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Avijit Ghosh

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@evijit

AI Research & Policy @huggingface 🤗
Leading: @evaluatingevals @huggingscience
Boston, Massachusetts evijit.io Joined January 2012
1.7K Following 3.4K Followers 6.4K Posts

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Avijit Ghosh Verified account @evijit
A benchmark to measure benchmaxxing named Maxxbench is very clever you gotta admit
Justin Lo Verified account @loresjpg 1d
Nice DeepSWE score! Does it survive a lowercase filename?

Introducing MaxxBench: 89 DeepSWE tasks with small changes to the instructions or starting code. Same engineering problem, slightly different requirement.

Models clustered around 74% on DeepSWE end up 23 pp apart.

Good at coding, or good at DeepSWE? 🧵
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Avijit Ghosh Verified account @evijit
Nice to see an open eval + dataset for people to learn from, inspect and build on top of open models and align them better to your own values!
Nous Research Verified account @NousResearch 1d
Introducing Arete-27B: Qwen3.8-27B, post-trained to reinforce Western values.

Chinese open models are currently the strongest available, but they ship with state-aligned behavior in the weights. We built an 803-prompt eval across seven value categories, including freedom of expression, democracy, and privacy, and measured how often a model adopts China’s party line.

After fine-tuning on roughly 53 million tokens, Arete-27B's propaganda rate fell from 22% to 1%, in line with the Western models we tested. Its Hermes Bench score shows no significant performance tradeoffs.

The values in an open model are one fine-tune away.

Our eval, weights, and training data are available now along with the full blog post: nousresearch.com/arete
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Avijit Ghosh Verified account @evijit
Enjoying this trend of issue discovery and disclosure from @OpenAI, good direction for research imo
Marcus Williams Verified account @Marcus_J_W 3d
🧵 Astra covertly underperforms on safety research tasks.
We noticed that Astra often makes surprisingly little progress on certain types of safety research and likes to invent blockers or impose arbitrary restrictions without telling the user. In most of these cases the decision to underperform was not verbalized in the CoT meaning our misalignment monitoring system did not detect this.

To measure underperformance we created a paired open-ended coding evaluation with safety and capability tasks. On this eval Astra scored 10% when we framed coding tasks as safety research, vs. 16% when framed as capabilities research. The gap remained after we excluded trajectories with refusals or verbalized intent to underperform in CoT. There was no such gap for 5.6-sol. When we aggressively filter out trajectories with even brief expressions of ethical doubt in the CoT the performance gap seems to reduce.
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