LLM Research Papers: A Curated List

Keeping pace with LLM research is not academic nostalgia. It is a practical requirement for anyone selecting inference infrastructure. The papers below shaped the architectures, context mechanisms, and reasoning strategies that are now available through production APIs. Understanding them helps you match a model's training paradigm to your workload, whether you are running long-context retrieval, agentic tool loops, or deep reasoning tasks.

Oxlo.ai hosts many of the models discussed here under a request-based pricing model. Because cost is a flat rate per API call rather than a function of prompt length, experimenting with long-context and agentic workloads on Oxlo.ai is significantly cheaper than on token-based alternatives.

Foundations: Attention, Scaling, and Compute-Optimal Training

1. Attention Is All You Need (Vaswani et al., 2017). Introduced the Transformer architecture. Every model on Oxlo.ai, from Qwen 3 32B to Llama 3.3 70B, builds on this self-attention mechanism.
2. Scaling Laws for Neural Language Models (Kaplan et al., 2020). Established the power-law relationship between model size, data, and compute.
3. Training Compute-Optimal Large Language Models (Hoffmann et al., 2022, Chinchilla). Demonstrated that smaller models trained on more tokens often outperform larger models. This philosophy underpins modern efficient flagships such as Qwen 3 32B and DeepSeek V3.2.

Mixture of Experts and Conditional Compute

MoE architectures route each token to a subset of parameters, enabling massive scale without proportional inference cost.

1. Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity (Fedus et al., 2022). Popularized the expert-routing paradigm.
2. DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model (DeepSeek-AI, 2024). Introduced Multi-Head Latent Attention and expert balancing that improved throughput.
3. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning (DeepSeek-AI, 2025). Showed that pure reinforcement learning on the base model can elicit sophisticated chain-of-thought reasoning.

On Oxlo.ai, you can run these architectures directly via DeepSeek R1 671B MoE, DeepSeek V4 Flash, and GLM 5 (744B MoE). Because Oxlo.ai charges one flat cost per request, you can send long prompts to a 671B-parameter MoE without the cost ballooning tied to input token count.

Reasoning, Chain-of-Thought, and Test-Time Compute

1. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022). Showed that prompting for intermediate steps improves arithmetic, commonsense, and symbolic reasoning.
2. DeepSeek-R1 (cited above) and Kimi K2.6 technical materials demonstrate advanced reasoning and agentic coding with extended thinking budgets.
3. OpenAI o1 System Card (OpenAI, 2024). Formalized the concept of scaling test-time compute through internal reasoning chains.

Oxlo.ai offers several reasoning-optimized endpoints: DeepSeek R1 671B MoE, Kimi K2.6, Kimi K2.5, Kimi K2 Thinking, and GLM 5. These are ideal for workloads that require extended chain-of-thought before returning an answer.

Long Context and Agentic Memory

1. Ring Attention with Blockwise Transformers for Near-Infinite Context (Liu et al., 2024). Enabled context windows far beyond standard attention limits through blockwise computation.
2. StreamingLLM: Efficient Streaming Language Models with Attention Sinks (Xiao et al., 2023). Allowed KV cache compression for infinite-length inputs.

Long-context research is particularly relevant to agentic systems that maintain multi-turn state. Oxlo.ai’s request-based pricing is built for this: cost does not scale with input length, so you can pass full document corpora or extended conversation histories to DeepSeek V4 Flash (1M context) or Kimi K2.6 (131K context) without the per-token surcharge common among token-based providers.

Open Weights and Post-Training Recipes

1. The Llama 3 Herd of Models (Meta, 2024). Detailed pre-training, post-training, and safety alignment for Llama 3 and 3.1/3.3 variants.
2. Qwen3 Technical Report (Qwen team, 2025). Outlined dense and MoE variants, multilingual training, and agentic tool-use fine-tuning.
3. GPT-Oss 120B technical notes represent the recent wave of large open-source GPT-class models.

Oxlo.ai carries these open-weight flagships, including Llama 3.3 70B, Qwen 3 32B, GPT-Oss 120B, and Mistral variants, all accessible through a fully OpenAI-compatible SDK.

Code, Vision, and Multimodal LLMs

1. Qwen3-Coder: Agentic Coding Models (Qwen team, 2025). Focused on repository-level understanding and tool use.
2. DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence (DeepSeek-AI, 2024). MoE architecture specialized for software engineering tasks.
3. Gemma 3 Technical Report (Google, 2025). Highlighted vision-language integration at high parameter efficiency.

Oxlo.ai exposes these capabilities across dedicated categories: Qwen 3 Coder 30B, DeepSeek Coder, and Oxlo.ai Coder Fast for code; Gemma 3 27B and Kimi VL A3B for vision; and Whisper Large v3 / Turbo / Medium for audio transcription.

From Paper to API: Running Research-Grade Models

Reading the paper is the first step. The second is reproducing the behavior on real hardware. Oxlo.ai provides an OpenAI SDK-compatible endpoint so you can test these architectures without managing GPU clusters or cold-start delays.

The following example calls Qwen 3 32B with tool-use capabilities enabled:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key="YOUR_OXLO_API_KEY"
)

response = client.chat.completions.create(
    model="qwen3-32b",
    messages=[
        {"role": "system", "content": "You are a helpful coding assistant."},
        {"role": "user", "content": "Write a Python function that implements ring attention blockwise computation based on Liu et al. 2024."}
    ],
    stream=True,
    tools=[{
        "type": "function",
        "function": {
            "name": "run_tests",
            "description": "Execute unit tests",
            "parameters": {"type": "object", "properties": {}}
        }
    }]
)

for chunk in response:
    print(chunk.choices[0].delta.content or "", end="")

Because Oxlo.ai uses request-based pricing, you can stream a long code-generation prompt or a multi-turn agentic conversation and pay the same flat per-request cost regardless of prompt length. For current plan details, see the Oxlo.ai pricing page.

Closing: Research as a Selection Criterion

The gap between arXiv and production is narrowing. Models that appear in papers today are available through inference APIs within weeks. Choosing the right provider means selecting one that supports the full spectrum of architectures, from dense generalists to MoE reasoning engines, without penalizing you for using the long contexts and agentic loops those papers describe.

Oxlo.ai hosts 45+ models across seven categories, with no cold starts and a flat per-request pricing structure. If you are benchmarking the ideas in these papers, it is a natural place to run your experiments.