# Demystifying the Roles of LLM Layers in Retrieval, Knowledge, and Reasoning

arXiv: https://arxiv.org/abs/2510.02091 · HTML: https://mlsanity.com/paper/2510.02091/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 30 of its 38 references that are in the map.
- First posted on arXiv 2 Oct 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 30 of 30)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://mlsanity.com/paper/2311.12022/) (2023)
- [Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering](https://mlsanity.com/paper/1809.02789/) (2018)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints](https://mlsanity.com/paper/2305.13245/) (2023)
- [The LAMBADA dataset: Word prediction requiring a broad discourse context](https://mlsanity.com/paper/1606.06031/) (2016)
- [MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms](https://mlsanity.com/paper/1905.13319/) (2019)
- [KILT: a Benchmark for Knowledge Intensive Language Tasks](https://mlsanity.com/paper/2009.02252/) (2020)
- [Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth](https://mlsanity.com/paper/2103.03404/) (2021)
- [ShortGPT: Layers in Large Language Models are More Redundant Than You Expect](https://mlsanity.com/paper/2403.03853/) (2024)
- [Layer by Layer: Uncovering Hidden Representations in Language Models](https://mlsanity.com/paper/2502.02013/) (2025)
- [The Unreasonable Ineffectiveness of the Deeper Layers](https://mlsanity.com/paper/2403.17887/) (2024)
- [Compact Language Models via Pruning and Knowledge Distillation](https://mlsanity.com/paper/2407.14679/) (2024)
- [Low-Rank Bottleneck in Multi-head Attention Models](https://mlsanity.com/paper/2002.07028/) (2020)
- [The Curse of Depth in Large Language Models](https://mlsanity.com/paper/2502.05795/) (2025)
- [Spike No More: Stabilizing the Pre-training of Large Language Models](https://mlsanity.com/paper/2312.16903/) (2023)
- [Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN](https://mlsanity.com/paper/2412.13795/) (2024)
- [AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models](https://mlsanity.com/paper/2410.10912/) (2024)
- [Random Fully Connected Neural Networks as Perturbatively Solvable Hierarchies](https://mlsanity.com/paper/2204.01058/) (2022)
- [A deeper look at depth pruning of LLMs](https://mlsanity.com/paper/2407.16286/) (2024)
- [Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanism](https://mlsanity.com/paper/2504.18574/) (2025)
- [Change Is the Only Constant: Dynamic LLM Slicing based on Layer Redundancy](https://mlsanity.com/paper/2411.03513/) (2024)
- [Layer Importance for Mathematical Reasoning is Forged in Pre-Training and Invariant after Post-Training](https://mlsanity.com/paper/2506.22638/) (2025)

## Built on by (most cited first, 15 of 15)

- [When Fewer Layers Break More Chains: Layer Pruning Harms Test-Time Scaling in LLMs](https://mlsanity.com/paper/2510.22228/) (2025)
- [On the Limits of Layer Pruning for Generative Reasoning in Large Language Models](https://mlsanity.com/paper/2602.01997/) (2026)
- [When Context Returns: Toward Robust Internalization in On-Policy Distillation](https://mlsanity.com/paper/2606.11627/) (2026)
- [Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training](https://mlsanity.com/paper/2607.01232/) (2026)
- [MTA: Multi-Granular Trajectory Alignment for Large Language Model Distillation](https://mlsanity.com/paper/2605.01374/) (2026)
- [From One-to-One to Many-to-Many: Dynamic Cross-Layer Injection for Deep Vision-Language Fusion](https://mlsanity.com/paper/2601.10710/) (2026)
- [Not All Layers Need Tuning: Selective Layer Restoration Recovers Diversity](https://mlsanity.com/paper/2602.06665/) (2026)
- [Layer-wise Representation Dynamics: An Empirical Investigation Across Embedders and Base LLMs](https://mlsanity.com/paper/2605.12714/) (2026)
- [OI-Bench: An Option Injection Benchmark for Evaluating LLM Susceptibility to Directive Interference](https://mlsanity.com/paper/2601.13300/) (2026)
- [AdaCultureSafe: Adaptive Cultural Safety Grounded by Cultural Knowledge in Large Language Models](https://mlsanity.com/paper/2603.08275/) (2026)
- [TAPIOCA: Why Task- Aware Pruning Improves OOD model Capability](https://mlsanity.com/paper/2605.14738/) (2026)
- [Understanding helpfulness and harmless tension in reward models](https://mlsanity.com/paper/2606.13209/) (2026)
- [Peeking Inside LLMs: Leveraging Internal Artifacts of LLMs for Enhancing Reliability in Legal Classification](https://mlsanity.com/paper/2606.20929/) (2026)
- [What Does Layer-Importance Reveal About Transformers and State-Space Models?](https://mlsanity.com/paper/2609.16537/) (2026)
- [Attention-Aware Routing: Coupling Routing and Attention in MoEs](https://mlsanity.com/paper/2609.20974/) (2026)

## Abstract (arXiv)

Recent studies suggest that the deeper layers of Large Language Models (LLMs) contribute little to representation learning and can often be removed without significant performance loss. However, such claims are typically drawn from narrow evaluations and may overlook important aspects of model behavior. In this work, we present a systematic study of depth utilization across diverse dimensions, including evaluation protocols, task categories, and model architectures. Our analysis confirms that very deep layers are generally less effective than earlier ones, but their contributions vary substantially with the evaluation setting. Under likelihood-based metrics without generation, pruning most layers preserves performance, with only the initial few being critical. By contrast, generation-based evaluation uncovers indispensable roles for middle and deeper layers in enabling reasoning and maintaining long-range coherence. We further find that knowledge and retrieval are concentrated in shallow components, whereas reasoning accuracy relies heavily on deeper layers -- yet can be reshaped through distillation. These results highlight that depth usage in LLMs is highly heterogeneous and context-dependent, underscoring the need for task-, metric-, and model-aware perspectives in both interpreting and compressing large models.