Knowledge catalogue
A lot of institutions and companies won’t die because of AI. They’ll die because of their own internal inability to learn. Someone recently told me that at a big company, legal had to review and appro
arXiv:2504.02181v2 Announce Type: replace Abstract: The rapid advancements in large Language models (LLMs) have significantly enhanced their reasoning capabilities, driven by various strategies such a
arXiv:2603.23089v2 Announce Type: replace Abstract: Multi-view capture systems have been an important tool in research for recording human motion under controlling conditions. Most existing systems ar
arXiv:2604.20305v1 Announce Type: new Abstract: Realizing active visual tracking with a single unified model across diverse robots is challenging, as the physical constraints and motion dynamics vary
arXiv:2604.20134v1 Announce Type: cross Abstract: Security Operations Centers (SOCs) increasingly encounter difficulties in correlating heterogeneous alerts, interpreting multi-stage attack progressio
arXiv:2604.20372v1 Announce Type: cross Abstract: We report the first systematic evidence of hallucination in AI models of fluid dynamics, demonstrated in the canonical problem of hydrodynamically uns
arXiv:2604.20166v1 Announce Type: new Abstract: Building trustworthy AI systems for mental health support is a shared priority across stakeholders from multiple disciplines. However, 'trustworthy' rem
arXiv:2604.20199v1 Announce Type: new Abstract: Multilingual Retrieval-Augmented Generation (mRAG) leverages cross-lingual evidence to ground Large Language Models (LLMs) in global knowledge. However,
arXiv:2604.20572v1 Announce Type: new Abstract: Online lifelong learning enables agents to accumulate experience across interactions and continually improve on long-horizon tasks. However, existing me
arXiv:2604.17511v2 Announce Type: replace-cross Abstract: Autonomous systems increasingly execute actions that directly modify shared state, creating an urgent need for precise control over which tran
arXiv:2604.20704v1 Announce Type: cross Abstract: Adversarial robustness evaluation underpins every claim of trustworthy ML deployment, yet the field suffers from fragmented protocols and undetected g
arXiv:2604.19780v1 Announce Type: new Abstract: Scaling test-time compute via extended reasoning has become a key paradigm for improving the capabilities of large language models (LLMs). However, exis
arXiv:2501.18873v4 Announce Type: replace Abstract: Reinforcement Learning from Human Feedback (RLHF) has emerged as a powerful approach for aligning generative models, but its reliance on learned rew
arXiv:2604.19984v1 Announce Type: cross Abstract: Research has documented LLMs' name-based bias in hiring and salary recommendations. In this paper, we instead consider a setting where LLMs generate c
arXiv:2601.03396v2 Announce Type: replace Abstract: Procedural content generation has enabled vast virtual worlds through levels, maps, and quests, but large-scale character generation remains underex
arXiv:2602.15861v2 Announce Type: replace-cross Abstract: Text analysis of tabular data relies on two core operations: summarization for corpus-level theme extraction and tagging for row-level labelin
arXiv:2604.19856v1 Announce Type: cross Abstract: Large Language Models (LLMs) show promise for generating Register-Transfer Level (RTL) code from natural language specifications, but single-shot gene
arXiv:2603.25383v3 Announce Type: replace Abstract: CLIP aligns image and text embeddings via contrastive learning and demonstrates strong zero-shot generalization. Its large-scale architecture requir
arXiv:2510.18471v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) excel at code generation by learning from vast code corpora, a fundamental semantic gap remains between the
arXiv:2604.20027v1 Announce Type: cross Abstract: For state-of-the-art image understanding, Vision Transformers (ViTs) have become the standard architecture but their processing diverges substantially
arXiv:2603.23694v2 Announce Type: replace Abstract: Medical image registration is a fundamental task in medical image analysis, enabling the alignment of images from different modalities or time point
arXiv:2604.20763v1 Announce Type: cross Abstract: Retrieval quality is the primary bottleneck for accuracy and robustness in retrieval-augmented generation (RAG). Current evaluation relies on heuristi
arXiv:2505.04897v2 Announce Type: replace-cross Abstract: Interactive imitation learning makes an agent's control policy robust by stepwise supervisions from an expert. The recent algorithms mostly em
arXiv:2604.20771v1 Announce Type: cross Abstract: The Internet of Vehicles (IoV) is advancing modern transportation by improving safety, efficiency, and intelligence. However, the reliance on the Cont
arXiv:2604.20492v1 Announce Type: cross Abstract: In this paper, it is shown, for the first time, that centralized performance is achievable in decentralized learning without sharing the local dataset
arXiv:2604.20811v1 Announce Type: new Abstract: As LLMs are increasingly integrated into agentic systems, they must adhere to dynamically defined, machine-interpretable interfaces. We evaluate LLMs as
arXiv:2510.03013v3 Announce Type: replace Abstract: We propose a distributional framework for offline Inverse Reinforcement Learning (IRL) that jointly models uncertainty over reward functions and ful
arXiv:2603.00696v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as reasoning engines in autonomous driving, yet their decision-making remains opaque. We propose
Dubious predictions that have yet to be fulfilled have given dubious people like Sam Altman and Elon Musk extraordinary power over society. @carissaveliz's lecture @@TEDTalks (which I was lucky enough
arXiv:2604.19980v1 Announce Type: new Abstract: This paper presents a model-based reinforcement learning (RL) framework for optimal closed-loop control of nonlinear robotic systems. The proposed appro
arXiv:2501.07399v2 Announce Type: replace Abstract: Consistent maps are key for most autonomous mobile robots, and they often use SLAM approaches to build such maps. Loop closures via place recognitio
arXiv:2604.20012v1 Announce Type: cross Abstract: Vision-Language-Action Models (VLAs) inherit their visual and linguistic capabilities from Vision-Language Models (VLMs), yet most VLAs are built from
arXiv:2604.19839v1 Announce Type: cross Abstract: Vision-language models (VLMs) have shown strong perception and reasoning abilities for instruction-following embodied agents. However, despite these a
arXiv:2601.14295v3 Announce Type: replace Abstract: Large language models increasingly function as artificial reasoners: they evaluate arguments, assign credibility, and express confidence. Yet their
arXiv:2601.09871v2 Announce Type: replace Abstract: Human-AI complementarity is the claim that a human supported by an AI system can outperform either alone in a decision-making process. Since its int
arXiv:2604.20295v1 Announce Type: new Abstract: Tactile sensors are increasingly integrated into dexterous robotic manipulators to enhance contact perception. However, learning manipulation policies t
arXiv:2604.20577v1 Announce Type: cross Abstract: An assurance case is a structured argument document that justifies claims about a system's requirements or properties, which are supported by evidence
arXiv:2603.15867v2 Announce Type: replace Abstract: The growing use of Machine Learning (ML) tools comes with critical challenges, such as limited model explainability. We propose a global explainabil
arXiv:2604.19755v1 Announce Type: new Abstract: Anti-money laundering (AML) transaction monitoring generates large volumes of alerts that must be rapidly triaged by investigators under strict audit an
arXiv:2604.19763v1 Announce Type: cross Abstract: Speech Emotion Recognition (SER) systems have growing applications in sensitive domains such as mental health and education, where biased predictions
arXiv:2407.11933v4 Announce Type: replace Abstract: Target-group detection is the task of detecting which group(s) a piece of content is ``directed at or about''. Applications include targeted marketi
arXiv:2506.20910v2 Announce Type: replace-cross Abstract: We study value-iteration (VI) algorithms for solving general (a.k.a. multichain) Markov decision processes (MDPs) under the average-reward cri
arXiv:2604.20689v1 Announce Type: new Abstract: Dexterous robotic manipulation requires comprehensive perception across all phases of interaction: pre-contact, contact initiation, and post-contact. Su
arXiv:2507.23115v2 Announce Type: replace-cross Abstract: Previous work on data privacy in federated learning systems focuses on privacy-preserving operations for data from users who have agreed to sh
arXiv:2604.20038v1 Announce Type: new Abstract: Recent advances in text-guided image editing and 3D Gaussian Splatting (3DGS) have enabled high-quality 3D scene manipulation. However, existing pipelin
arXiv:2604.20011v1 Announce Type: cross Abstract: AI companion chatbots increasingly shape how people seek social and emotional connection, sometimes substituting for relationships with romantic partn
arXiv:2604.20055v1 Announce Type: new Abstract: Hospital Quality Improvement (QI) plays a critical role in optimizing healthcare delivery by translating high-level hospital goals into actionable solut
arXiv:2510.12817v3 Announce Type: replace-cross Abstract: Human Label Variation (HLV) refers to legitimate disagreement in annotation that reflects the diversity of human perspectives rather than mere
arXiv:2604.20300v1 Announce Type: new Abstract: For LLM agents, memory management critically impacts efficiency, quality, and security. While much research focuses on retention, selective forgetting--
arXiv:2604.20093v1 Announce Type: new Abstract: Single-view 3D scene reconstruction involves inferring both object geometry and spatial layout. Existing methods typically reconstruct objects independe
arXiv:2604.20288v1 Announce Type: new Abstract: Flight diversions are rare but high-impact events in aviation, making their reliable prediction vital for both safety and operational efficiency. Howeve
arXiv:2604.20707v1 Announce Type: new Abstract: Digital twins of natural systems must remain aligned with physical systems that evolve over time, are only partially observed, and are typically modeled
arXiv:2604.20382v1 Announce Type: new Abstract: Rising demand for mental health support has increased interest in using Large Language Models (LLMs) for counseling. However, adapting LLMs to this high
arXiv:2604.20659v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has advanced the reasoning capabilities of Large Language Models (LLMs) by leveraging direct out
arXiv:2604.19790v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed under diverse numerical precision configurations, including standard floating-point formats (e.g.
arXiv:2604.20328v1 Announce Type: new Abstract: Chain-of-Thought (CoT) reasoning significantly elevates the complex problem-solving capabilities of multimodal large language models (MLLMs). However, a
arXiv:2604.20244v1 Announce Type: cross Abstract: Knowledge distillation (KD) is a powerful paradigm for compressing large language models (LLMs), whose effectiveness depends on intertwined choices of
arXiv:2503.08321v2 Announce Type: replace Abstract: Vision graph neural networks have emerged as a popular approach for modeling the global and spatial context for image recognition. However, a signif
A 7B agent that beats GPT-4o. Lossless weight compression that speeds up inference by 177%. An arena where 23 teams battled across 103,000 adversarial rounds. This year at ICLR, Lambda is presenting t
I’m in Madrid this week for the first in-person meeting of the United Nations Independent International Scientific Panel on AI. I'm thrilled to be part of this distinguished group of 40 international