2025 Model Evolution White Paper: Post-Transformer Era of Long Context, Sparsity, and Architecture

Preface:
In 2023-2024, we were accustomed to asking: "How many billions of parameters does this model have?"
By 2025, the question has become: "How many books can this model digest?" and "How many cents does it cost to infer 1 million tokens?"

This shift in questioning marks the transition of Large Language Models (LLMs) from the stage of "Brute Force Aesthetics" to "Precision Engineering". The marginal utility of parameter size is diminishing, while architectural efficiency, context length, and inference costs have become the new battlegrounds. This article deeply analyzes the three core trends of the AI model technology stack in 2025 from first principles.

Chapter 1: The Context Revolution: From 128k to "Infinite"

If parameter size determines a model's "IQ," then the Context Window determines its "memory" and "workbench size." In 2025, million-level (1M+) token contexts have become standard, and ten-million-level (10M+) are on the way.

1.1 Core Technologies Breaking Length Limits

Why couldn't previous models read long books? Because the time complexity of the Transformer's Self-Attention mechanism is $O(N^2)$. Doubling the input length quadruples the computational load and GPU memory usage.

Mainstream models in 2025 have broken this curse through the following technologies:

1.1.1 Ring Attention

This is a victory for distributed training.

Principle: Splitting long sequences into multiple Blocks, distributing them across different GPUs for attention calculation, and passing intermediate results (Key/Value blocks) between GPUs.
Mathematical Beauty: It allows us to process theoretically infinite sequences without approximating attention scores, limited only by the cluster's total GPU memory.
Engineering Implementation: Mainstream frameworks (like Megatron-LM, DeepSpeed) now have built-in Ring Attention, enabling 10M context training on clusters with thousands of H100s.

1.1.2 Evolution of RoPE Scaling (YaRN and LongRoPE)

Positional Encoding is key for the model to distinguish between the "first word" and the "tenth word."

NTK-Aware Scaled RoPE: Shined in 2024, achieving extrapolation by dynamically adjusting the base of rotation angles.
LongRoPE (2025): Through non-uniform interpolation strategies, it expanded the context window by over 8 times without degrading short-text performance. It solved the industry problem where "fine-tuning for long text degrades short text capabilities."

1.2 "Needle in a Haystack" and "Lost in the Middle"

Having a long window doesn't mean having long logic.

Lost in the Middle Phenomenon: Early long-context models tended to remember the beginning and end but ignore information in the middle.
2025 Solutions:
1. Synthetic Data: Using Synthetic Data to specifically construct training samples where "the answer is hidden in the middle."
2. Hierarchical Compression: Introducing mechanisms similar to human "long-term memory" and "working memory." Compressing historical information into a Summary Vector, retaining only key indices.

Chapter 2: Sparsity: The Total Dominance of MoE

In 2025, apart from very specific research purposes, few enterprises start training a Dense model from scratch. Mixture of Experts (MoE) rules both open source and closed source worlds with its extreme cost-performance ratio.

2.1 The Economics of MoE

Dense Model: Training a 100B model requires activating 100B parameters for every token during inference. Expensive and slow.
MoE Model: Total parameters might reach 500B, but it consists of 64 small experts. During inference, each token activates only 2 experts (Active Parameters approx. 15B).
Result: You possess the knowledge reserve of a 500B model but pay the inference electricity bill of a 15B model.

2.2 New MoE Variants in 2025

2.2.1 DeepSeek-V3 and Fine-grained Experts

Traditional MoE had only 8 or 16 experts. The architecture proposed by DeepSeek slices experts much finer (e.g., 256 experts) and introduces Shared Experts.

Shared Expert: Regardless of routing, a few fixed experts are always activated. They are responsible for capturing general grammatical and logical knowledge.
Routed Experts: Responsible for extremely vertical domain knowledge (like "Baroque Architecture History" or "Python Async Programming").

2.2.2 Lossless Load Balancing

MoE fears "expert hotspots." If 90% of requests flood to the same expert, the advantage of parallelism is lost.

Auxiliary Loss: Previously, to force load balancing, an auxiliary loss function was added, which hurt model performance.
Expert-choice Routing: Letting experts pick tokens, rather than tokens picking experts. This thoroughly solved the load imbalance problem.

Chapter 3: War of Architectures: Is Transformer Truly Invincible?

Transformer has ruled the AI world for nearly 8 years (since 2017). In 2025, challengers finally moved from labs to industry. Linear Attention and State Space Models (SSM) are showing potential to surpass Transformers in specific domains.

3.1 The Rise of Mamba and SSM

Mamba (State Space Models) is the most competitive challenger.

Core Advantage: Inference VRAM usage is $O(1)$ (constant), not Transformer's $O(N)$ (growing with length). This means Mamba can infer infinite sequences without running out of memory.
2025 Progress:
- Jamba (Joint Attention Mamba): A hybrid architecture launched by AI21 Labs. Using Mamba for 80% of the bottom layers to handle massive context, and Attention for the top 20% to enhance "recall capability." This hybrid architecture is proven to be the current optimal solution for cost-performance.
- Code Generation Applications: Since code relies on extremely long contexts (entire repositories), SSM architectures surpassed Transformers of the same parameter size in code completion tasks for the first time.

3.2 RWKV: The Renaissance of RNN

RWKV (Receptance Weighted Key Value) proved that RNNs (Recurrent Neural Networks), empowered by parallel training technologies, are still formidable.

Advantages: Extremely low inference VRAM usage, extremely fast token generation speed, and fully open source.
Ecosystem: In 2025, the RWKV community has already produced models of 14B and even 30B, becoming the preferred architecture for edge devices (phones, Raspberry Pi).

Chapter 4: Collapse and Reconstruction of Evaluation Systems

With the improvement of model capabilities, traditional benchmarks (like MMLU, GSM8K) have failed. Current models easily score 90+ on these leaderboards, suffering from severe score inflation and "question brushing" (Data Contamination).

4.1 New Generation Evaluation Standards of 2025

4.1.1 Dynamic Benchmarking

LiveCodeBench: Extracting test questions from weekly new problems on LeetCode and GitHub. It's impossible for models to have seen these in training data (because they were published yesterday).
Weight Increase of Chatbot Arena: Blind testing based on real human perception (Elo rating) has become the only recognized "Gold Standard."

4.1.2 Scenario-based Long Text Evaluation (Needle In A Haystack ++)

No longer simple "name finding," but requiring the model to read 100 financial reports and answer: "If the exchange rate fluctuation in Q2 2023 is calculated according to Q1 2024, what would be this company's net profit?"
This Multi-hop Reasoning capability is what enterprise applications truly care about.

Chapter 5: Industry Insights: How Should Enterprises Choose?

Based on the above technological trends, we offer the following suggestions for enterprise AI selection in 2025:

Don't Worship Parameter Size: For specific tasks (like extracting invoice information), a 7B MoE model fine-tuned with high-quality data often outperforms a 70B general model, at two orders of magnitude lower cost.
Long Context > RAG?: For documents under 100k words, throwing them directly into a Long Context window usually works better than RAG (slicing and retrieval). The future of RAG lies in "Massive Knowledge Bases" (TB level), not "Single Document QA."
Embrace Hybrid Architectures: Pay attention to Mamba-Transformer hybrid models; they might be the key to future cost reduction and efficiency enhancement.

Conclusion

Model evolution in 2025 is no longer a "Battle of Gods" where only Google and OpenAI can participate.
With the popularization of MoE, diversification of architectures, and decentralization of training technologies, we are entering a "Cambrian Explosion" era. Every architecture and size of model can find its ecological niche. For developers, this is not just a difficulty of choice, but a liberation of creativity.

This document is written by the Augmunt Institute for Frontier Technology, based on public technical literature and arXiv preprints from Q1 2025. Unauthorized reproduction is prohibited.