AdaptiveEmbed：面向多模态检索的样本自适应多向量表征
这篇 arXiv 论文提出 AdaptiveEmbed，一种用于多模态检索的样本自适应多向量表征方法。论文指出，多向量表征可通过为每个样本提供多个互补嵌入，更细粒度地捕捉跨模态信息。现有方法通常采用固定的多向量配置，而该工作关注根据样本特征进行自适应表征的可能性。该方向有望为多模态搜索、图文检索及 Agent 检索模块带来更具针对性的嵌入设计。
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以前瞻提升训练效率：用于自回归图像生成的多 Token 辅助监督
该论文以自回归（AR）图像生成为研究对象，将图像建模为离散 Token 序列，以实现可扩展的高保真生成。论文指出，传统的下一 Token 预测（NTP）训练仍面临监督信号稀疏且短视的问题。根据标题，研究提出多 Token 辅助监督，以增强训练过程中的前瞻性和效率。该工作以 arXiv:2608.25386v1 形式发布，原始摘要未提供具体实验数据和性能结论。
RSFusionDet：水下 RGB-声呐多模态目标检测
论文《RSFusionDet：水下 RGB-声呐多模态目标检测》以 arXiv:2608.25367v1 的新论文形式发布。研究聚焦水下单模态目标检测面临的传感器成像挑战：光学图像易受水下噪声和可视距离限制，声呐图像则缺少充分的目标结构信息。该工作提出将 RGB 光学图像与声呐信息结合，用于提升水下目标检测能力。其意义在于为水下机器人、海洋观测和自主巡检等应用提供多模态感知方案参考。
MulVec：面向免训练零样本组合图像检索的细粒度角色感知匹配
该论文提出 MulVec，聚焦免训练的零样本组合图像检索任务：系统根据参考图像和文本编辑指令，在图库中检索目标图像。论文强调，该方法无需使用任务特定的图像三元组进行训练。摘要指出，现有方法通常将目标图像作为整体进行描述和匹配，而 MulVec 旨在通过细粒度、角色感知的匹配改善这一问题。该研究对希望在无需额外任务训练数据的条件下构建组合式图像搜索与编辑检索能力的开发者具有参考价值。
降低 AI 驱动检测门槛：用于自动化结构缺陷检测的无代码工作流
该论文提出一种面向自动化结构缺陷检测的无代码工作流，旨在降低 AI 驱动结构检测的使用门槛。论文指出，结构健康监测（SHM）是现代工程中的关键环节，可为基于状态的维护、全生命周期评估和预测性决策提供数据支持。传统 SHM 通常依赖人工目视检查以识别裂缝等缺陷；该研究的意义在于推动 AI 检测能力向更易部署和使用的工作流演进。原文提供的摘要内容不完整，未披露具体实验数据或性能结论。
用于商业果园早期绿色果实解剖结构分类的轻量级多模态视觉语言框架
该研究提出了一种面向商业果园的轻量级多模态视觉语言框架，用于早期绿色苹果幼果的解剖结构分类。其识别目标包括花萼、幼果主体和果梗等关键结构，这些信息对机器人疏果、作物负载管理及其他精准果园作业十分重要。原文摘要指出，准确识别这些早期果实结构是实现自动化果园管理的基础。所提供原文未包含模型架构、实验数据或性能对比等进一步细节。
融合感知视觉专家与多模态大语言模型，实现可解释的植物病害诊断：从基准图像到真实世界机器人田间验证
研究团队提出混合分层多智能体框架（H$^{2}$MAF），在决策层融合 EfficientNet-B3 和 ConvNeXt-Tiny，并由 Gemma 4 E4B、Qwen3.5 4B 等开源权重多模态大语言模型进行语义仲裁。该系统基于结构化 JSON 证据输出可解释诊断、风险等级、治疗紧急程度和财务风险评估，并在 PlantDoc 及两套康奈尔大学机器人田间数据集上完成测试。在 PlantDoc 上，Gemma 将准确率从 63.9% 提升至 68.5%，在 CNN 预测冲突的 41.7% 样本中提升 7.6 个百分点；在康奈尔田间数据集上的准确率分别达到 99.3% 和 98.9%。研究表明，多模态大语言模型可在视觉模型意见冲突时提供有效仲裁，但风险评估能力高度依赖校准：Gemma 的关键风险误差较低，而 Qwen 存在较明显的风险过度标记问题。
网球多模态伤病风险预测
该研究探索利用多模态数据预测网球运动员的伤病风险。论文指出，机器学习已显著促进运动表现和伤病风险预测，但现有研究大多依赖主观观察与专家评估，因此效果受到限制。对于网球等运动项目，更丰富、客观的多模态数据有望提升伤病风险评估的准确性，并为教练、运动队医和开发者构建预防性分析工具提供参考。
Understanding the Energy Scaling of Large Language Model Inference Across Context Lengths and Attention Architectures
arXiv:2608.25096v1 Announce Type: new Abstract: The growing adoption of large language models (LLMs) has raised increasing concerns about the energy consumption and environmental impact of inference. This paper presents a systematic empirical study of decode-phase energy consumption across represen
MSR-IVA: Masked Structural Residual Independent Vector Analysis for State-Aware Fusion of Structural MRI and Dynamic Functional Network Connectivity
arXiv:2608.24978v1 Announce Type: new Abstract: Multimodal fusion of structural MRI (sMRI) and dynamic functional network connectivity (dFNC) can reveal how brain structure relates to changing functional states. When the same structural latent representation is coupled with multiple states, applyin
AFDBench: A Reasoning-First AI Scientist for NationalWeather Service Forecast Discussions
arXiv:2608.24954v1 Announce Type: new Abstract: Large language models (LLMs) hallucinate numerical values when generating high-stakes meteorological text, posing risks for weather communication. We present AFDBench, an AI meteorologist that generates professional Area Forecast Discussions (AFDs) by
Demystifying Reinforcement Learning Post-Training of Language Models
arXiv:2608.24949v1 Announce Type: new Abstract: Reinforcement learning (RL) post-training has emerged as a powerful framework for enhancing the capabilities of large language models (LLMs), enabling impressive reasoning, math, and coding capabilities. Yet for many researchers and practitioners, the
CAT-GS: Balanced Multimodal Learning via Calibrated Gating and Fusion Surgery
arXiv:2608.24947v1 Announce Type: new Abstract: End-to-end training of multimodal neural networks often exhibits unstable neural dynamics characterized by three coupled failure modes that degrade learning: (i) modality imbalance, where one branch dominates gradient-based optimization; (ii) unstable
MacroAgent: Regularity-Aware Macro Legalization with LLM-Agent-Designed Contour Algorithms
arXiv:2608.24946v1 Announce Type: new Abstract: Macros constitute a large part of the core area in modern very large-scale integration (VLSI) designs. Moreover, macro positions have a significant impact on the final quality of result (QoR), and macro legalization is typically the final step in dete
GreenLeaf Law Embed Tiny: A Compact Embedding Model for Legal Domain Retrieval
arXiv:2608.24936v1 Announce Type: new Abstract: We present GreenLeaf Law Embed Tiny, a 0.6B parameter embedding model for legal domain retrieval. GreenLeaf-Tiny achieves 75.11% on the Massive Legal Embedding Benchmark (MLEB) and 64.38% on MTEB(Law, v1),demonstrating competitive performance among mo
VietAIDetector: An Open-Source Zero-Shot Detector for Vietnamese AI-Generated Text
arXiv:2608.25478v1 Announce Type: new Abstract: In recent years, distinguishing between AI-generated text and human-written text has remained a challenge. In this paper, we introduce VietAIDetector, an open-source tool designed specifically for detecting Vietnamese AI-generated text. It allows user
MathAdv: What Theorem Provers Know, Reason, Formalize, and Generalize
arXiv:2608.25449v1 Announce Type: new Abstract: Formal theorem proving enables machine-verifiable evaluation of mathematical reasoning, yet existing benchmarks often emphasize aggregate proof accuracy, concentrate on a narrow range of mathematics, and provide limited evidence of robustness to equiv
DCGC: Draft-Conditioned Global Correction for Complex Reasoning with Masked Diffusion Models
arXiv:2608.25428v1 Announce Type: new Abstract: Correcting flawed reasoning traces remains a significant challenge for Large Language Models (LLMs), whose autoregressive generation can propagate early mistakes into subsequent reasoning. We introduce DCGC, a Masked Diffusion Model (MDM) framework fo
OmniPhys: A Unified Multimodal Benchmark for Physics Understanding and Generation from Chinese Educational Corpora
arXiv:2608.25398v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated strong abilities in solving diverse visual and textual reasoning tasks. However, their development in the physics domain is significantly hindered by the lack of a comprehensive benchmark. To
Adaptive Triggering for Bias Correction in LLM Reasoning
arXiv:2608.25379v1 Announce Type: new Abstract: Chain-of-thought prompting can expose and amplify demographic stereotypes within an LLM's intermediate reasoning and create a failure mode that final-answer debiasing alone cannot address. Mitigating such bias during generation presents a fundamental
Routed Graph Handoff: Adaptive Format Selection for Multi-Agent LLM Delegation
arXiv:2608.25277v1 Announce Type: new Abstract: Multi-agent LLM systems coordinate through natural-language messages that consume 40--60\% of their token budget. Replacing these with structured graphs reduces cost but fails on tasks requiring adaptive reasoning. We propose \textbf{Routed Graph Hand
From Memorization to Absorption: Mixed-Policy RL for Continual Knowledge Injection
arXiv:2608.25243v1 Announce Type: new Abstract: Continual knowledge injection is essential for keeping large language models up-to-date in a fast-evolving world. Existing methods rely on supervised fine-tuning (SFT), which memorizes injected facts in their training format but fails to generalize ac
Belief Cascades Drive Persuasion in LLM Agent Networks
arXiv:2608.25152v1 Announce Type: new Abstract: Multi-agent LLM systems increasingly debate answers, coordinate research, simulate users, and mediate information flows, making agent-to-agent persuasion a basic but undermeasured capability. We introduce a controlled testbed for studying how goal-dir
SelfGraphRAG: Bridging the Supervision Gap in Graph-Based RAG with Synthetic QA Generation
arXiv:2608.25123v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) improves large language models by incorporating external knowledge without retraining, but existing methods often underuse the relational structure encoded in knowledge graphs. Graph-based RAG can capture entity re
Less can be More: Relieving RAG Bottlenecks via Evidence Frontloading and Pressure-Adaptive Budgeting
arXiv:2608.25115v1 Announce Type: new Abstract: Existing methods for improving Retrieval-Augmented Generation (RAG) efficiency mainly optimize downstream LLM generation, such as context compression or serving optimization. However, RAG is an end-to-end system, and its bottleneck can shift between u
MTDiag: A Multi-Turn Diagnostic Dataset Towards Clinically Meaningful LLM Evaluation
arXiv:2608.25085v1 Announce Type: new Abstract: Clinical diagnosis is fundamentally interactive and incremental, yet the dominant paradigm for evaluating Large Language Models (LLMs) in medicine remains static QA benchmarks or template-based dialogues. These benchmarks say little about whether a mo
HealthBench-Psych: A Mental Health Subset of OpenAI's HealthBench
arXiv:2608.25071v1 Announce Type: new Abstract: General-purpose health benchmarks increasingly anchor claims about LLM medical performance, but they are not always resolved by clinical specialty, making domain-specific performance hard to isolate. Mental health is of acute public-health concern as
Padamitra: Grounded Glossary Generation for Classical Sanskrit
arXiv:2608.25038v1 Announce Type: new Abstract: We introduce grounded glossary generation, a structured task requiring models to recover semantically meaningful Sanskrit phrases and produce translation-grounded meanings from a sloka-translation pair, formalizing the traditional patha commentary pra
A Primer on Computational Semantics for Artificial Intelligence Systems
arXiv:2608.25022v1 Announce Type: new Abstract: As people adopt transformer-based language models (e.g., ChatGPT and Gemini) for an increasing number of use-cases, it is important to know how such models learn and represent the meaning of the language, and to be more informed about what language is
The Imperfective Paradox Is Not Necessarily in Large Language Models: A Benchmark Failure Before a Model Failure
arXiv:2608.25005v1 Announce Type: new Abstract: The imperfective paradox provides a useful test of compositional semantic analysis. Recent work constructs an NLI benchmark and reports that models frequently infer completed telic events from progressive descriptions, attributing this behavior to a T