1
Multimodal LLMs under Pairwise Modalities
打破多模态数据对齐瓶颈，论文提出仅用成对模态训练MLLM，显著提升跨域可扩展性。
arXiv:2605.21059v1 Announce Type: cross Abstract: Despite the impressive results achieved by multimodal large language models (MLLMs), their training …
2
Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution
提出小语言模型Med-V1，零样本实现生物医学证据归因，兼顾规模与可扩展性
arXiv:2603.05308v2 Announce Type: replace Abstract: Assessing whether an article supports an assertion is essential for hallucination detection and cl…
3
CodeScaler: Scaling Code LLM Training and Test-Time Inference via Reward Models
用奖励模型突破测试用例限制，实现代码大模型训练与推理阶段的可扩展强化学习。
arXiv:2602.17684v2 Announce Type: replace Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has driven recent progress in code large lan…
4
Learning complex goals with iterated amplification
OpenAI提出迭代放大技术，通过任务分解实现超人类复杂目标的AI安全方法，虽处早期但具可扩展潜力。
We’re proposing an AI safety technique called iterated amplification that lets us specify complicated behaviors and goals that are beyond human scale,…
5
The Hive Mind: Scaling Multi-Agent AI State with AWS Lambda and Amazon EFS
多智能体AI状态传输的架构困境，AWS Lambda+EFS给出弹性解决新方案
If you are building a multi-agent AI system on AWS, you will quickly hit a massive, hidden architectural wall: State Transfer. In a multi-agent framew…
6
Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs
提出力感知神经切线核方法，攻克机器学习力场主动学习在大规模候选池及偏差下的扩展性与鲁棒性难题
arXiv:2605.13788v2 Announce Type: replace Abstract: Active learning for machine-learning interatomic potentials (MLIPs) must address several challenge…
7
How OpenEvidence built a healthcare AI that physicians actually trust
医生信赖的医疗AI如何通过Vercel实现零妥协扩展，从TikTok爆红到系统稳如磐石。
Andy Yoon was scrolling through Slack when he saw the message: OpenEvidence had gone viral on TikTok. Not "gaining traction.” Actually viral, reaching…