Model compression is increasingly essential for deploying large language models (LLMs), yet existing comparative studies largely focus on pruning and quantization evaluated primarily on knowledge-centric benchmarks. Thus, we introduce UniComp, a unified evaluation framework for comparing pruning, quantization, and knowledge distillation. UniComp evaluates compressed models along three dimensions: performance, reliability, and efficiency, using a diverse set of capability- and safety-oriented benchmarks together with a hardware-aware efficiency analysis. Through evaluation of seven compression techniques across over 40 datasets, we observe (i) a consistent knowledge bias, where factual recall is largely preserved while multi-step reasoning, multilingual, and instruction-following capabilities degrade; (ii) a deployment-critical performance-reliability decoupling, where retained performance does not indicate preserved safety, fairness and privacy; and (iii) that task-specific calibration can yield up to 50% relative improvement in reasoning performance in pruned models.

As ideation agents become more capable, evaluating them poses an increasing challenge. LLM-as-a-judge approaches are brittle and gameable, while human evaluation is expensive and hard to scale. To address this, we introduce HorizonBench, an evaluation methodology leveraging future scientific discoveries. HorizonBench uses published papers as ground truth for ideation quality. Specifically, ideation agents pretraining and context retrieval data are strictly constrained to pre-publication knowledge, and scored on how well their predicted ideas align with subsequently published work. Using HorizonBench, we conduct the first unified comparison of five published ideation agents across multiple frontier model backbones. We verify that our soft recall metric captures meaningful ideation quality aligned to human judgement via a user study with 51 domain experts. We then uncover a surprising finding: a naïve baseline that receives only the research goal as an input consistently matches or outperforms all existing agents that require retrieval or multi-step reasoning, highlighting the importance of the research goal for ideation. Based on this result, we develop OSCAR, an ideation agent that distils retrieved papers into insights and explicitly reinforces the research goal in each step of the pipeline. OSCAR outperforms all evaluated agents on HorizonBench. We publish the code and an online version of HorizonBench.

Scholar Inbox is a new open-access platform designed to address the challenges researchers face in staying current with the rapidly expanding volume of scientific literature. We provide personalized recommendations, continuous updates from open-access archives (arXiv, bioRxiv, etc.), visual paper summaries, semantic search, and a range of tools to streamline research workflows and promote open research access. The platform’s personalized recommendation system is trained on user ratings, ensuring that recommendations are tailored to individual researchers’ interests. To further enhance the user experience, Scholar Inbox also offers a map of science that provides an overview of research across domains, enabling users to easily explore specific topics. We use this map to address the cold start problem common in recommender systems, as well as an active learning strategy that iteratively prompts users to rate a selection of papers, allowing the system to learn user preferences quickly. We evaluate the quality of our recommendation system on a novel dataset of 800k user ratings, which we make publicly available, as well as via an extensive user study.