OrangeBot.AI Digest — 2025-10-08

Stremio - Freedom to Stream

Infisical is the open-source platform for secrets management, PKI, and SSH access.

The AI Browser Automation Framework

所有小初高、大学PDF教材。

The power of Claude Code / GeminiCLI / CodexCLI + [Gemini / OpenAI / OpenRouter / Azure / Grok / Ollama / Custom Model / All Of The Above] working as one.

Open-source infrastructure for Computer-Use Agents. Sandboxes, SDKs, and benchmarks to train and evaluate AI agents that can control full desktops (macOS, Linux, Windows).

Desktop App for downloading Videos and Audios from hundreds of sites

The most popular open source electronic health records and medical practice management solution.

Open-source IoT Platform - Device management, data collection, processing and visualization.

Free, local, open-source AI app builder ✨ v0 / lovable / Bolt alternative 🌟 Star if you like it!

A set of beautifully-designed, accessible components and a code distribution platform. Works with your favorite frameworks. Open Source. Open Code.

Open Source Alternative to NotebookLM / Perplexity, connected to external sources such as Search Engines, Slack, Linear, Jira, ClickUp, Confluence, Notion, YouTube, GitHub, Discord and more. Join our discord: https://discord.gg/ejRNvftDp9

Hierarchical Reasoning Model (HRM) is a novel approach using two small neural networks recursing at different frequencies. This biologically inspired method beats Large Language models (LLMs) on hard puzzle tasks such as Sudoku, Maze, and ARC-AGI while trained with small models (27M parameters) on small data (around 1000 examples). HRM holds great promise for solving hard problems with small networks, but it is not yet well understood and may be suboptimal. We propose Tiny Recursive Model (TRM), a much simpler recursive reasoning approach that achieves significantly higher generalization than HRM, while using a single tiny network with only 2 layers. With only 7M parameters, TRM obtains 45% test-accuracy on ARC-AGI-1 and 8% on ARC-AGI-2, higher than most LLMs (e.g., Deepseek R1, o3-mini, Gemini 2.5 Pro) with less than 0.01% of the parameters.

Tool-integrated reasoning has emerged as a key focus for enabling agentic applications. Among these, DeepResearch Agents have gained significant attention for their strong performance on complex, open-ended information-seeking tasks. We introduce Fathom-DeepResearch, an agentic system composed of two specialized models. The first is Fathom-Search-4B, a DeepSearch model trained from Qwen3-4B and optimized for evidence-based investigation through live web search and targeted webpage querying. Its training combines three advances: (i) DUETQA, a 5K-sample dataset generated via multi-agent self-play that enforces strict web-search dependence and heterogeneous source grounding; (ii) RAPO, a zero-overhead extension of GRPO that stabilizes multi-turn Reinforcement Learning with Verifiable Rewards through curriculum pruning, reward-aware advantage scaling, and per-prompt replay buffers; and (iii) a steerable step-level reward that classifies each tool call by cognitive behavior and marginal utility, enabling explicit control over search trajectory breadth, depth, and horizon. These improvements enable reliable extension of tool-calling beyond 20 calls when warranted. The second is Fathom-Synthesizer-4B, trained from Qwen3-4B, which converts multi-turn DeepSearch traces into structured, citation-dense DeepResearch Reports for comprehensive synthesis. Evaluated on DeepSearch benchmarks (SimpleQA, FRAMES, WebWalker, Seal0, MuSiQue) and DeepResearch-Bench, the system achieves state-of-the-art performance in the open-weights category while demonstrating strong generalization to diverse reasoning tasks including HLE, AIME-25, GPQA-Diamond, and MedQA.

Process Reward Models (PRMs) have recently emerged as a powerful framework for enhancing the reasoning capabilities of large reasoning models (LRMs), particularly in the context of test-time scaling (TTS). However, their potential for supervising LRMs on tabular reasoning domains remains underexplored. Through detailed empirical analyses, we identify that existing PRMs, though widely adopted for supervising text-only reasoning steps, struggle with table-specific operations such as sub-table retrieval and schema interaction, leading to critical performance bottlenecks. To address this limitation, we propose TaTToo, a novel table-grounded PRM framework that (i) reasons explicitly over tabular reasoning steps and (ii) integrates tool-based verification to provide precise reward supervision. Concretely, we first design a scalable data curation pipeline that constructs over 60k high-quality step-level annotations by integrating table verification rationales with tool-based executions. Building on the collected data, we train TaTToo with a dual-stage paradigm: cold-start supervised fine-tuning to capture tool-use reasoning patterns, followed by reinforcement learning with tool-grounded reward shaping to align our model with table-based verification. We provide a comprehensive evaluation of the policy improvement induced by our newly designed PRM. Across 5 challenging tabular reasoning benchmarks covering numerical reasoning, fact-checking, and data analysis, TaTToo improves downstream policy LRMs by 30.9% at inference, surpasses strong PRM baselines such as Qwen-2.5-Math-PRM-72B with only 8B parameters, and demonstrates strong generalizability across diverse TTS strategies.

Autoregressive (AR) large language models (LLMs) have achieved remarkable performance across a wide range of natural language tasks, yet their inherent sequential decoding limits inference efficiency. In this work, we propose Fast-dLLM v2, a carefully designed block diffusion language model (dLLM) that efficiently adapts pretrained AR models into dLLMs for parallel text generation, requiring only approximately 1B tokens of fine-tuning. This represents a 500x reduction in training data compared to full-attention diffusion LLMs such as Dream (580B tokens), while preserving the original model's performance. Our approach introduces a novel training recipe that combines a block diffusion mechanism with a complementary attention mask, enabling blockwise bidirectional context modeling without sacrificing AR training objectives. To further accelerate decoding, we design a hierarchical caching mechanism: a block-level cache that stores historical context representations across blocks, and a sub-block cache that enables efficient parallel generation within partially decoded blocks. Coupled with our parallel decoding pipeline, Fast-dLLM v2 achieves up to 2.5x speedup over standard AR decoding without compromising generation quality. Extensive experiments across diverse benchmarks demonstrate that Fast-dLLM v2 matches or surpasses AR baselines in accuracy, while delivering state-of-the-art efficiency among dLLMs - marking a significant step toward the practical deployment of fast and accurate LLMs. Code and model will be publicly released.

Diffusion language models promise bidirectional context and infilling capabilities that autoregressive coders lack, yet practical systems remain heavyweight. We introduce CoDA, a 1.7B-parameter diffusion coder trained on TPU with a fully open-source training pipeline. CoDA pairs large-scale diffusion pre-training with code-centric mid-training and instruction tuning, enabling confidence-guided sampling that keeps inference latency competitive. On Humaneval, MBPP, and EvalPlus, CoDA-1.7B-Instruct matches or surpasses diffusion models up to 7B parameters. Our release includes model checkpoints, evaluation harnesses, and TPU training pipelines to accelerate research on lightweight diffusion-based coding assistants.

Diffusion and flow-based non-autoregressive (NAR) models have shown strong promise in large language modeling, however, their potential for automatic speech recognition (ASR) remains largely unexplored. We propose Drax, a discrete flow matching framework for ASR that enables efficient parallel decoding. To better align training with inference, we construct an audio-conditioned probability path that guides the model through trajectories resembling likely intermediate inference errors, rather than direct random noise to target transitions. Our theoretical analysis links the generalization gap to divergences between training and inference occupancies, controlled by cumulative velocity errors, thereby motivating our design choice. Empirical evaluation demonstrates that our approach attains recognition accuracy on par with state-of-the-art speech models while offering improved accuracy-efficiency trade-offs, highlighting discrete flow matching as a promising direction for advancing NAR ASR.

Outcome-driven reinforcement learning has advanced reasoning in large language models (LLMs), but prevailing tool-augmented approaches train a single, monolithic policy that interleaves thoughts and tool calls under full context; this scales poorly with long horizons and diverse tools and generalizes weakly to new scenarios. Agentic systems offer a promising alternative by decomposing work across specialized modules, yet most remain training-free or rely on offline training decoupled from the live dynamics of multi-turn interaction. We introduce AgentFlow, a trainable, in-the-flow agentic framework that coordinates four modules (planner, executor, verifier, generator) through an evolving memory and directly optimizes its planner inside the multi-turn loop. To train on-policy in live environments, we propose Flow-based Group Refined Policy Optimization (Flow-GRPO), which tackles long-horizon, sparse-reward credit assignment by converting multi-turn optimization into a sequence of tractable single-turn policy updates. It broadcasts a single, verifiable trajectory-level outcome to every turn to align local planner decisions with global success and stabilizes learning with group-normalized advantages. Across ten benchmarks, AgentFlow with a 7B-scale backbone outperforms top-performing baselines with average accuracy gains of 14.9% on search, 14.0% on agentic, 14.5% on mathematical, and 4.1% on scientific tasks, even surpassing larger proprietary models like GPT-4o. Further analyses confirm the benefits of in-the-flow optimization, showing improved planning, enhanced tool-calling reliability, and positive scaling with model size and reasoning turns.

Reasoning capability is pivotal for Large Language Models (LLMs) to solve complex tasks, yet achieving reliable and scalable reasoning remains challenging. While Chain-of-Thought (CoT) prompting has become a mainstream approach, existing methods often suffer from uncontrolled generation, insufficient quality, and limited diversity in reasoning paths. Recent efforts leverage code to enhance CoT by grounding reasoning in executable steps, but such methods are typically constrained to predefined mathematical problems, hindering scalability and generalizability. In this work, we propose Caco (Code-Assisted Chain-of-ThOught), a novel framework that automates the synthesis of high-quality, verifiable, and diverse instruction-CoT reasoning data through code-driven augmentation. Unlike prior work, Caco first fine-tunes a code-based CoT generator on existing math and programming solutions in a unified code format, then scales the data generation to a large amount of diverse reasoning traces. Crucially, we introduce automated validation via code execution and rule-based filtering to ensure logical correctness and structural diversity, followed by reverse-engineering filtered outputs into natural language instructions and language CoTs to enrich task adaptability. This closed-loop process enables fully automated, scalable synthesis of reasoning data with guaranteed executability. Experiments on our created Caco-1.3M dataset demonstrate that Caco-trained models achieve strong competitive performance on mathematical reasoning benchmarks, outperforming existing strong baselines. Further analysis reveals that Caco's code-anchored verification and instruction diversity contribute to superior generalization across unseen tasks. Our work establishes a paradigm for building self-sustaining, trustworthy reasoning systems without human intervention.

Reasoning models enhance performance by tackling problems in a step-by-step manner, decomposing them into sub-problems and exploring long chains of thought before producing an answer. However, applying extended reasoning to every step introduces substantial redundancy, as sub-problems vary widely in difficulty and complexity: a small number of pivotal steps are genuinely challenging and decisive for the final answer, while many others only involve straightforward revisions or simple computations. Therefore, a natural idea is to endow reasoning models with the ability to adaptively respond to this variation, rather than treating all steps with the same level of elaboration. To this end, we propose MixReasoning, a framework that dynamically adjusts the depth of reasoning within a single response. The resulting chain of thought then becomes a mixture of detailed reasoning on difficult steps and concise inference on simpler ones. Experiments on GSM8K, MATH-500, and AIME show that MixReasoning shortens reasoning length and substantially improves efficiency without compromising accuracy.

Recent Large Language Model (LLM) post-training methods rely on token-level clipping mechanisms during Reinforcement Learning (RL). However, we identify a fundamental flaw in this Outcome-Supervised RL (OSRL) paradigm: the Importance Sampling (IS) ratios of positive-advantage tokens are mismatched, leading to unbalanced token weighting for positive and negative tokens. This mismatch suppresses the update of low-probability tokens while over-amplifying already high-probability ones. To address this, we propose Asymmetric Importance Sampling Policy Optimization (ASPO), which uses a simple yet effective strategy that flips the IS ratios of positive-advantage tokens, aligning their update direction with the learning dynamics of negative ones. AIS further incorporates a soft dual-clipping mechanism to stabilize extreme updates while maintaining gradient flow. Comprehensive experiments on coding and mathematical reasoning benchmarks demonstrate that ASPO significantly mitigates premature convergence, improves training stability, and enhances final performance over strong GRPO-based baselines. Our analysis provides new insights into the role of token-level weighting in OSRL and highlights the critical importance of correcting IS in LLM RL. The code and models of ASPO are available at https://github.com/wizard-III/Archer2.0.

Video-conditioned 4D shape generation aims to recover time-varying 3D geometry and view-consistent appearance directly from an input video. In this work, we introduce a native video-to-4D shape generation framework that synthesizes a single dynamic 3D representation end-to-end from the video. Our framework introduces three key components based on large-scale pre-trained 3D models: (i) a temporal attention that conditions generation on all frames while producing a time-indexed dynamic representation; (ii) a time-aware point sampling and 4D latent anchoring that promote temporally consistent geometry and texture; and (iii) noise sharing across frames to enhance temporal stability. Our method accurately captures non-rigid motion, volume changes, and even topological transitions without per-frame optimization. Across diverse in-the-wild videos, our method improves robustness and perceptual fidelity and reduces failure modes compared with the baselines.

Multimodal large language models (MLLMs) have recently achieved remarkable progress in radiology by integrating visual perception with natural language understanding. However, they often generate clinically unsupported descriptions, known as medical hallucinations, which pose serious risks in medical applications that demand accuracy and image-grounded outputs. Through empirical analysis, we find that prompt-induced hallucinations remain prevalent in radiology MLLMs, largely due to over-sensitivity to clinical sections. To address this, we introduce Clinical Contrastive Cecoding (CCD), a training-free and retrieval-free inference framework that integrates structured clinical signals from task-specific radiology expert models. CCD introduces a dual-stage contrastive mechanism to refine token-level logits during generation, thereby enhancing clinical fidelity without modifying the base MLLM. Experiments on three datasets and multiple models demonstrate that CCD consistently improves overall performance on radiology report generation (RRG). On the MIMIC-CXR dataset, it yields up to a 17% improvement in RadGraph-F1 when applied to state-of-the-art RRG models. Our approach provides a lightweight and generalisable solution for mitigating medical hallucinations, effectively bridging expert models and MLLMs in radiology.

Recent advances in generative medical models are constrained by modality-specific scenarios that hinder the integration of complementary evidence from imaging, pathology, and clinical notes. This fragmentation limits their evolution into foundation models that can learn and reason across the full spectrum of biomedical data. We propose MeDiM, the first medical discrete diffusion model that learns shared distributions across modalities without modality-specific components. MeDiM unifies multiple generative tasks: translating between images and text, and jointly producing image-report pairs across domains in response to prompts. Built on a discrete diffusion framework, MeDiM bridges vision and language representations through a shared probabilistic space. To enable unified and flexible medical generation, we employ a multimodal large language model (MLLM) as the diffusion backbone, leveraging its prior knowledge and cross-modal reasoning. Two key designs are introduced: (1) removing the causal attention mask for bidirectional context, and (2) injecting continuous timestep embeddings for diffusion awareness. Experiments demonstrate high-fidelity medical generation (FID 16.60 on MIMIC-CXR and FID 24.19 on PathGen) and accurate report generation (METEOR 0.2650 and 0.2580). Jointly generated image-report pairs further enhance downstream performance (plus6.43 percent BLEU-1, plus18.57 percent BLEU-2, plus31.58 percent BLEU-3, plus4.80 percent METEOR), showing that MeDiM supports coherent and clinically grounded multimodal outputs.

We present OneFlow, the first non-autoregressive multimodal model that enables variable-length and concurrent mixed-modal generation. Unlike autoregressive models that enforce rigid causal ordering between text and image generation, OneFlow combines an insertion-based Edit Flow for discrete text tokens with Flow Matching for image latents. OneFlow enables concurrent text-image synthesis with hierarchical sampling that prioritizes content over grammar. Through controlled experiments across model sizes from 1B to 8B, we demonstrate that OneFlow outperforms autoregressive baselines on both generation and understanding tasks while using up to 50% fewer training FLOPs. OneFlow surpasses both autoregressive and diffusion-based approaches while unlocking new capabilities for concurrent generation, iterative refinement, and natural reasoning-like generation.

A key component of in-context reasoning is the ability of language models (LMs) to bind entities for later retrieval. For example, an LM might represent "Ann loves pie" by binding "Ann" to "pie", allowing it to later retrieve "Ann" when asked "Who loves pie?" Prior research on short lists of bound entities found strong evidence that LMs implement such retrieval via a positional mechanism, where "Ann" is retrieved based on its position in context. In this work, we find that this mechanism generalizes poorly to more complex settings; as the number of bound entities in context increases, the positional mechanism becomes noisy and unreliable in middle positions. To compensate for this, we find that LMs supplement the positional mechanism with a lexical mechanism (retrieving "Ann" using its bound counterpart "pie") and a reflexive mechanism (retrieving "Ann" through a direct pointer). Through extensive experiments on nine models and ten binding tasks, we uncover a consistent pattern in how LMs mix these mechanisms to drive model behavior. We leverage these insights to develop a causal model combining all three mechanisms that estimates next token distributions with 95% agreement. Finally, we show that our model generalizes to substantially longer inputs of open-ended text interleaved with entity groups, further demonstrating the robustness of our findings in more natural settings. Overall, our study establishes a more complete picture of how LMs bind and retrieve entities in-context.

群晖今年早些时候做出了一项受争议决策：2025 年款 Plus 系列 NAS 产品只兼容自有品牌硬盘。群晖声称，如果安装不兼容的硬盘，NAS 设备可能无法创建存储池。群晖并不生产硬盘，它主要是重新包装来自希捷和东芝的硬盘，群晖品牌硬盘通常比相似规格的第三方型号略贵。举例来说，群晖 Plus 系列 8TB 3.5 英寸 HDD HAT3310 在其官网上的售价为 210 美元。HAT3310 原装硬盘之一——东芝 N300 在多个网店售价为 173 美元。群晖此举招致了消费者的广泛批评，消费者们选择了用脚投票，其产品过去几个月销量暴跌。现在群晖释出了 DSM 7.3，悄悄撤销了这一受争议政策，使用第三方硬盘不会再触发警告或限制功能。批评人士认为这一事件损害了群晖的声誉。

2025 年诺贝尔化学奖授予了日本科学家 Susumu Kitagawa、英国科学家 Richard Robson 和美国科学家 Omar M. Yaghi，表彰他们“在金属有机框架开发领域的贡献”。他们开发了一种新的分子结构。在结构中，金属离子作为由长有机（碳基）分子连接的基石。金属离子和分子结合在一起，形成了包含大空洞的晶体。这些多孔材料被称为金属有机框架（MOF）。通过改变 MOF 中使用的构建块，化学家可以设计它们来捕获和存储特定的物质。MOF 还可以驱动化学反应或导电。在这奖者的突破性发现之后，化学家们已经构建了成千上万种不同的MOF。其中一些可能有助于解决人类面临的一些最大挑战，包括从水中分离 PFAS，分解环境中的药物痕迹，捕获二氧化碳或从沙漠空气中收集水。

生活在天然林里的蝴蝶与生活在人工林里的蝴蝶是不同的。在天然林中，五彩斑斓的颜色攸关蝴蝶的生存，有助于吸引配偶和躲避捕食者。但随着人类砍伐森林，将其改造为单一作物的人工林，生活在其中的蝴蝶也变得暗淡无光。巴西研究人员发现，天然林生活了 31 种各种颜色的蝴蝶，而人工林中只生活着 21 种，且以棕色为主。天然林为蝴蝶提供了多样化的栖息地，而在背景单一的人工林颜色单调的物种则更容易生存。蝴蝶是世界上颜色最丰富的物种之一，它们能对环境变化迅速做出反应。当动物颜色的多样性减少时，通常也意味着生态环境及其功能在衰退。

微软最近再次上调了 Xbox Series X 和 Series S 游戏机的售价，将订阅服务 Xbox Game Pass Ultimate 价格上涨 50%。一系列动作让很多人不看好微软游戏机业务的未来，包括 Costco 在内的零售商决定将 Xbox 产品下架。索尼 PS5 之后有 PS6，但 Xbox Series X 之后是否还会有新 Xbox？对于它可能放弃硬件制造的传言，微软周一发表声明重审它仍然致力于开发 Xbox 游戏机，继续与 AMD 公司在硬件方面进行合作。微软和索尼目前游戏机都使用 AMD 提供的 CPU 和 GPU 方案。微第一方 Xbox 掌机的计划据报道已经取消，原因据称是 AMD 在合同中要求销量至少要达到一千万，而 Steam Deck 自 2022 年发布以来销量也只有 400-500 万台。

在 Ubuntu 25.10 即将释出之际，Canonical 宣布下一个 LTS（长期支持版）Ubuntu 26.04 的代号为 Resolute Raccoon。Ubuntu 25.10 只支持九个月，而 Ubuntu 26.04 将支持五年，预计 2026 年 4 月释出。Ubuntu 25.10 的主要特性包括：Linux 6.17，GCC 15，使用 Rust 语言开发的系统组件 sudo-rs 和 Rust Coreutils，默认桌面环境 GNOME 49，等等。Ubuntu 26.04 的具体特性将在未来几个月逐步揭晓。

2025 年诺贝尔物理学奖授予了美国科学家 John Clarke、Michel H. Devoret 和 John M. Martinis，以表彰他们发现了电路中的宏观量子力学隧穿效应和能量量子化。物理学中的一个主要问题是，能展示量子力学效应的系统最大尺度是多少。今年的诺贝尔奖获得者通过一个电路进行了实验，在该系统中，他们同时演示了量子力学隧穿效应和能量量子化，而这个系统的尺寸大到足以用手握住。在 1984 年和 1985 年，John clarke、Michel H. Devoret 和 John M. Martini 使用由超导体构建的电子电路进行了一系列实验。在电路中，超导元件被一层薄薄的绝缘材料隔开，这种结构被称为约瑟夫森结。通过精化并测量其电路的各种特性，他们能够控制并探索当电流通过时出现的现象。共同在超导体中移动的带电粒子构成了一个系统，其行为就好像它们是填充整个电路的单个粒子一样。这个宏观的类粒子系统最初处于一种电流流动而没有任何电压的状态。系统被束缚在这种状态中，就像被困在一个无法穿越的势垒后面。在实验中，系统通过成功隧穿脱离零电压状态，展示了其量子特性。系统状态的改变通过电压的出现而被检测到。

根据上周悉尼国际宇航大会上发表的一项研究，如果能清理掉低地球轨道上最具有危险性的 50 块太空垃圾，那么新生成碎片的数量将能整体减半。论文主要作者是 Darren McKnight，他们计算了最可能与其它碎片碰撞产生更多碎片的低轨道物体。50 块最具危险性的太空垃圾有 34 块来自俄罗斯/苏联，10 块来自中国，美国 4 块，欧洲 2 块，日本 1 块。即使只清理掉其中最危险的 10 块，新太空碎片数量也能减少 30%。McKnight 指出，大部分太空垃圾来自于 2000 年之前，50 块最具有危险性的太空垃圾有 76% 是上个世纪留下的，88% 是遗留在太空的火箭残骸。坏消息是，自 2024 年 1 月 1 日以来，遗留在低地球轨道上的火箭残骸达到了 26 枚，它们会在轨道上停留逾 25 年。这 26 枚中有 21 枚是中国发射的，另外 5 枚来自美国、俄罗斯、印度和伊朗。随着中国加速发射和部署数量数以千计的国网和千帆星座，低轨道上的火箭残骸数量可能会继续增加。自去年发射国网和千帆星座以来，中国在轨道上遗留了9 枚火箭上面级的残骸，未来可能会遗留逾 100 枚。不过中国航天局的一名官员表示正在研究如何清理轨道上的太空垃圾。

美国上半年经济增长率 1.6%，大部分增长来自对 AI 的投资。如果没有 AI 方面的投资，经济增长率将会只有这一数字的三分之一。AI 支出的巨大经济影响力表明，硅谷正以史无前例的规模押注 AI 技术将会彻底改变生活工作的各个方面。科技巨头如 Google、Meta、Microsoft 和 Amazon 今年预计在数据中心上的投资将会接近 4000 亿美元。如果这次押注失败，如此规模的经济影响力意味着，其经济损失将会远大于硅谷本身。科技圈和金融圈对 AI 投资的潜在泡沫的担忧日益加剧。ChatGPT 等 AI 工具深受企业和消费者的欢迎，过去三年 AI 领域已投入了数千亿美元。但 AI 公司至今都无法盈利，然而需要巨额利润才能让巨大的投资物有所值。科技公司如今主导着公开市场，其业绩和股价的任何变化会对股指、401(k)退休金以及更广泛的经济产生巨大影响。独立研究公司 MacroStrategy Partnership 估计，AI 泡沫的规模是互联网泡沫的 17 倍，是次贷泡沫的 4 倍。从未有过如此大规模的资金被如此迅速的投入到一项尽管潜力巨大，但其盈利商业模式尚未得到证实的技术上。

天文学家发现了一个至今最遥远、信号最强的「奇异电波圈」（Odd Radio Circle 或 ORC）。这个神秘的天体，让科学家对星系及中心超大质量黑洞之间的互动获得新的线索。所谓「奇异电波圈」，是巨大的环状结构，目前仅在射电波段被观测到。ORC 直到六年前才第一次被发现，目前天文学家在可观测的宇宙中仅确认少数几个，每一个的尺寸都比我们的银河系大十倍以上。至于其成因，天文学界原本推测可能与星系合并或超大质量黑洞碰撞所产生的冲击波有关。而最新研究提出另一种解释：这些巨环或许是螺旋星系在喷发「超风外流」（superwind outflow）时形成的。这种超风由星遽增（starburst）活动所驱动，能将能量与物质吹送至星系之外，甚至扩展成庞大的电波泡泡。在某些情况下，黑洞活动也可能参与其中，使外流更为剧烈。根据这项研究，研究团队发现的奇异电波圈编号为 RAD J131346.9+500320，距离我们极为遥远，观测到的光线对应于宇宙年龄仅为现今一半时的景象。它是目前已知最远且电波最强的奇异电波圈。更特别的是，它拥有两个彼此交错的环状结构，目前仅有两个已知的奇异电波圈呈现出这种双环交错的结构。这些观测结果显示，奇异电波圈可能是星系与超大质量黑洞共同成长的线索，由黑洞喷流、星系风与周围环境交织而成的庞大等离子体结构。

2025 年诺贝尔生理学或医学奖授予了美国科学家 Mary E. Brunkow、Fred Ramsdell 和日本科学家 Shimon Sakaguchi，以表彰他们在防止免疫系统伤害身体的外周免疫耐受方面做出的开创性发现。人体强大的免疫系统必须受到调控，否则它可能攻击我们自身的器官。每天，我们的免疫系统会保护我们免受成千上万种不同微生物的入侵。它们都有不同的外观，其中许多还进化出与人类细胞相似的特征作为伪装。那么免疫系统是如何决定它应该攻击什么，防御什么呢？今年的获奖者识别出了免疫系统的"保安"——调节性T细胞，这些细胞能阻止免疫细胞攻击我们自身的身体。

由 AI 辅助的蛋白工程正在蛋白设计领域实现突破，但它们同时也带来了与产生潜在有害蛋白相关的生物安全挑战。实验室制造蛋白的必要步骤是订购编码该蛋白的 DNA。提供这些合成核酸的公司会用生物安全筛查软件（BSS）筛选客户订单，旨在发现和阻断可编码令人担忧蛋白的基因。而 AI 设计的氨基酸序列可能会因为差异足够大而逃避检测。根据发表在《科学》期刊上的一项研究，研究人员采用一种“AI 红队演练”法来评估 BSS 模型，旨在改进这些模型以增强生物安全性。他们利用开源 AI 蛋白质设计软件生成了超过 7 万 5000 种蛋白危险变体，并将其提交给四家不同的 BSS 开发商；他们发现，虽然所有工具在筛选原始野生型蛋白质时表现近乎完美，但它们检测重新设计变体的能力却不稳定。这些结果表明，尽管当前的 BSS 系统对未改变的序列仍然有效，但在面对通过现代生成式 AI 方法设计的蛋白序列同源物时，它们仍缺乏稳定一致的灵敏度。研究人员与 BSS 供应商合作开发了软件补丁，并由四家 BSS 中的三家部署到其系统之中。这些更新提高了该软件对 AI 生成变体的检测率，但假阳性却并未显著增加。