OrangeBot.AI Digest — 2025-06-23

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Language model inversion seeks to recover hidden prompts using only language model outputs. This capability has implications for security and accountability in language model deployments, such as leaking private information from an API-protected language model's system message. We propose a new method -- prompt inversion from logprob sequences (PILS) -- that recovers hidden prompts by gleaning clues from the model's next-token probabilities over the course of multiple generation steps. Our method is enabled by a key insight: The vector-valued outputs of a language model occupy a low-dimensional subspace. This enables us to losslessly compress the full next-token probability distribution over multiple generation steps using a linear map, allowing more output information to be used for inversion. Our approach yields massive gains over previous state-of-the-art methods for recovering hidden prompts, achieving 2--3.5 times higher exact recovery rates across test sets, in one case increasing the recovery rate from 17% to 60%. Our method also exhibits surprisingly good generalization behavior; for instance, an inverter trained on 16 generations steps gets 5--27 points higher prompt recovery when we increase the number of steps to 32 at test time. Furthermore, we demonstrate strong performance of our method on the more challenging task of recovering hidden system messages. We also analyze the role of verbatim repetition in prompt recovery and propose a new method for cross-family model transfer for logit-based inverters. Our findings show that next-token probabilities are a considerably more vulnerable attack surface for inversion attacks than previously known.

Watermarking the outputs of generative models has emerged as a promising approach for tracking their provenance. Despite significant interest in autoregressive image generation models and their potential for misuse, no prior work has attempted to watermark their outputs at the token level. In this work, we present the first such approach by adapting language model watermarking techniques to this setting. We identify a key challenge: the lack of reverse cycle-consistency (RCC), wherein re-tokenizing generated image tokens significantly alters the token sequence, effectively erasing the watermark. To address this and to make our method robust to common image transformations, neural compression, and removal attacks, we introduce (i) a custom tokenizer-detokenizer finetuning procedure that improves RCC, and (ii) a complementary watermark synchronization layer. As our experiments demonstrate, our approach enables reliable and robust watermark detection with theoretically grounded p-values.

Combining pre-trained expert models offers substantial potential for scalable multimodal reasoning, but building a unified framework remains challenging due to the increasing diversity of input modalities and task complexity. For instance, medical diagnosis requires precise reasoning over structured clinical tables, while financial forecasting depends on interpreting plot-based data to make informed predictions. To tackle this challenge, we introduce MEXA, a training-free framework that performs modality- and task-aware aggregation of multiple expert models to enable effective multimodal reasoning across diverse and distinct domains. MEXA dynamically selects expert models based on the input modality and the task-specific reasoning demands (i.e., skills). Each expert model, specialized in a modality task pair, generates interpretable textual reasoning outputs. MEXA then aggregates and reasons over these outputs using a Large Reasoning Model (LRM) to produce the final answer. This modular design allows flexible and transparent multimodal reasoning across diverse domains without additional training overhead. We extensively evaluate our approach on diverse multimodal benchmarks, including Video Reasoning, Audio Reasoning, 3D Understanding, and Medical QA. MEXA consistently delivers performance improvements over strong multimodal baselines, highlighting the effectiveness and broad applicability of our expert-driven selection and aggregation in diverse multimodal reasoning tasks.

Vision-language models (VLMs) excel at multimodal understanding, yet their text-only decoding forces them to verbalize visual reasoning, limiting performance on tasks that demand visual imagination. Recent attempts train VLMs to render explicit images, but the heavy image-generation pre-training often hinders the reasoning ability. Inspired by the way humans reason with mental imagery-the internal construction and manipulation of visual cues-we investigate whether VLMs can reason through interleaved multimodal trajectories without producing explicit images. To this end, we present a Machine Mental Imagery framework, dubbed as Mirage, which augments VLM decoding with latent visual tokens alongside ordinary text. Concretely, whenever the model chooses to ``think visually'', it recasts its hidden states as next tokens, thereby continuing a multimodal trajectory without generating pixel-level images. Begin by supervising the latent tokens through distillation from ground-truth image embeddings, we then switch to text-only supervision to make the latent trajectory align tightly with the task objective. A subsequent reinforcement learning stage further enhances the multimodal reasoning capability. Experiments on diverse benchmarks demonstrate that Mirage unlocks stronger multimodal reasoning without explicit image generation.

One promise that Vision-Language-Action (VLA) models hold over traditional imitation learning for robotics is to leverage the broad generalization capabilities of large Vision-Language Models (VLMs) to produce versatile, "generalist" robot policies. However, current evaluations of VLAs remain insufficient. Traditional imitation learning benchmarks are unsuitable due to the lack of language instructions. Emerging benchmarks for VLAs that incorporate language often come with limited evaluation tasks and do not intend to investigate how much VLM pretraining truly contributes to the generalization capabilities of the downstream robotic policy. Meanwhile, much research relies on real-world robot setups designed in isolation by different institutions, which creates a barrier for reproducibility and accessibility. To address this gap, we introduce a unified probing suite of 50 simulation-based tasks across 10 subcategories spanning language instruction, vision, and objects. We systematically evaluate several state-of-the-art VLA architectures on this suite to understand their generalization capability. Our results show that while VLM backbones endow VLAs with robust perceptual understanding and high level planning, which we refer to as good intentions, this does not reliably translate into precise motor execution: when faced with out-of-distribution observations, policies often exhibit coherent intentions, but falter in action execution. Moreover, finetuning on action data can erode the original VLM's generalist reasoning abilities. We release our task suite and evaluation code to serve as a standardized benchmark for future VLAs and to drive research on closing the perception-to-action gap. More information, including the source code, can be found at https://ai4ce.github.io/INT-ACT/

State-of-the-art text-to-speech (TTS) systems realize high naturalness in monolingual environments, synthesizing speech with correct multilingual accents (especially for Indic languages) and context-relevant emotions still poses difficulty owing to cultural nuance discrepancies in current frameworks. This paper introduces a new TTS architecture integrating accent along with preserving transliteration with multi-scale emotion modelling, in particularly tuned for Hindi and Indian English accent. Our approach extends the Parler-TTS model by integrating A language-specific phoneme alignment hybrid encoder-decoder architecture, and culture-sensitive emotion embedding layers trained on native speaker corpora, as well as incorporating a dynamic accent code switching with residual vector quantization. Quantitative tests demonstrate 23.7% improvement in accent accuracy (Word Error Rate reduction from 15.4% to 11.8%) and 85.3% emotion recognition accuracy from native listeners, surpassing METTS and VECL-TTS baselines. The novelty of the system is that it can mix code in real time - generating statements such as "Namaste, let's talk about <Hindi phrase>" with uninterrupted accent shifts while preserving emotional consistency. Subjective evaluation with 200 users reported a mean opinion score (MOS) of 4.2/5 for cultural correctness, much better than existing multilingual systems (p<0.01). This research makes cross-lingual synthesis more feasible by showcasing scalable accent-emotion disentanglement, with direct application in South Asian EdTech and accessibility software.

Unified image understanding and generation has emerged as a promising paradigm in multimodal artificial intelligence. Despite recent progress, the optimal architectural design for such unified models remains an open challenge. In this work, we start by analyzing the modality alignment behaviors of task-specific expert models for understanding and generation, as well as current unified models. Our analysis reveals a crucial observation: understanding tasks benefit from a progressively increasing modality alignment across network depth, which helps build up semantic information for better comprehension; In contrast, generation tasks follow a different trend: modality alignment increases in the early layers but decreases in the deep layers to recover spatial details. These divergent alignment patterns create a fundamental conflict in fully shared Transformer backbones, where a uniform representational flow often leads to performance compromises across two tasks. Motivated by this finding, we introduce UniFork, a novel Y-shaped architecture that shares the shallow layers for cross-task representation learning, while employing task-specific branches in deeper layers to avoid task interference. This design effectively balances shared learning and task specialization. Through extensive ablation experiments, we demonstrate that Unifork consistently outperforms conventional fully shared Transformer architectures, and achieves performance on par with or better than task-specific models.

An ideal traffic simulator replicates the realistic long-term point-to-point trip that a self-driving system experiences during deployment. Prior models and benchmarks focus on closed-loop motion simulation for initial agents in a scene. This is problematic for long-term simulation. Agents enter and exit the scene as the ego vehicle enters new regions. We propose InfGen, a unified next-token prediction model that performs interleaved closed-loop motion simulation and scene generation. InfGen automatically switches between closed-loop motion simulation and scene generation mode. It enables stable long-term rollout simulation. InfGen performs at the state-of-the-art in short-term (9s) traffic simulation, and significantly outperforms all other methods in long-term (30s) simulation. The code and model of InfGen will be released at https://orangesodahub.github.io/InfGen

3D AI-generated content (AIGC) is a passionate field that has significantly accelerated the creation of 3D models in gaming, film, and design. Despite the development of several groundbreaking models that have revolutionized 3D generation, the field remains largely accessible only to researchers, developers, and designers due to the complexities involved in collecting, processing, and training 3D models. To address these challenges, we introduce Hunyuan3D 2.1 as a case study in this tutorial. This tutorial offers a comprehensive, step-by-step guide on processing 3D data, training a 3D generative model, and evaluating its performance using Hunyuan3D 2.1, an advanced system for producing high-resolution, textured 3D assets. The system comprises two core components: the Hunyuan3D-DiT for shape generation and the Hunyuan3D-Paint for texture synthesis. We will explore the entire workflow, including data preparation, model architecture, training strategies, evaluation metrics, and deployment. By the conclusion of this tutorial, you will have the knowledge to finetune or develop a robust 3D generative model suitable for applications in gaming, virtual reality, and industrial design.

Modern multimodal large language models (MLLMs) can reason over hour-long video, yet their key-value (KV) cache grows linearly with time--quickly exceeding the fixed memory of phones, AR glasses, and edge robots. Prior compression schemes either assume the whole video and user query are available offline or must first build the full cache, so memory still scales with stream length. InfiniPot-V is the first training-free, query-agnostic framework that enforces a hard, length-independent memory cap for streaming video understanding. During video encoding it monitors the cache and, once a user-set threshold is reached, runs a lightweight compression pass that (i) removes temporally redundant tokens via Temporal-axis Redundancy (TaR) metric and (ii) keeps semantically significant tokens via Value-Norm (VaN) ranking. Across four open-source MLLMs and four long-video and two streaming-video benchmarks, InfiniPot-V cuts peak GPU memory by up to 94%, sustains real-time generation, and matches or surpasses full-cache accuracy--even in multi-turn dialogues. By dissolving the KV cache bottleneck without retraining or query knowledge, InfiniPot-V closes the gap for on-device streaming video assistants.

In visual generation, the quadratic complexity of attention mechanisms results in high memory and computational costs, especially for longer token sequences required in high-resolution image or multi-frame video generation. To address this, prior research has explored techniques such as sparsification and quantization. However, these techniques face significant challenges under low density and reduced bitwidths. Through systematic analysis, we identify that the core difficulty stems from the dispersed and irregular characteristics of visual attention patterns. Therefore, instead of introducing specialized sparsification and quantization design to accommodate such patterns, we propose an alternative strategy: *reorganizing* the attention pattern to alleviate the challenges. Inspired by the local aggregation nature of visual feature extraction, we design a novel **Pattern-Aware token ReOrdering (PARO)** technique, which unifies the diverse attention patterns into a hardware-friendly block-wise pattern. This unification substantially simplifies and enhances both sparsification and quantization. We evaluate the performance-efficiency trade-offs of various design choices and finalize a methodology tailored for the unified pattern. Our approach, **PAROAttention**, achieves video and image generation with lossless metrics, and nearly identical results from full-precision (FP) baselines, while operating at notably lower density (~20%-30%) and bitwidth (**INT8/INT4**), achieving a **1.9x** to **2.7x** end-to-end latency speedup.

Retrieval-Augmented Generation (RAG) systems have revolutionized information retrieval and question answering, but traditional text-based chunking methods struggle with complex document structures, multi-page tables, embedded figures, and contextual dependencies across page boundaries. We present a novel multimodal document chunking approach that leverages Large Multimodal Models (LMMs) to process PDF documents in batches while maintaining semantic coherence and structural integrity. Our method processes documents in configurable page batches with cross-batch context preservation, enabling accurate handling of tables spanning multiple pages, embedded visual elements, and procedural content. We evaluate our approach on a curated dataset of PDF documents with manually crafted queries, demonstrating improvements in chunk quality and downstream RAG performance. Our vision-guided approach achieves better accuracy compared to traditional vanilla RAG systems, with qualitative analysis showing superior preservation of document structure and semantic coherence.

Modern Parameter-Efficient Fine-Tuning (PEFT) methods such as low-rank adaptation (LoRA) reduce the cost of customizing large language models (LLMs), yet still require a separate optimization run for every downstream dataset. We introduce Drag-and-Drop LLMs (\textit{DnD)}, a prompt-conditioned parameter generator that eliminates per-task training by mapping a handful of unlabeled task prompts directly to LoRA weight updates. A lightweight text encoder distills each prompt batch into condition embeddings, which are then transformed by a cascaded hyper-convolutional decoder into the full set of LoRA matrices. Once trained in a diverse collection of prompt-checkpoint pairs, DnD produces task-specific parameters in seconds, yielding i) up to 12,000times lower overhead than full fine-tuning, ii) average gains up to 30\% in performance over the strongest training LoRAs on unseen common-sense reasoning, math, coding, and multimodal benchmarks, and iii) robust cross-domain generalization despite never seeing the target data or labels. Our results demonstrate that prompt-conditioned parameter generation is a viable alternative to gradient-based adaptation for rapidly specializing LLMs. Our project is available at https://jerryliang24.github.io/DnD{https://jerryliang24.github.io/DnD}.

Generating unbiased summaries in real-world settings such as political perspective summarization remains a crucial application of Large Language Models (LLMs). Yet, existing evaluation frameworks rely on traditional metrics for measuring key attributes such as coverage and faithfulness without verifying their applicability, and efforts to develop improved summarizers are still nascent. We address these gaps by (1) identifying reliable metrics for measuring perspective summary quality, and (2) investigating the efficacy of LLM-based methods beyond zero-shot inference. Namely, we build a test set for benchmarking metric reliability using human annotations and show that traditional metrics underperform compared to language model-based metrics, which prove to be strong evaluators. Using these metrics, we show that reranking-based methods yield strong results, and preference tuning with synthetically generated and reranking-labeled data further boosts performance. Our findings aim to contribute to the reliable evaluation and development of perspective summarization methods.

3D panorama synthesis is a promising yet challenging task that demands high-quality and diverse visual appearance and geometry of the generated omnidirectional content. Existing methods leverage rich image priors from pre-trained 2D foundation models to circumvent the scarcity of 3D panoramic data, but the incompatibility between 3D panoramas and 2D single views limits their effectiveness. In this work, we demonstrate that by applying multi-plane synchronization to the operators from 2D foundation models, their capabilities can be seamlessly extended to the omnidirectional domain. Based on this design, we further introduce DreamCube, a multi-plane RGB-D diffusion model for 3D panorama generation, which maximizes the reuse of 2D foundation model priors to achieve diverse appearances and accurate geometry while maintaining multi-view consistency. Extensive experiments demonstrate the effectiveness of our approach in panoramic image generation, panoramic depth estimation, and 3D scene generation.

根据发表于《自然》的全球作物产量分析，到本世纪末，每变暖 1℃，人均可用粮食每天将减少约121千卡。研究论文作者、美国伊利诺伊大学厄巴纳-香槟分校的 Andrew Hultgren 说，根据目前轨迹，在 3℃ 的变暖情景下，“这相当于每个人都不再用早餐”。Hultgren 和同事收集了世界上六大主粮作物的产量数据。这些作物提供了全球人类所需热量的 2/3 以上。他们发现，对于除水稻外的所有作物，高温会造成巨大的损失，因为水稻在更暖的夜晚生长得更好。根据预测，到本世纪末，全球变暖情况下玉米产量比没有全球变暖的情况下降 12% 或28%，具体下降程度取决于温室气体排放量是适中还是非常高。如果农民不采取措施适应气候变化，那么在本世纪末变暖程度高的情景下，作物损失将增加约1 /3。不过，研究人员指出，即便采取了上述农业适应措施，也不太可能弥补气候变化造成的巨大作物损失。

英特尔新 CEO 陈立武正致力于削减开支重整业务，作为新战略的一部分，芯片巨人正将营销业务外包给 Accenture。英特尔表示相信 Accenture 利用 AI 将能更好的与客户沟通。英特尔表示将在 7 月 11 日前通知大部分营销人员它是否计划裁员。英特尔称，营销和运营职能的转型将导致团队结构的巨大变化，可能导致人员削减，只保留精简的团队。英特尔拒绝披露会有多少员工失去工作，也拒绝透露其营销部门有多少员工。

NASA 宣布 Psyche 探测器切换到了备用燃料管线，继续朝目标灵神星（Psyche）前进。Psyche 探测器于 2023 月 10 月发射升空，其目标灵神星位于火星和木星之间的小行星带，距离地球大约 40 亿公里，被认为含有铁、镍、白金、稀土等元素，潜在价值巨大。探测器预计将在 2029 年抵达。它使用了四个以氙气为燃料的电推进器，比传统火箭推进器更节省燃料。推进器一直工作正常，直到今年 4 月 1 日燃料管线内压力下降，探测器根据压力信号关闭了推进器。好消息是电推进器的一大优势是灵活性，而传统推进器的点火必须指定时间内进行，在切换到备用燃料管线之后，电推进器关闭两个月并没有影响抵达灵神星的时间。NASA 称，导致这起事故的原因被认为是主管线内的一个阀门可能发生故障。

天文学家在观测宇宙最大的星系聚集区域— Shapley 超星系团（Shapley Supercluster）时，发现一条长达约2,300 万光年的细丝状高温星际气体，可能正是寻找失落普通物质的关键线索。这条高温气体位于 Shapley 超星系团中的 4 个次星系团；A3528N、A3528S与A3530、A3532之 间，此细丝状气体以超过一千万度的高温辐射 X 射线，总质量约相当于 10 个银河系。研究指出，这正符合宇宙学模拟中预测，存在宇宙网中的细丝状星际气体的特征。宇宙网是由暗物质构成的庞大纤维状结构，贯穿星系间的星际空间，成为星系之间相互交换气体的通道。 根据宇宙微波背景辐射（CMB）推算，宇宙初期的普通物质数量明确可知；然而，当今宇宙中能直接观测到的恒星、星系、行星、气体与尘埃，仅占预期的一半。在宇宙学中被称为「普通物质遗失问题」。由于物质不会凭空消失，因此这些「消失的」普通物质究竟隐藏在何处，一直是近年来天文观测与理论研究的重要议题。这项发现提供了有力证据，支持「失落普通物质」分布于星系间极度稀薄、难以直接探测的宇宙网细丝状结构中的说法。

地球正在快速变暖，但你知道吗？早在 3 亿多年前，类似的气候剧变就曾引发海洋生命的巨大波动。南京大学研究团队在《Science Advances》发表研究报告：在晚古生代（约3.4亿至2.5亿年前），地球缓慢变冷时，海洋生物加速演化、种类剧增；而一旦气候急剧变暖，尤其是火山喷发带来的升温，便引发大规模物种灭绝。研究主角是一群叫做䗴类有孔虫（fusuline）的远古单细胞海洋生物，它们个体虽小，但数量惊人（图1），曾主宰海底世界，被誉为“碳酸盐岩工厂”。团队发现，9180 多万年间，这些生物经历了两次“多样化爆发”和四次“灭绝危机”。尤其在 2.6亿 年前峨眉山大规模火山喷发前后，体型较大的䗴类几乎绝迹；而 2.52 亿年前的二叠纪末期超级火山事件，更彻底终结了这个庞大家族的演化历程。值得警惕的是，人类活动引发的现代全球变暖，其速度已远超古代峨眉山玄武岩和二叠纪末火山事件带来的变暖速率。当前的海洋生态系统或正经历类似䗴类曾遭遇的命运考验。

在人类的演化过程中，寄生虫如头虱、跳蚤和绦虫一直伴随左右。但现代最强大的寄生物并非是吸血的无脊椎动物，而是智能手机。智能手机寄生于我们的时间、注意力和个人信息，为科技公司及其广告商谋利。从演化和寄生的角度看，智能手机对社会构成了独一无二的风险。寄生虫的生存依赖于宿主，离开宿主会很快死亡，以头虱为例，它给人类带来的代价主要是痒。智能手机改变了我们的生活，以至于很多人都离不开它。它带来的代价是一部分人沦为其奴隶，导致睡眠不足、线下关系薄弱以及情绪紊乱。人类与智能手机的关系一开始是互利共生（mutualism），但逐渐的演变为寄生关系。它提供的流行应用不是为了用户的利益，而是通过操纵我们的行为和情绪为其开发商和广告商谋利。用户是宿主，而智能手机就是寄生物。我们需要对其进行限制，至少能恢复部分互利共生的关系，但科技寡头们的实力非普通人能抵挡。

中科院的研究人员报告成功培育出一只来自双父亲的小鼠，并且健康成年。生物铭印（Biological imprinting）是一种基因表达的遗传模式，涉及到特定基因的激活或关闭，取决于它们来自父亲还是母亲。研究人员发现，如果调整铭印基因的选择，能为独特的生殖能力开启大门，如完全来自父系的胚胎。当小鼠胚胎以正常方式形成时，父系和母系 DNA 结合在一起。此种结合产生了铭印基因的精确平衡。仅有父系的胚胎中，与生长相关的特定基因可能会被过度刺激，研究团队选择性地修改这些基因，使得纯父系胚胎能成熟。研究负责人表示，该研究提供了强有力的证据，表明铭印基因异常是哺乳动物单性生殖的主要障碍。