Knowledge catalogue
arXiv:2604.23546v1 Announce Type: cross Abstract: Optical chemical structure recognition (OCSR) translates molecular images into machine-readable representations like SMILES strings or molecular graph
arXiv:2604.23017v1 Announce Type: new Abstract: Stochastic Gradient Descent (SGD) is a known stochastic iterative method popular for large-scale convex optimization problems due to its simple implemen
arXiv:2604.23112v1 Announce Type: new Abstract: Multimodal Federated Learning (MMFL) enables privacy-preserving collaborative training, but real-world clinical applications often suffer from within-mo
arXiv:2604.24745v1 Announce Type: new Abstract: In this paper, we propose a harmonized rotational gradient method, termed HRGrad, for simultaneously tackling multiscale time-dependent kinetic problems
arXiv:2504.02316v4 Announce Type: replace-cross Abstract: Recent advances in zero-shot text-to-3D generation have revolutionized 3D content creation by enabling direct synthesis from textual descripti
arXiv:2604.22905v1 Announce Type: cross Abstract: Whole-body Positron Emission Tomography (PET) registration is essential for multi-parametric tumor characterization and assessment of metastatic disea
arXiv:2601.13262v2 Announce Type: replace Abstract: While large language models (LLMs) have shown to perform well on monolingual mathematical and commonsense reasoning, they remain unreliable for mult
arXiv:2510.05799v2 Announce Type: replace-cross Abstract: Aligning text-to-speech (TTS) system outputs with human feedback through preference optimization has been shown to effectively improve the rob
arXiv:2601.16046v2 Announce Type: replace-cross Abstract: Language-driven dexterous grasp generation requires the models to understand task semantics, 3D geometry, and complex hand-object interactions
arXiv:2604.23557v1 Announce Type: cross Abstract: Building scalable and reusable multi-agent decision policies from offline datasets remains a challenge in offline multi-agent reinforcement learning (
arXiv:2604.23488v1 Announce Type: new Abstract: Reward hacking in code generation, where models exploit evaluation loopholes to obtain full reward without correctly solving the tasks, poses a critical
arXiv:2604.23494v1 Announce Type: new Abstract: Graph-based anti-money laundering (AML) systems on blockchain networks can score suspicious activity at two granularity levels -- transactions or actor
arXiv:2604.24320v1 Announce Type: new Abstract: Large language model (LLM) agents that follow the sequential 'reason-then-act' paradigm have achieved superior performance in many complex tasks.However
arXiv:2604.24357v1 Announce Type: cross Abstract: Diffusion language models generate without a fixed left-to-right order, making token ordering a central algorithmic choice: which tokens should be rev
arXiv:2512.03847v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has shown strong performance in LLM post-training, but real-world deployment often involves noisy or incomplete su
arXiv:2604.23325v1 Announce Type: cross Abstract: Emotionally talking head video generation aims to generate expressive portrait videos with accurate lip synchronization and emotional facial expressio
arXiv:2604.23336v1 Announce Type: cross Abstract: Unlike traditional fact-based retrieval, rationale-based retrieval typically necessitates cross-encoding of query-document pairs using large language
arXiv:2511.13312v2 Announce Type: replace-cross Abstract: Acting in human environments is a crucial capability for general-purpose robots, necessitating a robust understanding of natural language and
Foo Yun Chee / Reuters: EU countries and lawmakers reach an impasse on a deal watering down the EU's AI Act due to some parties seeking exemptions for already regulated industries — EU countries and E
arXiv:2510.10930v2 Announce Type: replace-cross Abstract: Reasoning is not just about solving problems -- it is also about evaluating which problems are worth solving at all. Evaluations of artificial
arXiv:2604.24176v1 Announce Type: new Abstract: We reformulate explanation quality assessment as a ranking problem rather than a generation problem. Instead of optimizing models to produce a single 'b
arXiv:2604.24545v1 Announce Type: cross Abstract: In many areas of medicine, security, and life sciences, we want to allocate limited resources to different sources in order to detect extreme values.
arXiv:2604.23990v1 Announce Type: new Abstract: This paper presents PSA-Eval, a failure-centered runtime evaluation framework for deployed trilingual public-space agents. The central claim is that, wh
arXiv:2604.22885v1 Announce Type: cross Abstract: Federated cross-modal retrieval faces severe challenges from heterogeneous client data, particularly non-IID semantic distributions and missing modali
arXiv:2602.07605v3 Announce Type: replace-cross Abstract: Any entity in the visual world can be hierarchically grouped based on shared characteristics and mapped to fine-grained sub-categories. While
arXiv:2604.23588v1 Announce Type: new Abstract: Financial AI systems must produce answers grounded in specific regulatory filings, yet current LLMs fabricate metrics, invent citations, and miscalculat
From what I can tell, Elon Musk just stepped on his own toes at the trial, making it all about him instead of the promises Altman and Brockman broke. OpenAI will slay his ego on cross. He should have
arXiv:2604.23947v1 Announce Type: new Abstract: We introduce GameDAI, a hierarchical multi-agent framework that transforms instructor-provided questions into fully playable, pedagogically grounded edu
This post by AI researcher Gary Marcus contrasts Google's original 'Don't Be Evil' corporate motto from 2001 with a critical question about the company's 2026 practices, likely arguing that Google has
arXiv:2604.23982v1 Announce Type: new Abstract: Digital pathology has fundamentally altered diagnostic workflows by enabling the computational analysis of gigapixel Whole Slide Images (WSIs), yet effe
arXiv:2604.23988v1 Announce Type: cross Abstract: Time series models predict numbers; decision-makers need advisory -- directional signals with reasoning, actionable suggestions, and risk management.
arXiv:2511.11218v3 Announce Type: replace Abstract: Humanoid robots have demonstrated strong capabilities for interacting with static scenes across locomotion and manipulation, yet dynamic real-world
arXiv:2604.22817v1 Announce Type: cross Abstract: Recent advances in speech-aware language models have coupled strong acoustic encoders with large language models, enabling systems that move beyond tr
In this informal poll, only a fifth of all coders and software engineers have gone over full time to vibe coding. Most use vibe coding; few use it exclusively. Most still do some hand coding. Amateurs
arXiv:2602.23024v2 Announce Type: replace Abstract: Mobile manipulation is a fundamental capability for general-purpose robotic agents, requiring both coordinated control of the mobile base and manipu
arXiv:2604.22966v1 Announce Type: cross Abstract: Each major technological revolution inverts a particular scarcity and rebuilds institutions around the shift. The near-consensus diagnosis of the AI r
arXiv:2604.22832v1 Announce Type: cross Abstract: Microscopy-based phenotypic profiling is scalable for drug discovery but lacks the mechanistic depth of transcriptomics, which remains costly and scar
arXiv:2604.24114v1 Announce Type: new Abstract: Curriculum learning helps language models tackle complex reasoning by gradually increasing task difficulty. However, it often fails to generate consiste
arXiv:2508.01191v5 Announce Type: replace Abstract: Chain-of-Thought (CoT) prompting has been shown to be effective in eliciting structured reasoning (i.e., CoT reasoning) from large language models (
arXiv:2603.06589v2 Announce Type: replace-cross Abstract: Model calibration and debiasing are fundamental yet operationally expensive challenges in large-scale recommendation systems. Existing approac
arXiv:2604.24082v1 Announce Type: cross Abstract: Large (vision-)language models exhibit remarkable capability but remain highly susceptible to jailbreaking. Existing safety training approaches aim to
arXiv:2604.24031v1 Announce Type: new Abstract: The encoder-decoder framework has become widely popular nowadays. In this model, the encoder extracts informative visual features from an input image, a
arXiv:2604.23056v1 Announce Type: cross Abstract: We propose a simple yet effective alternative to reward normalization in policy gradient reinforcement learning by integrating a 1D Kalman filter for
arXiv:2604.23320v1 Announce Type: new Abstract: The Convolutional Neural Networks (CNNs) have been the dominant and effective approach for general computer vision tasks. Recently, Kolmogorov-Arnold ne
arXiv:2604.23957v1 Announce Type: new Abstract: Proactive watermarking offers a promising approach for deepfake tamper detection and localization in short-form videos. However, existing methods often
arXiv:2604.23125v1 Announce Type: new Abstract: Real-world data often exhibit long-tailed distributions with numerous noisy labels, substantially degrading the performance of deep models. While prior
arXiv:2604.23000v1 Announce Type: new Abstract: In behavioral cloning (BC), policy performance is fundamentally limited by demonstration data quality. Real-world datasets contain trajectories of varyi
arXiv:2604.24681v1 Announce Type: new Abstract: Human videos contain rich manipulation priors, but using them for robot learning remains difficult because raw observations entangle scene understanding
arXiv:2604.23855v1 Announce Type: new Abstract: We present a deployed system that automates end-to-end customer support workflows inside an enterprise Business Process Management (BPM) platform. The a
arXiv:2509.00084v2 Announce Type: replace-cross Abstract: Test-time scaling (TTS) has gained widespread attention for enhancing LLM reasoning. Existing approaches such as Best-of-N and majority voting
arXiv:2405.20642v3 Announce Type: replace Abstract: Machine learning has become increasingly popular in informing data-driven policy-making. Policies influence behavior in individuals or populations,
arXiv:2604.23950v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have recently demonstrated remarkable capabilities in visual understanding and reasoning, but they also impose significant
arXiv:2603.20806v2 Announce Type: replace Abstract: Multi-label fundus diagnosis requires features that capture both fine-grained lesions and large-scale retinal structure. Many multi-scale medical vi
arXiv:2604.00829v3 Announce Type: replace-cross Abstract: Adapting pretrained language models (LMs) into vision-language models (VLMs) can degrade their native linguistic capability due to representat
arXiv:2604.23578v1 Announce Type: cross Abstract: Human daily behavior unfolds as complex sequences shaped by intentions, preferences, and context. Effectively modeling these behaviors is crucial for
arXiv:2603.03269v2 Announce Type: replace Abstract: Feedforward geometric foundation models achieve strong short-window reconstruction, yet scaling them to minutes-long videos is bottlenecked by quadr
arXiv:2604.22896v1 Announce Type: cross Abstract: Indoor positioning is an essential technology for a wide range of applications in GNSS-denied environments, including indoor navigation and IoT system
arXiv:2510.17590v2 Announce Type: replace Abstract: We present MERIT, an inference-time modular framework for multimodal misinformation detection that decomposes verification into four specialized mod
arXiv:2604.24178v1 Announce Type: cross Abstract: Multi-Objective Alignment aims to align Large Language Models (LLMs) with diverse and often conflicting human values by optimizing multiple objectives
arXiv:2604.24374v1 Announce Type: new Abstract: Representation learning is fundamental to NLP, but building embeddings that work well at different computational budgets is challenging. Matryoshka Repr