explicit balancing, this can result in under-performance of critical sub-systems and dramatically reduce the overall efficacy of a learning model.

Traditional algorithmic approaches, such as naive dataset-level balancing or uniform update scheduling, fail to account for the intrinsic and extrinsic variation in data availability and utility. Modern LCE strategies instead deploy dynamic, data-driven corrective mechanisms at both the global (dataset) and local (sample/batch) levels, enabling the system to self-assess, reweight, and adapt learning signals to meet real-time performance and capability demands.

2. Mathematical Formulation and Core Mechanisms

Recent frameworks operationalize LCE using explicit weighting factors that modulate gradient updates for different learning modules or modalities. In the context of multi-modal learning under missing data regimes (Zhao et al., 12 Oct 2025), LCE is realized via:

• Global Update Factor (𝒜ₘ): For modality $m$, the global factor

$\mathcal{A}_{m} = N / \sum_{n} \mathcal{I}_{n,m}$

where $N$ is the sample count and $\mathcal{I}_{n,m}$ indicates the presence of modality $m$ in sample $n$. This factor, normalized across modalities, ensures that modalities with higher missing rates (lower $\sum_n \mathcal{I}_{n,m}$) receive proportionally stronger gradient signals when available, thereby rectifying update frequency imbalances at scale.

• Batch/Instance-level Learning State (𝓑ₘ): At the sample or mini-batch granularity, the marginal contribution of each modality is quantified via a Shapley value calculation,

$$\phi_{m} = \sum_{n=1}^{N_{b}} \sum_{\mathcal{S} \subseteq \mathcal{M}_n\setminus\{m\}} \frac{(|\mathcal{S}|! (|\mathcal{M}_n|-|\mathcal{S}|-1)!)}{|\mathcal{M}_n|!}(v_n(\mathcal{S} \cup \{m\}) - v_n(\mathcal{S}))$$

The capability gap is then determined via

$\Delta_{m} = \mathcal{U}_{m} - \phi_{m}$

where $\mathcal{U}_{m}$ is the unimodal upper bound. The normalized learning-state factor is

$$\mathcal{B}_m = \frac{\mathbb{I}\{\Delta_m > 0\}\sum_{n=1}^{N_b} \mathcal{I}_{n,m}\Delta_{m}}{\sum_{n=1}^{N_b} \mathcal{I}_{n,m}}$$

This design ensures that under-performing modalities are identified in situ, and their optimization signal—the loss or gradient—is adaptively amplified according to their current learning gap.

The integration of these factors into the overall optimization objective, such as in weighted loss formulations,

$$\mathcal{L}_{\mathrm{single}} = \sum_{m} \mathcal{A}_{m} \mathcal{B}_{m} \cdot \rho(f_{\mathrm{dec}}^{m}(\mathbf{h}_m), \mathcal{Y}),$$

ensures direct supervision is both globally and locally balanced to promote capability enhancement.

3. Interaction with Representation and Systemic Adaptation

In advanced multi-component systems, LCE is often coupled with Representation Capability Enhancement (RCE) (see (Zhao et al., 12 Oct 2025)) and auxiliary completion tasks. The dynamic factors learned and maintained by the LCE mechanism (𝒜, 𝓑) serve to reweight not only primary loss terms but also auxiliary tasks such as cross-modal completion or subset prediction. This synergy addresses two central objectives:

• Robust feature learning, even with arbitrary or missing modalities,
• Prevention of overfitting to dominant or more frequent modalities, ensuring semantic transfer and redundancy across the system.

This integrated approach collectively improves both learning dynamics and representational robustness, critical for resilient deployment in open-world settings with nonuniform data availability.

4. Empirical Evidence and Benchmarking

Extensive benchmarking on multi-modal learning datasets, such as nuScenes (urban segmentation), BraTS2020 (brain tumor segmentation), IEMOCAP (emotion recognition), and AudiovisionMNIST (audio-visual digit recognition), substantiates the effectiveness of LCE (Zhao et al., 12 Oct 2025). Empirical findings indicate:

Configuration	IoU (nuScenes)	Accuracy (IEMOCAP)
RCE only	lower	lower
RCE + LCE (𝒜,𝓑)	+2%	+1%
State-of-the-art SOTA	typically <	typically <

The observed performance improvements are attributed to the dynamic rebalancing of learning signals, wherein under-represented, but potentially informative, modalities are prevented from lagging in representational quality.

5. Broader Impact and Theoretical Significance

LCE extends beyond multi-modal learning to general scenarios with resource heterogeneity, missing data, and dynamic environments. Its core design principle—continuous, data-driven rediscovery and rebalancing of system learning progress—has implications for constructing adaptive, scalable, and reliable AI models.

Notably:

• Compressed string processing uses LCE-like mechanisms at the algorithmic level, enabling sublinear queries, dynamic updates, and real-time adaptability without decompressing data (Nishimoto et al., 2015, Nishimoto et al., 2016).
• Organizational and neural learning systems reflect LCE at the meta-level, where systemic adaptability and the ability to integrate new information without stagnant components are prerequisites for effective generalization and robustness (Nguyen et al., 2016, Wang et al., 2020).
• Dynamic environments (autonomous driving, streaming analytics) benefit critically from LCE to avoid catastrophic forgetting and dominance of spurious signals (Hu et al., 14 Dec 2024, Wei et al., 19 Jan 2025).

6. Challenges, Limitations, and Future Directions

Current LCE frameworks rely on precise quantification of learning gaps and representative metrics for both global and local learning progress. Accurately estimating unimodal upper bounds (𝒰ₘ) and decomposing marginal contributions in large-scale non-linear systems remains computationally intensive for high-dimensional or real-time data streams.

Potential directions include:

• Efficient estimation of modality importance and performance ceiling without exhaustive unimodal pretraining.
• Extension of LCE principles to hierarchical, lifelong, and reinforcement learning settings, where learning progress is more complexly structured.
• Unifying LCE with probabilistic or uncertainty-driven frameworks to further refine dynamic capacity allocation under partial observability or heavy-tailed missingness.

LCE remains a central and evolving component of the broader paper of adaptive and scalable learning systems, underpinning the quest for systems capable of robust generalization and continual adaptation in the presence of real-world data imperfections and resource imbalance.