Is Complex Query Answering Really Complex?
arXiv:2410.12537v4 Announce Type: replace Abstract: Complex query answering (CQA) on knowledge graphs (KGs) is gaining momentum as a challenging reasoning task. In this paper, we show that the current
Knowledge catalogue
arXiv:2410.12537v4 Announce Type: replace Abstract: Complex query answering (CQA) on knowledge graphs (KGs) is gaining momentum as a challenging reasoning task. In this paper, we show that the current
arXiv:2610.16547v1 Announce Type: cross Abstract: Sentence representation learning is still an open challenge in Natural Language Processing. In this work, we propose a new self-supervised framework f
arXiv:2610.17697v1 Announce Type: new Abstract: AI systems now write long and difficult mathematical proofs, and checking their step-by-step validity is becoming a bottleneck. A learned verifier could
arXiv:2606.12475v2 Announce Type: replace Abstract: Human-robot collaboration (HRC) combines the complementary strengths of humans and robots to improve task efficiency. However, many existing collabo
arXiv:2610.16966v1 Announce Type: new Abstract: Symbolic regression (SR) seeks explicit equations that explain observed data, and recent methods increasingly employ large language models (LLMs) and ag
arXiv:2610.14302v1 Announce Type: new Abstract: Humans and animals do not learn vision from a fixed set of data: they move, interact, and learn from the visual consequences of their actions. Motivated
arXiv:2603.03172v2 Announce Type: replace Abstract: Certified machine unlearning aims to provably remove the influence of a deletion set U from a model trained on a dataset S, by producing an unlearne
arXiv:2610.18243v1 Announce Type: new Abstract: Given the growing global burden of liver tumors and diffuse liver diseases, non-contrast computed tomography (NCCT) is increasingly used for opportunist
arXiv:2610.14550v1 Announce Type: cross Abstract: Dynamic Product Advertising (DPA) retrieval systems are a core component of modern e-commerce advertising; however, their evaluation remains challengi
arXiv:2610.14325v1 Announce Type: new Abstract: Position tracking is a well-studied problem in Euclidean navigation, yet comparatively little attention has been given to navigation on general manifold
arXiv:2610.18478v1 Announce Type: new Abstract: Large vision-language models (LVLMs) have shown strong potential for embodied vision-language navigation (VLN), yet their performance degrades on long-h
arXiv:2610.18851v1 Announce Type: cross Abstract: Multi-objective molecular optimization is a critical process in drug discovery that aims to modify a given molecule to simultaneously improve multiple
arXiv:2610.16252v1 Announce Type: new Abstract: Whole-body human-object interaction (HOI) generation requires coordinating body and finger motion, object trajectories, and time-varying contacts. Exist
arXiv:2610.17866v1 Announce Type: new Abstract: Understanding fluorescence microscopy images is fundamental to automating biological research. However, existing methods typically learn predefined conc
Old-school physics-based weather forecasting and even new AI models typically become less reliable beyond two weeks, a problem Microsoft claims to have solved using machine learning. Researchers with
arXiv:2610.16919v1 Announce Type: cross Abstract: Machine learning engineering (MLE) agents are emerging as a new paradigm for automating end-to-end ML pipeline development. Using large language model
arXiv:2610.14716v1 Announce Type: new Abstract: Random walk (RW)-based algorithms are widely used in decentralized learning due to their low communication overhead and scalability. While most existing
arXiv:2610.17320v1 Announce Type: new Abstract: Stochastic optimization models sequential decision-making under uncertainty, typically assuming known input distributions. When these distributions are
arXiv:2610.18379v1 Announce Type: new Abstract: With the rapid development of deep learning, supervised convolutional neural network methods have greatly improved the performance of low-dose computed
arXiv:2610.15223v1 Announce Type: cross Abstract: Recent observations that deep learning models train at the edge of stability have renewed interest in understanding stability properties of classical
arXiv:2610.16452v1 Announce Type: new Abstract: Explainable face forgery detection requires not only correct judgments but also forensic evidence supported by the input image. However, training cues a
arXiv:2610.15414v1 Announce Type: new Abstract: Multimodal remote sensing image generation emerges as a promising paradigm for recovering complete and complementary observations from heterogeneous and
arXiv:2610.18714v1 Announce Type: cross Abstract: Non-clashing teaching minimizes teaching complexity among models satisfying the Goldman--Mathias collusion-avoidance criterion. While the Vapnik--Cher
arXiv:2607.11432v2 Announce Type: replace Abstract: We study reinforcement learning (RL) from pairwise human-generated trajectory comparisons. We generalize preference-based RL by formalizing a novel
arXiv:2610.16815v1 Announce Type: cross Abstract: Calibration has been extensively studied for classifiers, but remains largely unexplored for crowdsourcing aggregation methods, which are increasingly
arXiv:2610.15177v1 Announce Type: new Abstract: As agentic AI systems take on increasingly complex and consequential tasks, securing them against prompt injection becomes critical. Existing defenses o
arXiv:2610.14022v1 Announce Type: new Abstract: On-policy distillation (OPD) improves language-model by supervising a student on prefixes sampled from its own policy, but its supervision remains confi
arXiv:2610.15224v1 Announce Type: cross Abstract: Natural language specifications let non-expert users task robot teams, and large language models (LLMs) have recently proven effective at grounding su
arXiv:2610.18485v1 Announce Type: new Abstract: Recent progress in generative models has made talking-head deepfakes highly realistic and easy to create, jeopardizing public trust. However, most audio
arXiv:2610.16066v1 Announce Type: new Abstract: Detailed video captioning has become an essential semantic interface for multi-modal understanding and generation. While existing methods mainly target
arXiv:2610.14713v1 Announce Type: new Abstract: While diffusion models achieve impressive image generation capabilities, they suffer from high computational costs, implausible artifacts, and limited a
arXiv:2610.14704v1 Announce Type: new Abstract: Coupling a pretrained vision-language model (VLM) with a diffusion or flow-matching action expert has become the mainstream vision-language-action (VLA)
arXiv:2610.18683v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as the primary paradigm for optimizing Large Language Model (LLM) agents, yet efficient critic-free algorithms (
arXiv:2601.13155v3 Announce Type: replace-cross Abstract: Long-context inference enhances the reasoning capability of Large Language Models (LLMs), but incurs significant computational overhead. Token
arXiv:2610.16592v1 Announce Type: cross Abstract: Agentic reinforcement learning (RL) is commonly used to enhance the capabilities of large language models (LLMs) in solving complex multi-turn tasks.
arXiv:2610.17265v1 Announce Type: new Abstract: Federated learning allows hospitals to train a shared mortality model without pooling their records, but published comparisons of aggregation strategies
arXiv:2610.15669v1 Announce Type: new Abstract: Weak bag-level labels are widely available in computational pathology, whereas reliable instance-level annotations for whole-slide images remain expensi
arXiv:2610.17667v1 Announce Type: new Abstract: Diffusion models and Flow Matching can represent flexible distributions over robot actions, but many reinforcement learning algorithms also require the
arXiv:2610.17829v1 Announce Type: new Abstract: Multimodal Diffusion Transformers (MM-DiTs) have recently emerged as a powerful framework for unified text-vision synthesis, surpassing traditional U-Ne
arXiv:2512.23080v2 Announce Type: replace-cross Abstract: The discovery of novel odorant molecules is key for the fragrance and flavor industries, yet efficiently navigating the vast chemical space to
arXiv:2610.15101v1 Announce Type: new Abstract: Weighted sums of random kitchen sinks (W-RKS) is a commonly used feature extraction technique for supervised learning tasks. A major drawback of this me
arXiv:2610.17971v1 Announce Type: cross Abstract: The semantic entropy of a language model is the entropy of the meanings of its sampled answers. The meanings are never observed, and the entropy is es
Reachy Mini is an open-source robot from Pollen Robotics and Hugging Face, run by a Raspberry Pi with 4 GB of RAM. We wanted it to do what a good host does: greet you by name the second time you walk
arXiv:2610.15079v1 Announce Type: new Abstract: Squeeze-and-Excitation (SE) modules improve convolutional networks by modeling channel-wise dependencies, but global average pooling collapses spatial s
arXiv:2605.06046v2 Announce Type: replace Abstract: Auto-regressive token generation in large language models is memory-bound because it requires 'attending to' key and value tensors (KV cache) of all
arXiv:2610.14500v1 Announce Type: new Abstract: Many widely used activation functions, including ReLU, GELU, Swish, and Sigmoid, produce predominantly positive outputs, resulting in a nonzero post-act
arXiv:2610.14423v1 Announce Type: cross Abstract: Frontier AI systems are increasingly natively multimodal, jointly pretrained on multiple modalities, such as text, vision, and audio. However, the sci
arXiv:2610.18039v1 Announce Type: new Abstract: Vision Transformers achieve strong performance but incur high computational costs from quadratic token interactions. Training-free token reduction metho
arXiv:2610.15734v1 Announce Type: new Abstract: We present ScenePlacer, a framework for scene-level pose estimation of objects generated by state-of-the-art native shape generation models, placing gen
arXiv:2610.14523v1 Announce Type: cross Abstract: ScienceBuddy-PV adapts the ScienceBuddy recursive-in-recursive design to panvascular intervention research through a structured evidence ledger, devic
arXiv:2610.17939v1 Announce Type: cross Abstract: Traditional time-series classification methods often operate as black boxes, providing a class prediction without indicating which time point or time
arXiv:2610.14968v1 Announce Type: cross Abstract: Large Language Models (LLMs) can generate functionally correct code while introducing exploitable security weaknesses, particularly when developers sp
arXiv:2610.16234v1 Announce Type: new Abstract: Visual grounding is a central challenge for GUI agents, requiring an instruction to be mapped to a target within a crowded screenshot. Coordinate genera
arXiv:2605.05965v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a key approach for improving the reasoning abilities of large language models. Howe
arXiv:2603.08185v2 Announce Type: replace Abstract: Post-training quantization (PTQ) has emerged as a prevailing technique for deploying large language models (LLMs) efficiently in terms of both memor
arXiv:2602.16805v2 Announce Type: replace-cross Abstract: Code evolution is a family of techniques that rely on large language models to search through possible computer programs by evolving existing
arXiv:2610.18807v1 Announce Type: new Abstract: Agentic reinforcement learning (RL) enables large language models (LLMs) to learn from multi-turn interactions with verifiable outcomes. Textual skills
arXiv:2610.15204v1 Announce Type: cross Abstract: Large language models are increasingly used in computer science education. However, the teaching abilities of even the most powerful LLMs are limited:
arXiv:2610.15968v1 Announce Type: new Abstract: Spiking Transformers achieve energy efficiency by discarding softmax normalization in favor of thresholded, binary attention. However, this removal brea
arXiv:2610.18486v1 Announce Type: new Abstract: Membership Inference Attacks (MIAs) are currently the dominant approach to evaluating privacy in machine learning applications. Despite their significan