Knowledge catalogue
arXiv:2606.27216v1 Announce Type: cross Abstract: Muon-type optimizers construct update directions for dense neural-network weights by applying a finite Newton-Schulz map to momentum-gradient matrices
arXiv:2606.26100v1 Announce Type: new Abstract: Media bias detection is a critical task for ensuring fair and balanced information dissemination, yet existing sentence-level approaches classify each s
arXiv:2606.26316v1 Announce Type: new Abstract: We study first-order methods for smooth objectives satisfying the Polyak-L{}ojasiewicz (PL) condition when gradient samples are generated by an exogenou
arXiv:2606.26614v1 Announce Type: cross Abstract: Large language model (LLM) agents enable natural language interaction for scientific visualization (SciVis). Still, prior systems have essentially pri
arXiv:2606.26164v1 Announce Type: new Abstract: Finding all modes of a multimodal black-box function is a fundamental challenge in optimization, Bayesian inference, and scientific computing. Existing
arXiv:2603.06480v2 Announce Type: replace Abstract: Vision-Language Navigation (VLN) enables robots to follow natural-language instructions in visually grounded environments, serving as a key capabili
arXiv:2510.15238v2 Announce Type: replace-cross Abstract: Optimizing a single advertising campaign across heterogeneous channels is a central challenge in industrial autobidding. Auction mechanisms va
arXiv:2606.26346v1 Announce Type: new Abstract: Agentic benchmarks have emerged across general-purpose and domain-specific settings, including finance, coding, law, and drug discovery, yet energy-doma
arXiv:2606.27282v1 Announce Type: new Abstract: Time-series forecasting research has been moving steadily toward larger architectures, from specialized transformers to general-purpose foundation model
arXiv:2606.27275v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly critical to digital library workflows, yet their ability to process historical language remains poorly und
arXiv:2606.27061v1 Announce Type: new Abstract: External indexes can be used for cluster evaluation when ground truth is available. We review the most common external validity indexes focusing on set-
arXiv:2510.26518v2 Announce Type: replace Abstract: Human feedback is critical for aligning AI systems to human values. As AI capabilities improve and AI is used to tackle more challenging tasks, veri
arXiv:2606.26855v1 Announce Type: new Abstract: Imitating human demonstrations has emerged as a dominant paradigm for learning humanoid loco-manipulation policies. However, scaling these approaches re
arXiv:2606.27239v1 Announce Type: new Abstract: High-quality demonstration data are essential for humanoid robot skill learning, especially for whole-body behaviors that require coordinated perception
arXiv:2606.26502v1 Announce Type: new Abstract: Large reasoning models (LRMs) take longer on harder problems, just as humans do. This surface similarity hides an opposite pattern within items. When an
arXiv:2508.18486v2 Announce Type: replace-cross Abstract: Over the past few years, machine learning-based data-driven weather prediction has been transforming operational weather forecasting by provid
arXiv:2606.26373v1 Announce Type: cross Abstract: Dense embeddings power semantic search and retrieval-augmented generation, but embedding-inversion attacks can reconstruct source text from a vector:
arXiv:2606.26744v1 Announce Type: cross Abstract: We present HyperDFlash, a block-parallel speculative decoding framework tailored to the novel multi-hyper-connection (MHC) architecture proposed by De
arXiv:2606.26575v1 Announce Type: cross Abstract: Complex multi-agent control tasks remain challenging for traditional rule-based and model-based approaches, motivating the adoption of learning-based
arXiv:2606.26829v1 Announce Type: new Abstract: Robots operating in real-world environments must in general be able to recognize previously unseen objects. As robotic systems move toward open-world au
arXiv:2606.26168v1 Announce Type: new Abstract: Living systems navigate environments using noisy and incomplete sensory signals. In unicellular algae, phototaxis is often modeled as a mechanistic run-
arXiv:2512.23816v2 Announce Type: replace-cross Abstract: In this paper, we study the private and robust alignment of language models from a theoretical perspective by establishing upper bounds on the
arXiv:2606.27025v1 Announce Type: new Abstract: Building general-purpose role-playing agents that faithfully portray any character from a natural-language profile remains challenging. The dominant par
arXiv:2606.26801v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for generalizable robotic manipulation. During fine-tuning, however, action supervisio
arXiv:2606.26981v1 Announce Type: cross Abstract: Synthesizing human motion from textual descriptions is essential for immersive digital applications, yet existing methods face a persistent trade-off
arXiv:2606.26588v1 Announce Type: new Abstract: A central challenge in deploying learned robot policies is inference-time behavior steering: redirecting a policy at test time to satisfy user preferenc
arXiv:2606.26875v1 Announce Type: cross Abstract: Reasoning capability has advanced rapidly in large language models (LLMs), leading to an increasing size of key-value (KV) cache in both prefilling an
arXiv:2606.27091v1 Announce Type: cross Abstract: LLMs fine-tuned for security classification are usually evaluated on held-out examples from the same distribution as their training data. We show that
arXiv:2606.26356v1 Announce Type: new Abstract: Practitioners of prompt-composed agentic systems report a recurring failure mode: editing one prompt module silently shifts the behavior of others despi
arXiv:2606.26228v1 Announce Type: cross Abstract: We review the concepts of interpretability and explainability as they apply to machine learning in physics. We define interpretability as concerning t
arXiv:2606.26706v1 Announce Type: new Abstract: Intracranial aneurysms are often asymptomatic until rupture, which carries high mortality. Rupture risk assessment and treatment planning depend on both
arXiv:2606.27002v1 Announce Type: cross Abstract: This paper presents a deep learning-assisted methodology for the inverse synthesis of a compact, wideband inverted Doherty power amplifier (PA). Convo
arXiv:2606.26103v1 Announce Type: cross Abstract: Large Language Models (LLMs) have rapidly influenced many aspects of society, particularly education, due to their demonstrated ability to complete as
arXiv:2606.26936v1 Announce Type: cross Abstract: With a profusion of jailbreaks for LLMs now widely known, a growing concern is that non-expert malicious actors ('the average Jane') could elicit acti
arXiv:2606.27136v1 Announce Type: new Abstract: For LLM agents in multi-step interactive environments, a key challenge is to make effective use of accumulated interaction experience. Existing work has
arXiv:2602.07533v2 Announce Type: replace Abstract: Reward models are critical for reinforcement learning from human feedback, as they determine the alignment quality and reliability of generative mod
arXiv:2606.27023v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) applied to Medical Visual Question Answering (VQA) tend to produce overconfident outputs regardless of actual
arXiv:2606.26710v1 Announce Type: new Abstract: Combined-cycle gas turbines (CCGTs) play a key role in modern power generation, offering both high efficiency and reduced environmental impact. However,
arXiv:2606.26807v1 Announce Type: new Abstract: We propose a new method that allows an LLM to automatically pull in factual knowledge from a knowledge base during token generation. This means that (1)
arXiv:2606.26179v1 Announce Type: cross Abstract: While WGS-based AMR prediction has reached high accuracy, existing models lack a mechanism to ground neural attributions in established biological pat
arXiv:2606.26101v1 Announce Type: cross Abstract: Reliable evaluation of large language models should separate supported answering from unsupported guessing without conflating either with data contami
arXiv:2606.26205v1 Announce Type: new Abstract: Patients increasingly seek medication information online, yet safety knowledge for psychiatric drugs is split between regulatory adverse-event records,
arXiv:2606.27126v1 Announce Type: new Abstract: Kolmogorov Arnold networks (KAN) have recently been introduced as a (deep) neural network architecture whose trainable parameters adapt the activation f
arXiv:2606.26321v1 Announce Type: new Abstract: Mobile manipulators need world models that are current, queryable, semantically meaningful, and usable under edge-compute constraints. This technical re
arXiv:2606.27295v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are commonly pretrained on robot demonstrations by jointly mapping visual observations and language instructions to
arXiv:2606.26246v1 Announce Type: cross Abstract: Lacuna is a research map for machine learning that uses LLMs to turn papers and scholarly metadata into markdown summaries, concept elements, research
arXiv:2606.26661v1 Announce Type: cross Abstract: Motion forecasting is essential for autonomous driving systems to enable safe decision-making and planning in complex driving scenarios. While existin
arXiv:2606.27334v1 Announce Type: new Abstract: Digital twins have emerged as a promising paradigm for personalized healthcare, enabling modeling of individual behavior and health trajectories. In cog
arXiv:2606.26592v1 Announce Type: cross Abstract: We propose latent-space diffusion posterior sampling (L-DPS), an approximate Bayesian framework for high-dimensional inverse problems governed by part
arXiv:2603.05310v3 Announce Type: replace-cross Abstract: While existing audio watermarking techniques have achieved strong robustness against traditional digital signal processing (DSP) attacks, they
arXiv:2606.26379v1 Announce Type: new Abstract: Visual prompt tuning has emerged as a parameter-efficient fine-tuning approach for adapting large-scale Vision Transformers (ViTs) to downstream tasks.
arXiv:2606.26315v1 Announce Type: new Abstract: Disturbance-robust UAV position control is easy to demonstrate in benign simulations but much harder to make fast in approach, well behaved near the tar
arXiv:2606.26647v1 Announce Type: new Abstract: 3D/2D registration serves as a cornerstone technique in surgical navigation. Traditional iterative optimization algorithms suffer from low efficiency an
arXiv:2606.26857v1 Announce Type: new Abstract: The interpretation phase of life cycle assessment often lacks structured mechanisms for translating quantified improvement opportunities addressing envi
arXiv:2606.26171v1 Announce Type: cross Abstract: Recent image generation models achieve impressive quality in single-image synthesis, but often fail to maintain consistency across sequential outputs,
arXiv:2606.26778v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have driven substantial progress in image and video generation but suffer from prohibitive computational costs. Feature ca
arXiv:2606.26828v1 Announce Type: new Abstract: Accurate seedling detection during early growth stages is essential for timely replanting and effective crop management in precision agriculture. Howeve
arXiv:2209.01754v5 Announce Type: replace-cross Abstract: The empirical risk minimization approach to data-driven decision making requires access to training data drawn under the same conditions as th
arXiv:2604.04535v2 Announce Type: replace Abstract: Modern machine learning systems, such as generative models and recommendation systems, often evolve through a cycle of deployment, user interaction,
arXiv:2606.18195v2 Announce Type: replace Abstract: On-policy self-distillation (OPSD) has proven effective for post-training large language models (LLMs), yet its application to diffusion LLMs (dLLMs