Knowledge catalogue
arXiv:2605.19876v1 Announce Type: new Abstract: Text-to-3D generation based on diffusion models often suffers from the Janus problem, leading to inconsistent geometry across viewpoints. This work iden
arXiv:2605.19931v1 Announce Type: cross Abstract: Estimating forest aboveground biomass (AGB) from Earth observation combines two structurally incompatible label sources: spaceborne lidar provides can
arXiv:2605.19264v1 Announce Type: new Abstract: Voting methods weighted by stakes are the fundamental governance paradigm in Proof-of-Stake (PoS) blockchains. Such a paradigm is known to be prone to p
arXiv:2605.18816v1 Announce Type: cross Abstract: Neural surrogates enable orders-of-magnitude acceleration of computational fluid dynamics (CFD) simulations, with the potential to transform engineeri
arXiv:2603.07018v2 Announce Type: replace-cross Abstract: Treatment effects estimated from a randomized controlled trial are local not only to the study population but also to the time at which the tr
arXiv:2605.18843v1 Announce Type: new Abstract: Backtesting large language models on historical events requires reasoning exclusively from information available before a specified cutoff date. Yet mod
arXiv:2605.20066v1 Announce Type: new Abstract: Knowledge graph question answering seeks to translate natural language questions into executable queries over knowledge graphs, but existing approaches
arXiv:2605.19638v1 Announce Type: cross Abstract: As large language models (LLMs) demonstrate increasing competence in synthesizing functional user interfaces, a fundamental question emerges in access
The consensus of analysts is that AI capital investments will rise by 20% a year for five years while revenues are expected to grow 15% resulting in negative returns. 'The AI boom will become a story
the crazy part is that people are “clowning” me without knowing anything about the training or whether anything else other than scaled changed or how the model does on anything else. (or what it costs
This video from the World Science Festival, shared by cognitive scientist Gary Marcus, examines the gap between AI systems' claimed reasoning capabilities and their actual mechanisms, likely arguing t
think you know @garymarcus because read a few of his tweets? try watching this, to see the real deal, and why, for example, the US Senate invited him to testify. Superb conversation between @bgreene a
arXiv:2605.20087v1 Announce Type: cross Abstract: Conversational AI has now reached billions of users, yet existing datasets capture only what people say, not what they think. We introduce ThoughtTrac
arXiv:2605.19064v1 Announce Type: cross Abstract: Workforce transformations are difficult to forecast and costly to mismanage. In particular, the integration of artificial intelligence into knowledge
arXiv:2605.20074v1 Announce Type: new Abstract: Distillation transfers knowledge from a large model trained on broad data to a smaller, more efficient model suitable for deployment. In structured pred
arXiv:2605.19035v1 Announce Type: new Abstract: The rapid advancement of Large Language Models has given rise to autonomous LLM-based agents capable of complex reasoning and execution. As these agents
arXiv:2602.11767v3 Announce Type: replace Abstract: Advances in large language models (LLMs) are driving a shift toward using reinforcement learning (RL) to train agents from iterative, multi-turn int
two trillion dollars to build “pathologically dishonest” AI I think @RyanPGreenblatt's recent post summarizes the vibe of this behavior well. Not the sort of thing that would be acceptable for a human
arXiv:2603.18003v4 Announce Type: replace Abstract: Multimodal large language models (MLLMs) exhibit strong visual-language reasoning, yet cannot process structured, non-visual data such as human skel
arXiv:2605.20037v1 Announce Type: cross Abstract: Reward-poisoning attacks present a significant risk to learning-based wireless control systems. Given this, we propose a Disagreement-Guided Reward Po
arXiv:2605.19839v1 Announce Type: new Abstract: Preference alignment aims to guide generative models by learning from comparisons between preferred and non-preferred samples. In practice, most existin
arXiv:2605.19662v1 Announce Type: new Abstract: Tabular foundation models based on pretrained prior-data fitted networks~(PFNs) have shown strong generalization on diverse tabular tasks, but they are
arXiv:2605.19425v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become the dominant paradigm for advanced reasoning in Large Language Models (LLMs), but rol
arXiv:2602.07008v2 Announce Type: replace Abstract: Reliable models should not only predict correctly, but also justify decisions with acceptable evidence. Yet conventional supervised learning typical
arXiv:2605.19214v1 Announce Type: cross Abstract: Diagnostic performance in medical AI varies systematically across demographic groups, yet subgroup AUC can mask clinically important disparities. At a
arXiv:2605.16913v1 Announce Type: cross Abstract: Neural networks trained with gradient-based methods exhibit a strong simplicity bias: they learn simpler statistical features of their data before mov
arXiv:2605.18224v1 Announce Type: cross Abstract: This note studies exact constant collapse in variational autoencoders, where the encoder mean becomes independent of the input. The goal is to make th
A large-scale meta-analysis examining over 361,000 job applications across nearly 30 countries spanning 40 years found that hiring discrimination based on protected characteristics (such as race, gend
arXiv:2504.14820v2 Announce Type: replace Abstract: For peg-in-hole tasks, humans rely on binocular visual perception to locate the peg above the hole surface and then proceed with insertion. This pap
arXiv:2605.18743v1 Announce Type: new Abstract: Inspired by the emergent behaviors in large language models that generalized human intelligence, the research community is pursuing similar emergent cap
arXiv:2602.02236v4 Announce Type: replace-cross Abstract: We study online fine-tuning of pretrained control policies for autonomous driving using Real-Time Recurrent Reinforcement Learning (RTRRL), a
arXiv:2605.18459v1 Announce Type: new Abstract: Adaptive experimentation enables efficient estimation of causal effects, but existing methods are not designed for survival data with censoring, where e
arXiv:2605.17609v1 Announce Type: new Abstract: Many inference-time language-model pipelines combine a cheap reward signal with an expensive verifier, such as exact answer checking in mathematical rea
arXiv:2605.17517v1 Announce Type: new Abstract: Recent advances in Vision-Language-Action (VLA) models have shown strong potential for general-purpose robotic manipulation. However, the visual represe
arXiv:2605.17698v1 Announce Type: new Abstract: The deployment of Large Language Models (LLMs) as autonomous economic agents introduces systemic risks that extend beyond individual capability failures
arXiv:2605.17634v1 Announce Type: cross Abstract: Prompt injection is the most critical vulnerability in deployed AI agents. Despite recent progress, we show that the prevailing defense paradigm (data
arXiv:2605.16291v1 Announce Type: cross Abstract: With the growing adoption of AI systems, reasoning about how society can exert control over AI becomes an increasingly urgent problem. Existing work o
arXiv:2605.16905v1 Announce Type: cross Abstract: Post-hoc saliency methods are widely used to interpret deep neural networks, but their faithfulness is difficult to evaluate reliably. Existing evalua
arXiv:2605.17010v1 Announce Type: cross Abstract: Algorithmic feeds have become primary environments for encountering information online, yet while they shape what people see, less is known about how
arXiv:2511.06316v3 Announce Type: replace Abstract: In low- and middle-income countries, public safety and urban planning initiatives frequently face a critical shortage of accurate, location-specific
arXiv:2605.16516v1 Announce Type: cross Abstract: Long-term interaction with LLM-based systems may produce alignment drift: a gradual process in which system outputs become less constrained by the use
arXiv:2605.17352v1 Announce Type: new Abstract: Despite substantial advances in large language models (LLMs), generating factually consistent responses for knowledge-intensive question answering remai
arXiv:2605.18529v1 Announce Type: new Abstract: The alignment of Large Language Models (LLMs) for complex reasoning heavily relies on Reinforcement Learning with Verifiable Rewards (RLVR). However, st
arXiv:2605.18648v1 Announce Type: cross Abstract: Central to human-aligned AI is understanding the benefits of human-elicited labels over synthetic alternatives. While human soft-labels improve calibr
arXiv:2605.17921v1 Announce Type: new Abstract: Streaming video requires handling dynamic information density under strict latency budgets. Yet, existing methods typically employ static strategies, su
arXiv:2605.18133v1 Announce Type: cross Abstract: LLM-based chatbot agents increasingly process user requests by combining natural-language reasoning with external tools such as web browsing. These ca
arXiv:2605.17071v1 Announce Type: new Abstract: Radiology report generation (RRG) aims to automatically produce clinically accurate textual reports from medical images. Existing methods predominantly
arXiv:2605.17212v1 Announce Type: new Abstract: A unified framework for learning under covariate shift is presented, in which a constrained density-ratio network approximates the Radon-Nikodym derivat
arXiv:2603.04727v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have demonstrated impressive general competence in video understanding, yet their reliability for rea
arXiv:2603.11395v2 Announce Type: replace-cross Abstract: Continual reinforcement learning challenges agents to acquire new skills while retaining previously learned ones with the goal of improving pe
arXiv:2605.17228v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in high-stakes domains such as clinical decision support and medical documentation. However, the
As a Soviet historian who has spent years writing about the extreme, repressive control Soviet Communism exercised over its unfortunate citizens, I find it really hard to bring a similar accusation ag
arXiv:2605.17765v1 Announce Type: new Abstract: Recent healthcare foundation models have achieved strong predictive performance through large scale self supervised learning, yet their latent represent
arXiv:2603.12145v2 Announce Type: replace-cross Abstract: Translating complex reinforcement learning (RL) environments into high-performance implementations has traditionally required months of specia
arXiv:2605.17602v1 Announce Type: new Abstract: Aligning Text-to-Image (T2I) generation models with human preferences increasingly relies on image reward models that score or rank generated images acc
arXiv:2605.16446v1 Announce Type: cross Abstract: Semi-supervised learning (SSL) enables prediction with limited labels, but high-stakes tabular applications (medical, credit, recidivism) require stat
arXiv:2605.18491v1 Announce Type: new Abstract: Methods: Nine SSL methods spanning four pretext-task families were pretrained from scratch using the same 10{,}412 3D CT scans (1.89~M 2D axial slices)
arXiv:2605.16272v1 Announce Type: cross Abstract: Mainstream creativity support design prioritizes compliant AI for seamless writing interactions, but concerns over inappropriate AI reliance highlight
arXiv:2508.03018v2 Announce Type: replace Abstract: Large Language Reasoning Models have demonstrated remarkable success on static tasks, yet their application to multi-round agentic planning in inter
arXiv:2506.01523v2 Announce Type: replace Abstract: Alignment via reinforcement learning from human feedback (RLHF) has become the dominant paradigm for controlling the quality of outputs from large l