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      "source": "arxiv",
      "fetched_at": "2026-07-14T11:12:23.148Z",
      "count": 50,
      "items": [
        {
          "id": "arxiv_2607.11881v1",
          "title": "Metacognition in LLMs: Foundations, Progress, and Opportunities",
          "url": "http://arxiv.org/abs/2607.11881v1",
          "summary": "Metacognition is a foundational component of intelligence critical to effective learning, problem solving, decision-making, communication, and more. In recent years, it has become increasingly recogni",
          "authors": [
            "Gabrielle Kaili-May Liu",
            "Areeb Gani",
            "Jacqueline Lu",
            "Jordan Thomas"
          ],
          "published": "2026-07-13T17:58:37Z"
        },
        {
          "id": "arxiv_2607.11875v1",
          "title": "Invariant Learning Dynamics of Transformers in Inductive Reasoning Tasks",
          "url": "http://arxiv.org/abs/2607.11875v1",
          "summary": "We present a theoretical framework to explain the emergence of inductive reasoning abilities in Transformer language models. While previous works on Transformer learning dynamics have so far been most",
          "authors": [
            "Tiberiu Musat",
            "Tiago Pimentel",
            "Nicholas Zucchet",
            "Thomas Hofmann"
          ],
          "published": "2026-07-13T17:56:11Z"
        },
        {
          "id": "arxiv_2607.11874v1",
          "title": "A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation",
          "url": "http://arxiv.org/abs/2607.11874v1",
          "summary": "Recent work in humanoid whole-body control has found success with a simple recipe: retarget human motion to robot kinematic references, then train policies via reinforcement learning (RL) to track the",
          "authors": [
            "Yunhai Feng",
            "Natalie Leung",
            "Jiaxuan Wang",
            "Lujie Yang"
          ],
          "published": "2026-07-13T17:56:08Z"
        },
        {
          "id": "arxiv_2607.11871v1",
          "title": "Inside the Unfair Judge: A Mechanistic Interpretability Account of LLM-as-Judge Bias",
          "url": "http://arxiv.org/abs/2607.11871v1",
          "summary": "Existing studies of LLM-as-judge scoring bias work predominantly at the input-output level: they perturb inputs, measure score deltas, and propose prompt-level mitigations. We argue that the same bias",
          "authors": [
            "Zixiang Xu",
            "Sixian Li",
            "Huaxing Liu",
            "Xiang Wang"
          ],
          "published": "2026-07-13T17:55:19Z"
        },
        {
          "id": "arxiv_2607.11862v1",
          "title": "Evidence-Backed Video Question Answering",
          "url": "http://arxiv.org/abs/2607.11862v1",
          "summary": "Current Video Large Language Models (Video LLMs) excel in question answering (QA) but largely operate as black boxes, providing textual answers without verifiable visual grounding. Existing explainabi",
          "authors": [
            "Shijie Wang",
            "Honglu Zhou",
            "Ziyang Wang",
            "Ran Xu"
          ],
          "published": "2026-07-13T17:49:10Z"
        },
        {
          "id": "arxiv_2607.11839v1",
          "title": "LoRA-Based Cascaded Multimodal Fusion for Action Recognition in Medical Training Environments",
          "url": "http://arxiv.org/abs/2607.11839v1",
          "summary": "This paper presents a cascaded Low-Rank Adaptation (LoRA)-based multimodal fusion framework for action and activity recognition in healthcare-oriented training environments. The proposed architecture ",
          "authors": [
            "Divya Mereddy",
            "Jeevan Beedareddy"
          ],
          "published": "2026-07-13T17:27:58Z"
        },
        {
          "id": "arxiv_2607.11826v1",
          "title": "Transformer-Guided Swarm Intelligence for Frugal Neural Architecture Search",
          "url": "http://arxiv.org/abs/2607.11826v1",
          "summary": "Neural Architecture Search (NAS) has automated the design of deep learning models but traditionally requires massive computational resources, often measured in thousands of GPU-days. In this paper, we",
          "authors": [
            "Romain Amigon"
          ],
          "published": "2026-07-13T17:18:24Z"
        },
        {
          "id": "arxiv_2607.11818v1",
          "title": "MM-ToolSandBox: A Unified Framework for Evaluating Visual Tool-Calling Agents",
          "url": "http://arxiv.org/abs/2607.11818v1",
          "summary": "We introduce MM-ToolSandBox, a benchmark and evaluation framework for visually grounded tool-calling agents. The framework provides a stateful execution environment spanning 500+ tools across 16 appli",
          "authors": [
            "Kaixin Ma",
            "Di Feng",
            "Alexander Metz",
            "Jiarui Lu"
          ],
          "published": "2026-07-13T17:13:09Z"
        },
        {
          "id": "arxiv_2607.11808v1",
          "title": "Introducing Human-Centeredness in AI-Assisted Lexicography",
          "url": "http://arxiv.org/abs/2607.11808v1",
          "summary": "This paper proposes a human-centered artificial intelligence (HCAI) framework for AI-assisted lexicography. While generative AI offers significant opportunities to enhance lexicographic work, it also ",
          "authors": [
            "Antonio San Martin",
            "Catherine Trekker"
          ],
          "published": "2026-07-13T17:00:39Z"
        },
        {
          "id": "arxiv_2607.11801v1",
          "title": "Encoder-Side Neuron Identification and Amplification for Acoustic Perception in Large Audio-Language Models",
          "url": "http://arxiv.org/abs/2607.11801v1",
          "summary": "Large audio-language models (LALMs) often underperform on fine-grained, non-semantic attributes of speech, such as a speaker's emotion, despite strong performance on speech content. Improving this wit",
          "authors": [
            "Yu-Han Huang",
            "Chih-Kai Yang",
            "Ke-Han Lu",
            "An-Yu Cheng"
          ],
          "published": "2026-07-13T16:53:08Z"
        },
        {
          "id": "arxiv_2607.11798v1",
          "title": "StoryTeller: Training-Free Narrative Grounding for Long-Form Audio Description",
          "url": "http://arxiv.org/abs/2607.11798v1",
          "summary": "Long-form audio description (AD) requires more than describing visible actions: it must preserve characters, events, relationships, and story context across scenes so that blind and low-vision (BLV) a",
          "authors": [
            "Seung Hyun Hahm",
            "Minh T. Dinh",
            "SouYoung Jin"
          ],
          "published": "2026-07-13T16:50:03Z"
        },
        {
          "id": "arxiv_2607.11771v1",
          "title": "Evaluating RE Practices for Explainability: Synthesizing Insights from Daimler Truck into an Explainable RE Framework Proposal",
          "url": "http://arxiv.org/abs/2607.11771v1",
          "summary": "Explainability has emerged as a critical requirement for AI-based systems, particularly in safety-critical and regulated domains. Although prior research has proposed frameworks, patterns, and user-ce",
          "authors": [
            "Umm-e- Habiba",
            "Lucas Mauser",
            "Jonas Fritzsch",
            "Justus Bogner"
          ],
          "published": "2026-07-13T16:28:45Z"
        },
        {
          "id": "arxiv_2607.11749v1",
          "title": "Playful AI in Professional Email: A Field Experiment on Tone and Recipient Engagement",
          "url": "http://arxiv.org/abs/2607.11749v1",
          "summary": "Large language models (LLMs) are rapidly reshaping workplace communication, yet whether AI-assisted writing changes how recipients actually behave, and through what channel, remains unknown. Here, in ",
          "authors": [
            "Ziv Ben-Zion",
            "Teddy Lazebnik"
          ],
          "published": "2026-07-13T16:06:23Z"
        },
        {
          "id": "arxiv_2607.11725v1",
          "title": "Time-Lag-Aware Deep Reinforcement Learning for Flexible Job-Shop Scheduling in PPVC Module Factories",
          "url": "http://arxiv.org/abs/2607.11725v1",
          "summary": "Prefabricated prefinished volumetric construction moves most building work into module factories, whose production floor operates as a flexible job shop. A major complication is decisive: long post-op",
          "authors": [
            "Ziheng Zhang",
            "Wei Zhang"
          ],
          "published": "2026-07-13T15:50:00Z"
        },
        {
          "id": "arxiv_2607.11720v1",
          "title": "Active Offline-to-Online Reinforcement Learning",
          "url": "http://arxiv.org/abs/2607.11720v1",
          "summary": "Background: Offline reinforcement learning (RL) enables effective policies to be trained from large, previously collected datasets and subsequently improved through limited online interaction. This of",
          "authors": [
            "Alper Kamil Bozkurt",
            "Shangtong Zhang",
            "Yuichi Motai"
          ],
          "published": "2026-07-13T15:46:11Z"
        },
        {
          "id": "arxiv_2607.11707v1",
          "title": "An Explainable Agentic System for Detection of Conversational Scams with Summary-Based Memory",
          "url": "http://arxiv.org/abs/2607.11707v1",
          "summary": "Following the rapid progress of generative Artificial Intelligence, there is a growing threat posed by conversational scams. These scams often span over multiple weeks or months, gradually build trust",
          "authors": [
            "Ahmed Omar Salim Adnan",
            "Yogananda Manjunath",
            "Shivanjali Khare"
          ],
          "published": "2026-07-13T15:36:19Z"
        },
        {
          "id": "arxiv_2607.11706v1",
          "title": "VoxENES 2026: Benchmarking Generalization of Speech Spoofing Detectors Against LLM-Era TTS and Voice Conversion",
          "url": "http://arxiv.org/abs/2607.11706v1",
          "summary": "Modern LLM-driven text-to-speech (TTS) and voice conversion (VC) systems produce synthetic speech that differs from the generators represented in many legacy spoofing benchmarks. This mismatch creates",
          "authors": [
            "Aastha Sharma",
            "Guangjing Wang"
          ],
          "published": "2026-07-13T15:35:29Z"
        },
        {
          "id": "arxiv_2607.11698v1",
          "title": "Agent Hacks Agent: Autoresearch for Production-Agent Red-Teaming",
          "url": "http://arxiv.org/abs/2607.11698v1",
          "summary": "Production LLM agents such as Claude Code and Codex operate over untrusted content, files, commands, and workspace state, making safety failures directly actionable. Red-teaming must therefore keep pa",
          "authors": [
            "Xutao Mao",
            "Xiang Zheng",
            "Cong Wang"
          ],
          "published": "2026-07-13T15:31:04Z"
        },
        {
          "id": "arxiv_2607.11696v1",
          "title": "Think Through a Bottleneck: Hourglass Reasoning for Rigorous Induction",
          "url": "http://arxiv.org/abs/2607.11696v1",
          "summary": "Self-refinement often fails to strengthen few-shot inductive reasoning in large language models. Prompting a model to explicitly state its inferred rule does little on its own. What actually matters i",
          "authors": [
            "Huan Zhu"
          ],
          "published": "2026-07-13T15:29:24Z"
        },
        {
          "id": "arxiv_2607.11689v1",
          "title": "From World Action Models to Embodied Brains: A Roadmap for Open-World Physical Intelligence",
          "url": "http://arxiv.org/abs/2607.11689v1",
          "summary": "Artificial general intelligence ultimately requires agents that can reason and act in the physical world. Action models, vision-language-action policies, and world models have advanced this goal, whil",
          "authors": [
            "Yuanzhi Liang",
            "Xufeng Zhan",
            "Haibin Huang",
            "Chi Zhang"
          ],
          "published": "2026-07-13T15:22:56Z"
        },
        {
          "id": "arxiv_2607.11683v1",
          "title": "RAGU: A Multi-Step GraphRAG Engine with a Compact Domain-Adapted LLM",
          "url": "http://arxiv.org/abs/2607.11683v1",
          "summary": "Graph retrieval-augmented generation (GraphRAG) enhances large language models with structured knowledge, yet existing systems construct knowledge graphs in a single extraction pass, producing noisy e",
          "authors": [
            "Mikhail Komarov",
            "Ivan Bondarenko",
            "Stanislav Shtuka",
            "Oleg Sedukhin"
          ],
          "published": "2026-07-13T15:20:51Z"
        },
        {
          "id": "arxiv_2607.11649v1",
          "title": "Closing the Loop: An Access-Control Architecture for Automated, Anomaly-Driven Network Revocation in IoT Deployments",
          "url": "http://arxiv.org/abs/2607.11649v1",
          "summary": "Network-based anomaly detection for IoT devices has matured to the point of reporting strong detection accuracy, yet most published systems stop at raising an alert and leave the question of automated",
          "authors": [
            "Muhammet Emir Korkmaz",
            "Kemal Bicakci",
            "Yusuf Uzunay"
          ],
          "published": "2026-07-13T15:02:09Z"
        },
        {
          "id": "arxiv_2607.11643v1",
          "title": "Xiaomi-Robotics-U0: Unified Embodied Synthesis with World Foundation Model",
          "url": "http://arxiv.org/abs/2607.11643v1",
          "summary": "Recent foundation image and video generation models offer strong generalization and controllability, but their direct application to embodied scenarios is limited by requirements for multi-view consis",
          "authors": [
            "Xinghang Li",
            "Jun Guo",
            "Qiwei Li",
            "Long Qian"
          ],
          "published": "2026-07-13T14:57:58Z"
        },
        {
          "id": "arxiv_2607.11632v1",
          "title": "Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling",
          "url": "http://arxiv.org/abs/2607.11632v1",
          "summary": "Human choice behavior, including route choice, exhibits systematic behavioral biases that deviate from the assumptions of full rationality. Cumulative prospect theory (CPT) has been widely recognized ",
          "authors": [
            "Jiangtao Han",
            "Shoufeng Ma",
            "Shuxian Xu",
            "Geng Li"
          ],
          "published": "2026-07-13T14:48:23Z"
        },
        {
          "id": "arxiv_2607.11621v1",
          "title": "Lesioned Multimodal Language Models Reproduce Aphasic Picture-Naming Patterns",
          "url": "http://arxiv.org/abs/2607.11621v1",
          "summary": "Aphasia following stroke commonly produces systematic naming errors with characteristic profiles, but whether general-purpose language models not designed for clinical simulation can reproduce these p",
          "authors": [
            "Yong Yang",
            "Xiang Guan",
            "Sophie Arheix-Parras",
            "Saeed Ahmadi"
          ],
          "published": "2026-07-13T14:43:31Z"
        },
        {
          "id": "arxiv_2607.11614v1",
          "title": "Extending LLM Context via Associative Recurrent Memory",
          "url": "http://arxiv.org/abs/2607.11614v1",
          "summary": "Extending the context length of large language models (LLMs) is critical for many real-world applications, yet standard transformers remain constrained by quadratic compute and linear memory scaling. ",
          "authors": [
            "Gleb Kuzmin",
            "Ivan Rodkin",
            "Aydar Bulatov",
            "Yuri Kuratov"
          ],
          "published": "2026-07-13T14:37:24Z"
        },
        {
          "id": "arxiv_2607.11607v1",
          "title": "Auditing the Risk Claims of Distributional Reinforcement Learning",
          "url": "http://arxiv.org/abs/2607.11607v1",
          "summary": "Distributional reinforcement learning agents learn full return distributions that are increasingly read at face value: for interpretability, risk-sensitive control, and safety monitoring. We ask a que",
          "authors": [
            "Hari Prasad"
          ],
          "published": "2026-07-13T14:30:01Z"
        },
        {
          "id": "arxiv_2607.11598v1",
          "title": "Interaction Scaling: Grounding the Third Axis of Test-Time Compute",
          "url": "http://arxiv.org/abs/2607.11598v1",
          "summary": "There are two standard ways to spend more compute at test time: let a model reason longer, or sample more attempts and keep one. Both share a hidden limit: they are internal. Every extra token comes f",
          "authors": [
            "Bojie Li",
            "Noah Shi"
          ],
          "published": "2026-07-13T14:22:06Z"
        },
        {
          "id": "arxiv_2607.11594v1",
          "title": "MAGIC: Transition-Aware Generation of Navigable Multi-Scene Game Worlds with Large Language Models",
          "url": "http://arxiv.org/abs/2607.11594v1",
          "summary": "Multi-scene navigation (clearing an objective in one bounded space and then crossing a portal into the next) is a defining feature of contemporary 3D games, but authoring it is laborious: every portal",
          "authors": [
            "Tsz Hei Fan",
            "Choi Wing Fung",
            "Yuxuan Wan",
            "Shuqing Li"
          ],
          "published": "2026-07-13T14:16:33Z"
        },
        {
          "id": "arxiv_2607.11586v1",
          "title": "HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference",
          "url": "http://arxiv.org/abs/2607.11586v1",
          "summary": "Mixture-of-Experts (MoE) large language models (LLM) activate only a small number of experts during inference, but token routing introduces persistent expert hotness skew: a small set of hot experts c",
          "authors": [
            "Yongqin Zhang"
          ],
          "published": "2026-07-13T14:05:55Z"
        },
        {
          "id": "arxiv_2607.11578v1",
          "title": "DiffEEG: A Self-Supervised Denoising Diffusion Model for Learning EEG Generic Representations",
          "url": "http://arxiv.org/abs/2607.11578v1",
          "summary": "Deep learning for EEG-based seizure detection faces critical challenges: severe annotation scarcity and extreme class imbalance, where ictal events comprise less than 10\\% of clinical recordings. We p",
          "authors": [
            "Abdulkader Helwan",
            "Lina Abou-Abbas",
            "Hussein El Amouri",
            "Belkacem Chikhaoui"
          ],
          "published": "2026-07-13T13:59:45Z"
        },
        {
          "id": "arxiv_2607.11577v1",
          "title": "Structure-Feature Aligned Graph Learning via Alternating Constrained Optimization",
          "url": "http://arxiv.org/abs/2607.11577v1",
          "summary": "We introduce a constrained two-view framework for node prediction that aligns structure-conditioned GNN embeddings with a structure-free feature prior learned by an anchor model. Conventional Graph Ne",
          "authors": [
            "Chengcheng Yan",
            "Qingsong Wang"
          ],
          "published": "2026-07-13T13:58:52Z"
        },
        {
          "id": "arxiv_2607.11565v1",
          "title": "Heuristic Learning for Active Flow Control Using Coding Agents",
          "url": "http://arxiv.org/abs/2607.11565v1",
          "summary": "Active flow control involves nonlinear dynamics, partial observations, and computationally expensive simulations, making controller design particularly challenging. Deep reinforcement learning (DRL) h",
          "authors": [
            "Paul Garnier",
            "Jonathan Viquerat",
            "Elie Hachem"
          ],
          "published": "2026-07-13T13:47:17Z"
        },
        {
          "id": "arxiv_2607.11560v1",
          "title": "Technical Report on the CVPR 2026@AdvML Workshop Challenge",
          "url": "http://arxiv.org/abs/2607.11560v1",
          "summary": "Vision-language agents (VLAs) are increasingly used to interpret complex driving scenes and support safety-critical reasoning. This report presents the CVPR 2026@AdvML Workshop Challenge on adversaria",
          "authors": [
            "Tianyuan Zhang",
            "Zonglei Jing",
            "Jiangfan Liu",
            "Ligong Zhang"
          ],
          "published": "2026-07-13T13:42:48Z"
        },
        {
          "id": "arxiv_2607.11530v1",
          "title": "Learning Residual Kinematic Corrections for Continuous Neural Decoding via Reinforcement Learning",
          "url": "http://arxiv.org/abs/2607.11530v1",
          "summary": "Decoding continuous three-dimensional (3D) motor imagery (MI) using non-invasive electroencephalography (EEG)-based brain--computer interfaces (BCIs) remains challenging due to signal variability and ",
          "authors": [
            "Jiamian Li",
            "Niall McShane",
            "Attila Korik",
            "Naomi du Bois"
          ],
          "published": "2026-07-13T13:15:18Z"
        },
        {
          "id": "arxiv_2607.11526v1",
          "title": "AutoMatBench: An Automatic Optimization Toolkit for the Acceleration of Material Properties Prediction Benchmarking",
          "url": "http://arxiv.org/abs/2607.11526v1",
          "summary": "Material property prediction (MPP) infers key properties from chemical composition and structure, accelerating the discovery and optimization of novel materials. In the realm of MPP, MatBench is a wid",
          "authors": [
            "Hongxiao Li",
            "Wanling Gao"
          ],
          "published": "2026-07-13T13:11:40Z"
        },
        {
          "id": "arxiv_2607.11523v1",
          "title": "Vinci2: Providing Proactive Assistance in Continuous Egocentric Videos",
          "url": "http://arxiv.org/abs/2607.11523v1",
          "summary": "When should an intelligent assistant speak up without being asked? Continuous egocentric video offers rich, evolving context that enables a new form of assistance: one that is proactive rather than me",
          "authors": [
            "Gong Sitong",
            "Tianyu Yan",
            "Caixin Kang",
            "Bo Zheng"
          ],
          "published": "2026-07-13T13:09:45Z"
        },
        {
          "id": "arxiv_2607.11512v1",
          "title": "Toward Inclusive Avatar Design with Limb Differences Through Artificial Intelligence",
          "url": "http://arxiv.org/abs/2607.11512v1",
          "summary": "As extended reality becomes more popular for social interaction and entertainment, 3D avatars must represent the full diversity of body types. Most 3D avatar systems only support normative bodies and ",
          "authors": [
            "Fernanda Miyuki Yamada",
            "João Paulo Gois",
            "Hiroki Takahashi"
          ],
          "published": "2026-07-13T13:00:45Z"
        },
        {
          "id": "arxiv_2607.11508v1",
          "title": "CDFM: Towards a General-Purpose Causal Discovery Foundation Model",
          "url": "http://arxiv.org/abs/2607.11508v1",
          "summary": "Causal discovery, the process of recovering underlying causal structures from observational data, is a fundamental pursuit across scientific disciplines. Over the past decades, numerous algorithms hav",
          "authors": [
            "Jie Qiao",
            "Ruichu Cai",
            "Zijian Li",
            "Weilin Chen"
          ],
          "published": "2026-07-13T12:58:28Z"
        },
        {
          "id": "arxiv_2607.11505v1",
          "title": "Proxy Exploration and Reusable Guidance: A Modular LLM Post-Training Paradigm via Proxy-Guided Update Signals",
          "url": "http://arxiv.org/abs/2607.11505v1",
          "summary": "Post-training is essential for refining the domain-specific capabilities of large language models (LLMs), yet existing reward optimization and distribution matching methods tightly couple policy explo",
          "authors": [
            "Daocheng Fu",
            "Rong Wu",
            "Yu Yang",
            "Xuemeng Yang"
          ],
          "published": "2026-07-13T12:56:21Z"
        },
        {
          "id": "arxiv_2607.11501v1",
          "title": "Comparative Analysis of GAT and BERT for Human-Like Playtesting",
          "url": "http://arxiv.org/abs/2607.11501v1",
          "summary": "Accurately modeling and understanding player experience is crucial for designing engaging puzzle games. To achieve this, a common approach involves collecting diverse user data to train predictive pla",
          "authors": [
            "Kleio Fragkedaki",
            "Theodoros Panagiotakopoulos",
            "Matteo Biasielli",
            "Hui Wang"
          ],
          "published": "2026-07-13T12:54:18Z"
        },
        {
          "id": "arxiv_2607.11498v1",
          "title": "See like a Robot: Robot-Centric Pointmaps for Vision-Language-Action Models",
          "url": "http://arxiv.org/abs/2607.11498v1",
          "summary": "Vision-language-action (VLA) models predict robot actions from visual observations and language instructions. These actions are defined in the robot's own 3D coordinate frame, yet most VLAs observe th",
          "authors": [
            "Byungkun Lee",
            "Dongyoon Hwang",
            "Dongjin Kim",
            "Hojoon Lee"
          ],
          "published": "2026-07-13T12:50:32Z"
        },
        {
          "id": "arxiv_2607.11497v1",
          "title": "IG-GAN: A Generative Adversarial Network for Aerodynamic Data Generation Based on Intrinsic Geometry",
          "url": "http://arxiv.org/abs/2607.11497v1",
          "summary": "Existing generative models learn data distributions in flat Euclidean space. However, most data in our real world are manifolds embedded in high dimensional Euclidean space. Therefore, we propose an i",
          "authors": [
            "Ying Yan",
            "Liwei Hu",
            "Xiaoming Zhang"
          ],
          "published": "2026-07-13T12:49:48Z"
        },
        {
          "id": "arxiv_2607.11493v1",
          "title": "Agentic Skill Optimization over Lie Algebroids",
          "url": "http://arxiv.org/abs/2607.11493v1",
          "summary": "Agentic systems increasingly improve themselves by editing skills: prompts, rubrics, plans, tool contracts, examples, validators, and traces. Skill edits are not independent coordinates in a vector sp",
          "authors": [
            "Sridhar Mahadevan"
          ],
          "published": "2026-07-13T12:48:09Z"
        },
        {
          "id": "arxiv_2607.11492v1",
          "title": "Enhancing Query Efficiency for d-DNNF Representations Through Preprocessing",
          "url": "http://arxiv.org/abs/2607.11492v1",
          "summary": "In this paper, we investigate preprocessing techniques aimed at improving the efficiency of accessing models of propositional formulas represented in conjunctive normal form (CNF). We focus on three f",
          "authors": [
            "Jean Marie Lagniez",
            "Emmanuel Lonca"
          ],
          "published": "2026-07-13T12:47:24Z"
        },
        {
          "id": "arxiv_2607.11487v1",
          "title": "LightMem-Ego: Your AI Memory for Everyday Life",
          "url": "http://arxiv.org/abs/2607.11487v1",
          "summary": "Personal AI assistants on mobile and wearable devices continuously perceive users' daily lives through visual and audio streams. However, answering queries about past experiences requires lightweight ",
          "authors": [
            "Yijun Chen",
            "Boyi Xiao",
            "Yixian Zhao",
            "Haoting Xia"
          ],
          "published": "2026-07-13T12:40:17Z"
        },
        {
          "id": "arxiv_2607.11459v1",
          "title": "A Multimodal Dataset for Large Language Model Applications in the Energy Domain",
          "url": "http://arxiv.org/abs/2607.11459v1",
          "summary": "This paper presents the mAIEnergy dataset, an open-access, multimodal corpus developed to support Large Language Model (LLM) applications in the energy sector. The dataset integrates approximately 50,",
          "authors": [
            "Costas Mylonas",
            "Magda Foti"
          ],
          "published": "2026-07-13T12:08:43Z"
        },
        {
          "id": "arxiv_2607.11436v1",
          "title": "The Ebb and Flow of Multimodal Focus: Scheduling Visual Relay Windows for Grounded VLM Reasoning",
          "url": "http://arxiv.org/abs/2607.11436v1",
          "summary": "Vision-language models increasingly succeed on multimodal reasoning benchmarks, yet their visual evidence often becomes unstable once it enters the language stack, weakening evidence-grounded reasonin",
          "authors": [
            "Wencheng Ye",
            "Yi Bin",
            "Yujuan Ding",
            "Hongye Fang"
          ],
          "published": "2026-07-13T11:44:23Z"
        },
        {
          "id": "arxiv_2607.11433v1",
          "title": "Omni-Decision: A Progressive Evidence-State Agent System for Omni-Modal QA",
          "url": "http://arxiv.org/abs/2607.11433v1",
          "summary": "Omni-modal evidence-seeking QA requires agents to answer questions whose evidence is sparsely distributed across videos, audio, images, web pages, and computation results. Existing agentic multimodal ",
          "authors": [
            "Ming Ma",
            "Yi Zhu",
            "Yiran Zhong",
            "Feida Zhu"
          ],
          "published": "2026-07-13T11:39:27Z"
        },
        {
          "id": "arxiv_2607.11412v1",
          "title": "Uncertainty Quantification for EO Regression Tasks: Building Height, Tree Canopy Height and Above-ground Biomass Estimation",
          "url": "http://arxiv.org/abs/2607.11412v1",
          "summary": "Earth Observation regression tasks such as building height, canopy height, and above-ground biomass estimation underpin critical applications in urban planning, forest monitoring, and climate policy, ",
          "authors": [
            "Ritu Yadav",
            "Andrea Nascetti",
            "Yifang Ban"
          ],
          "published": "2026-07-13T11:18:10Z"
        }
      ]
    }
  }
}