Knowledge catalogue
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
arXiv:2604.23788v1 Announce Type: new Abstract: Appreciating multi-figure paintings requires understanding how characters relate through subtle cues like gaze alignment, gesture, and spatial arrangeme
arXiv:2604.24280v1 Announce Type: new Abstract: We present a framework using Relative Entropy Inverse Reinforcement Learning (RE-IRL) to recover investor reward functions from observed investment acti
arXiv:2604.23264v1 Announce Type: new Abstract: Text-to-motion generation aims to generate 3D human motions that are tightly aligned with the input text while remaining physically plausible and rich i
arXiv:2604.22881v1 Announce Type: cross Abstract: Generative recommendation (GR) offers superior modeling capabilities but suffers from prohibitive inference costs due to the repeated encoding of long
arXiv:2604.24186v1 Announce Type: cross Abstract: Diagnostic prediction and clinical reasoning are critical tasks in healthcare applications. While Large Language Models (LLMs) have shown strong capab
arXiv:2508.07555v3 Announce Type: replace Abstract: We consider a remote inference system with multiple modalities, where a multimodal machine learning (ML) model performs real-time inference using fe
arXiv:2508.07220v2 Announce Type: replace-cross Abstract: Learning stochastic functions from partially observed context-target pairs requires models that are expressive, uncertainty-aware, and strongl
arXiv:2604.22837v1 Announce Type: cross Abstract: SAM-based dense trackers provide strong short-term mask propagation but remain fragile under long occlusion, fast motion, viewpoint change, and distra
arXiv:2604.24191v1 Announce Type: new Abstract: Omnimodal understanding entails a massive, highly redundant search space of cross-modal interactions, demanding focused and deliberative reasoning. Curr
arXiv:2512.09297v3 Announce Type: replace Abstract: Learning dexterous bimanual manipulation policies critically depends on large-scale, high-quality demonstrations, yet current paradigms face inheren
OpenAI, which squandered its tremendous lead and is now missing its projections, is in trouble. There’s no two ways about it. 'OpenAI Chief Financial Officer Sarah Friar has told other company leaders
arXiv:2604.23540v1 Announce Type: new Abstract: Text-to-image diffusion models have achieved remarkable generative capabilities, yet accurately aligning complex textual prompts with synthesized layout
arXiv:2502.04274v4 Announce Type: replace Abstract: End-to-end representation learning has become a powerful tool for estimating causal quantities from high-dimensional observational data, but its eff
arXiv:2604.23412v1 Announce Type: new Abstract: While annotated corpora are crucial in the field of natural language processing (NLP), those containing copyrighted material are difficult to exchange a
arXiv:2604.22971v1 Announce Type: cross Abstract: The TRUST democratic discourse analysis pipeline exposes its large language model (LLM) components to peer model identity through multiple structural
arXiv:2604.23600v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in persona-driven applications such as education, customer service, and social platforms, where m
arXiv:2604.23574v1 Announce Type: new Abstract: Existing image-to-video generation methods often produce physically implausible motions and lack precise control over object dynamics. While prior appro
arXiv:2604.24171v1 Announce Type: new Abstract: Current visual text generation models struggle with the trade-off between text accuracy and overall image coherence. We find that achieving high text ac
arXiv:2604.23036v1 Announce Type: cross Abstract: Despite MoE models leading many benchmarks, supervised fine-tuning (SFT) for the MoE architectures remains difficult because its router layers are fra
arXiv:2604.24642v1 Announce Type: new Abstract: The dream of instantly creating rich 360-degree panoramic worlds from text is rapidly becoming a reality, yet a crucial gap exists in our ability to rel
arXiv:2604.22891v1 Announce Type: cross Abstract: LLM-as-a-Judge has become a dominant approach in automated evaluation systems, playing critical roles in model alignment, leaderboard construction, qu
arXiv:2604.23702v1 Announce Type: new Abstract: Humanoid robots operating in human-centered environments (e.g., homes, hospitals, and offices) must mitigate foot--ground impact transients, as impact-i
arXiv:2604.23696v1 Announce Type: new Abstract: Haptic feedback has been a long-missed feature in robotic-assisted surgery, one that would allow surgeons to perceive tissue properties and apply contro
arXiv:2604.22911v1 Announce Type: new Abstract: Humanoid robots operating in unstructured environments must recover from unexpected disturbances-a capability that remains challenging for end-to-end co
arXiv:2603.06165v2 Announce Type: replace-cross Abstract: The growing demand for text-to-image generation has led to rapid advances in generative modeling. Recently, text-to-image diffusion models tra
arXiv:2603.25562v2 Announce Type: replace-cross Abstract: On-policy distillation (OPD) is increasingly used in LLM post-training because it can leverage a teacher model to provide dense supervision on
arXiv:2604.24198v1 Announce Type: cross Abstract: Process Reward Models (PRMs) have achieved remarkable success in augmenting the reasoning capabilities of Large Language Models (LLMs) within static d