Knowledge catalogue
Cal Newport discusses whether recent AI advances represent a breakthrough in mathematical problem-solving capabilities, examining the implications of AI systems' improved performance on complex mathem
arXiv:2605.27418v1 Announce Type: cross Abstract: The imminent integration of autonomous vehicles and mobile robots in urban settings presents a critical safety challenge for future intelligent transp
arXiv:2512.02019v3 Announce Type: replace-cross Abstract: Diffusion models excel at sampling from complex, unnormalized distributions. In this work, we extend Maximum Entropy Reinforcement Learning (M
arXiv:2605.28456v1 Announce Type: new Abstract: Existing Visual Speech Recognition (VSR) systems commonly rely on left-to-right autoregressive decoding, which can force premature decisions on visually
arXiv:2605.28491v1 Announce Type: new Abstract: We study real-time audio-responsive character control as a deployment-faithful problem: strictly causal, bounded-latency streaming that must generate co
arXiv:2605.27823v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly vulnerable to adversarial prompts that exploit semantic ambiguities to bypass safety mechanisms, resulti
arXiv:2605.28678v1 Announce Type: new Abstract: Speculative reasoning has recently been proposed as a means to accelerate reasoning-intensive generation in large multimodal models, but its effectivene
arXiv:2605.27846v1 Announce Type: new Abstract: Large Reasoning Models are typically trained via reinforcement learning from verifiable rewards (RLVR). However, existing approaches adopt fixed weights
arXiv:2602.02150v2 Announce Type: replace-cross Abstract: Test-time reinforcement learning generates multiple candidate answers via repeated rollouts and performs online updates using pseudo-labels co
arXiv:2603.09882v2 Announce Type: replace-cross Abstract: Extrinsic dexterity leverages environmental contact to overcome the limitations of prehensile manipulation. However, achieving such dexterity
arXiv:2605.28630v1 Announce Type: new Abstract: Zero-Shot Anomaly Detection (ZSAD) aims to detect anomalies in unseen domains without target-domain adaptation. Recent CLIP-based methods have shown pro
arXiv:2605.27741v1 Announce Type: new Abstract: Audio and omni-modal large language models exhibit impressive cross-modal reasoning capabilities. However, applying standard reinforcement learning post
arXiv:2605.28598v1 Announce Type: cross Abstract: LLM-powered social agents are increasingly used to simulate online social behavior, yet their realism remains difficult to validate. Existing work has
arXiv:2605.27671v1 Announce Type: cross Abstract: Safety defenses for large language models (LLMs) are typically trained and evaluated on single-turn prompts, yet real attacks often unfold as indirect
arXiv:2605.28098v1 Announce Type: new Abstract: Multi-agent systems are increasingly deployed to support various tasks where agents interact to achieve individual and collective objectives. Although t
F Scott Fitzgerald, writing about someone who may as well have been Peter Thiel: “They were careless people, Tom and Daisy- they smashed up things and creatures and then retreated back into their mone
arXiv:2605.28389v1 Announce Type: new Abstract: While large language models have made significant progress in mathematical reasoning, they remain unreliable at judging the correctness of their own sol
arXiv:2605.27892v1 Announce Type: new Abstract: Synthetic Electronic Health Record (EHR) generation provides a promising avenue for data augmentation and cross-hospital modeling in privacy-constrained
arXiv:2605.27451v1 Announce Type: new Abstract: The 10th Affective & Behavior Analysis in-the-Wild (ABAW) Workshop and Competition, held at CVPR 2026, continues to advance research on modelling, analy
arXiv:2605.27387v1 Announce Type: cross Abstract: Diffusion models promise efficient parallel text generation but rely on bidirectional attention, creating a structural mismatch with pre-trained Autor
arXiv:2605.28483v1 Announce Type: new Abstract: Linking learning resources to a structured competency framework is key to enabling competency-based search and curriculum analytics in Learning Manageme
arXiv:2605.28820v1 Announce Type: new Abstract: Current vision-language models (VLMs) typically stitch together separate image encoders and language decoders via multi-stage alignment, a modular frame
Gary Marcus shares a discussion thread about consciousness with user @Grimezsz on X (formerly Twitter), likely exploring philosophical, scientific, or technical perspectives on consciousness and relat
arXiv:2605.27491v1 Announce Type: new Abstract: We introduce GE-Sim 2.0 (Genie Envisioner World Simulator 2.0), a closed-loop video world simulator for robotic manipulation. Building on the action-con
arXiv:2605.27934v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards improves language model reasoning, but its reliance on domain-specific verifiers, sparse outcome rewards,
arXiv:2605.27970v1 Announce Type: new Abstract: While large language models (LLMs) are trained purely on textual data, prior work has shown that their internal representations can exhibit rich geometr
arXiv:2605.28233v1 Announce Type: cross Abstract: Fairness-accuracy trade-offs are a central concern in the deployment of fairness-aware machine learning methods. When sensitive attributes are unavail
arXiv:2605.27766v1 Announce Type: new Abstract: LLM safety evaluations predominantly test models in isolation, yet deployed AI agents increasingly operate within persistent social environments alongsi
arXiv:2605.27488v1 Announce Type: cross Abstract: Agentic systems increasingly run user-authored orchestration code that invokes tools, spawns subtasks, and delegates work across machines and clouds.
arXiv:2605.27494v1 Announce Type: cross Abstract: Modern retrieval-augmented generation(RAG) deployments increasingly rely on caching to reduce token cost and time-to-first-token(TTFT). Prefix-level K
arXiv:2605.28520v1 Announce Type: new Abstract: Accurately forecasting the impact of salient financial events on markets is critical for investors and policymakers. However, existing multimodal time-s
arXiv:2511.20934v2 Announce Type: replace Abstract: Compositional explanations are a family of methods that aim to describe the spatial alignment between neurons' receptive field activations and conce
arXiv:2503.11477v2 Announce Type: replace Abstract: Understanding the factors that trigger or prevent undesirable health outcomes across patient subpopulations is essential for designing targeted inte
arXiv:2605.28198v1 Announce Type: new Abstract: Existing approaches for synthetic tabular data generation are based on either purely generative models or LLMs, both of which struggle with data heterog
arXiv:2605.27758v1 Announce Type: cross Abstract: Automotive crashworthiness optimization remains a safety-critical challenge, requiring the management of large-scale nonlinear structural deformations
arXiv:2508.15130v2 Announce Type: replace Abstract: Despite significant progress in no-reference image quality assessment (NR-IQA), dataset biases and reliance on subjective labels continue to hinder
Holy shit! They changed the rules for Elon again... They waved the profitability rule & are adding SpaceX to indices only 5 days after IPO... normally it's 90 This forces 401k retirement & passive fun
arXiv:2605.27662v1 Announce Type: cross Abstract: Equivariant neural networks encode geometric symmetries by construction, yet they are often difficult to optimize and can underperform less constraine
arXiv:2605.28726v1 Announce Type: cross Abstract: We discover that VLA architectures fail in fundamentally different, predictable ways at the motor-command level. Running VQ-BeT, Diffusion Policy, and
arXiv:2605.28114v1 Announce Type: new Abstract: As autonomous AI agents are deployed in persistent, interacting networks -- coordinating tasks, routing resources, and accumulating reputational histori
I read this garbage (in a big UK newspaper) when Google can’t even count reliably and wonder why people don’t spend more time learning about AI’s actual strengths and weakness before running their mou
arXiv:2605.28097v1 Announce Type: new Abstract: Canary deployment routes a fraction of traffic to a new software version, monitors metrics, and rolls back on regression. Mainstream controllers (Argo R
arXiv:2605.27374v1 Announce Type: new Abstract: Recent advances in multimodal large language models (MLLMs) and diffusion models (DMs) have opened new possibilities for AI-generated content. Yet, pers
arXiv:2601.04716v3 Announce Type: replace Abstract: While Large Language Model (LLM) role-playing agents have advanced rapidly, it remains unclear which profile elements genuinely drive role-playing q
arXiv:2311.02304v3 Announce Type: replace Abstract: Control of legged robots is a challenging problem that has been investigated by different approaches, such as model-based control and learning algor
Important context for latest OpenAI announcement. Especially (5:30): 'The model spit out a long transcript of an answer. Then a team of expert mathematicians poured over this [transcript] and identifi
arXiv:2605.28279v1 Announce Type: new Abstract: IMU preintegration is widely used in factor-graph-based visual--inertial, lidar--inertial, and radar--inertial state estimation, yet it is often treated
arXiv:2605.27583v1 Announce Type: new Abstract: Electrocardiograms (ECGs) are widely used non-invasive measurements of cardiac activity and play a central role in clinical diagnosis. Recent multimodal
arXiv:2605.27395v1 Announce Type: cross Abstract: As the rapid proliferation of AI systems and harms spurs efforts in AI governance around the world, prioritizing among competing policy options has be
arXiv:2605.28327v1 Announce Type: cross Abstract: Traditional insurance pricing relies on risk-based principles that ensure actuarial fairness and solvency but do not explicitly account for policyhold
arXiv:2605.27628v1 Announce Type: new Abstract: As autonomous and agentic AI systems scale in robotic and human-machine environments, managing hallucination and persistent but unjustified action remai
arXiv:2605.28609v1 Announce Type: new Abstract: Forensic vision-language models (VLMs) have recently been developed to detect image tampering and provide natural-language explanations. However, their
arXiv:2605.28184v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as the standard paradigm for improving reasoning capability of large language models,
just imagine what will happen to the economy and people’s retirement funds if these projections from FT are correct. brace for bailouts. The AI numbers are starting to look very ugly. Even under 'best
arXiv:2605.28617v1 Announce Type: new Abstract: LLM agents increasingly act by writing code, yet a split persists between the runtime that drives the agent and the code the model writes. The runtime o
arXiv:2507.06999v2 Announce Type: replace-cross Abstract: Reasoning is essential for large language models (LLMs), especially in complex tasks such as mathematical problem solving. However, multimodal
arXiv:2605.28612v1 Announce Type: new Abstract: Parity functions are fundamental Boolean operations with critical applications across machine learning, cryptography, and error correction. Yet, learnin
arXiv:2605.27999v1 Announce Type: cross Abstract: We address the problem of learning to assign prediction tasks to one agent from a set of available human or AI agents. In particular, we focus on the
arXiv:2605.28133v1 Announce Type: new Abstract: We study the problem of learning to bid when the bidder's value is dynamic, i.e., when the current value depends on past outcomes. Specifically, we cons
arXiv:2410.12035v2 Announce Type: replace-cross Abstract: Several variational bounds involving importance weighting ideas generalize the Evidence Lower BOund (ELBO) for marginal likelihood optimizatio