Improving LLM Predictions via Inter-Layer Structural Encoders
arXiv:2603.22665v1 Announce Type: new Abstract: The standard practice in Large Language Models (LLMs) is to base predictions on the final-layer token representations. Recent studies, however, show that intermediate layers encode substantial information, which may contain more task-relevant features than the final-layer representations alone. Importantly, it was shown that for different tasks, different layers may be optimal. In this work we introduce Inter-Layer Structural Encoders (ILSE), a powerful structural approach to learn one effective representation from the LLM's internal layer representations all together. Central to ILSE is Cayley-Encoder, a mathematically grounded geometric encoder that leverages expander Cayley graphs for efficient inter-layer information propagation. We evaluate ILSE across 13 classification and semantic similarity tasks with 9 pre-trained LLMs ranging from 14 million to 8 billion parameters. ILSE consistently outperforms baselines and existing approache
arXiv:2603.22665v1 Announce Type: new Abstract: The standard practice in Large Language Models (LLMs) is to base predictions on the final-layer token representations. Recent studies, however, show that intermediate layers encode substantial information, which may contain more task-relevant features than the final-layer representations alone. Importantly, it was shown that for different tasks, different layers may be optimal. In this work we introduce Inter-Layer Structural Encoders (ILSE), a powerful structural approach to learn one effective representation from the LLM's internal layer representations all together. Central to ILSE is Cayley-Encoder, a mathematically grounded geometric encoder that leverages expander Cayley graphs for efficient inter-layer information propagation. We evaluate ILSE across 13 classification and semantic similarity tasks with 9 pre-trained LLMs ranging from 14 million to 8 billion parameters. ILSE consistently outperforms baselines and existing approaches, achieving up to 44% improvement in accuracy and 25% in similarity metrics. We further show that ILSE is data-efficient in few-shot regimes and can make small LLMs competitive with substantially larger models.
Executive Summary
This article introduces Inter-Layer Structural Encoders (ILSE), a novel framework that enhances LLM predictions by aggregating information from intermediate layers rather than relying exclusively on final-layer representations. Leveraging a Cayley-Encoder grounded in expander Cayley graphs, ILSE aggregates inter-layer data efficiently. Empirical evaluation across 13 tasks with multiple LLMs shows significant gains—up to 44% in accuracy and 25% in similarity metrics—indicating a robust, data-efficient alternative to conventional methods. The work addresses a critical gap in LLM utilization by exploiting underused intermediate representations.
Key Points
- ▸ ILSE aggregates intermediate layer information instead of solely final-layer representations
- ▸ Cayley-Encoder uses expander Cayley graphs for efficient propagation
- ▸ Empirical results show up to 44% accuracy improvement and 25% in similarity metrics
Merits
Innovation
ILSE introduces a geometrically grounded, mathematically rigorous structural approach that fills a critical gap in LLM representation selection by leveraging underutilized intermediate layers.
Effectiveness
The empirical validation demonstrates substantial performance gains across diverse models and tasks, validating the practical impact of ILSE.
Demerits
Generalizability Concern
While results are compelling, the evaluation is limited to pre-trained models and specific tasks; real-world applicability in dynamic or domain-specific contexts remains untested.
Expert Commentary
The introduction of ILSE marks a substantive advancement in LLM representation engineering. By shifting focus from the conventional final-layer paradigm to a structural aggregation model, the authors address a persistent bottleneck in representation utilization. The Cayley-Encoder’s use of expander Cayley graphs is particularly noteworthy—it offers both mathematical elegance and computational efficiency, aligning theoretical rigor with practical performance. Moreover, the ability to empower smaller models against larger counterparts through layer-aggregation is a democratizing effect that resonates deeply with open-source and resource-constrained communities. While the current evaluation is commendable, the next frontier will involve longitudinal studies on dynamic task evolution and cross-domain adaptation, which may reveal subtler limitations or broader applicability. Overall, ILSE represents a pivotal step toward more intelligent, adaptive LLM architectures.
Recommendations
- ✓ Integrate ILSE into open-source LLM repositories as a modular enhancement layer for improved prediction accuracy.
- ✓ Fund comparative studies on ILSE performance across domain-specific LLMs and evolving task paradigms to validate long-term efficacy.
Sources
Original: arXiv - cs.CL
