Knowledge catalogue
arXiv:2603.18579v2 Announce Type: replace-cross Abstract: Evaluating whether explanations faithfully reflect a model's reasoning remains an open problem. Existing benchmarks use single interventions w
arXiv:2606.17372v3 Announce Type: replace-cross Abstract: Two recent studies itep{jones2026llms, zeng2026lvlms} reach apparently contradictory conclusions about whether large vision-language models (L
arXiv:2609.01800v1 Announce Type: new Abstract: Automatic Target Recognition (ATR) in Synthetic Aperture Sonar (SAS) is a task largely dominated by deep neural networks (DNNs). Most SAS-ATR models use
arXiv:2609.02855v1 Announce Type: cross Abstract: We study how far gradient descent (GD) can be accelerated by predetermined stepsizes in smooth convex optimization. Going beyond the classical Omega(n
arXiv:2609.02004v1 Announce Type: cross Abstract: Accurate segmentation of polyps and skin lesions is pivotal for clinical diagnosis, yet existing methods struggle with low contrast, ambiguous boundar
It is inevitable that all AI will converge towards symbolic learning (i.e. modeling data by finding the shortest symbolic program that explains it), since that is the optimally efficient form of AI. B
arXiv:2609.01976v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly embedded in organizational work, yet their errors often pass human review. Prior research locates such f
arXiv:2609.02079v1 Announce Type: new Abstract: Intermittent state measurements pose fundamental challenges to model predictive control of constrained nonlinear systems because prediction uncertainty
arXiv:2603.18593v2 Announce Type: replace Abstract: We propose a method that represents language models by log-likelihood vectors over prompt-response pairs and constructs model maps for comparing the
arXiv:2609.02634v1 Announce Type: new Abstract: Vision-Language-Action (VLA) Models are increasingly used in robotics for their ability to ground language and perception into action, yet the internal
arXiv:2609.02568v1 Announce Type: cross Abstract: Volumetric video based on point cloud representations enables immersive virtual and augmented reality applications but introduces significant challeng
arXiv:2609.01687v1 Announce Type: new Abstract: Grounded question answering systems should answer only when the supplied evidence supports the answer. In multi-hop QA, this requirement is difficult be
arXiv:2609.02833v1 Announce Type: cross Abstract: Meshfree methods such as smoothed particle hydrodynamics (SPH) with kernel corrections, radial basis function-generated finite differences (RBF-FD), a
arXiv:2609.02194v1 Announce Type: new Abstract: Classical constitutive modeling of path-dependent inelastic materials relies on internal state variables whose evolution equations must be postulated ba
arXiv:2609.01924v1 Announce Type: new Abstract: Recent work identifies a mid-depth band of verbalisable, causally potent representations in a standard feedforward transformer --- a functional analogue
arXiv:2609.01961v1 Announce Type: new Abstract: Augmenting the dexterity of human surgeons has the potential to free them from tedious subtasks. We consider debridement (removal of diseased or dead ti
arXiv:2609.02565v1 Announce Type: new Abstract: Text-video retrieval requires representations that can distinguish videos with similar scenes, actions, and temporal patterns. Recent multimodal large l
arXiv:2609.01689v1 Announce Type: new Abstract: We revisit median-of-means estimation from a deterministic optimization viewpoint and develop a family of block-Lp estimators for robust learning with h
arXiv:2609.01685v1 Announce Type: new Abstract: With the development of artificial intelligence (AI), the landscape of meta-ethics, which has largely centred on human ethics, faces pressures that may
arXiv:2609.02060v1 Announce Type: new Abstract: Mineral exploration requires integrating heterogeneous geochemical, geophysical, and geological evidence, yet existing prospectivity systems often provi
arXiv:2606.17352v2 Announce Type: replace-cross Abstract: We introduce MM++ (Multilayer Mahalanobis++), a strictly post-hoc, and scale-invariant framework for Out-of-Distribution (OOD) detection. To a
arXiv:2405.00448v5 Announce Type: replace Abstract: This paper introduces MMTryon, a multi-modal multi-reference VIrtual Try-ON (VITON) framework, which can generate high-quality compositional try-on
arXiv:2505.21907v3 Announce Type: replace Abstract: AI copilots represent a new generation of AI-powered systems designed to assist users, particularly knowledge workers and developers, in complex, co
arXiv:2609.02728v1 Announce Type: cross Abstract: We study when and how momentum improves large-batch training in the one-pass regime, using power-law kernel regression as a tractable setting. We firs
arXiv:2410.23158v3 Announce Type: replace Abstract: Semi-supervised anomaly detection is based on the principle that any record that looks different from normal training data is a potential anomaly. H
arXiv:2609.02493v1 Announce Type: new Abstract: Accurate scene understanding in confined, cluttered spaces such as shelves is essential for service robots, as many everyday tasks require them to locat
arXiv:2609.02751v1 Announce Type: new Abstract: As generative AI tools become increasingly powerful and easy to use, people can easily edit portrait images with a prompt, necessitating the task of ima
arXiv:2609.02366v1 Announce Type: cross Abstract: Named Entity Recognition (NER) has achieved substantial progress since the advent of large language models (LLMs). Nevertheless, the recognition of lo
arXiv:2609.02727v1 Announce Type: cross Abstract: We approximate strongly continuous convex monotone semigroups by learning their Chernoff-type one-step operators with neural operators. First, we intr
arXiv:2502.09884v4 Announce Type: replace-cross Abstract: We consider linear two-time-scale stochastic approximation algorithms driven by martingale noise. Recent applications in machine learning moti
arXiv:2609.01662v1 Announce Type: cross Abstract: For embodied systems, predictive agreement alone does not determine whether evidence warrants action; evidential origin matters. Repeated inference ov
arXiv:2609.02672v1 Announce Type: new Abstract: Hyper-Connections (HC) replace the single residual stream of a Transformer with n parallel ones, mixing them at every layer with a learned n imes n resi
arXiv:2607.19128v2 Announce Type: replace Abstract: Vision-language models (VLMs) provide a unified representation space for textual and visual information, yet their potential as general-purpose back
arXiv:2609.02145v1 Announce Type: cross Abstract: We study online maximization of nonnegative, non-monotone DR-submodular functions over compact convex down-closed subsets of the d-dimensional unit cu
arXiv:2609.01933v1 Announce Type: new Abstract: Modern supply chain operations can require coordinating replenishment across thousands of heterogeneous items under correlated stochastic demand, hetero
arXiv:2609.01896v1 Announce Type: cross Abstract: Power-outage planning requires scenarios before an event occurs. These scenarios must represent uncertainty in magnitude, timing, and duration while p
arXiv:2609.02272v1 Announce Type: cross Abstract: Faithfully translating research papers into repository-level implementations remains challenging because papers often describe methods at a high level
arXiv:2607.27287v2 Announce Type: replace Abstract: Planning over Temporal Dynamic Knowledge Graphs (TDKGs) presents theoretical challenges in open-world environments with incomplete information. Exis
arXiv:2609.02860v1 Announce Type: new Abstract: Modern crop breeding demands precise organ-level analysis for trait quantification, making plant point cloud segmentation (PPCS) increasingly important.
arXiv:2607.18151v2 Announce Type: replace Abstract: We present a novel Generalized Scene Reconstruction (GSR) approach called Plenoptic Condensation (PCon). PCon uses a multi-stage reconstruction pipe
arXiv:2609.02328v1 Announce Type: cross Abstract: The PGM-index (Ferragina and Vinciguerra, VLDB'20) is one of the most practical learned indexes, owing to its theoretical elegance and consistently st
arXiv:2609.02391v1 Announce Type: cross Abstract: Political evasion refers to responses that engage with a question while withholding the requested information. Recent NLP work frames political evasio
arXiv:2609.01999v1 Announce Type: cross Abstract: We study a variant of the Thompson Sampling (TS) algorithm, called alpha-TS, for solving stochastic generalized linear bandit problems. Existing analy
arXiv:2609.02108v1 Announce Type: cross Abstract: Diffusion language models (DLMs) have emerged as a promising alternative to the auto-regressive paradigm. With bidirectional attention and any-order g
arXiv:2609.02606v1 Announce Type: new Abstract: Loneliness is a critical public health issue among older adults, linked to higher risks of depression, cognitive decline, and mortality. Scalable, objec
arXiv:2609.01667v1 Announce Type: cross Abstract: Artificial Intelligence has shown to help improve agricultural practices, yet adoption remains limited: 69% of U.S. farmers have privacy concerns with
arXiv:2609.02549v1 Announce Type: cross Abstract: Drug-target interaction (DTI) prediction is an important task in AI-driven drug discovery. Although recent biochemical representation learning methods
arXiv:2504.03626v2 Announce Type: replace-cross Abstract: We present quantum speedups for sampling from distributions of the form pipropto e^{-f} on R^d. We consider two stochastic oracle models: a st
arXiv:2609.01671v1 Announce Type: cross Abstract: Security teams and researchers choose knowledge-graph extraction tooling for threat reports on the strength of published triple-F1 scores, yet those s
arXiv:2609.02237v1 Announce Type: new Abstract: Scaling offline goal-conditioned reinforcement learning (GCRL) to long-horizon tasks is difficult because (1) long-range value learning depends on short
arXiv:2609.01942v1 Announce Type: new Abstract: Bitcoin's pseudonymous nature makes it challenging to analyze user-level activity, since a single user may control multiple identifiers (addresses). Exi
arXiv:2609.02507v1 Announce Type: new Abstract: Test-Time Adaptation (TTA) has recently emerged as a promising strategy that allows the adaptation of pre-trained models to changing data distributions
arXiv:2609.02268v1 Announce Type: cross Abstract: With the rapid proliferation of generative models on Machine Learning as a Service (MLaaS) platforms, reliably tracing the provenance of synthetic med
arXiv:2602.10118v2 Announce Type: replace Abstract: Peer review is central to scientific quality, yet reliance on simple heuristics, namely lazy thinking and non-specific critiques, has threatened rev
arXiv:2609.02847v1 Announce Type: new Abstract: Novel view synthesis from sparse inputs requires both geometric grounding from the observed views and generative priors of unobserved regions, motivatin
arXiv:2609.02247v1 Announce Type: cross Abstract: Semi-supervised learning has shown great potential for reducing annotation costs in medical image segmentation. However, most existing methods mainly
arXiv:2609.02196v1 Announce Type: cross Abstract: Generative modeling directly on geometric manifolds can avoid errors introduced by flattening non-Euclidean data, repeated ambient projection, and coo
arXiv:2512.15823v3 Announce Type: replace-cross Abstract: Immersive formats such as 360{eg} and 6DoF point cloud videos require high bandwidth and low latency, posing challenges for real-time AR/VR st
arXiv:2511.20279v3 Announce Type: replace Abstract: End-to-end transformer architectures have driven significant progress in multi-object tracking by unifying detection and association into a single,
arXiv:2509.02191v3 Announce Type: replace Abstract: Predict-Then-Optimize combines machine learning predictions with downstream optimization to support decision-making when problem parameters are unkn