Knowledge catalogue
arXiv:2606.02016v1 Announce Type: new Abstract: Algorithm Selection (AS) aims to automatically identify the most suitable optimization algorithm for a given problem instance by leveraging measurable p
arXiv:2511.20615v2 Announce Type: replace-cross Abstract: This study aimed to explore the application of deep neural networks for whole-body human posture prediction during dynamic load-reaching activ
arXiv:2603.23485v2 Announce Type: replace-cross Abstract: Standard evaluation practices assume that large language model (LLM) outputs are stable when prompts are embedded in contextually equivalent d
arXiv:2606.02307v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are increasingly used as generalist robot policies, yet their evaluation still relies largely on static benchmarks t
arXiv:2606.01220v1 Announce Type: cross Abstract: Generating molecules that simultaneously satisfy drug-like properties and conform to the 3D structure of a target protein is a core challenge in struc
arXiv:2602.23197v2 Announce Type: replace Abstract: Transformer-based large language models exhibit in-context learning, enabling adaptation to downstream tasks via few-shot prompting with demonstrati
arXiv:2606.00083v1 Announce Type: cross Abstract: Reinforcement learning relies on accurate reward functions, which are often hand-crafted or even unavailable in real-world applications, such as robot
arXiv:2511.06676v3 Announce Type: replace Abstract: Now that AI-driven moderation has become pervasive in everyday life, we often hear claims that 'the AI is biased'. While this is often said jokingly
arXiv:2405.14782v3 Announce Type: replace Abstract: Reliable evaluation of language models (LMs) remains an open challenge. Re- searchers and engineers face methodological issues such as the sensitivi
arXiv:2603.26779v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, yet they struggle with spatial tasks that require mental sim
arXiv:2602.06806v2 Announce Type: replace Abstract: Text-to-image diffusion models achieve impressive generation quality but inherit and amplify training-data biases, skewing coverage of semantic attr
arXiv:2606.02252v1 Announce Type: new Abstract: Model merging offers a training-free way to combine multiple post-trained expert models, but merging experts obtained through reinforcement learning (RL
arXiv:2604.01841v2 Announce Type: replace Abstract: Clinical prediction from structured electronic health records (EHRs) is challenging due to high dimensionality, heterogeneity, class imbalance, and
arXiv:2603.09292v2 Announce Type: replace-cross Abstract: Measurement of task progress through explicit, actionable milestones is critical for robust robotic manipulation. This progress awareness enab
arXiv:2606.00928v1 Announce Type: new Abstract: Multiplexed fluorescence microscopy improves tissue segmentation by providing complementary channels including nuclear (DAPI) and membrane (E-cadherin),
arXiv:2606.00267v1 Announce Type: cross Abstract: Video world models (WMs) have shown promise for policy evaluation and improvement by imagining realistic future observations conditioned on ego-robot
arXiv:2511.21397v2 Announce Type: replace-cross Abstract: How does irrelevant information (i.e., distractors) affect test-time scaling in vision-language models (VLMs)? Prior work on text-only languag
arXiv:2605.31438v1 Announce Type: new Abstract: Time series forecasting often requires learning nonlinear and time-delayed dependencies. A paradigmatic class of forecasting models are nonlinear vector
arXiv:2605.31503v1 Announce Type: new Abstract: Humans easily determine which color belongs to which shape in multi-object scenes, an ability known as concept binding. Vision-language embedding models
arXiv:2503.14190v3 Announce Type: replace Abstract: A common goal in analyzing time series data is to understand how events cause observed variations. We study whether Large Language Models (LLMs) can
arXiv:2605.31158v1 Announce Type: new Abstract: Interactive video world models generate video chunk by chunk in response to user-controlled camera movements, enabling applications such as real-time ga
Open models! Introducing MiniMax M3: The First Open-Weights Model to Combine Three Frontier Capabilities - Coding & Agentic Frontier: 59.0% SWE-Bench Pro, 66.0% Terminal Bench 2.1, 34.8% SWE-fficiency
arXiv:2605.31196v1 Announce Type: cross Abstract: Safe human--robot collaboration requires more than visual description: a monitor must determine whether the robot body is safely separated, already co
arXiv:2605.31106v1 Announce Type: new Abstract: Riemannian diffusion models generalize score-based generative modeling to manifold-supported data via stochastic diffusion equations on the manifold. Ho
arXiv:2602.22971v2 Announce Type: replace Abstract: As LLMs achieved breakthroughs in general reasoning, their proficiency in specialized scientific domains reveals pronounced gaps in existing benchma
arXiv:2605.31404v1 Announce Type: cross Abstract: Large Language Model (LLM)-based navigation systems commonly construct explicit spatial representations (e.g., topological graphs, semantic raster map
Video agent models represent the next frontier in AI by extending language model capabilities to process, understand, and act on video content in real-time, enabling autonomous agents to perceive and
The Top AI Papers of the Week (May 24 - May 31) - SkillOpt - AutoScientists - The Efficiency Frontier - Language Models Need Sleep - Adapting the Interface, Not the Model - Forecasting Scientific Prog
arXiv:2510.10020v4 Announce Type: replace-cross Abstract: Generative models frequently suffer miscalibration, wherein statistics of the sampling distribution, such as the fraction of generations in a
arXiv:2605.30153v1 Announce Type: cross Abstract: Score-based diffusion models have demonstrated remarkable empirical success in learning high-dimensional distributions, particularly those exhibiting
arXiv:2605.29572v1 Announce Type: new Abstract: Human tactile perception of materials relies on complex multisensory touch cues, yet the relationship between low-level tactile signals and perceptual r
arXiv:2411.14279v2 Announce Type: replace-cross Abstract: Large vision-language models (LVLMs) have achieved impressive results in various vision-language tasks. However, despite showing promising per
arXiv:2601.14758v4 Announce Type: replace-cross Abstract: Post-training pretrained autoregressive models (ARMs) into masked diffusion models (MDMs) has emerged as a cost-effective way to overcome the
arXiv:2601.22139v2 Announce Type: replace-cross Abstract: Reasoning-oriented Large Language Models (LLMs) have achieved remarkable progress with Chain-of-Thought (CoT) prompting, yet they remain funda
arXiv:2605.30251v1 Announce Type: cross Abstract: Large language models (LLMs) often solve a task when all instructions are given in a single prompt, but fail when the same information is revealed gra
arXiv:2605.28842v1 Announce Type: cross Abstract: The success of large language models (LLMs) across diverse NLP tasks has elevated the importance of reasoning chain optimization as a critical step in
arXiv:2605.30343v1 Announce Type: cross Abstract: To improve the reasoning capabilities of large language models, test-time compute is typically scaled by generating intermediate tokens before the fin
arXiv:2605.30170v1 Announce Type: cross Abstract: While Large Vision-Language Models (VLMs) excel at interpolation, they suffer catastrophic failures in systematic generalization, most notably in visu
arXiv:2512.00837v2 Announce Type: replace Abstract: Watermarking acts as a critical safeguard in text generated by Large Language Models (LLMs). By embedding identifiable signals into model outputs, w
arXiv:2604.00913v2 Announce Type: replace-cross Abstract: 2D assembly diagrams are often abstract and hard to follow, creating a need for intelligent assistants that can monitor progress, detect error
arXiv:2605.28792v1 Announce Type: new Abstract: Electroencephalography (EEG) is a critical, non-invasive method to monitor electrical brain activity. EEGs can span anywhere from a couple seconds to mu
arXiv:2308.04823v5 Announce Type: replace Abstract: This paper unveils CG-Eval, the first-ever comprehensive and automated evaluation framework designed for assessing the generative capabilities of la
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:2510.11234v3 Announce Type: replace Abstract: Efficient compression of language model weights is increasingly critical as model scale and deployment grow. Yet, most existing methods rely on hand
arXiv:2509.26476v2 Announce Type: replace-cross Abstract: We study code-to-metric regression: predicting numeric outcomes of code executions, a challenging task due to the open-ended nature of program
arXiv:2605.28740v1 Announce Type: cross Abstract: As large language models are increasingly deployed for clinical text, ensuring they can reliably signal their own uncertainty becomes critical. Most e
arXiv:2605.28306v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models have emerged as a dominant paradigm for efficient LLM scaling, yet adapting them to non-English downstream tasks remai
arXiv:2605.28814v1 Announce Type: new Abstract: Search has been proposed as an effective method for self-improving language models and agentic systems, both for post-training sample generation and for
Yann LeCun discusses his perspectives on large language models (LLMs) and reinforcement learning (RL) as approaches for developing world models, presenting arguments he frequently advocates for in the
10/ The bigger point: your product is the best RL environment you'll ever have. Frontier labs ship models that are good at everything. The opportunity is a model that's great at your thing. Product, u
arXiv:2511.16870v3 Announce Type: replace Abstract: Enforcing alignment between the internal representations of diffusion or flow-based generative models and those of pretrained self-supervised encode
arXiv:2601.22519v2 Announce Type: replace-cross Abstract: Discrete flow models (DFMs) have been proposed to learn the data distribution on finite state space, offering a flexible framework as an alter
arXiv:2605.27310v1 Announce Type: new Abstract: Cross-view spatial reasoning remains a weak spot for vision-language models (VLMs): they often reason in language and lose the fine-grained geometry nee
arXiv:2605.26409v1 Announce Type: cross Abstract: Evaluating and mitigating a generative system's susceptibility to jailbreak attacks is critical to its safe deployment. Given the number of deployable
arXiv:2605.27101v1 Announce Type: cross Abstract: A key capability for video understanding is reliably linking subjects to events across time, yet whether Video Large Language Models (VideoLLMs) actua
arXiv:2605.26133v1 Announce Type: cross Abstract: Large Language Models (LLMs) have become the predominant paradigm in NLP, advancing both research and industry. As model sizes and pretraining data gr
arXiv:2605.26778v1 Announce Type: new Abstract: Retrieval-augmented generation promises to ground language model outputs in external evidence, yet the field has no reliable way to verify whether retri
This is a live broadcast or discussion from ComfyUI's X (Twitter) account covering recent developments and updates in AI models and workflows. The content likely showcases new features, model releases
arXiv:2605.26368v1 Announce Type: cross Abstract: Geometry estimation from perspective images has greatly advanced, maturing to the point where off-the-shelf foundation models are able to reconstruct
What does JEPA actually learn? We can finally prove it 🌍 So excited to share our theory of identifiable World Models: LeJEPA recovers the latent variables of the world. Plan in the learned World Model