A Geometric Taxonomy of Hallucinations in LLMs
arXiv:2602.13224v3 Announce Type: replace Abstract: Hallucinations in deployed language models can have real consequences for downstream decisions in domains such as healthcare, legal, and financial s
Knowledge catalogue
arXiv:2602.13224v3 Announce Type: replace Abstract: Hallucinations in deployed language models can have real consequences for downstream decisions in domains such as healthcare, legal, and financial s
arXiv:2605.06681v1 Announce Type: cross Abstract: A hierarchical ensemble pipeline is introduced to address anomaly detection in multivariate telemetry data provided by European Space Agency (ESA). Th
arXiv:2410.18103v2 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) are becoming increasingly popular for EEG-based depression detection. However, previous GNN-based methods fail to
arXiv:2602.02320v3 Announce Type: replace-cross Abstract: Molecular function is largely determined by structure. Accurately aligning molecular structure with natural language is therefore essential fo
arXiv:2605.06762v1 Announce Type: cross Abstract: Robust genotype-to-phenotype (G2P) prediction is essential for accelerating breeding decisions and genetic gain. However, it remains challenging to me
arXiv:2605.07388v1 Announce Type: new Abstract: Marine debris detection for ocean robot is crucial for ecological protection, yet performance is often degraded by low-quality images with blur, complex
arXiv:2508.15294v4 Announce Type: replace Abstract: In the current field of agent memory, extensive explorations have been conducted in the area of memory retrieval, yet few studies have focused on ex
arXiv:2605.08072v1 Announce Type: cross Abstract: L_1-Approximating polynomials, i.e., polynomials that approximate indicator functions in L_1-norm under certain distributions, are widely used in comp
arXiv:2605.07596v1 Announce Type: cross Abstract: Contrastive Representation Learning (CRL) has achieved strong empirical success in multiple machine learning disciplines, yet its theoretical sample c
arXiv:2605.07170v1 Announce Type: new Abstract: Metaphor is pervasive in everyday language, yet token-level computational identification of metaphor-related words in Chinese under the MIPVU framework
arXiv:2605.06937v1 Announce Type: new Abstract: This methods article presents a reproducible calibration workflow for prompt-based large language models (LLMs) in structured evidence-synthesis tasks.
arXiv:2503.12285v2 Announce Type: replace-cross Abstract: We study bi-criteria combinatorial optimization under noisy function evaluations. While resilience and black-box offline-to-online reductions
arXiv:2605.06821v1 Announce Type: cross Abstract: Cohen et al. (arXiv:2207.14484) observed that adaptive gradient methods such as Adam operate at the edge of stability. While there has been significan
arXiv:2605.06737v1 Announce Type: cross Abstract: Autonomous agents based on Large Language Models (LLMs) are increasingly being utilized in complex software systems. However, reliability remains a si
arXiv:2605.06749v1 Announce Type: cross Abstract: As learning systems increasingly shape everyday decisions, Algorithmic Collective Action (ACA), i.e., users coordinating changes to shared data to ste
arXiv:2601.16736v5 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful technique for real-time novel view synthesis. As an explicit representation optimized through
arXiv:2605.07032v1 Announce Type: cross Abstract: The evolution of generative models from next-token predictors to autonomous engines of complex systems necessitates rigorous safety hardening. Adversa
arXiv:2605.06819v1 Announce Type: new Abstract: Autoregressive generation lies at the heart of the mechanism of large language models. It can be viewed as the repeated application of a next-token gene
arXiv:2601.15507v2 Announce Type: replace Abstract: Recent image generation models produce impressive composites, but often fail to preserve the identity of user-provided content when editing specific
arXiv:2605.07466v1 Announce Type: new Abstract: Non-alcoholic fatty pancreas disease (NAFPD) is an underdiagnosed condition associated with metabolic syndrome, insulin resistance, and increased risk o
arXiv:2605.06829v1 Announce Type: cross Abstract: We survey continuous-time generative modeling methods based on transporting a simple reference distribution to a data distribution via stochastic or d
arXiv:2605.06678v1 Announce Type: new Abstract: According to the United Nations Office for Disaster Risk Reduction (2025), the average annual cost of natural catastrophes increased from 70--80 billion
arXiv:2605.06924v1 Announce Type: cross Abstract: Synthesizing consistent and coherent long video remains a fundamental challenge. Existing methods suffer from semantic drift and narrative collapse ov
arXiv:2605.08011v1 Announce Type: new Abstract: Recent efforts to improve the reasoning abilities of Large Language Models (LLMs) have focused on integrating formal logic solvers within neurosymbolic
arXiv:2605.07444v1 Announce Type: cross Abstract: The simulation of fluid flows is computationally expensive due to the complexity of its governing partial differential equations. Machine learning mod
arXiv:2605.06900v1 Announce Type: cross Abstract: We present an accelerated relax-and-round algorithm for concave coverage problems, which generalize the classic maximum coverage problem. Building on
arXiv:2605.08048v1 Announce Type: new Abstract: Measuring the breadth of a word's meaning, or its spread across contexts, has become feasible with contextualized token embeddings. A word type can be r
arXiv:2605.07324v1 Announce Type: cross Abstract: Backdoor attacks on language models pose a significant threat to AI safety, where models behave normally on most inputs but exhibit harmful behavior w
arXiv:2605.08020v1 Announce Type: new Abstract: We present an active embodiment identification method for legged robots that jointly learns information-seeking behavior and explicit embodiment predict
arXiv:2605.05703v2 Announce Type: replace-cross Abstract: Optimizing the communication structure of large language model based multi-agent systems (LLM-MAS) has been shown to improve downstream perfor
arXiv:2310.15288v3 Announce Type: replace Abstract: Reward learning techniques enable machine learning systems to learn objectives from human feedback. A core limitation of these systems is their assu
arXiv:2605.07857v1 Announce Type: new Abstract: Optimizing dynamic risk with stochastic policies is challenging in both policy updates and value learning. The former typically requires transition pert
arXiv:2605.07094v1 Announce Type: new Abstract: This paper introduces the Active-Importance-Sampling Actor-Critic (AISAC) algorithm, an extension of the Actor-Critic framework for reducing variance in
arXiv:2602.13357v2 Announce Type: replace-cross Abstract: Diffusion Transformers (DiTs) achieve state-of-the-art performance in high-fidelity image and video generation but suffer from expensive infer
arXiv:2604.02525v2 Announce Type: replace Abstract: Hadamard transforms have become a key tool for stabilizing low-precision training, but existing methods apply them uniformly across tensors and comp
arXiv:2509.08461v3 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLMs) have demonstrated their remarkable capacity to process and reason over structured and unstruct
arXiv:2605.07002v1 Announce Type: new Abstract: A major bottleneck in characterizing the failure modes of generative AI systems is the cost and time of annotation and evaluation. Consequently, adaptiv
arXiv:2605.08028v1 Announce Type: new Abstract: Traffic state estimation from sparse fixed sensors is challenging because physics-informed neural networks (PINNs) tend to over-smooth the shockwaves ad
arXiv:2605.06946v1 Announce Type: cross Abstract: Sequence models face a fundamental tradeoff between memory capacity and computational efficiency. Transformers achieve expressive context modeling at
arXiv:2605.07137v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a highly effective method for improving the reasoning abilities of Large Language Mod
arXiv:2605.07892v1 Announce Type: new Abstract: Sparse training reduces the memory and computational costs of deep neural networks. However, sparse optimization methods, e.g., those adding an ell_1 pe
arXiv:2605.07257v1 Announce Type: new Abstract: Generative personalization often suffers from the semantic collapsing problem (SCP), where a learned personalized concept overpowers the rest of the tex
arXiv:2605.07121v1 Announce Type: new Abstract: Temporal knowledge graphs (TKGs) represent time-stamped relational facts and support a wide range of reasoning tasks over evolving events. However, exis
arXiv:2605.07863v1 Announce Type: new Abstract: We present Agentic Decentralized Knowledge Optimization (ADKO), a framework for collaborative black-box optimization across autonomous agents that achie
arXiv:2605.06876v1 Announce Type: new Abstract: Adaptive density control in 3D Gaussian Splatting (3DGS) repeatedly grows the Gaussian population through fixed-cardinality random splitting to discover
arXiv:2605.06912v1 Announce Type: new Abstract: The proliferation of generative video technologies has intensified the need for reliable methods to detect and characterize synthetic media. To address
arXiv:2605.07885v1 Announce Type: new Abstract: The robustness of event cameras to high dynamic range and motion blur holds the potential to improve visual odometry systems in challenging environments
arXiv:2605.06218v2 Announce Type: replace Abstract: Piecewise affine neural networks (PANNs) provide a principled geometric perspective on neural network expressivity by characterizing the input--outp
arXiv:2605.07142v1 Announce Type: new Abstract: Accurate 3D brain MRI subtype classification benefits from both localized anatomical cues and long-range contextual reasoning. We present AGA3DNet, a re
arXiv:2605.07926v1 Announce Type: new Abstract: As LLM-based agents increasingly rely on external tools, it is important to evaluate their ability to sustain tool-grounded reasoning beyond familiar wo
arXiv:2605.06713v1 Announce Type: cross Abstract: Agentic AI systems can plan, call tools, inspect code, interact with web applications, and coordinate multi-step workflows. These same capabilities ch
arXiv:2605.06717v1 Announce Type: cross Abstract: Coding agents are rapidly changing the landscape of software development, moving from inline completion to autonomous systems that edit repositories,
arXiv:2605.06869v1 Announce Type: new Abstract: AI agent research spans a wide spectrum: from RL agents that learn from scratch to foundation model agents that leverage pre-trained knowledge, yet no u
arXiv:2512.10371v2 Announce Type: replace Abstract: The rapid development of mobile GUI agents has stimulated growing research interest in long-horizon task automation. However, building agents for th
arXiv:2605.07963v1 Announce Type: new Abstract: Aggregating conformal predictors is a standard way of balancing their predictive and computational efficiency while retaining their validity, at least a
arXiv:2602.04672v3 Announce Type: replace Abstract: Reconstructing dynamic hand-object interactions from monocular videos is critical for dexterous manipulation data collection and creating realistic
arXiv:2605.06841v1 Announce Type: new Abstract: In model-based learning, the agent learns behaviors by simulating trajectories based on world model predictions. Standard world models typically learn a
arXiv:2605.06965v1 Announce Type: cross Abstract: As language-based AI systems become more anthropomorphic, the question of whether they can have subjective experience is increasingly pressing. I focu
arXiv:2605.06607v2 Announce Type: replace-cross Abstract: Recent LLM-based agents have closed substantial portions of the scientific discovery loop in software-only machine-learning research, in chemi
arXiv:2605.07003v1 Announce Type: new Abstract: The interaction of robots with bendable objects in midair presents significant challenges in control, often resulting in performance degradation and pot