Knowledge catalogue
arXiv:2605.24517v1 Announce Type: cross Abstract: CLI agents are the closest thing language models have to an embodied setting: the model emits commands, the terminal executes them, and the returned s
arXiv:2605.24225v1 Announce Type: new Abstract: In this paper, we introduce a model of evolution and learning in robots that co-optimizes a distribution of latent design vectors (genotypes) and a mixt
arXiv:2605.24566v1 Announce Type: cross Abstract: Human motion diffusion models can synthesize action sequences from text, but controlling motion intensity remains challenging. Existing approaches rel
arXiv:2602.01992v4 Announce Type: replace Abstract: Analogy is a central faculty of human intelligence, enabling abstract patterns discovered in one domain to be applied to another. Despite its centra
arXiv:2605.24926v1 Announce Type: new Abstract: Runtime fairness is not a one-time constraint but a dynamic property evaluated over a sequence of decisions. To ensure fairness at runtime, it is necess
arXiv:2605.24172v1 Announce Type: new Abstract: Secure patient-provider messages contain clinically important communication behaviors that are difficult to characterize manually at scale. The Electron
arXiv:2605.25297v1 Announce Type: cross Abstract: Effective features are crucial for predictive model performance, but creating them often requires domain expertise, limiting scalability across applic
Europe’s sovereign AI moment arrives in Heilbronn next week. At TECH by Handelsblatt 2026, Cohere CEO and Co-founder, @aidangomez, will join leaders across business, policy, and industry to discuss ho
even by the standards of the last few years, we are in some truly insane territory here. “Unserious, empty, hallucinatory, and borderline dishonest” - the prospectus for a company that the S&P 500 is
arXiv:2605.25120v1 Announce Type: cross Abstract: Radiology reports remain the primary mechanism by which imaging findings are communicated to clinical teams. However, much of the structured informati
arXiv:2605.25091v1 Announce Type: new Abstract: As modern air combat evolves toward beyond-visual-range (BVR) multi-aircraft cooperative engagements, autonomous decision-making for unmanned combat aer
arXiv:2605.24345v1 Announce Type: new Abstract: In online reinforcement learning, data scarcity creates epistemic uncertainty that makes robustness important early in learning, whereas sufficient expl
arXiv:2605.24173v1 Announce Type: cross Abstract: Memorization in large language models has been studied almost exclusively through prefix-conditioned extraction, a natural choice for autoregressive m
arXiv:2605.25582v1 Announce Type: cross Abstract: Reinforcement learning for large language models faces a fundamental trade-off between sample efficiency and asymptotic performance: strictly on-polic
arXiv:2602.16229v2 Announce Type: replace Abstract: Learning latent actions from action-free video has emerged as a powerful paradigm for scaling up controllable world model learning. Latent actions p
arXiv:2510.22827v3 Announce Type: replace-cross Abstract: Evaluating text-to-image (T2I) systems requires judging not only whether an image matches a prompt, but also whether socially salient attribut
arXiv:2605.23970v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as automatic judges for summarization and dialogue evaluation. Prior work has documented biases such
arXiv:2605.24286v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning is useful for monitoring language models only when the reasoning trace faithfully reflects the computation that produ
arXiv:2603.06218v2 Announce Type: replace Abstract: Accurate physics simulation is essential for robotic learning and control, yet analytical simulators often fail to capture complex contact dynamics,
arXiv:2605.25258v1 Announce Type: cross Abstract: Recommender systems generally optimises user engagement, but this approach is dangerous in mental health contexts. When vulnerable users show signs of
arXiv:2511.03548v2 Announce Type: replace Abstract: Understanding the generalization behavior of learning algorithms is a central goal of learning theory. A recently emerging explanation is that learn
arXiv:2605.25226v1 Announce Type: new Abstract: Large language models are widely deployed in high-stakes NLP tasks, yet risks such as bias, hallucination, adversarial vulnerability and unreliable gene
arXiv:2602.00491v2 Announce Type: replace Abstract: Public health reasoning requires population level inference grounded in scientific evidence, expert consensus, and safety constraints. However, it r
arXiv:2507.10644v4 Announce Type: replace Abstract: The Web of Agents (WoA) transforms the document-centric Web into an environment of autonomous agents acting on users' behalf, a vision newly tractab
arXiv:2506.11027v3 Announce Type: replace-cross Abstract: Generating accurate and executable code using Large Language Models (LLMs) remains a significant challenge for underrepresented programming la
arXiv:2605.25459v1 Announce Type: cross Abstract: Language models are pretrained as passive predictors with no incentive to model the consequences of their own outputs. Post-training changes this: a m
arXiv:2605.25239v1 Announce Type: new Abstract: We present FusionCore, an open-source ROS 2 sensor fusion package that fuses IMU, wheel encoder odometry, GPS, and Visual SLAM pose into a single 100 Hz
arXiv:2603.10250v2 Announce Type: replace Abstract: A commonly used family of RL algorithms for diffusion policies conducts softmax reweighting over samples from the behavior policy, which often induc
arXiv:2605.24405v1 Announce Type: cross Abstract: We study sequential decision-making with offline reinforcement learning (RL). Traditional offline RL policies may result in out-of-distribution (OOD)
arXiv:2602.01576v2 Announce Type: replace-cross Abstract: Mobile Graphical User Interface (GUI) World Models (WMs) offer a promising path for improving mobile GUI agent performance at train- and infer
arXiv:2605.25447v1 Announce Type: new Abstract: Generating structured, editable diagrams remains a significant challenge for contemporary large language models, despite their proficiency in general-pu
arXiv:2605.24243v1 Announce Type: cross Abstract: In 3D scene understanding, deep learning models rely on large models and extensive training to capture basic geometric structures that are present in
arXiv:2605.26078v1 Announce Type: new Abstract: Wasserstein policy gradient (WPG) is a policy optimization method for reinforcement learning (RL) that exploits the optimal-transport geometry of action
arXiv:2605.24939v1 Announce Type: new Abstract: We study the global convergence of policy gradient for infinite-horizon entropy-regularized Markov decision processes (MDPs) with continuous state and a
arXiv:2603.06626v2 Announce Type: replace-cross Abstract: Traditional Mixture-of-Experts (MoE) training typically proceeds without any structural priors, effectively requiring the model to simultaneou
arXiv:2605.25170v1 Announce Type: cross Abstract: Training data for olfaction is scattered through disparate, non-standardized datasets that limit the ability to build representative world models. Olf
arXiv:2605.25443v1 Announce Type: new Abstract: Post-training has significantly enhanced the reasoning capability of Large Reasoning Models (LRMs), especially with Reinforcement Learning (RL) like Gro
arXiv:2605.25967v1 Announce Type: new Abstract: As policy catches up with the capabilities of generative AI, watermarking is central to content provenance efforts. Inference-time watermarks for autore
arXiv:2605.24042v1 Announce Type: cross Abstract: Of 1{,}536 Gaussian release covariances we tested for single-layer hidden-state privacy, zero achieve both moderate utility and moderate privacy again
arXiv:2507.09179v3 Announce Type: replace Abstract: Decentralized finance (DeFi) has introduced a new era of permissionless financial innovation but also led to unprecedented market manipulation. With
arXiv:2605.25198v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a powerful paradigm for improving language models on reasoning-intensive tasks, but i
arXiv:2605.25790v1 Announce Type: new Abstract: The increasing use of drones in human-centric applications highlights the need for designs that can survive collisions and recover rapidly, minimizing r
arXiv:2605.24351v1 Announce Type: new Abstract: Large language models (LLMs) can support scientific literature synthesis, but remain prone to hallucinated references, uneven coverage, and weakly groun
arXiv:2605.24749v1 Announce Type: cross Abstract: Reward modeling is not only a prediction problem: in KL-regularized policy optimization, the learned reward is exponentiated to define the deployed po
arXiv:2605.25414v1 Announce Type: new Abstract: Distribution shift in imitation learning refers to the problem that the agent cannot plan proper actions for a state that has not been visited during th
arXiv:2605.24934v1 Announce Type: cross Abstract: Human egocentric video captures rich manipulation demonstrations without any robot hardware, yet transferring these skills to robots remains challengi
arXiv:2605.25685v1 Announce Type: new Abstract: Robust and accurate perception of humans in their 3D scene context is essential for integrating robots into everyday environments. Existing approaches,
I feel like I'm eating crazy pills when I read the countless bad takes around how the Vatican would have virtually anointed Anthropic. When if you read the Pope's encyclical it's actually a COMPLETE r
arXiv:2605.24217v1 Announce Type: new Abstract: As Large Language Models (LLMs) transition from research environments to production deployments, evaluating their performance against strict Service Lev
⚠️⚠️⚠️if you believe this you and think you are safe, you don’t understand how S&P is about to change the rules and what that means. run don’t walk to read my essay “This one weird trick might cost yo
Gary Marcus discusses a potentially overlooked financial risk that could have massive implications for retirement savings, presenting his analysis in an essay format on X. The post appears to emphasiz
Import AI 458 discusses perspectives on AI's future trajectory and potential long-term scenarios, likely including analysis of singularity concepts and their implications. The newsletter entry examine
arXiv:2603.05691v2 Announce Type: replace Abstract: It is increasingly common in machine learning to use learned models to label data and then employ such data to train more capable models. The phenom
arXiv:2605.24009v1 Announce Type: cross Abstract: Convective available potential energy (CAPE) is an important variable for forecasting severe weather and understanding deep convection and precipitati
arXiv:2605.24247v1 Announce Type: cross Abstract: Many automated labeling pipelines classify inputs into categories defined by a written specification, content moderation being a prominent use case. S
arXiv:2605.23924v1 Announce Type: new Abstract: Segment-level disclosures are a central component of financial reporting, providing insight into firms' internal organization and the allocation of econ
In our new paper, EDGE-OPD (EviDence GuidEd On-Policy Distillation), we introduce two key ideas for improving On-Policy Distillation (OPD). First, we use guided rollouts that inject privileged context
arXiv:2605.25123v1 Announce Type: cross Abstract: We study inference-time alignment for diffusion-based generative models, aiming to steer a base model toward high-reward outputs without updating its
arXiv:2605.05226v2 Announce Type: replace-cross Abstract: The central challenge of reinforcement learning for reasoning lies not only in the sparsity of outcome-level supervision, but more fundamental
arXiv:2605.24883v1 Announce Type: new Abstract: The widespread integration of Large Language Models (LLMs) necessitates rigorous and systematic safety evaluation. Existing paradigms either rely on con