Knowledge catalogue
arXiv:2606.00647v1 Announce Type: cross Abstract: Detecting psychological defense mechanisms in conversational text remains a challenging clinical NLP problem. For the PsyDefDetect 2026 shared task (n
arXiv:2606.00418v1 Announce Type: new Abstract: Soft robotics is increasingly explored in artistic contexts, where tactile interaction provides audiences with embodied engagement beyond visual or audi
arXiv:2602.23881v2 Announce Type: replace-cross Abstract: Speculative decoding accelerates autoregressive large language model (LLM) inference by using a lightweight draft model to propose candidate t
Research demonstrates that LLM-based agents can generate functionally correct patches that pass all tests while still containing security vulnerabilities, challenging the assumption that test-passing
arXiv:2606.00718v1 Announce Type: new Abstract: While Large Language Models (LLMs) have recently shown promise in Automated Heuristic Design (AHD), existing methods typically generate and evolve heuri
arXiv:2606.02438v1 Announce Type: new Abstract: Learned heuristics have recently become a competitive alternative to traditional domain-independent heuristics for satisficing planning. Existing approa
arXiv:2509.20070v2 Announce Type: replace Abstract: We present LLM Trainer, a fully automated pipeline that leverages the world knowledge of Large Language Models (LLMs) to transform a small number of
arXiv:2606.00031v1 Announce Type: cross Abstract: Coronary artery disease (CAD) remains one of the leading causes of death globally, highlighting the need for reliable predictive systems to support ea
arXiv:2606.00324v1 Announce Type: cross Abstract: Multimodal LLMs use dedicated encoders to bridge non-language modalities (vision encoders for images, depth models for audio codec tokens) because raw
arXiv:2505.14752v3 Announce Type: replace Abstract: Macro-aligned micro-records are crucial for credible simulations in social science and urban studies. For example, epidemic models are only reliable
arXiv:2606.00022v1 Announce Type: cross Abstract: Humor generation remains difficult not only because producing fluent, novel jokes is hard, but because 'funny' is audience-dependent and supervision i
arXiv:2606.00345v1 Announce Type: new Abstract: Wearable and mobile sensing technologies enable continuous monitoring of human behavior and health in real-world settings. However, predictive modeling
arXiv:2606.00975v1 Announce Type: new Abstract: LLM chatbots increasingly serve as a first source of support for people in psychological distress, including those whose distress is entangled with delu
arXiv:2606.00636v1 Announce Type: cross Abstract: This tech note describes the architecture and execution results of the LPDDR5X-PIM simulator, developed by Samsung Electronics. Based on the latest re
arXiv:2606.00060v1 Announce Type: cross Abstract: This paper investigates whether machine learning forecasts of hourly BTC-USDT returns can be converted into economically meaningful trading performanc
arXiv:2606.00985v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown remarkable progress for mobile manipulation, but their performance on long-horizon tasks remains poor. Th
arXiv:2606.00675v1 Announce Type: new Abstract: Water research in Brazil largely overlooks the widespread damming of small streams for agricultural uses such as watering cattle, farm-scale hydropower,
arXiv:2606.01970v1 Announce Type: new Abstract: Reliable autonomous UAV swarms in Search and Rescue (SAR) missions require fault-tolerant coordination capable of sustaining operations despite agent de
arXiv:2606.02470v1 Announce Type: new Abstract: The Model Context Protocol (MCP) has emerged as a transformative standard for connecting large language models (LLMs) with external data sources and too
arXiv:2606.00049v1 Announce Type: cross Abstract: Large language models (LLMs) are widely recognised for their applications in natural language generation and are increasingly used for code generation
arXiv:2606.01914v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) remain unreliable on spatial multiple-choice questions, and their failures are often attributed to poorly atten
arXiv:2506.10858v2 Announce Type: replace-cross Abstract: Medical image segmentation is a fundamental task in computer-aided diagnosis and treatment. Existing approaches based on CNNs, ViTs, Mamba, an
arXiv:2606.00672v1 Announce Type: new Abstract: Financial exploitation is a growing concern for people with Alzheimer's disease, especially during periods of reduced cognitive stability. Conventional
Ram Iyer / TechCrunch: Microsoft releases ASSERT, an open-source framework that lets developers generate and run AI behavior tests using natural-language descriptions — AI researchers and labs have ad
Microsoft announced two new text LLMs this morning - MAI-Thinking-1 (reasoning, 35B parameters, available to 'select early partners') and MAI-Code-1-Flash (5B parameters, 'purpose-built for GitHub Cop
arXiv:2606.01099v1 Announce Type: cross Abstract: Command understanding systems in smart home ecosystems can automate device control and substantially improve user experience. However, while they perf
arXiv:2606.01063v1 Announce Type: new Abstract: Theory of Mind (ToM) enables an agent to reason about another actor's beliefs, goals, and intentions, which is essential for human-centered embodied ass
arXiv:2606.00017v1 Announce Type: new Abstract: Training language model agents for multi-agent strategic interaction presents a core difficulty: the quality of any action may depend on future events t
arXiv:2606.00240v1 Announce Type: new Abstract: Effective real-world assistance requires AI agents with robust Theory of Mind (ToM): inferring human mental states from their behavior. Despite recent a
arXiv:2512.06906v2 Announce Type: replace-cross Abstract: Detecting the anomalies of web applications, important infrastructures for running modern companies and governments, is crucial for providing
arXiv:2603.06989v3 Announce Type: replace Abstract: This paper introduces MipSLAM, a frequency-aware 3D Gaussian Splatting (3DGS) SLAM framework capable of high-fidelity anti-aliased novel view synthe
arXiv:2606.01926v1 Announce Type: new Abstract: Generations from large language models often fail to conform to desired constraints such as JSON schema. Existing locally constrained decoding (LCD) app
arXiv:2606.00891v1 Announce Type: new Abstract: Multi-modal Domain Generalization (MMDG) seeks to leverage complementary modalities to enhance model robustness on unseen domains. Despite extensive pro
arXiv:2606.01993v1 Announce Type: cross Abstract: Abundant procedural knowledge on the Web holds great potential for helping agents solve long-horizon tasks. However, such knowledge is often multimoda
arXiv:2606.01640v1 Announce Type: new Abstract: Human mobility generation aims to synthesize realistic trip chains for target populations based on individual features. Existing paradigms, including de
arXiv:2606.02198v1 Announce Type: new Abstract: Prediction tasks over individual futures, which are inherently noisy, often admit multiple similarly accurate models. When these models produce differen
arXiv:2602.17588v3 Announce Type: replace Abstract: Despite rapid progress in autonomous web agents, human involvement remains essential for shaping preferences and correcting agent behavior as tasks
arXiv:2606.00162v1 Announce Type: cross Abstract: Robotic systems generate large volumes of multimodal sensor data, but converting ROS bag recordings into machine learning datasets is often handled by
arXiv:2606.00844v1 Announce Type: cross Abstract: Bounding-box regression is a fundamental component of object detection, playing a critical role in precise object localization. Existing Intersection-
arXiv:2512.02328v2 Announce Type: replace-cross Abstract: Selecting an effective docking algorithm is highly context-dependent, and no single method performs reliably across structural, chemical, and
arXiv:2606.02522v1 Announce Type: cross Abstract: Video multimodal large language models (MLLMs) have made rapid progress on general and long-form video understanding, yet their ability to preserve br
arXiv:2606.00708v1 Announce Type: new Abstract: Automated data science is a structured model-selection problem. A solution must choose data transformations, feature representations, architecture, trai
arXiv:2606.00107v1 Announce Type: cross Abstract: Electrocardiography (ECG) remains central to cardiovascular screening, yet interpretation remains largely manual and episodic. Clinical practice relie
arXiv:2606.01533v1 Announce Type: cross Abstract: Computer use agents (CUAs) today are primarily deployed as single serial agents. This setup is suboptimal for complex long-horizon tasks that benefit
arXiv:2606.02352v1 Announce Type: new Abstract: Robust self-supervised learning of multi-modal video representations is critical for real-world applications such as driver distraction detection, where
arXiv:2601.18783v2 Announce Type: replace-cross Abstract: Balancing safety, efficiency, and operational costs in highway driving poses a challenging decision-making problem for heavy-duty vehicles. A
arXiv:2606.02147v1 Announce Type: cross Abstract: Idiomatic expressions pose a major challenge for multilingual NLP because their meanings shift between figurative and literal usage, often requiring c
arXiv:2606.00174v1 Announce Type: cross Abstract: Electromyography (EMG) directly reflects muscle activation and is a key sensing modality for gesture recognition, prosthetic control, and wearable int
arXiv:2606.01402v1 Announce Type: cross Abstract: Neural network compression is commonly achieved by pruning parameters based on local importance scores, e.g., magnitude-based pruning. We propose a co
arXiv:2602.01460v3 Announce Type: replace-cross Abstract: Policy-gradient methods are widely used in reinforcement learning, yet training often becomes unstable or slows down as learning progresses. W
arXiv:2503.07325v2 Announce Type: replace Abstract: Understanding and certifying the behavior of modern deep neural networks remains a fundamental challenge in reliable machine learning. We introduce
arXiv:2601.00389v2 Announce Type: replace-cross Abstract: Timing and burst patterns can leak through encryption, and an adaptive adversary can exploit them. This undermines metadata-only detection in
arXiv:2606.01148v1 Announce Type: new Abstract: Natural-language explanations are often treated as a unified interface for understanding model behavior, but different explanation sources may support s
arXiv:2606.01803v1 Announce Type: new Abstract: The explosive growth of Text-to-Image (T2I) models, from large-scale versions to lightweight, real-time ones, now faces diminishing marginal returns fro
arXiv:2606.00135v1 Announce Type: cross Abstract: Tool-calling is a central component of modern large language model (LLM) agents, equipping them with skills beyond their parametric knowledge. This pa
arXiv:2602.22101v3 Announce Type: replace-cross Abstract: Many real-world applications generate continuous data streams for regression. Hoeffding trees and their variants have a long-standing traditio
arXiv:2606.01075v1 Announce Type: new Abstract: Recent work suggests that large language models (LLMs) can improve through self-evolution (SE), using supervision signals generated by the model itself.
arXiv:2606.02437v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) is usually treated as a cheaper alternative to full fine-tuning. We study a broader role: small trainable adapt
arXiv:2606.01427v1 Announce Type: cross Abstract: Foundation models (FMs) have achieved substantial success in generalizing across tasks without problemspecific training or fine-tuning. However, many
arXiv:2510.17532v2 Announce Type: replace Abstract: Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinica