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RY DATASET 55a00d48a02213cfcebceda1b6fdb3d5e2b140a1442dc844f0a72013fc90b0a6 64 for tracking purposes. o3-mini system card ↩ Claude 3.7 Sonnet System Card ↩ ↩ 2 ↩ 3 ↩ 4 DeepSeek-R1 Github ↩ Safety cases at UK AISI ↩ Anthropic’s Recommendations to OSTP for the U.S. AI Action Plan ↩ (Between December 2024 and March 2025) ↩ We think these considerations can apply to non-reasoning models as well, since they also often think out loud, though for such models the line between reasoning and output is blu

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nue rapid development over the coming months in both evaluation methods and increasing agent performance. Explore the dataset itself on Huggingface . Use the evaluation harness on GitHub . See the agent framework on GitHub . Why? The Need for a Bioinformatics Benchmark Modern AI models and agents are commonly evaluated using standardized benchmarks, testing abilities in areas like image recognition and natural language understanding. However, common benchmarks fall short when trying to evaluate

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en with instructions on entering your OpenAI API key, selecting a local model, and running either Minion or MinionS: Example code To run Minion or MinionS programmatically with Python, the minions package can be used. Minion First create a file named example.py and add the following contents: Then run the example: MinionS With a few modifications, the same code can be used to run the MinionS protocol: After making the modifications, re-run the example: Minions GitHub repository; Blog post; Paper

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user is prompted to “monitor” what Operator is doing, what text it types and buttons it clicks. It’s unclear for which pages this screen appears, so far, I mostly only tested with Github and Outlook but I assume it’s based on some data classification model that might detect user PII on screen or similar, rather than specific domain names. Mitigation 2: Inline Confirmation Requests Within the chat conversation, Operator may ask the user if a certain action should be performed or request clarifica

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doesn’t require any custom attention mask, or anything of the likes, the zero-shot pipeline is very simple to set up and use: For more, you’ll want to check out our mini cookbook GitHub repository, with examples on how to fine-tune the model! Encoder models traditionnally perform best on all tasks with a task-specific head. While not necessarily an issue, this feels like a bit of a waste: the MLM head, its original pre-training head, is fully discarded. In practice, this works, but it also feel

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ons seeking to build, test, and deploy LLM applications at scale without sacrificing performance or simplicity. If you’re as excited as we are, don’t wait! Clone the repo: https://github.com/vllm-project/production-stack; Kick the tires; Let us know what you think!; Interest Form Join us to build a future where every application can harness the power of LLM inference—reliably, at scale, and without breaking a sweat. Happy deploying! What AIBrix adds as a Kubernetes control plane for vLLM: LoRA m

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ples in the code to learn how to write it. In retrospect, we should have included this in the initial prompt, or in a playbook; Finally, we gave it one single round of feedback in GitHub, and this was enough to merge Link to session: Implementing Sui blockchain integration Sui is a blockchain, similar to Ethereum and Solana. This task was much harder than the above, adding a new chain required changing code across multiple layers, as well as adding examples; In this case, our prompt for Devin wa

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. There are still a few usability concerns in XGrammar v0 integration to match feature parity with all use cases: It is yet to support grammars other than GBNF format (PR on vLLM: github); It is yet to support regex; It is yet to support complex JSON that uses regex patterns or numeric ranges There are a few PR trying to cover this usage. There was one bugfix PR on vLLM and one upstream vLLM now has a basic support for XGrammar by default. In case where we know XGrammar is insufficient to serve

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boundaries of innovation while making it accessible to all. For collaboration requests or inquiries, reach out at vllm-questions@lists.berkeley.edu. Join our growing community on GitHub or connect with us on the vLLM Slack. Together, let’s drive AI innovation forward. Acknowledgments We extend our gratitude to the uv community — particularly Charlie Marsh — for creating a fast, innovative package manager. Special thanks to Kevin Luu (Anyscale), Daniele Trifirò (Red Hat), and Michael Goin (Neura

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rsor, a popular IDE, that already ships with Model Context Protocol (MCP) support. MCP turns a local agent into a swiss‑army knife by letting it spin up arbitrary servers – Slack, GitHub, databases – and call their tools from natural language. That power, however, intrinsically compromises the agent. The tools expose the agent to external and untrusted data, which can affect the agent’s control-flow. This in turn, allows attackers to hijack the agent’s session and take advantage of the agent’s p

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Devin independently completing an Upwork bounty, installing and running a PyTorch project without human intervention.1 The company claimed Devin could resolve 13.86% of real-world GitHub issues end-to-end on the SWE-bench benchmark - 3x times better than previous systems. Only a select group of users could access it initially, leading to breathless tweets about how this would revolutionize software development. As a team at Answer.AI that routinely experiments with AI developer tools, something

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Sonnet. SWE-bench is an AI evaluation benchmark that assesses a model's ability to complete real-world software engineering tasks. Specifically, it tests how the model can resolve GitHub issues from popular open-source Python repositories. For each task in the benchmark, the AI model is given a set up Python environment and the checkout (a local working copy) of the repository from just before the issue was resolved. The model then needs to understand, modify, and test the code before submitting

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ng a 7B parameter metagenomic foundation model designed for pandemic monitoring, trained on over 1.5T base pairs of DNA and RNA sequenced from wastewater. Paper: metagene-1-paper; Github: github.com/metagene-ai; Hugging Face: huggingface.co/metagene-ai A collaboration between researchers at USC, Prime Intellect, and the Nucleic Acid Observatory. Overview We pretrain METAGENE-1, a 7-billion-parameter autoregressive transformer language model, which we refer to as a metagenomic foundation model, o

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her Devin sessions start in existing or new Slack threads, whether Devin waits for you to approve its plan, and more in Settings Customization. Devin can send Slack updates on its Github activity. Configure the channel these updates are sent to in Settings Integrations. Share Devin sessions you start in the web app to Slack. You can now change the default channel. Configure + monitor Devin’s machine If you need to increase Devin’s machine size (disk space, RAM, CPU), we’ve added additional optio

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ng test results as feedback; The problem space is well-defined and structured; and Output quality can be measured objectively. In our own implementation, agents can now solve real GitHub issues in the SWE-bench Verified benchmark based on the pull request description alone. However, whereas automated testing helps verify functionality, human review remains crucial for ensuring solutions align with broader system requirements. Appendix 2: Prompt engineering your tools No matter which agentic syst

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s a low-priority task in the Dagger project. While it’s impressive that Devin is able to eventually solve the issue, several rounds of back and forth are sometimes needed. Devin’s Github integration works with PR comments and CI checks to make this process smoother. Any PR comments are automatically sent to Devin by a webhook. Finally, to get the most out of Devin, don’t expect 100% perfect PRs. While Devin can get you 80% of the way there, a human should still ensure quality of the final result

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kinda help people running Redis, in some way or the other. (Amazingly, one of the repositories associated with this idea is still online, showing commits of 15 years ago: https://github.com/antirez/redis-tools ) I was about to try some kind of open core approach; I also remember I was thinking about delaying the BSD license for new code for six months, in order to create some kind of advantage for paying users. Now I don’t believe I would be a jerk and do strange games with my users, but I woul

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ce, creating simple but powerful solutions that help humans and AI work together more effectively. ShellSage is just one example of this approach, and we’re excited to see how the community helps us evolve it further. If you’re interested in contributing or have ideas for improvements, check out our GitHub repository. We’d love to hear your thoughts on how we can make ShellSage even more helpful for your command-line adventures, and how we can better support the future of human-AI collaboration.

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p toward democratizing AI development and preventing the consolidation of AI capabilities within a few organizations. By open-sourcing INTELLECT-1's model, checkpoints, and training framework, we invite the global AI community to join us in pushing the boundaries of distributed training. To get involved, visit our GitHub repository or join our Discord community. Together, we can build a more open, collaborative future for AI development. ‍ Rewriting Prime Agent in Rust On the Nature of the Swarm

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$ (sentiment) and $\pi^r {-}$ (sentiment), are statistically significant Cited as: Weng, Lilian. “Reward Hacking in Reinforcement Learning”. Lil’Log (Nov 2024). https://lilianweng.github.io/posts/2024-11-28-reward-hacking/. Or [1] Andrew Ng & Stuart Russell. “Algorithms for inverse reinforcement learning.”. ICML 2000. [2] Amodei et al. “Concrete problems in AI safety: Avoid reward hacking.” arXiv preprint arXiv:1606.06565 (2016). [3] Krakovna et al. “Specification gaming: the flip side of AI ing

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