Knowledge catalogue
arXiv:2603.09007v2 Announce Type: replace-cross Abstract: Audio deepfake detection aims to detect real human voices from those generated by Artificial Intelligence (AI) and has emerged as a significan
arXiv:2605.08178v1 Announce Type: cross Abstract: Federated Graph Learning (FGL) enables collaborative learning over distributed graph data, yet existing approaches largely rely on a closed-world assu
arXiv:2511.17879v4 Announce Type: replace Abstract: Most applications of generative AI involve a sequential interaction in which a person inputs a prompt and waits for a response, and where reaction t
Good science starts with intellectual honesty, and I am not seeing it. I 100% believe the quote below and zero percent believe the seemingly similar– but actually very different—quote that @geoffreyhi
arXiv:2605.05248v2 Announce Type: replace-cross Abstract: AI systems increasingly synthesize executable structure at runtime: LLMs generate programs, agents construct workflows,self-improving systems
arXiv:2605.09534v1 Announce Type: cross Abstract: Engineering managers increasingly must decide how to introduce generative artificial intelligence (AI), retrieval-augmented generation, and coding age
arXiv:2605.10051v1 Announce Type: cross Abstract: Inference-time guidance is essential for steering generative robot policies toward dynamic objectives without retraining, yet existing methods are lar
Happy One Year Anniversary, AGI!* What an amazing, hallucination-free year it has been!* Glad we can totally trust you now!* *I was being sarcastic. @codeslubber tyler cowen literally said o3 was AGI.
arXiv:2605.09971v1 Announce Type: cross Abstract: Text-to-vibration generation converts natural language into haptic feedback, enabling vibration-effect designers to get scenarios-fitted vibrations mo
arXiv:2602.09524v3 Announce Type: replace Abstract: Unsupervised industrial anomaly detection (UAD) is essential for modern manufacturing inspection, where defect samples are scarce and reliable detec
arXiv:2605.09243v1 Announce Type: new Abstract: If a person can solve a task, can measuring their brain make it easier to train a model to solve that task too? Recent NeuroAI work suggests that supple
arXiv:2605.08283v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a pivotal technique for enhancing the reasoning capabilities of Large Language Mo
https://x.com/xiaochuan8688/status/2053007309459341699 'ByteDance has quietly shut down 30% of its AI projects' Even ByteDance faces the same fate as OpenAI's Sora. While the Chinese public enjoys 'AI
arXiv:2605.08158v1 Announce Type: cross Abstract: Long-video understanding with multimodal language models suffers from three compounding bottlenecks: heavy decode cost to obtain dense RGB frames, qua
arXiv:2605.09392v1 Announce Type: new Abstract: Understanding the intricate mappings between visual stimuli and neural responses is a fundamental challenge in cognitive neuroscience. While current app
arXiv:2602.06527v2 Announce Type: replace Abstract: Scaling test-time compute with multi-path chain-of-thought improves reasoning accuracy, but its effectiveness depends critically on the exploration-
arXiv:2601.22904v2 Announce Type: replace-cross Abstract: Recent studies have explored using pretrained Vision Foundation Models (VFMs) such as DINO for generative autoencoders, showing strong generat
I sat down with @JonHernandezIA in Madrid to discuss the growing risks and impacts of AI and the urgent need to improve our social, political, and technical safeguards. Thanks for an excellent convers
arXiv:2605.10523v1 Announce Type: new Abstract: The field of image-to-video generation has made remarkable progress. However, challenges such as human limb twisting and facial distortion persist, espe
arXiv:2605.08950v1 Announce Type: cross Abstract: Lexical difficulty prediction is a fundamental problem in language learning and readability assessment, requiring models to estimate word difficulty a
arXiv:2603.00918v3 Announce Type: replace-cross Abstract: Text-to-image generation powers content creation across design, media, and data augmentation. Post-training of text-to-image generative models
arXiv:2605.08131v1 Announce Type: new Abstract: Inverse reinforcement learning (IRL) learns a reward function and a corresponding policy that best fit the demonstration data of an expert. However, in
arXiv:2605.09090v1 Announce Type: cross Abstract: Visual Grounding benchmarks assume that the object described by a referring expression is always present in the image, and grounding models are theref
arXiv:2605.08281v1 Announce Type: new Abstract: Implicit neural representations (INRs) encode images as neural-network weights, making image classification a problem of weight-space classifiability. A
arXiv:2412.18798v3 Announce Type: replace-cross Abstract: Transformer-based models have achieved remarkable success in multivariate time series forecasting (MTSF) by capturing long-range dependencies.
arXiv:2605.08686v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems often rely on a controller to coordinate a pool of heterogeneous models, yet existing controllers are typ
arXiv:2512.11321v3 Announce Type: replace Abstract: Facial animation is a core component for creating digital characters in Computer Graphics (CG) industry. A typical production workflow relies on spa
arXiv:2410.14022v2 Announce Type: replace-cross Abstract: Human dexterity arises from combining high-level task reasoning with finger-level dexterity control and physical compliance at the muscle and
arXiv:2605.08755v1 Announce Type: new Abstract: Large reasoning models (LRMs) reach competition-level math and coding accuracy via long autoregressive decoding, making per-token decoding cost a primar
arXiv:2605.09009v1 Announce Type: cross Abstract: Large language models (LLMs) have shown remarkable in-context learning (ICL) capabilities, yet their potential for sequential decision-making remains
arXiv:2605.08496v1 Announce Type: new Abstract: Current adversarial robustness methods for large language models require extensive datasets of harmful prompts (thousands to hundreds of thousands of ex
arXiv:2605.08279v1 Announce Type: cross Abstract: Learning predictive world models from visual observations is a core problem in embodied AI, with applications to model-based reinforcement learning an
arXiv:2603.03759v2 Announce Type: replace-cross Abstract: Many large-scale platforms and networked control systems have a centralized decision maker interacting with a massive population of agents und
arXiv:2605.09217v1 Announce Type: new Abstract: For AI systems to be useful to humans, they must understand and act in accordance with our values and preferences. Since specifying preferences is a har
arXiv:2605.09859v1 Announce Type: new Abstract: Fine-grained image retrieval (FGIR) typically relies on supervision from seen categories to learn discriminative embeddings for retrieving unseen catego
arXiv:2605.09818v1 Announce Type: new Abstract: Reinforcement learning (RL) in healthcare has had mixed results, with reward sparsity, unreliable off-policy evaluation, and deployment-simulation gap a
arXiv:2605.08978v1 Announce Type: new Abstract: Recent advancements in agentic test-time scaling allow models to gather environmental feedback before committing to final actions. A key limitation of e
arXiv:2605.09183v1 Announce Type: new Abstract: Behavior cloning provides strong imitation learning guarantees when training and test environments share the same dynamics. However, in many deployment
arXiv:2605.09404v1 Announce Type: cross Abstract: Targeted data selection aims to identify training samples from a large candidate pool that improve performance on a specific downstream task. Many rec
arXiv:2509.20786v3 Announce Type: replace Abstract: Training deep neural networks with noise and data heterogeneity is a major challenge. We introduce Lightweight Learnable Adaptive Weighting (LiLAW),
arXiv:2505.17204v3 Announce Type: replace-cross Abstract: The sliced Wasserstein flow (SWF), a nonparametric and implicit generative gradient flow, is transformed into a Liouville partial differential
arXiv:2605.08326v1 Announce Type: cross Abstract: As Large Language Models (LLMs) transition into conversational agents, generative advertising emerges as a crucial monetization strategy. However, emb
arXiv:2605.08898v1 Announce Type: cross Abstract: Large Language Models (LLMs) increasingly employ alignment techniques to prevent harmful outputs. Despite these safeguards, attackers can circumvent t
arXiv:2605.09518v1 Announce Type: new Abstract: Meta-learning for algorithm selection relies on a meta-dataset in which each row corresponds to a supervised learning dataset described by meta-features
arXiv:2605.05812v2 Announce Type: replace Abstract: Off-policy, value-based reinforcement learning methods such as Q-learning are appealing because they can learn from arbitrary experience, including
arXiv:2605.09948v1 Announce Type: new Abstract: Current Vision-Language-Action (VLA) models typically treat the deepest representation of a vision-language backbone as universally optimal for action p
arXiv:2605.08843v1 Announce Type: new Abstract: Neural surrogate models for physical simulations are trained on discretized samples of continuous domains, where the induced empirical measure leads to
arXiv:2411.08443v2 Announce Type: replace-cross Abstract: Machine unlearning is an emerging technology that removes a subset of the training data from a trained model without significantly affecting t
arXiv:2605.09418v1 Announce Type: new Abstract: Multi-modal cross-view place recognition remains a fundamental challenge in computer vision and robotics due to the severe viewpoint, modality, and spat
arXiv:2605.09649v1 Announce Type: new Abstract: The key-value (KV) cache is a major bottleneck in long-context inference, where memory and computation grow with sequence length. Existing KV eviction m
arXiv:2511.19279v4 Announce Type: replace-cross Abstract: A cognitive map is an internal model which encodes the abstract relationships among entities in the world, giving humans and animals the flexi
“Marcus's repeated warnings about the 'wall of generalization' since 1998 have once again been proven true.” Marcus氏が1998年から繰り返す'汎化の壁'の警告。またも証明された。完璧なAIを待つより、今の限界を熟知して使いこなすチームが勝つ。少人数ゆえの意思決定の速さとリスク許容度が
arXiv:2605.08527v1 Announce Type: cross Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has significantly improved the reasoning capabilities of large language models (LLMs), particula
arXiv:2511.01008v2 Announce Type: replace Abstract: Large Language Models (LLMs) often struggle with the precise logic and schema alignment required for complex Text-to-SQL tasks. While current method
arXiv:2605.10784v1 Announce Type: new Abstract: Multi-negative preference optimization under the Plackett--Luce (PL) model extends Direct Preference Optimization (DPO) by leveraging comparative signal
arXiv:2605.09317v1 Announce Type: new Abstract: GUI agents are beginning to operate the web, mobile, and desktop as interactive worlds, where successful control depends on carrying forward visual, pro
arXiv:2605.06225v2 Announce Type: replace-cross Abstract: Steering large language models (LLMs) is usually done by either instruction prompting or activation steering. Prompting often gives strong con
arXiv:2602.07940v3 Announce Type: replace Abstract: To cope with uncertain changes of the external world, intelligent systems must continually learn from complex, evolving environments and respond in
Foo Yun Chee / Reuters: Meta offers to give rival AI chatbots free access to WhatsApp for a month while it discusses commitments with EU antitrust regulators to address their concerns — Meta Platforms
arXiv:2505.16741v4 Announce Type: replace Abstract: Minimum attention applies the least action principle to changes of control concerning state and time, first proposed by Brockett. The involved regul