Knowledge catalogue
arXiv:2608.03275v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) enables efficient adaptation of large language models, but existing MoE-based PEFT methods typically improve capa
arXiv:2608.03664v1 Announce Type: new Abstract: Accurate diagnosis in Digital Pathology (DP) relies on high-resolution whole-slide images, yet clinical deployment is often limited by hardware costs. S
arXiv:2608.02674v1 Announce Type: cross Abstract: With the widespread deployment of large foundation models (LFMs) in open environments, safety threats are shifting from black-box jailbreaks toward wh
arXiv:2402.13822v2 Announce Type: replace Abstract: Most of the previous approaches to Time Series Classification (TSC) highlight the significance of receptive fields and frequencies while overlooking
arXiv:2608.03474v1 Announce Type: new Abstract: Recent advances in Large Vision-Language Models (LVLMs) have demonstrated impressive capabilities in web UI generation. However, existing benchmarks pre
arXiv:2608.03636v1 Announce Type: cross Abstract: Large language model-based automated heuristic design (LLM-AHD) has shown strong potential in discovering effective heuristics for combinatorial optim
arXiv:2108.04694v2 Announce Type: cross Abstract: We introduce the problem of multi-camera trajectory forecasting (MCTF), which involves predicting the trajectory of a moving object across a network o
arXiv:2608.03419v1 Announce Type: cross Abstract: Audio-based piano transcription performs well on onset, pitch, and velocity, but the sustain pedal lets sound persist long after key release, so audio
arXiv:2608.03708v1 Announce Type: new Abstract: Text-to-image diffusion models enable personalization of specific visual concepts from a small number of reference images. However, generating a single
arXiv:2608.03882v1 Announce Type: cross Abstract: Geospatial reasoning, i.e., computing distances, containment, and other spatial relations over real-world entities, is central to navigation and logis
arXiv:2608.02629v1 Announce Type: cross Abstract: The use of variable well perforation and injection strategies can improve the efficiency of geological carbon storage operations. We develop a new mul
arXiv:2608.03109v1 Announce Type: new Abstract: Plant root phenotyping is fundamental to understanding below-ground structures, optimizing crop management, and improving agricultural sustainability. T
arXiv:2608.03941v1 Announce Type: new Abstract: Muon is a recent optimizer that orthogonalizes the update to each weight matrix with a Newton-Schulz iteration, which performs steepest descent under th
arXiv:2608.03885v1 Announce Type: new Abstract: Vision-language models exhibit remarkable zero-shot capabilities but suffer significant performance degradation under distribution shifts. While test-ti
arXiv:2608.02843v1 Announce Type: cross Abstract: Persistent agent memory must adapt as later outcomes change earlier evidence, yet mutable retrieval weights create an attribution problem: reviewers m
arXiv:2607.18660v2 Announce Type: replace Abstract: Robot-assisted minimally invasive surgery (RMIS) offers major benefits over open and conventional laparoscopic procedures, yet it still lacks tactil
arXiv:2608.03257v1 Announce Type: new Abstract: Precise 3D characterization of nanomaterials is essential for unlocking structure-property relationships. However, standard electron tomography is funda
arXiv:2608.02700v1 Announce Type: cross Abstract: Analog compute-in-memory (CIM) enables energy-efficient neural network inference, but device variation and read noise can severely degrade low-bit qua
arXiv:2608.03895v1 Announce Type: new Abstract: Camera-based bird's-eye-view (BEV) 3D detection typically assumes accurate and fixed camera extrinsics. In detectors using spatial cross-attention (SCA)
arXiv:2604.01973v2 Announce Type: replace Abstract: When evaluating identity-focused tasks such as personalized generation and image editing, existing vision encoders entangle object identity with bac
arXiv:2608.02624v1 Announce Type: cross Abstract: Refinement operators generate many functions used in wavelet constructions, subdivision schemes, and geometric modeling. Their finite iterates can dev
arXiv:2608.02778v1 Announce Type: new Abstract: We present a novel application of Neural Networks with Local Converging Inputs (NNLCI) to improve the efficiency of existing numerical methods for prici
arXiv:2608.02993v1 Announce Type: new Abstract: (Flat) Reinforcement Learning (RL) agents face significant challenges in environments with sparse rewards that require long-horizon reasoning. A compell
arXiv:2608.03277v1 Announce Type: new Abstract: Differentially private zeroth-order optimization (DP-ZO) enables memory-efficient private fine-tuning of large language models using only forward evalua
arXiv:2608.02845v1 Announce Type: new Abstract: Tabular model performance degrades when feature distributions change over time or the relationship between features and outcome variables change over ti
arXiv:2608.03113v1 Announce Type: new Abstract: Adulteration of bovine milk using urea remains a major food quality and health concern, motivating the development of rapid and quantitative screening t
arXiv:2608.02762v1 Announce Type: new Abstract: Fine-grained wildlife classification in aerial imagery is limited not only by model performance, but also by unreliable labels: animals occupy few pixel
arXiv:2608.03839v1 Announce Type: new Abstract: Training-free speculative decoding drafts by matching an exact suffix of the context against a pool of earlier context. That lookup misses correct draft
arXiv:2608.03428v1 Announce Type: cross Abstract: Image based dietary assessment offers a scalable alternative to self reported food diaries, yet fine-grained food recognition remains challenging due
arXiv:2608.03887v1 Announce Type: new Abstract: Fine-tuning a large language model on new data degrades what it previously learned. We present Omega-S, a drop-in penalty computed from the weight matri
arXiv:2608.03812v1 Announce Type: new Abstract: Omni-modal large language models (Omni-LLMs) have achieved remarkable performance on audio-visual understanding tasks, but processing long and highly re
arXiv:2608.03233v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable potential for analogy making, a core cognitive capability that drives novelty and creativity.
arXiv:2608.03197v1 Announce Type: new Abstract: Sharpness-Aware Minimization (SAM) improves generalization by seeking parameters whose loss is robust to local adversarial perturbations, but the quanti
arXiv:2608.02996v1 Announce Type: new Abstract: Latin America is missing a foundational layer for native AI development: the benchmark layer. The benchmark layer does two things no other layer can - i
arXiv:2608.02949v1 Announce Type: new Abstract: Latin America is missing two foundational layers of AI infrastructure: the dataset layer and the benchmark layer. This paper targets the dataset layer.
arXiv:2608.02999v1 Announce Type: new Abstract: Statistical regularities are routinely offered as evidence that undeciphered sign systems encode language; the Indus script debate is the canonical exam
arXiv:2608.02671v1 Announce Type: cross Abstract: Malware detection using Hardware Performance Counters (HPC) has emerged as a promising solution to improve the security of computing systems as a comp
arXiv:2608.02615v1 Announce Type: cross Abstract: Cancer diagnosis and characterization require integrating complementary evidence from radiology, pathology, genomics, and clinical metadata. However,
arXiv:2608.03249v1 Announce Type: new Abstract: Cold-Start Active Learning (CSAL) aims to select a valuable subset from an unlabeled pool without any prior knowledge or human assistance. Existing meth
arXiv:2507.07754v3 Announce Type: replace-cross Abstract: Machine unlearning is usually evaluated by what the classifier outputs: forget-set accuracy, confidence, membership-inference scores. We show
arXiv:2608.03225v1 Announce Type: new Abstract: Human-interpretable computer-aided diagnosis is crucial for clinical decision making. Concept-based models excel by providing transparent reasoning and
arXiv:2608.03878v1 Announce Type: new Abstract: Synthetic power-grid scenarios are essential for planning, resilience assessment, contingency analysis, and data-driven power-system applications. Recen
arXiv:2608.02942v1 Announce Type: new Abstract: Diffusion language models (dLLMs) can predict many tokens in parallel, but accurate generation still requires many iterative denoising steps. Few-step d
arXiv:2608.03114v1 Announce Type: cross Abstract: Artificial intelligence (AI) is increasingly integrated into medical decision-making, yet its liability implications remain complex, particularly when
arXiv:2608.00151v2 Announce Type: replace-cross Abstract: Current evaluation frameworks for artificial intelligence focus mainly on capability, safety, and proxies such as adoption, engagement, effici
arXiv:2602.13769v3 Announce Type: replace Abstract: Automating heuristic design in complex, experiment-driven domains requires more than iterative mutation of solution algorithms. Current LLM-based ev
arXiv:2608.02691v1 Announce Type: cross Abstract: The key-value (KV) cache has become a major memory and bandwidth bottleneck in long-context large language model inference, making ultra-low-bit quant
arXiv:2608.03034v1 Announce Type: cross Abstract: Reasoning-enhanced large language models have achieved remarkable improvements in planning tasks, yet their deployment in embodied systems remains imp
arXiv:2509.21690v5 Announce Type: replace Abstract: Humanoid table tennis (TT) demands rapid perception, proactive whole-body motion, and agile footwork under strict timing--capabilities that remain d
arXiv:2607.28587v2 Announce Type: replace-cross Abstract: SWE-bench-like benchmarks are widely used for evaluating LLM's issue resolution capability. They typically follow a common construction pipeli
arXiv:2608.03017v1 Announce Type: new Abstract: There has been significant interest in using machine learning algorithms to predict kidney transplant outcomes, such as the number of years until a graf
arXiv:2608.03077v1 Announce Type: new Abstract: Multi-domain machine translation (MDMT) requires more than fluent generation: it demands domain-sensitive translation decisions such as domain disambigu
arXiv:2608.02844v1 Announce Type: cross Abstract: We develop a class of diffusion-based stochastic particle optimisation methods for loss functions with intractable gradients. Specifically, we conside
arXiv:2608.04010v1 Announce Type: cross Abstract: Existing scaling strategies for Multimodal Large Language Models (MLLMs) typically expand either model parameters or sequential inference computation,
arXiv:2511.13300v1 Announce Type: cross Abstract: Generative models have shown remarkable performance in speech enhancement (SE), achieving superior perceptual quality over traditional discriminative
arXiv:2608.03060v1 Announce Type: new Abstract: This paper presents purely convex programs for passively safe impulsive rendezvous and proximity operations in cislunar orbits. Approach, arrival, and a
arXiv:2608.04003v1 Announce Type: new Abstract: Recursive self-improvement requires agents to turn accumulated experience into better future behavior. Personal AI agents offer a concrete setting for s
arXiv:2603.13888v2 Announce Type: replace Abstract: Long-range navigation is commonly addressed through hierarchical pipelines in which a global planner generates a path, decomposed into waypoints, an
arXiv:2408.02677v2 Announce Type: replace-cross Abstract: This study proposes a novel, integrative framework for patient-centered data science in the digital health era. We developed a multidimensiona
arXiv:2608.03691v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are increasingly used to translate webpage screenshots into front-end code, but repeated UI patterns may sway