Knowledge catalogue
arXiv:2605.28729v1 Announce Type: cross Abstract: Robustness of neural networks is commonly quantified via local or global Lipschitz constants. However, Lipschitz continuity can be overly coarse or ov
arXiv:2605.28739v1 Announce Type: cross Abstract: Tabular data in knowledge-rich domains often carries a latent prior in the form of Boolean implication relationships (BIRs) between pairs of features.
arXiv:2605.27969v1 Announce Type: new Abstract: Post-trained language-model assistants are often optimized to avoid under-answering, encouraging complete, helpful, cautious, and proactive responses. W
arXiv:2605.27737v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly attractive for multimodal quality assessment, but their default reliance on autoregressive text generatio
arXiv:2605.28253v1 Announce Type: new Abstract: The preservation of under-resourced languages requires digital tools and resources shaped by and for their speakers. We present the first dedicated ASR
arXiv:2603.12344v2 Announce Type: replace Abstract: Molecular Property Prediction (MPP) is a fundamental problem in drug discovery that has recently attracted growing attention. Large Language Models
arXiv:2602.12586v2 Announce Type: replace Abstract: While plan-and-infill decoding in Masked Diffusion Models (MDMs) shows promise for mathematical and code reasoning, performance remains highly sensi
arXiv:2605.28782v1 Announce Type: new Abstract: Discourse particles, such as extit{well} and extit{kind of}, are crucial components that enable LLMs to ``speak'' more like humans. They are used to con
arXiv:2605.28778v1 Announce Type: new Abstract: LLMs' linguistically expressed confidence should faithfully reflect their intrinsic uncertainty. While recent work shows LLMs struggle to use epistemic
arXiv:2605.27764v1 Announce Type: cross Abstract: Recent segmentation models couple large language models (LLMs) with mask decoders to ground complex language expressions into masks, yet their instruc
arXiv:2605.27835v1 Announce Type: cross Abstract: We introduce CAREF, a parameter-efficient fine-tuning framework that jointly optimizes predictive accuracy and explanation faithfulness via calibratio
arXiv:2603.22335v2 Announce Type: replace-cross Abstract: Direct Preference Optimization (DPO) guides large language models (LLMs) to generate recommendations aligned with user historical behavior dis
arXiv:2605.27725v1 Announce Type: cross Abstract: High-fidelity computational fluid dynamics (CFD) is widely used for thermal-fluid design, but repeated CFD solves remain expensive for design optimiza
arXiv:2605.28115v1 Announce Type: new Abstract: Vision-Language Models (VLMs) face severe memory and latency bottlenecks due to high-resolution visual tokens. While current token reduction methods the
arXiv:2605.28387v1 Announce Type: cross Abstract: Recognizing and continuously learning novel human actions without forgetting prior classes is a requirement for emerging AR/VR and robotics applicatio
arXiv:2605.28034v1 Announce Type: new Abstract: Clark Hash is a small method for storing neural embeddings in less space. It normalizes each database vector, applies a deterministic sparse signed John
Anthropic shipped Claude Opus 4.8 today. My favourite thing about it is this note in the release announcement: Users will find Opus 4.8 to be a modest but tangible improvement on its predecessor. Ther
arXiv:2603.26182v2 Announce Type: replace Abstract: While Large Language Models (LLMs) have demonstrated potential in healthcare, they often struggle with the complex, non-linear reasoning required fo
arXiv:2605.28521v1 Announce Type: new Abstract: ClinicalEncoder26AM is a multilingual Diagnosable ColBERT for clinical and biomedical texts, which aligns at multiple levels its token-level semantic wi
arXiv:2605.28056v1 Announce Type: new Abstract: Portrait animation methods have achieved substantial visual quality and lip synchronization, but fine-grained manipulation of the eye region still faces
arXiv:2605.27467v1 Announce Type: cross Abstract: Traditional Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) units operate on discrete time steps, often failing to capture the flui
arXiv:2602.12468v2 Announce Type: replace Abstract: Diffusion language models offer a promising alternative to autoregressive models due to their global, non-causal generation process, but their conti
Artificial intelligence cloud operator CoreWeave Inc. today announced the launch of a new offering that gives enterprise outfits the ability to deploy AI agents that can learn and improve themselves a
arXiv:2604.01604v2 Announce Type: replace Abstract: While modern LLMs are aligned to refuse harmful requests, it is essential to understand the underlying mechanistic basis of this refusal behavior fo
arXiv:2605.27584v1 Announce Type: new Abstract: The proliferation of social media platforms and online communities has inadvertently catalyzed the spread of cyberbullying, hate speech, and other forms
arXiv:2605.28139v1 Announce Type: new Abstract: Building competitive automatic speech recognition (ASR) models usually requires large-scale au- dio supervision, which makes reproduction and specializa
arXiv:2605.28340v1 Announce Type: cross Abstract: The use of residential photovoltaics has increased dramatically in recent years. With battery systems becoming more affordable, the optimal operation
arXiv:2605.28148v1 Announce Type: cross Abstract: The rapid development of LLMs coupled with the introduction of Model Context Protocol (MCP) has revolutionized how intelligent agents interact with AP
arXiv:2605.27809v1 Announce Type: new Abstract: Despite recent progress in backdoor attacks, existing methods remain susceptible to post-training defenses that erase the backdoor through fine-tuning o
arXiv:2605.28163v1 Announce Type: cross Abstract: Multilingual Large Language Models (mLLMs) leaderboards report per-language accuracy but rarely explain why disparities emerge, leaving systemic biase
arXiv:2605.27656v1 Announce Type: cross Abstract: Online recruitment platforms require recommendation methods capable of retrieving relevant job opportunities from large and heterogeneous collections
arXiv:2605.27418v1 Announce Type: cross Abstract: The imminent integration of autonomous vehicles and mobile robots in urban settings presents a critical safety challenge for future intelligent transp
arXiv:2605.27823v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly vulnerable to adversarial prompts that exploit semantic ambiguities to bypass safety mechanisms, resulti
arXiv:2605.28787v1 Announce Type: cross Abstract: In the era of autonomous agents, machine-actionable data is critical for data-driven workflows. For more than a decade, semantic metadata like schema.
arXiv:2605.27772v1 Announce Type: cross Abstract: Audio large language models (Audio LLMs) demonstrate strong performance on speech understanding tasks, yet their ability to understand paralinguistic
arXiv:2605.28515v1 Announce Type: cross Abstract: Large Language Models (LLMs) have become an integral part of software development, especially with the advent of agentic capabilities. Yet, many front
arXiv:2601.17363v2 Announce Type: replace-cross Abstract: This study investigates whether readers prefer AI-generated short stories in Italian over one written by a renowned Italian author. In a blind
arXiv:2605.27923v1 Announce Type: cross Abstract: The rapid growth of computer vision and increasingly complex image recognition tasks has exposed fundamental computational limitations of classical ma
arXiv:2605.27566v1 Announce Type: new Abstract: Progress in neural combinatorial optimization for Dynamic Flexible Job Shop Scheduling Problem (DFJSP) is currently hindered by a methodological tension
arXiv:2605.28101v1 Announce Type: cross Abstract: Predicting spatially varying Room Impulse Response (RIR) from sparse observations is a critical but highly challenging inverse problem for immersive s
arXiv:2605.27610v1 Announce Type: cross Abstract: The rapid growth of scientific publishing has made it increasingly difficult to track how fast-moving areas evolve. Search engines and LLM-based assis
arXiv:2603.09882v2 Announce Type: replace-cross Abstract: Extrinsic dexterity leverages environmental contact to overcome the limitations of prehensile manipulation. However, achieving such dexterity
arXiv:2605.27908v1 Announce Type: cross Abstract: Existing emotional support conversation (ESC) systems mainly rely on end-to-end response generation or coarse strategy supervision, offering limited i
arXiv:2605.27741v1 Announce Type: new Abstract: Audio and omni-modal large language models exhibit impressive cross-modal reasoning capabilities. However, applying standard reinforcement learning post
Google created MapReduce more than 20 years ago to solve the scaling problems in data processing that the then young company was running into. The AI era that we are in now demands efficient, large-sc
arXiv:2605.27390v1 Announce Type: cross Abstract: Speculative decoding accelerates Large Language Model inference via a draft-then-verify paradigm, yet the output projection layer becomes a bottleneck
arXiv:2603.14515v2 Announce Type: replace Abstract: Neural-network wave functions in Variational Monte Carlo (VMC) have achieved great success in accurately representing both ground and excited states
arXiv:2511.14558v2 Announce Type: replace Abstract: We present a clustering-based explainability technique for digital pathology models based on convolutional neural networks. Unlike commonly used met
arXiv:2603.22735v2 Announce Type: replace Abstract: Existing NLP work commonly treats contradictions as errors to be resolved by choosing which statements to accept or discard. Yet a key aspect of hum
arXiv:2605.27651v1 Announce Type: new Abstract: Autonomous space systems operating in extreme thermal environments require accurate and efficient thermal modeling to support both pre-mission system de
arXiv:2605.27486v1 Announce Type: new Abstract: Federated learning (FL) has broadened the horizon for multivariate time series anomaly detection (MTSAD). However, benchmarking such anomaly detection m
arXiv:2605.27896v1 Announce Type: new Abstract: Recently, large language models (LLMs) have achieved superior performance in static financial reasoning and simple dynamic trading tasks. However, exist
arXiv:2605.27452v1 Announce Type: new Abstract: Bridge inspection in Japan requires mandatory visual assessments every five years, yet qualitative damage ratings (levels a-e) assigned by different eng
arXiv:2510.02174v3 Announce Type: replace Abstract: Flatness of the loss landscape has been widely studied as an important perspective for understanding the behavior and generalization of deep learnin
arXiv:2605.28174v1 Announce Type: cross Abstract: Foundation models offer a promising route to transferable remote sensing representations, but many current approaches depend on very large pretraining
arXiv:2605.27590v1 Announce Type: new Abstract: Remote sensing question answering (RS-QA) often requires more than direct semantic prediction, especially in large-scale forest scenes where ecological
arXiv:2605.27451v1 Announce Type: new Abstract: The 10th Affective & Behavior Analysis in-the-Wild (ABAW) Workshop and Competition, held at CVPR 2026, continues to advance research on modelling, analy
arXiv:2603.20980v2 Announce Type: replace-cross Abstract: Time-varying causal models provide a powerful framework for studying dynamic scientific systems, yet most existing approaches assume that the
arXiv:2605.28483v1 Announce Type: new Abstract: Linking learning resources to a structured competency framework is key to enabling competency-based search and curriculum analytics in Learning Manageme
arXiv:2605.28548v1 Announce Type: new Abstract: Embodied Vision-Language Models (VLMs) have demonstrated impressive performance and generalization in robotics, particularly within Vision-Language-Acti