Knowledge catalogue
arXiv:2607.04710v1 Announce Type: new Abstract: Inducing cooperation among distributed agents is still a difficult problem in the field of multi-agent reinforcement learning (MARL), particularly in so
arXiv:2607.04259v1 Announce Type: new Abstract: Path planning is a fundamental component of autonomous vehicles, where achieving safe, comfortable, and dynamically feasible paths while ensuring comput
arXiv:2607.03125v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) offers powerful control for industrial cyber-physical systems (ICPSs), but its 'black-box' exploration risks violating
arXiv:2606.14606v2 Announce Type: replace Abstract: Dexterous manipulation is fundamentally a problem of interaction dynamics: the hand must track precise finger trajectories, regulate the contact for
arXiv:2607.04988v1 Announce Type: new Abstract: Unified models for robot manipulation aim to equip one policy with both the semantic priors of pretrained VLMs and the physical dynamics learned through
arXiv:2606.13156v2 Announce Type: replace-cross Abstract: Letting a vision-language model (VLM) think longer at test time has driven much recent progress. A natural way to bring this to spatial ground
arXiv:2607.03166v1 Announce Type: cross Abstract: Template-based contrastive synthesis is scalable, but its candidates often differ only in a few entity-slots while sequence-level optimization spreads
arXiv:2508.02296v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) systems are increasingly deployed in high-stakes domains, where safety depends not only on how a system answers
arXiv:2607.05061v1 Announce Type: new Abstract: Key-value (KV) cache growth is a major bottleneck in autoregressive decoding, as memory and bandwidth scale linearly with context length. Existing KV ev
arXiv:2607.03482v1 Announce Type: new Abstract: In this paper, we present the Invariant-Variant Disentangled State-Space Model (IVD-SSM), our submission to SemEval-2026 Task 4 on Narrative Story Simil
arXiv:2511.06148v4 Announce Type: replace-cross Abstract: As large language models (LLMs) are adopted into frameworks that grant them the capacity to make real decisions, it is increasingly important
arXiv:2607.02607v1 Announce Type: cross Abstract: Video reasoning requires Large Multimodal Models (LMMs) to remain grounded in dense evidence, yet existing systems largely adopt 'read-once, generate-
arXiv:2607.04270v1 Announce Type: cross Abstract: Large language models (LLMs) have recently emerged as powerful backbones for recommender systems by reformulating recommendation as a token-level gene
arXiv:2607.04638v1 Announce Type: new Abstract: Referring expression comprehension (REC) aims to localize the object in an image described by natural language. In Weakly supervised REC (WREC), existin
arXiv:2604.01641v2 Announce Type: replace Abstract: We introduce LivingWorld, an interactive framework for generating 4D worlds with environmental dynamics from a single image. While recent advances i
arXiv:2607.04412v1 Announce Type: new Abstract: Reinforcement learning (RL) for non-verifiable instruction following increasingly relies on LLM judges with prompt-specific rubrics as reward signals. W
arXiv:2508.16181v2 Announce Type: replace-cross Abstract: Cross-organizational collaboration in Model-Based Systems Engineering (MBSE) faces many challenges in achieving semantic alignment across inde
arXiv:2501.08579v3 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly employed for simulating human behaviors across diverse domains. However, our position is that current
arXiv:2607.02990v1 Announce Type: new Abstract: We propose MABLE (Masked Autoencoding with Bi-Lipschitz Decoding for Embeddings and Graph Metric Learning), a self-supervised framework for learning nod
arXiv:2509.23960v2 Announce Type: replace-cross Abstract: Co-optimizing safety and performance in large-scale multi-agent systems remains a fundamental challenge. Existing approaches based on multi-ag
arXiv:2607.02568v1 Announce Type: new Abstract: View-based point cloud completion aims to recover a complete 3D shape from a partial point cloud, guided by a single-view image. However, existing appro
arXiv:2603.25126v2 Announce Type: replace-cross Abstract: Multi-Behavior Recommendation (MBR) leverages multiple user interaction types (e.g., views, clicks, purchases) to enrich preference modeling a
arXiv:2607.05167v1 Announce Type: new Abstract: Many real-world systems are organized as networks where spatio-temporal dynamics unfold along connections and not discretely between nodes. Examples inc
arXiv:2607.03530v1 Announce Type: new Abstract: We introduce MentalThink, a visual-symbolic reasoning paradigm that equips Multimodal LLMs (MLLMs) with an executable mechanism for 'mental' visualizati
arXiv:2409.16663v5 Announce Type: replace-cross Abstract: We propose the use of latent space generative world models to address the covariate shift problem in autonomous driving. A world model is a ne
arXiv:2607.03517v1 Announce Type: cross Abstract: Brownian Bridge Diffusion Models (BBDM) offer an appealing framework for image restoration and inverse problems by constructing a stochastic bridge fr
More than half of Americans can’t afford food or gas. Meanwhile, the top .1% increased their wealth by $2.59T during Trump’s second term. Republicans ran on affordability, and then immediately passed
Recent breakthroughs in instruction-based image editing have captured significant attention, as models are now capable of handling real-world editing demands with the practicality required by everyday
arXiv:2607.04763v1 Announce Type: cross Abstract: We study on-policy distillation (OPD) for agentic tasks, where an LLM agent interacts with an environment over multiple turns and a student imitates a
arXiv:2602.06205v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis suggests that independently trained neural networks converge to increasingly similar latent spaces. How
arXiv:2607.05376v1 Announce Type: new Abstract: Recent advances in video diffusion models have enabled either long single-view generation through temporal autoregression, or short multi-view synthesis
arXiv:2607.03915v1 Announce Type: new Abstract: With the rapid development of sensor and artificial intelligence (AI) technologies, intelligent surface vehicles (ISVs) have gained increasing attention
arXiv:2607.03847v1 Announce Type: new Abstract: Understanding why discovered scenarios become critical in scenario-based testing is essential for effectively leveraging them in decision-making systems
arXiv:2607.03925v1 Announce Type: new Abstract: EEG foundation models have shown strong potential in learning generalized representations across subjects and tasks. However, most existing approaches f
new @WSJ editorial board piece on 'The Socialist Temptation of Sam Altman' highlights dangers of politicization of AI, regulatory capture, & bailouts. 'The larger harm will be to the U.S. economy if i
arXiv:2607.03441v1 Announce Type: cross Abstract: LLM agents often degrade over long episodes: as trajectories grow, they revisit explored states, repeat failed actions, and lose strategies that previ
arXiv:2607.04780v1 Announce Type: cross Abstract: Sequential Monte Carlo (SMC) methods are a natural tool for post-hoc conditioning of pretrained generative models, but in many applications the mutati
arXiv:2607.03038v1 Announce Type: new Abstract: Omnidirectional depth estimation from multi-fisheye camera rigs is complicated by visibility conflicts: wide baselines cause different cameras to observ
arXiv:2607.03723v1 Announce Type: cross Abstract: Visual policies learned from human videos, teleoperation, and robot demonstrations offer scalable motion priors, but often fail in contact-rich manipu
arXiv:2607.03948v1 Announce Type: new Abstract: We study the online routing problem in large language model serving, where requests arrive sequentially and must be dispatched to parallel decode worker
arXiv:2508.01389v3 Announce Type: replace Abstract: Person retrieval in surveillance videos often depends on attributes described by witnesses or operators. However, the most useful cues in practice a
arXiv:2607.05163v1 Announce Type: cross Abstract: AI systems may produce failures after deployment that pre-deployment safety assessments do not anticipate. Managing these failures requires what we re
OpenAI just asked the US government to take a 5% stake in the company. The pitch: give every citizen a share in the profits of AI. The problem: AI has no profits. So the real plan is to give every cit
arXiv:2601.07376v2 Announce Type: replace Abstract: We introduce extsc{OpenTinker}, an open infrastructure for training large language model (LLM) agents with many LoRA-backed policies over shared exe
arXiv:2607.02741v1 Announce Type: cross Abstract: This paper develops an Optimality-Informed Neural Network (OINN) approach for the energy-optimal, free-final-time powered descent of a lunar lander fr
arXiv:2607.02961v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) exhibit remarkable vision-language capabilities and are increasingly deployed in real-world applications such as pe
arXiv:2607.02914v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across diverse applications, yet ensuring their simultaneous safety, helpfulness,
arXiv:2508.04796v3 Announce Type: replace-cross Abstract: Tokenization is the first -- and often least scrutinized -- step of most NLP pipelines. Standard algorithms for learning tokenizers rely on fr
arXiv:2607.03692v1 Announce Type: new Abstract: Spectral methods are widely used to construct representations from the geometry of data, but they often rely on a fixed kernel, graph Laplacian, or manu
arXiv:2607.04637v1 Announce Type: new Abstract: Vision-Language-Action Models (VLAs), which leverage the advanced reasoning capabilities of Vision-Language Models (VLMs), show promising generalization
arXiv:2506.16995v4 Announce Type: replace Abstract: Proficient game agents with diverse play styles enrich the gaming experience and enhance the replay value of games. However, recent advancements in
arXiv:2506.23845v2 Announce Type: replace-cross Abstract: While sparse autoencoders (SAEs) have generated significant excitement, a series of negative results have added to skepticism about their usef
arXiv:2607.02637v1 Announce Type: cross Abstract: Recent generative models can produce high-quality synthetic images, offering scalable training training data for data-hungry models. Existing approach
arXiv:2607.04846v1 Announce Type: cross Abstract: Transformers follow implicit curricula whereby some tasks are learned before others. However, how explicit pretraining curricula influence learning, g
arXiv:2602.23297v2 Announce Type: replace Abstract: Medical diagnosis requires the effective synthesis of visual manifestations and clinical metadata. However, existing methods often treat metadata as
arXiv:2607.04880v1 Announce Type: new Abstract: Recent advances in large-scale pretrained vision-language-action models have improved robot policy learning, but directly deploying such policies in use
arXiv:2607.03797v1 Announce Type: new Abstract: Deep learning (DL) has shown strong performance in medical image classification, but its trustworthy deployment remains challenging in safety-critical c
arXiv:2607.04242v1 Announce Type: new Abstract: Group-based reinforcement learning (RL) has become an effective paradigm for improving large language model agents on long-horizon interactive tasks. To
arXiv:2304.13917v4 Announce Type: replace Abstract: In recent years, there has been a surge in effort to formalize notions of fairness in machine learning. We focus on centroid clustering--one of the
arXiv:2607.03732v1 Announce Type: new Abstract: Precise control over complex dynamics remains challenging for modern video generative models, as text prompts alone often cannot specify physically plau