I built a lightweight research assistant that ingests raw LLM paper text and returns structured summaries, key contributions, and implementation notes. It runs entirely on Oxlo.ai's flat per-request pricing, so analyzing a long excerpt costs the same as a short prompt. If you are a developer trying to keep up with the flood of preprints, this tool saves you from reading every page.
What you'll need
You need Python 3.10 or newer, the OpenAI SDK, and an Oxlo.ai API key from https://portal.oxlo.ai. Install the SDK with pip install openai. I use Llama 3.3 70B on Oxlo.ai for this agent because the request-based pricing means I can pass in thousands of words of context without token costs scaling up. If you prefer reasoning models, DeepSeek V3.2 or Kimi K2.6 are also available on Oxlo.ai and drop into the same code by changing the model string.
Step 1: Configure the Oxlo.ai client
Oxlo.ai exposes a fully OpenAI-compatible endpoint. Initialize the client once with the base URL set to https://api.oxlo.ai/v1 and reuse it for every request. Because the API matches the OpenAI spec, you can keep your existing instrumentation, retry logic, and Pydantic models without changes.
from openai import OpenAI
client = OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_OXLO_API_KEY"
)Step 2: Load a paper into a single string
I embed a real excerpt from the Attention Is All You Need paper so the script is self-contained and runnable. In production you might fetch this from arXiv or parse a PDF, but the key requirement is a single Python string containing the text you want analyzed. One advantage of Oxlo.ai is that request-based pricing removes the penalty for long inputs. On token-based providers, pasting a full introduction and abstract could consume tens of thousands of tokens before the model even responds. Here, the cost is flat per request, so you can be generous with context.
PAPER_TEXT = """Attention Is All You Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
Abstract
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature.
1 Introduction
Recurrent neural networks, long short-term memory and gated recurrent neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation. Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures.
Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states h_t, as a function of the previous hidden state h_{t-1} and the input at position t. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples. Recent work has achieved significant improvements in computational efficiency through factorization tricks and conditional computation, while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains.
Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences. In all but a few cases, however, such attention mechanisms are used in conjunction with a recurrent network.
In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.
2 Background
The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU, ByteNet and ConvS2S, all of which use convolutional neural networks as basic building blocks, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions. In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with multi-head attention.
3 Model Architecture
Most competitive neural sequence transduction models have an encoder-decoder structure. Here, the encoder maps an input sequence of symbol representations to a sequence of continuous representations. Given these, the decoder then generates an output sequence of symbols one element at a time. At each step the model is auto-regressive, consuming the previously generated symbols as additional input when generating the next.
3.1 Scaled Dot-Product Attention
We call our particular attention Scaled Dot-Product Attention. The input consists of queries and keys of dimension d_k, and values of dimension d_v. We compute the dot products of the query with all keys, divide each by the square root of d_k, and apply a softmax function to obtain the weights on the values. In practice, we compute the attention function on a set of queries simultaneously, packed together into a matrix Q. The keys and values are also packed together into matrices K and V.
"""Step 3: Define the analysis agent
I keep the system prompt strict. It forces structured output so I can scan results quickly instead of reading freeform paragraphs. The function passes the full paper text to Llama 3.3 70B via Oxlo.ai. If you are working with a 100-page survey, you could switch to Kimi K2.6 on Oxlo.ai to exploit its 131K context window, still under the same flat per-request cost structure.
SYSTEM_PROMPT = """You are a research assistant that analyzes LLM papers. Given the full text of a paper, produce a structured analysis with exactly these sections:
- Summary: One paragraph explaining the core idea.
- Key Contribution: The single most important novelty.
- Methods: Techniques or architectures introduced.
- Experiments: Datasets, metrics, and results.
- Implementation Notes: Practical advice for reproducing the work.
- Limitations: Weaknesses or caveats mentioned by the authors.
Be concise. Use bullet points where appropriate. If the text is truncated, note what is missing."""
def analyze_paper(paper_text: str) -> str:
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": paper_text},
],
)
return response.choices[0].message.contentStep 4: Execute the pipeline
With the client, prompt, and paper text ready, run the analysis and print the result. You can wrap this in a CLI using argparse if you want to pass paper files as arguments.
analysis = analyze_paper(PAPER_TEXT)
print(analysis)Run it
Save the script as research_agent.py and run it. On Oxlo.ai, there are no cold starts for Llama 3.3 70B, so the first request returns immediately. If you are batch-processing a reading list, the predictable per-request cost makes budgeting straightforward. Here is realistic output based on the excerpt above.
python research_agent.pySummary:
The authors introduce the Transformer, a novel sequence transduction architecture that replaces recurrent and convolutional layers with stacked self-attention and point-wise fully connected layers. It is designed for machine translation but is intended to generalize to other tasks.
Key Contribution:
Demonstrating that attention mechanisms alone, without any recurrence or convolution, are sufficient to achieve state-of-the-art translation quality while enabling massive parallelization during training.
Methods:
- Transformer architecture based entirely on attention.
- Multi-head self-attention and positional encodings.
- Encoder-decoder structure with residual connections and layer normalization.
- Scaled dot-product attention to stabilize gradients.
Experiments:
- WMT 2014 English-to-German: 28.4 BLEU (new best, +2.0 over prior ensembles).
- WMT 2014 English-to-French: 41.8 BLEU after 3.5 days on eight GPUs.
- English constituency parsing to show generalization.
Implementation Notes:
- Training requires eight P100 GPUs for several days.
- Highly parallelizable compared to RNNs, so batching and data pipeline efficiency matter.
- Positional encodings must be chosen carefully to inject order information.
Limitations:
- The excerpt does not list explicit limitations, but the authors note that the work focuses on translation and that broader task validation is ongoing.Wrap-up and next steps
This agent turns wall-of-text papers into skimmable briefs. Because Oxlo.ai charges per request rather than per token, you can feed it long introductions or related work sections without worrying about ballooning costs. Two concrete next steps: wire the script to the arXiv API so you can pass an ID instead of raw text, or add a second Oxlo.ai call using deepseek-v3.2 to generate PyTorch scaffolding from the methods section. You can also experiment with Qwen 3 32B on Oxlo.ai if your reading list includes multilingual papers. See https://oxlo.ai/pricing for details on request-based plans.
