[■] Open source research workspace
Read papers with an assistant that has the whole text.
Find what to read next, open any arXiv paper, PDF, or web page, and ask questions that come back with citations to the exact passage.
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- In the assistant's context
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- MIT
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SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
SWE-bench evaluates language models on 2,294 software-engineering problems drawn from real GitHub issuesthread · 4 and corresponding pull requests across 12 popular Python repositories. Given a codebase and an issue, a model is tasked with generating a patch that resolves the described problem. Resolving issues requires understanding and coordinating changes across multiple functions, classes, and files simultaneously.
Our evaluations show that both state-of-the-art proprietary models and our fine-tuned model SWE-Llama can resolve only the simplest issues…
[01] Discover
Know what to read next.
Describe a topic, a method, or an open question. A research agent searches the literature and the open web, reads what it finds, and returns a ranked shortlist of papers and lab blogs, each with a one-line reason to care.
- 01
Medusa: Simple LLM Inference Acceleration Framework
Multiple decoding heads, no draft model. The closest thing to a drop-in answer.
arXiv:2401.10774 - 02
EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty
Explains why naive draft and verify breaks down exactly in long-context regimes.
arXiv:2401.15077 - 03
How speculative decoding works in vLLM
The production tradeoffs you'll actually hit, from the team running it at scale.
vllm.ai / blog
Given just a handful of human-written examples of how an API can be used, we let the model annotate a large language-modeling dataset with potential API calls. We then use a self-supervised loss to determine which of these calls actually help the model predict future tokens.
[02] Review
Highlight a sentence. Ask about it.
The assistant has the full text in context. Ask for a derivation, pull definitions side by side, or highlight any passage to open a thread that stays pinned to it. Every answer cites its source: click the chip, land on the passage.
[04] Open source
Free, open source,
and yours to fork.
Artifact is built in the open. Read the code, file an issue, send a patch. Contributions are very much welcome.
View on GitHubOpen source, MIT-licensed
Read the code, run it yourself, fork it, send a patch. Use the hosted version free, or self-host the whole thing.
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Trust every answer
Every answer your assistant gives traces back to the PDF or page. Click a citation chip, land on the passage.
Start reading.
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