Open-Weight-Models

TL;DR for operators A benchmark score can become unreliable when test items, close paraphrases, or related material have entered training. Removing suspicious questions sounds straightforward, but any detector that misses contaminated items leaves score inflation behind, while filtering also changes the evaluation set. Chai, Zhe, and Sakuma propose DeconIEP,1 a white-box inference-time intervention that keeps the benchmark text and model weights fixed. Instead, it learns small, input-specific changes to the model’s embeddings so contaminated behavior moves closer to a comparatively less-contaminated reference model. ...

TL;DR for operators A team with an expensive research workflow faces a recurring choice: keep paying for the full workflow on every report, or use it to teach a cheaper system how to reproduce much of its behavior. O-Researcher shows why the second option is plausible.1 With the same GPT-5 model, changing research execution from sequential to parallel raises the reported Overall score from 42.92 to 49.60, while Comprehensiveness rises from 40.59 to 49.61. Workflow structure itself is contributing capability. ...

TL;DR for operators A scientific agent that generates 16 plausible hypotheses does not need every candidate to receive the same expensive treatment. The operational problem is deciding which hypotheses deserve simulation, experiment, or expert attention first. Rajwal, Das, and Ghosal show that, on a benchmark built from 1,323 published papers, simply measuring how strongly open-weight models internally support each candidate can rank the published hypothesis substantially better than asking GPT-5 to compare all candidates explicitly.1 Pooled first-place accuracy was 33.1% using normalized token likelihood and 32.8% using raw model logits, versus 16.6% for the tested zero-shot GPT-5 judge. ...

TL;DR for operators Open-weight model safety is not just a question of what the released model refuses to answer. Once weights are public, the more relevant question is what a capable actor can make the model do after post-training. That is the problem this paper tackles. The paper introduces malicious fine-tuning as a release-evaluation method: take the model, assume a sophisticated adversary with serious reinforcement-learning infrastructure, and try to elicit the maximum dangerous capability in high-risk domains. The authors apply this to gpt-oss-120b, focusing on biology and cybersecurity rather than self-improvement. ...