Knowledge catalogue
arXiv:2602.01051v4 Announce Type: replace Abstract: Repertoire-level analysis of T cell receptors offers a biologically grounded signal for disease detection and immune monitoring, yet practical deplo
arXiv:2604.20293v1 Announce Type: new Abstract: The increasing adoption of synthetic data in aviation research offers a promising solution to data scarcity and confidentiality challenges. This study i
arXiv:2604.19829v1 Announce Type: new Abstract: Tactile graphics require careful expert validation before reaching blind and visually impaired (BVI) learners, yet existing datasets provide only coarse
arXiv:2604.20179v1 Announce Type: cross Abstract: The rapidly evolving Node.js ecosystem currently includes millions of packages and is a critical part of modern software supply chains, making vulnera
arXiv:2509.21267v3 Announce Type: replace Abstract: Large language models often generate homogeneous outputs, but whether this is problematic depends on the specific task. For objective math tasks, re
arXiv:2508.18609v4 Announce Type: replace-cross Abstract: Post-Training Quantization (PTQ) is a critical strategy for efficient Large Language Models (LLMs) deployment. However, existing scaling laws
arXiv:2604.20472v1 Announce Type: cross Abstract: Recent advances in vision-language-action (VLA) models for robotics have highlighted the importance of reliable uncertainty quantification in sequenti
arXiv:2604.20156v1 Announce Type: new Abstract: Mixture-of-Experts models, now popular for scaling capacity at fixed inference speed, switch experts at nearly every token. Once a model outgrows availa
arXiv:2604.20269v1 Announce Type: cross Abstract: With the popularity of the large language models (LLMs), text steganography has achieved remarkable performance. However, existing methods still have
arXiv:2604.20216v1 Announce Type: new Abstract: Many applications of LLM-based text regression require predicting a full conditional distribution rather than a single point value. We study distributio
arXiv:2507.06803v3 Announce Type: replace-cross Abstract: This paper contributes to speeding up the design and deployment of engineering dynamical systems by proposing a strategy for exploiting domain
arXiv:2604.19803v1 Announce Type: new Abstract: Agentic AI is rapidly transforming the way research is conducted, from prototyping ideas to reproducing results found in the literature. In this paper,
arXiv:2407.17395v5 Announce Type: replace Abstract: Machine Learning research, including work promoting fair or equitable algorithms, often relies on the concept of a data-generating probability distr
arXiv:2504.19239v2 Announce Type: replace-cross Abstract: Quantum neural networks hold promise for tackling computationally challenging tasks that are intractable for classical computers. However, the
arXiv:2604.19810v1 Announce Type: new Abstract: Can scientific discovery be made arbitrarily easy by choosing the right representation, collecting enough data, and deploying sufficiently powerful algo
arXiv:2604.20665v1 Announce Type: cross Abstract: The rapid proliferation of Vision-Language Models (VLMs) is widely celebrated as the dawn of unified multimodal knowledge discovery but its foundation
arXiv:2604.20225v1 Announce Type: new Abstract: Evaluating the multilingual and multicultural capabilities of Large Language Models (LLMs) is essential for their global utility. However, current bench
arXiv:2601.09373v2 Announce Type: replace Abstract: Do Large Language Models (LLMs) genuinely grasp the compositional semantics of events, or do they rely on surface-level probabilistic heuristics? We
arXiv:2603.29025v2 Announce Type: replace-cross Abstract: Large language models systematically fail when a salient surface cue conflicts with an unstated feasibility constraint. We study this through
arXiv:2511.03690v2 Announce Type: replace-cross Abstract: Agents are now used widely in the process of software development, but building production-ready software engineering agents is a complex task
arXiv:2503.03816v2 Announce Type: replace-cross Abstract: Galaxies are often modelled as composites of separable components with distinct spectral signatures, implying that different wavelength ranges
arXiv:2604.20446v1 Announce Type: new Abstract: Full-batch gradient descent on neural networks drives the largest Hessian eigenvalue to the threshold 2/eta, where eta is the learning rate. This phenom
arXiv:2507.21166v2 Announce Type: replace-cross Abstract: Human intelligence scales through cumulative cultural evolution (CCE), a ratchet process in which innovations are retained against entropic dr
arXiv:2603.20714v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has become the method of choice for photo-realistic 3D reconstruction of scenes, due to being able to efficiently and a
arXiv:2604.19749v1 Announce Type: new Abstract: Equipping LLMs with external tools effectively addresses internal reasoning limitations. However, it introduces a critical yet under-explored phenomenon
arXiv:2604.19758v1 Announce Type: new Abstract: We present ThermoQA, a benchmark of 293 open-ended engineering thermodynamics problems in three tiers: property lookups (110 Q), component analysis (101
arXiv:2604.01577v2 Announce Type: replace-cross Abstract: We extend the recent latent recurrent modeling to sequential input streams. By interleaving fast, recurrent latent updates with self-organizat
arXiv:2502.11478v3 Announce Type: replace-cross Abstract: In high-noise environments such as factories, subways, and busy streets, capturing clear speech is challenging. Throat microphones can offer a
arXiv:2604.20117v1 Announce Type: new Abstract: Constructivist epistemology argues that knowledge is actively constructed rather than passively copied. Despite the generative nature of Large Language
arXiv:2604.20723v1 Announce Type: cross Abstract: The cost of simulator evaluations is a key practical bottleneck for Simulation Based Inference (SBI). In hierarchical settings with shared global para
arXiv:2604.20614v1 Announce Type: new Abstract: Modern neural networks can achieve high accuracy while remaining poorly calibrated, producing confidence estimates that do not match empirical correctne
arXiv:2604.20123v1 Announce Type: new Abstract: In natural images, object skeletons are used to represent geometric shapes. However, even slight variations in pose or movement can cause noticeable cha
arXiv:2604.20231v1 Announce Type: new Abstract: Connected autonomous vehicles (CAVs), which represent a significant advancement in autonomous driving technology, have the potential to greatly increase
arXiv:2604.20549v1 Announce Type: cross Abstract: As Large Language Models (LLMs) scale, data curation has shifted from maximizing volume to optimizing the signal-to-noise ratio by performing quality
arXiv:2604.20151v1 Announce Type: cross Abstract: Autonomous mechanical thrombectomy (MT) presents substantial challenges due to highly variable vascular geometries and the requirements for accurate,
arXiv:2604.20495v1 Announce Type: cross Abstract: Machine learning-based static malware detectors remain vulnerable to adversarial evasion techniques, such as metamorphic engine mutations. To address
arXiv:2408.00920v4 Announce Type: replace Abstract: In the field of machine unlearning, certified unlearning has been extensively studied in convex machine learning models due to its high efficiency a
arXiv:2604.20374v1 Announce Type: new Abstract: Automated Market Makers (AMMs), as a core infrastructure of decentralized finance (DeFi), uniquely drive on-chain asset pricing through a deterministic
arXiv:2602.10100v2 Announce Type: replace Abstract: Data privacy and eXplainable Artificial Intelligence (XAI) are two important aspects for modern Machine Learning systems. To enhance data privacy, r
arXiv:2604.19778v1 Announce Type: new Abstract: We present KokborokMT, a high-quality neural machine translation (NMT) system for Kokborok (ISO 639-3), a Tibeto-Burman language spoken primarily in Tri
arXiv:2602.12755v2 Announce Type: replace Abstract: Diffusion-based image generators are promising priors for ill-posed inverse problems like sparse-view X-ray Computed Tomography (CT). As most studie
arXiv:2604.20211v1 Announce Type: cross Abstract: Logging code plays an important role in software systems by recording key events and behaviors, which are essential for debugging and monitoring. Howe
arXiv:2604.19934v1 Announce Type: new Abstract: We study how large language models recall relational knowledge during text generation, with a focus on identifying latent representations suitable for r
arXiv:2602.10312v2 Announce Type: replace Abstract: We propose R2RAG-Flood, a training-free retrieval-augmented generation framework for flood damage nowcasting with reinforced reasoning. The framewor
arXiv:2604.20127v1 Announce Type: new Abstract: Failures in complex systems often emerge through gradual degradation and the propagation of stress across interacting components rather than through iso
arXiv:2601.20144v3 Announce Type: replace Abstract: Tool-calling agents are increasingly deployed in real-world customer-facing workflows. Yet most studies on tool-calling agents focus on idealized se
arXiv:2509.22343v2 Announce Type: replace-cross Abstract: Reasoning capability is essential to ensure the factual correctness of the responses of transformer-based Large Language Models (LLMs), and ro
arXiv:2604.19757v1 Announce Type: cross Abstract: This paper presents a transparent screening framework for estimating inference and training impacts of current large language models under limited obs
arXiv:2506.23040v4 Announce Type: replace-cross Abstract: Large language models are thought to have the potential to aid in medical decision making. This work investigates the degree to which this mig
arXiv:2604.20043v1 Announce Type: cross Abstract: Explainability for Large Language Model (LLM) agents is especially challenging in interactive, partially observable settings, where decisions depend o
arXiv:2604.20582v1 Announce Type: cross Abstract: We study emergent social dynamics in LLM agents playing The Resistance: Avalon, a hidden-role deception game. Unlike prior work on single-game perform
arXiv:2604.19769v1 Announce Type: cross Abstract: Key-value (KV) caching is critical for efficient inference in large language models (LLMs), yet its memory footprint scales linearly with context leng
arXiv:2604.17172v2 Announce Type: replace-cross Abstract: The rapid growth of large language models (LLMs) has made GPU communication a critical bottleneck. While prior work reduces communication volu
arXiv:2604.20255v1 Announce Type: cross Abstract: Anomaly detection in tabular data is challenging due to high dimensionality, complex feature dependencies, and heterogeneous noise. Many existing meth
arXiv:2603.16059v2 Announce Type: replace Abstract: Motion planning under dynamics constraints, i.e, kinodynamic planning, enables safe robot operation by generating dynamically feasible trajectories
arXiv:2512.12463v3 Announce Type: replace-cross Abstract: Classical statistical learning theory predicts a U-shaped relationship between test loss and model capacity, driven by the bias-variance trade
arXiv:2511.19328v2 Announce Type: replace Abstract: Language modeling has shown us that transformers can discover latent structure from context, but the dynamics of how they acquire different componen
arXiv:2604.19923v1 Announce Type: new Abstract: We introduce UniCon3R (Unified Contact-aware 3D Reconstruction), a unified feed-forward framework for online human-scene 4D reconstruction from monocula
arXiv:2604.20318v1 Announce Type: new Abstract: Composed image retrieval, multi-turn composed image retrieval, and composed video retrieval all share a common paradigm: composing the reference visual
arXiv:2604.20421v1 Announce Type: new Abstract: Prediction markets are markets for trading claims on future events, such as presidential elections, and their prices provide continuously updated signal