Knowledge catalogue
arXiv:2608.06637v1 Announce Type: cross Abstract: Robust aggregation methods are widely used in federated learning to mitigate the impact of adversarial client behavior. Distance-based aggregation rul
arXiv:2608.06908v1 Announce Type: cross Abstract: We propose Zero-phase Component Analysis (ZCA) whitening as a geometric pre-processing step for the Word Embedding Association Test (WEAT). WEAT is a
arXiv:2604.00310v2 Announce Type: replace-cross Abstract: Multimodal large-language models (MLLMs) often experience degraded safety alignment when harmful queries exploit cross-modal interactions. Mod
arXiv:2608.06557v1 Announce Type: cross Abstract: The reasoning and agentic capabilities of large language models have expanded the range of applications they support, from short interactive exchanges
arXiv:2608.06871v1 Announce Type: new Abstract: Complex systems, core objects of study in artificial life, model diverse phenomena through nonlinear, feedback-driven interactions that produce emergent
arXiv:2504.17584v2 Announce Type: replace-cross Abstract: Attention-FC Disaggregated (AFD) LLM inference systems offload memory-bound Attention operations to memory-rich accelerators (e.g., CPUs, HBM-
arXiv:2608.06532v1 Announce Type: new Abstract: LVLMs are increasingly used to read financial charts, tables, and documents, where a single misread figure can move a decision and the most authoritativ
arXiv:2608.06674v1 Announce Type: new Abstract: Adversarial vulnerabilities remain a major concern for the safe deployment of neural networks, particularly in object detection, a core task embedded in
arXiv:2607.16999v2 Announce Type: replace-cross Abstract: The Credit Assignment Problem (CAP) is fundamental to developing efficient and explainable Reinforcement Learning (RL) agents. Existing framew
arXiv:2608.07460v1 Announce Type: cross Abstract: While post-training improves the capabilities of large language models (LLMs), it generally lowers their output diversity and creativity, negatively i
arXiv:2608.06582v1 Announce Type: new Abstract: Flow-based generative models can efficiently produce candidate structures for crystal structure prediction (CSP), but their pretrained objectives do not
arXiv:2602.20860v2 Announce Type: replace Abstract: While existing unsupervised domain adaptation (UDA) methods greatly enhance target domain performance in semantic segmentation, they often neglect n
arXiv:2608.06939v1 Announce Type: new Abstract: Adverse weather causes diverse and complex image degradations, severely compromising the reliability of computer vision systems. Existing all-in-one res
arXiv:2608.06536v1 Announce Type: cross Abstract: Nonadiabatic dynamics needs an excited-state gradient and an interstate nonadiabatic coupling matrix element (NACME) at every nuclear geometry, and a
arXiv:2608.07147v1 Announce Type: new Abstract: Reinforcement learning with Verifiable Reward (RLVR) has emerged as a powerful paradigm for training coding agents, where the execution feedback from co
arXiv:2608.06613v1 Announce Type: cross Abstract: Self-supervised 3D medical foundation models are increasingly used as general-purpose feature extractors, yet their sensitivity to MRI artifacts remai
arXiv:2608.06949v1 Announce Type: new Abstract: Prior benchmarking work has shown that a single large language model (LLM), forced to make life-or-death resource-allocation decisions, exhibits measura
arXiv:2608.06943v1 Announce Type: new Abstract: Cross-modal Re-Identification (ReID) aims to retrieve the same identity across heterogeneous imaging modalities and has been widely studied in visible-i
arXiv:2608.06768v1 Announce Type: new Abstract: Diffusion models have strong generative capabilities. However, their maximum likelihood training objective only focuses on reconstructing the data distr
arXiv:2608.06876v1 Announce Type: cross Abstract: In the era of Industrial Internet of Things (IIoT) and Cyber-Physical Systems (CPS), Federated Learning (FL) offers a promising decentralized intellig
arXiv:2608.06732v1 Announce Type: new Abstract: Recent text-to-video (T2V) generation models enable fake news videos to be synthesized from scratch, shifting the threat beyond cheap fakes assembled fr
arXiv:2608.07301v1 Announce Type: new Abstract: We study what can be recovered about the transition probabilities of a Markov decision process from optimal actions alone. This is closely related to th
arXiv:2608.06861v1 Announce Type: new Abstract: Training large language model agents in long-horizon environments requires assigning credit from sparse terminal outcomes to individual actions. Existin
arXiv:2608.06779v1 Announce Type: cross Abstract: Large Language Models (LLMs) have accelerated drug discovery, particularly in the automated design of antimicrobial peptides (AMPs). However, current
arXiv:2608.06836v1 Announce Type: new Abstract: We study the problem of inserting new furniture into indoor scene images. Under masked single-view 2D image-plane conditioning, however, the physical sc
arXiv:2502.18816v3 Announce Type: replace Abstract: Significant progress has been achieved on the improvement and downstream usages of the Contrastive Language-Image Pre-training (CLIP) vision-languag
arXiv:2608.06834v1 Announce Type: new Abstract: Transformers provide a powerful architecture for global content-based matching, but reasoning problems may benefit from a stronger inductive bias toward
arXiv:2608.07281v1 Announce Type: cross Abstract: This paper investigates the asymptotic behavior of the out-of-sample prediction risk of the high-dimensional ridgeless least-squares estimator when th
arXiv:2608.07118v1 Announce Type: new Abstract: Credit assignment in multi-turn agent reinforcement learning operates at two levels: assigning trajectory-level credit to actions and distributing each
arXiv:2608.06898v1 Announce Type: cross Abstract: Researchers who seek to build social robot applications on foundation models are faced with a difficult question: how should we pick a model? Public l
Between chaotic levels of market fragmentation and economic volatility, marketing and communications agencies can no longer rely on the human intuition they’ve traditionally used to win clients and op
arXiv:2603.17889v4 Announce Type: replace Abstract: Recent advances in video synthesis have enabled realistic integration of real individuals, driving demand for identity-aware generation. While emerg
arXiv:2506.03392v2 Announce Type: replace Abstract: We propose a new ternary spiking neuron model to improve the representation capacity of binary spiking neurons in deep Q-learning. Although a ternar
arXiv:2608.07151v1 Announce Type: cross Abstract: Reinforcement learning policies are difficult to inspect, but interpreting them is a prerequisite for trustworthiness. Converting a trained policy int
arXiv:2608.06799v1 Announce Type: cross Abstract: Learning structured and control-relevant latent representations remains a key challenge for world models. Recent JEPA-based world models learn action-
arXiv:2608.07057v1 Announce Type: new Abstract: Knife-enabled violence presents a major public safety challenge, and law enforcement agencies require scalable tools for catalogue-level knife identific
arXiv:2608.06748v1 Announce Type: cross Abstract: Probabilistic long-term time-series forecasting commonly relies on trained models. Training-free conformal methods typically construct intervals aroun
arXiv:2608.07228v1 Announce Type: new Abstract: When a reinforcement learning agent cannot observe the full state, we usually blame its policies: it cannot see enough to represent a good one. We show
arXiv:2509.06296v2 Announce Type: replace-cross Abstract: Traditional on-policy reinforcement learning (RL) controllers for quadrupedal locomotion often suffer from low data efficiency, requiring mill
arXiv:2509.16462v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly used in high-stakes decision-making systems, where biased predictions can reinforce social and economi
arXiv:2608.06948v1 Announce Type: new Abstract: AI models are becoming increasingly adept at understanding and processing spatial information, thereby facilitating agentic problem-solving in spatial t
arXiv:2608.06481v1 Announce Type: cross Abstract: Training controllers that are safe and robust in simulation, and systematically assessing their readiness for real-world deployment, remain key challe
arXiv:2608.06929v1 Announce Type: cross Abstract: Regional image editing has attracted considerable attention for its spatial controllability. Although instruction-based and mask-reference-based editi
arXiv:2608.07068v1 Announce Type: new Abstract: Long-horizon agents accumulate growing contexts during interaction, impairing performance and stability. Compact memory mitigates this problem by compre
arXiv:2608.06745v1 Announce Type: new Abstract: Long-horizon agents rely on memory to reuse experiences, yet existing memory systems often assume that evidence can be directly consumed through a fixed
arXiv:2608.06752v1 Announce Type: new Abstract: This paper proposes DKG-MTI, a dual knowledge graph framework for unified multi-task user intent inference from online travel reviews. Existing approach
arXiv:2506.06793v2 Announce Type: replace-cross Abstract: Reward assignment from scarce demonstrations is a key challenge in both offline and online imitation learning. A common and intuitive strategy
arXiv:2608.06713v1 Announce Type: new Abstract: Biomedical knowledge graphs (KGs) accelerate drug discovery, but standard pipelines assume query molecules already exist as graph entities, leaving unre
arXiv:2608.06785v1 Announce Type: new Abstract: Cognitive distortion detection is a key task in computational mental health, yet existing approaches often overlook the psychological structure of disto
arXiv:2608.06378v1 Announce Type: cross Abstract: Driver emotions can affect risk perception, decision-making, and vehicle control under complex road conditions. Existing studies mainly focus on drive
arXiv:2608.06425v1 Announce Type: new Abstract: Comprehensive affective analysis is challenging for two reasons: it spans heterogeneous prediction tasks with continuous, ordinal, and multi-label outpu
arXiv:2608.07139v1 Announce Type: new Abstract: Uncertainty quantification is essential when deploying machine learning models in safety-critical applications. Online conformal prediction (OCP) provid
arXiv:2608.06794v1 Announce Type: new Abstract: While diffusion models have made significant progress in text-to-image tasks, they still exhibit limitations when directly optimizing downstream objecti
arXiv:2608.07367v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly used as a primary source of information and advice, understanding their alignment to humans in terms of
arXiv:2601.19499v2 Announce Type: replace Abstract: Task completion is difficult to certify when state aggregation, model mismatch, and disturbances invalidate nominal RL transitions. We present a rev
arXiv:2608.06792v1 Announce Type: cross Abstract: Foundation model(FM) for recommendation has shown strong ability to model long-horizon sequential user behavior. In practice, a single pretrained foun
arXiv:2608.06827v1 Announce Type: cross Abstract: Real-to-sim (R2S) depends on scene representations that render observations along robot ego trajectories, yet dense multi-view capture limits per-envi
arXiv:2608.07251v1 Announce Type: cross Abstract: This paper documents an applied natural-language-processing framework for measuring the tone of Brazilian Monetary Policy Committee (Copom) statements
Really important point and nicely explained! Two further points worth noting: (1) if an AI system relies on a harness (which as Gary notes in other writing is increasingly improtant in top models), th
arXiv:2608.07176v1 Announce Type: cross Abstract: Developing foundation generative models for endoscopy is limited by the gap between natural and clinical images and the computational cost of training