Knowledge catalogue
arXiv:2605.18933v1 Announce Type: new Abstract: Pre-norm Transformers with RMSNorm tolerate ternary {-1,0,+1} weight quantization with surprisingly small loss (Ma et al., 2024). We give a geometric ex
arXiv:2605.19166v1 Announce Type: cross Abstract: Reinforcement learning (RL)-based quadrotor control policies have achieved impressive performance in tasks such as fast navigation in cluttered enviro
arXiv:2605.19313v1 Announce Type: cross Abstract: In complex multivariate systems, interactions among variables are defined by dependency structures, often encoded as directed acyclic graphs (ext{DAGs
arXiv:2605.18798v1 Announce Type: new Abstract: We propose non-parametric estimators for the average run length (ARL) and average detection delay (ADD) in quickest changepoint detection (QCD) under fi
arXiv:2605.19202v1 Announce Type: cross Abstract: This paper addresses the problem of using a deep Reinforcement Learning (RL)-based low-level Quadrotor controller within an autonomous Quadrotor navig
In just a short time, we’ve seen AI transition from simple chat interfaces to autonomous agents capable of function calling, code execution, and persistent terminal use. But to orchestrate these capab
arXiv:2605.19503v1 Announce Type: cross Abstract: Reinforcement learning for legged locomotion has matured into a stack of multi-component reward functions and physics-engine benchmarks whose morpholo
arXiv:2605.19124v1 Announce Type: cross Abstract: Chemical disorder, originating from the mixed occupation of crystallographic sites by multiple elements, is widespread in alloys, ceramics, and compos
arXiv:2605.19485v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in solving complex problems by generating structured, step-by-step reasoning con
arXiv:2605.19136v1 Announce Type: new Abstract: Simulation is a central tool for scalable robot learning, but its effectiveness depends on the quality of object assets. While modern 3D datasets provid
arXiv:2605.19778v1 Announce Type: new Abstract: We introduce B-cos GNNs, an inherently explainable class of graph neural networks whose predictions decompose exactly into per-node, per-feature contrib
arXiv:2605.19433v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable success in complex reasoning tasks via long chain-of-thought (CoT), yet their immense computatio
arXiv:2605.19147v1 Announce Type: cross Abstract: Large language models (LLMs) are highly susceptible to backdoor attacks (BAs), wherein training samples are poisoned using trigger-based harmful conte
arXiv:2512.05721v2 Announce Type: replace Abstract: Traditional cellular traffic forecasting models are optimized for minimizing symmetric errors, leaving them indifferent to shifting operational prio
arXiv:2605.19919v1 Announce Type: new Abstract: Pretrained imitation policies have become a strong foundation for robot manipulation, but they often require online improvement to overcome execution er
arXiv:2605.19249v1 Announce Type: new Abstract: Time-series forecasting is critical in various scenarios, such as energy, transportation, and public health. However, most existing forecasters rely pri
arXiv:2605.20107v1 Announce Type: cross Abstract: JEPAs often regularize one-view embeddings toward an isotropic Gaussian, implicitly baking Euclidean symmetry into the representation. We show that th
arXiv:2605.19461v1 Announce Type: new Abstract: On-policy reinforcement learning methods like GRPO suffer from mode collapse: they exhibit reduced solution diversity, concentrating probability mass on
arXiv:2605.19532v1 Announce Type: new Abstract: Text-to-image diffusion models can synthesize high-quality images, yet the outcome is notoriously sensitive to the random seed: different initial seeds
arXiv:2605.19352v1 Announce Type: cross Abstract: Understanding how humans and artificial intelligence systems predict and plan by interacting with their environment is a fundamental challenge at the
⚠️👇 🚨Breaking ⚠️ If we can’t make AI agents follow rules, we are screwed. New study from METR reports that “when the agents were faced with hard tasks, they routinely violated constraints” This—routin
arXiv:2605.19837v1 Announce Type: cross Abstract: Adverse weather (rain, fog, sand, and snow) degrades camera-based object detection in autonomous vehicles. Existing enhancement-then-detect approaches
arXiv:2605.19229v1 Announce Type: new Abstract: Survey research faces mounting structural challenges: declining response rates, sample bias, block-wise missingness among at-risk respondents, and AI-as
arXiv:2605.19981v1 Announce Type: new Abstract: Humanoid robots have achieved impressive locomotion performance, yet contact-rich and long-horizon manipulation remains a major bottleneck. Manipulation
arXiv:2605.19436v1 Announce Type: cross Abstract: When a model produces a correct solution under reinforcement learning with verifiable rewards (RLVR), every token receives the same reward signal rega
arXiv:2512.20931v2 Announce Type: replace Abstract: Estimating the absolute orientation of a local system relative to a global navigation satellite system (GNSS) reference often suffers from local min
arXiv:2605.19091v1 Announce Type: new Abstract: Chess has long served as a canonical testbed for artificial intelligence, but modeling approaches for its central tasks have diverged. Maximizing playin
arXiv:2507.15698v2 Announce Type: replace-cross Abstract: Process Reward Models (PRMs) play a central role in evaluating and guiding multi-step reasoning in large language models (LLMs), especially fo
arXiv:2504.09188v2 Announce Type: replace Abstract: This paper introduces the Compliant Explicit Reference Governor (CERG), a modular reference management system that enables robots to interact physic
arXiv:2605.19374v1 Announce Type: cross Abstract: Vision-language alignment using chest X-rays and radiology reports has emerged as an advanced paradigm for zero-shot classification and grounding of c
arXiv:2603.11673v2 Announce Type: replace Abstract: Many physical systems exhibit a low-dimensional structure that varies with external parameters: link lengths in a robot, forcing constants in a flui
arXiv:2605.19314v1 Announce Type: cross Abstract: Long-horizon embodied agents increasingly delegate navigation, search, approach, and manipulation to specialist executors. As these executors become s
arXiv:2605.20075v1 Announce Type: cross Abstract: Chain-of-thought (CoT) is a standard approach for eliciting reasoning capabilities from large language models (LLMs). However, the common CoT paradigm
‼️ Could large language models turn out to be the tech industry’s Vietnam? All In’s @jason notes below that today’s students are speaking out against AI, just as students in the 60s and 70s spoke out
arXiv:2605.19665v1 Announce Type: cross Abstract: Pairwise human preference prediction is central to evaluating code-generation systems, where quality often depends on task-specific trade-offs beyond
arXiv:2510.13293v3 Announce Type: replace Abstract: While Text-to-Speech (TTS) systems enable emotional control via natural-language instructions, expressiveness, naturalness, and speech quality degra
arXiv:2605.19690v1 Announce Type: new Abstract: Navigation Foundation Models (NFMs) trained on large cross-embodied datasets have demonstrated powerful generalizability in various scenarios. Adopting
arXiv:2511.13588v2 Announce Type: replace-cross Abstract: Model Predictive Control (MPC) is a powerful framework for optimal control but can be too slow for low-latency applications. We present a data
arXiv:2605.19294v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) policies are typically deployed with asynchronous inference: the robot executes a previously predicted action chunk while
arXiv:2605.18793v1 Announce Type: cross Abstract: Accurate spatiotemporal pattern analysis is critical in fields such as urban traffic, meteorology, and public health monitoring. However, existing met
arXiv:2506.12218v2 Announce Type: replace-cross Abstract: Directed acyclic graphs (DAGs) are central to science and engineering applications including causal inference, scheduling, and neural architec
arXiv:2512.16856v2 Announce Type: replace Abstract: AI safety and alignment research has predominantly been focused on methods for safeguarding individual AI systems, resting on the assumption of an e
arXiv:2605.19293v1 Announce Type: cross Abstract: Wireless extended reality (XR) teleoperation provides embodied interaction capability for collecting humanoid robot demonstrations, but the large-scal
arXiv:2605.18899v1 Announce Type: cross Abstract: Generative LLM-based recommenders (LLM-Rec) require continual post-deployment updates, yet deployment logs provide only policy-shaped contextual bandi
arXiv:2603.23722v2 Announce Type: replace-cross Abstract: While Multi-Agent Reinforcement Learning (MARL) algorithms achieve unprecedented successes across complex continuous domains, their standard d
arXiv:2601.05391v2 Announce Type: replace Abstract: Accurate multistep forecasting of node-level attributes on dynamic graphs is critical for applications ranging from financial trust networks to biol
arXiv:2605.19322v1 Announce Type: new Abstract: Recent advances in Video Large Language Models (Video-LLMs) have greatly expanded multimodal reasoning capabilities. However, the massive number of visu
arXiv:2511.19741v3 Announce Type: replace Abstract: Optimal Transport (OT) offers a powerful framework for finding correspondences between distributions and addressing matching and alignment problems
arXiv:2605.18918v1 Announce Type: cross Abstract: Modern AI assistants are agentic. To answer a single user request, the underlying language model pulls in information from many sources, such as web s
arXiv:2605.18848v1 Announce Type: cross Abstract: This paper introduces Exact Linear Attention (ELA), a mechanism that achieves linear computational complexity for Transformer attention by leveraging
arXiv:2605.19321v1 Announce Type: cross Abstract: Large Language Model (LLM) alignment remains vulnerable to jailbreak attacks that elicit unsafe responses, motivating pre-model and post-model guards.
arXiv:2509.26464v2 Announce Type: replace Abstract: Self-preference is a fundamental feature of biological organisms. Since large language models (LLMs) lack sentience, they might be expected to avoid
arXiv:2410.15362v2 Announce Type: replace-cross Abstract: Aligned Large Language Models (LLMs) have attracted significant attention for their safety, particularly in the context of jailbreak attacks t
arXiv:2601.16200v3 Announce Type: replace-cross Abstract: Modern deep learning models exhibit strong capabilities across diverse applications, yet remain vulnerable to malicious inputs that induce err
arXiv:2605.19739v1 Announce Type: new Abstract: Recent advances in flow matching models have significantly improved text-to-image generation quality, but also introduce growing safety risks due to the
arXiv:2605.19604v1 Announce Type: new Abstract: Large Language Model (LLM) agents increasingly act inside real workspaces, where tools and skills determine whether model reasoning becomes reliable act
arXiv:2605.18841v1 Announce Type: new Abstract: Safety in reinforcement learning is often specified through cumulative cost constraints, but these trajectory-level guarantees do not directly prevent u
arXiv:2510.13727v2 Announce Type: replace Abstract: Generative AI systems are increasingly assisting and acting on behalf of end users in practical settings, from digital shopping assistants to next-g
arXiv:2605.19235v1 Announce Type: new Abstract: Competitive multi-agent reinforcement learning in imperfect-information games requires agents to act under partial observability and against adversarial
arXiv:2605.19457v1 Announce Type: new Abstract: Automated bidding is central to modern digital advertising. Early rule-based methods lacked adaptability, while subsequent Reinforcement Learning approa