Knowledge catalogue
arXiv:2601.15251v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved impressive proficiency in basic arithmetic, rivaling human-level performance on standard numerical tasks.
arXiv:2606.29832v1 Announce Type: new Abstract: Machine unlearning aims to eliminate the influence of specific data from trained models to safeguard privacy. However, this presents a significant chall
arXiv:2606.30574v1 Announce Type: new Abstract: Many modern generative modeling methods, including diffusion models, normalizing flows, and flow matching, estimate transport maps or plans between dist
arXiv:2606.30206v1 Announce Type: new Abstract: Modern Artificial Intelligence is often framed as limited by its own disembodiment, as if giving it a body would unlock its true potential. We argue to
arXiv:2606.29451v1 Announce Type: new Abstract: Self-supervised learning (SSL) pretrained models have become a dominant paradigm for visual representation learning, but they are vulnerable to backdoor
arXiv:2606.28529v1 Announce Type: cross Abstract: Embodied foundation models have recently been widely used to improve robot generalization and task success rates. Previous works apply lossy efficient
arXiv:2606.30308v1 Announce Type: new Abstract: 4D hand motion reconstruction from egocentric video is bottlenecked by clear limitations of existing methods: image-based pipelines depend on a detector
arXiv:2606.28359v1 Announce Type: cross Abstract: Dense embedding retrieval compresses all relevance information into a single inner product, imposing a fundamental geometric limit -- the Voronoi Bott
arXiv:2606.29067v1 Announce Type: new Abstract: We present ThinkProbe, a framework for structural analysis of LLM reasoning traces. ThinkProbe converts each trace into a Thought Graph a directed graph
arXiv:2606.29481v1 Announce Type: cross Abstract: While reinforcement learning (RL) significantly enhances LLM reasoning, its efficacy is severely undermined by Pre-RL data overlap, where RL datasets
arXiv:2606.30549v1 Announce Type: cross Abstract: AI code completion tools, such as Github Copilot, provide students with code suggestions to help them write programs. However, recent qualitative stud
arXiv:2407.07338v4 Announce Type: replace-cross Abstract: We study the problem of restricting a Markov equivalence class of maximal ancestral graphs (MAGs) to only those MAGs that contain certain edge
arXiv:2606.19781v2 Announce Type: replace-cross Abstract: Neural scaling laws describe how model performance improves as a power law in compute, model size, and dataset size. While well-established fo
arXiv:2606.29241v1 Announce Type: new Abstract: Data-generating priors are a central component of tabular foundation models because they define the task distribution used during pretraining. However,
arXiv:2504.17421v2 Announce Type: replace-cross Abstract: Large language models (LMs) offer broad generalization capabilities but require vast amounts of data and computational resources for domain-sp
arXiv:2606.30313v1 Announce Type: new Abstract: Longitudinal glioblastoma response assessment requires comparing subtle tumor changes across MRI time points using structured clinical criteria such as
arXiv:2606.30543v1 Announce Type: cross Abstract: With the proliferation of speech AI agents, understanding emotional entrainment in conversational interaction has become increasingly important. Emoti
arXiv:2606.30440v1 Announce Type: cross Abstract: We present a complete formal proof that transformer architectures, when their internal update mechanisms satisfy a Bayes joint-distribution condition,
arXiv:2606.28393v1 Announce Type: new Abstract: In longitudinal clinical practice, every chest X-ray is read in the context of the patients prior exam, and much of what the radiologist communicates is
arXiv:2606.28548v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) have become a useful tool for extracting interpretable features in language models. However, standard SAE architectures opera
arXiv:2606.29639v1 Announce Type: cross Abstract: Automatic prompt optimization is still underexplored for episodic few-shot relation extraction with smaller language models. We propose a two-stage fr
arXiv:2606.30020v1 Announce Type: new Abstract: Pathology foundation models (PFMs) offer generalizable representations for whole-slide image (WSI) analysis, yet their clinical adoption remains limited
arXiv:2606.29228v1 Announce Type: new Abstract: Despite the capability of parallel decoding, diffusion large language models (dLLMs) require many denoising steps to maintain generation quality, motiva
arXiv:2606.29460v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly embedded in social robots to support natural group interactions, yet their role in complex multi-party set
arXiv:2512.21078v3 Announce Type: replace Abstract: Visual Place Recognition (VPR) has been traditionally formulated as a single-image retrieval task. Using multiple views offers clear advantages, yet
arXiv:2606.29794v1 Announce Type: new Abstract: Existing 3D Gaussian Splatting (3DGS) frameworks rely on camera-specific rasterization, suffering from inconsistent solid-angle sampling and degraded pe
arXiv:2606.29714v1 Announce Type: new Abstract: Unified visual anomaly detection seeks to train a single detector that can be deployed across categories, domains, and application scenarios. In the few
arXiv:2202.08832v3 Announce Type: replace-cross Abstract: We study a general class of optimization problems with decision variable oldsymbol{Theta} in R^{p imes k} and cost function which is the sum o
arXiv:2606.29872v1 Announce Type: cross Abstract: This study analyzes the novelty distribution of scholarly papers in the field of Library and Information Science (LIS) in China, with a focus on diffe
arXiv:2606.28808v1 Announce Type: cross Abstract: We study the leading-order fluctuation of stochastic gradient Euler-Maruyama estimators for generalized non-reversible Langevin dynamics. Under struct
arXiv:2606.29632v1 Announce Type: cross Abstract: Audio-Visual Speech Recognition takes two input modalities, acoustic and visual streams, where visual information from lip movements aids recognition
arXiv:2603.25265v2 Announce Type: replace Abstract: We present ViewSplat, a view-adaptive 3D Gaussian splatting network for novel view synthesis from unposed images. While recent feed-forward 3D Gauss
arXiv:2606.28792v1 Announce Type: new Abstract: This paper presents an innovative approach that enables the users to capture their hand and try the jewel ring on their hand. The user captures the imag
arXiv:2606.29337v1 Announce Type: new Abstract: We summarize our submission to Sub-Challenge 1: W4A4 Quantization for Inference (HiF4 / MXFP4) of the ICME 2026 Low-Bit-width Large-Model Quantization C
arXiv:2511.16340v2 Announce Type: replace Abstract: Efficient Gaussian process (GP) inference is critical for sequential decision-making tasks such as active learning, online prediction, and Bayesian
arXiv:2504.10796v4 Announce Type: replace-cross Abstract: Distributionally robust optimization (DRO) is widely used for decision-making under uncertainty, but its adversarial focus on worst-case loss
arXiv:2606.28857v1 Announce Type: cross Abstract: While automatic tools for speech annotation are now commonplace within phonetic research pipelines, many tasks require substantial manual correction o
We can finally say AI isn't killing jobs. A new paper from me, @tryramp, and @RevelioLabs uses firm-level spend and workforce data across 21K U.S. businesses to measure AI's impact on jobs. Firms that
arXiv:2606.29047v1 Announce Type: cross Abstract: Extracting interpretable, localized physical mechanisms from complex spatiotemporal data is a foundational challenge across physics, biology, and engi
arXiv:2512.07569v2 Announce Type: replace-cross Abstract: Reliable forecasting of multivariate time series under anomalous conditions is crucial in applications such as ATM cash logistics, where sudde
We're coming out of stealth. We've built our first racks after a successful A0 tapeout, 1B+ in customer contracts, and 800m raised. Early customer tests show us achieving SOTA throughput, latency, and
arXiv:2606.28912v1 Announce Type: new Abstract: The light of the daytime sky contains a mixture of many colors yet is perceived as blue by human observers. This is largely due to the particular respon
arXiv:2606.29054v1 Announce Type: new Abstract: Large language models (LLMs) deployed for structured generation (NER, JSON extraction, QA, and classification) lack formal reliability guarantees, and s
arXiv:2603.15389v2 Announce Type: replace Abstract: Recent work has demonstrated the curse of depth in large language models (LLMs), where later layers contribute less to learning and representation t
arXiv:2606.29232v1 Announce Type: new Abstract: A synthetic measurement of model competence is useful only if it survives the move to real data, yet the real labels that would verify it are exactly wh
arXiv:2606.28469v1 Announce Type: new Abstract: Robot initiative is a central challenge in multi-party human-robot collaboration. A robot that contributes without being addressed may provide timely su
arXiv:2606.29248v1 Announce Type: new Abstract: Vegetable prices in Sri Lanka are highly volatile because the market is largely import-isolated, so supply disruptions quickly drive prices up. This stu
arXiv:2506.05808v2 Announce Type: replace Abstract: Imitation learning for generalizable performance often requires a large volume of demonstration data, making the process significantly costly. One p
arXiv:2606.29489v1 Announce Type: new Abstract: When humans translate, not every word depends equally on the surrounding context. Some tokens, particularly function words like pronouns and auxiliaries
arXiv:2606.30595v1 Announce Type: cross Abstract: Semantic communication (SemCom) aims to preserve semantic meaning and task-oriented information beyond conventional message recovery over wireless cha
arXiv:2601.13602v3 Announce Type: replace-cross Abstract: This paper introduces an analytical approach to quantifying and optimizing the distributional discrepancy in generative diffusion models. For
arXiv:2510.11103v3 Announce Type: replace-cross Abstract: Many robotic control tasks require policies to act on orientations, yet the geometry of SO(3) makes this nontrivial. Because SO(3) admits no g
arXiv:2606.27881v1 Announce Type: cross Abstract: Temporal variation poses a unique challenge for named entity recognition (NER) in historical texts, where entities drift in surface form and salience
arXiv:2606.27380v1 Announce Type: new Abstract: Automated coaching for oral presentations sits at the intersection of computer-assisted pronunciation training (CAPT), prosody modeling, and speech synt
A very damaged man who had the political power and the private wealth to save millions of lives but chose instead to allow suffering & death. Not only that, he uses his influence to sow ethnic and rel
arXiv:2606.27802v1 Announce Type: new Abstract: Hierarchical predictive coding provides an interpretable framework for perception as error-driven inference in multi-layer generative models, while spar
arXiv:2405.19466v4 Announce Type: replace Abstract: We pose uncertainty quantification and exploration in online decision-making as a problem of training and generation from an autoregressive sequence
arXiv:2512.19196v4 Announce Type: replace-cross Abstract: Solving high-dimensional Fokker-Planck (FP) equations remains a challenging problem in computational physics and stochastic dynamics, due to t
arXiv:2606.27685v1 Announce Type: cross Abstract: Pervasive data contamination -- stemming from measurement errors, outliers, or adversarial corruption -- has motivated the development of robust stati
arXiv:2606.27637v1 Announce Type: new Abstract: Recent advancements in synthetic data technology have opened a new era where images of remarkable quality are generated, blurring the lines between real