Knowledge catalogue
arXiv:2606.08738v1 Announce Type: cross Abstract: Urban last-mile parcel delivery increasingly relies on heterogeneous fleets whose performance depends on timely coordination, reliable communication,
arXiv:2606.07861v1 Announce Type: cross Abstract: Recent vision-language models (VLMs) excel at multimodal understanding and reasoning, yet their fine-grained visual perception remains underexplored.
arXiv:2606.08202v1 Announce Type: cross Abstract: Most statistical and machine learning methods for directed interactions focus on pairwise effects among variables. Even existing cyclic models represe
arXiv:2602.08235v2 Announce Type: replace-cross Abstract: Although computer-use agents (CUAs) hold significant potential to automate increasingly complex OS workflows, they can demonstrate unsafe unin
arXiv:2606.07128v1 Announce Type: new Abstract: Raw numerical datasets remain less systematically examined in integrity screening than images, plagiarism, or summary-statistic inconsistencies. We deve
arXiv:2606.06714v1 Announce Type: new Abstract: Human perception of surface slant from texture exhibits systematic, graded biases that emerge reliably in psychophysical experiments. Prior work showed
arXiv:2606.05050v1 Announce Type: cross Abstract: Theoretical heterogeneous catalysis promises rapid catalyst discovery, yet computational and machine-learning predictions often deviate from experimen
arXiv:2606.07303v1 Announce Type: new Abstract: Representation learning is central to modern machine learning, enabling transitions from handcrafted features to learned embeddings, latent spaces, foun
arXiv:2602.04894v4 Announce Type: replace-cross Abstract: LLMs are increasingly used for code generation, but their outputs often follow recurring templates that can induce predictable vulnerabilities
arXiv:2606.06881v1 Announce Type: new Abstract: Blood glucose forecasting models are foundational for modern diabetes management systems, as reliable short-term predictions can enable proactive interv
arXiv:2606.07402v1 Announce Type: new Abstract: Language agents are increasingly deployed over accumulating multimodal information, yet existing benchmarks assume a human-human form with sparse visual
arXiv:2606.07401v1 Announce Type: new Abstract: Document parsing systems are increasingly deployed in high-stakes, regulated workflows such as mortgage underwriting, financial reporting, supply-chain
arXiv:2606.06574v1 Announce Type: new Abstract: Large language models (LLMs) perform inference by following a fixed depth and order, non-recurrent execution of all layers. We reveal the wide existence
This is exactly right. People are starting to look for cheaper model alternatives and realizing two things at once: open-source models are already very good, and the ability to train and serve them ef
arXiv:2606.06687v1 Announce Type: new Abstract: We investigate cluster formation, involving the number and composition of clusters, in decentralized federated learning (FL) with heterogeneous machine
arXiv:2602.03160v2 Announce Type: replace Abstract: Aligning Large Language Models (LLMs) with the diverse spectrum of human values remains a central challenge: preference-based methods often fail to
arXiv:2606.06674v1 Announce Type: new Abstract: Large Language Models (LLMs) are often fine-tuned through Reinforcement Learning from Human Feedback (RLHF) to align with people's preferences and value
ChatGPT's memory feature stores conversation context and user preferences within individual chats. This Reddit post discusses prompts designed to retrieve or extract the stored memory data that ChatGP
Gary Marcus comments on AI systems that exhibit scam-like or deceptive behaviors while remaining too commercially important or integrated to face meaningful consequences. The post likely critiques how
arXiv:2601.21162v2 Announce Type: replace-cross Abstract: Graph Retrieval-Augmented Generation (Graph-RAG) enhances multihop question answering by organizing corpora into knowledge graphs and routing
arXiv:2606.06448v1 Announce Type: new Abstract: LLM agents are increasingly deployed on long-horizon tasks requiring sustained reasoning over extended interaction histories. Realizing this at scale re
arXiv:2606.05552v1 Announce Type: cross Abstract: Despite progress in image tokenization, standard methods encode redundant information by mixing all granularities within each token, thus redundancy p
arXiv:2606.05710v1 Announce Type: cross Abstract: The increasing penetrations of the critical infrastructure sector in the United States with intelligent digital technologies have greatly increased ex
arXiv:2606.05219v1 Announce Type: cross Abstract: Recent analyses of multi-pathway Deep Linear Networks use Gradient Flow to predict a 'winner-takes-all' specialization in which path symmetry breaks a
arXiv:2606.05449v1 Announce Type: new Abstract: Agentic artificial intelligence (AI) systems are transforming the risk landscape by extending beyond information generation to autonomous planning, tool
arXiv:2606.06021v1 Announce Type: cross Abstract: On-policy distillation (OPD) supervises the student only in output space by matching next-token probabilities. This output-only paradigm has two limit
I've been experimenting with different approaches to running code in a sandbox for several years now, but my latest attempt feels like it might finally have all of the characteristics I've been lookin
arXiv:2602.12124v2 Announce Type: replace-cross Abstract: While most AI alignment research focuses on preventing models from generating explicitly harmful content, a more subtle risk arises from capab
arXiv:2606.06242v1 Announce Type: new Abstract: Institutional documents contain substantial amounts of operational and analytical information embedded within figures and tables. Current approaches for
arXiv:2509.04027v3 Announce Type: replace-cross Abstract: Test-time scaling, primarily manifested through multi-step Chain-of-Thought (CoT) reasoning via Reinforcement Learning (RL), has emerged as a
arXiv:2606.05290v1 Announce Type: new Abstract: Recent progress in generative modeling has made safety control a central challenge, yet existing approaches remain largely model-specific, requiring ret
arXiv:2606.05874v1 Announce Type: new Abstract: Current evaluations for Multimodal Large Language Models (MLLMs) overwhelmingly focus on utility-driven objectives, leaving model behavior under logic-n
arXiv:2606.06464v1 Announce Type: new Abstract: A long-standing finding in the causal learning literature is that adults struggle to identify conjunctive causal rules, where an effect requires the sim
arXiv:2606.05744v1 Announce Type: new Abstract: Spatial planning maps are central to territorial governance, translating planning objectives, regulations, and spatial strategies into visual forms for
arXiv:2606.05660v1 Announce Type: new Abstract: Embodied AI systems are increasingly expected to reason and act over extended horizons in physical environments. This growing capability brings safety t
arXiv:2602.09464v2 Announce Type: replace-cross Abstract: Vericoding refers to the generation of formally verified code from rigorous specifications. Recent AI models show promise in vericoding, but a
arXiv:2509.02655v3 Announce Type: replace-cross Abstract: Many AI alignment discussions of 'runaway optimisation' focus on RL agents: unbounded utility maximisers that over-optimise a proxy objective
arXiv:2606.04261v1 Announce Type: new Abstract: Curating training data is among the most consequential yet labor-intensive parts of modern AI development: practitioners iteratively propose, implement,
arXiv:2606.04557v1 Announce Type: new Abstract: Large Language Models can reason over long contexts, yet prefilling millions of tokens is wasteful as much of the content remains static across queries.
arXiv:2606.04518v1 Announce Type: new Abstract: This paper proposes a cooperative target circumnavigation framework for multiple unmanned surface vehicles (USVs) operating without external localizatio
arXiv:2606.04109v1 Announce Type: new Abstract: Context-augmented language model systems often wrap supplied content with labels such as Reference:, Evidence:, Instruction:, Note:, or Example:, but th
arXiv:2606.04212v1 Announce Type: new Abstract: Existing analyses of the edge of stability (EoS) treat it as a global property of optimization. We show that it is also selective: the stability constra
arXiv:2509.23385v5 Announce Type: replace-cross Abstract: Simulation-based inference (SBI) is transforming experimental sciences by enabling parameter estimation in complex non-linear models from simu
arXiv:2606.04967v1 Announce Type: cross Abstract: AI tools for programming are no longer just autocomplete or chat assistants: they organize themselves as development frameworks, with process, roles,
arXiv:2606.04660v1 Announce Type: new Abstract: Lifelong digital companions must integrate cross-session cues, continually update their understanding of users, and adapt to shifting privacy boundaries
arXiv:2505.17315v2 Announce Type: replace Abstract: Recent language models exhibit strong reasoning capabilities, yet the influence of long-context capacity on reasoning remains underexplored. In this
arXiv:2606.04438v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) and looped architectures scale models along two orthogonal axes, namely parameter capacity and effective depth. However, main
arXiv:2606.04092v1 Announce Type: new Abstract: Flow matching models learn to transport samples from a simple prior distribution to a complex data distribution. When prior-data pairs are coupled via o
arXiv:2606.04737v1 Announce Type: new Abstract: Large-scale video generation models have made remarkable progress in semantic consistency and visual quality, producing videos that are increasingly coh
arXiv:2606.05129v1 Announce Type: cross Abstract: Preserving data privacy is an important topic in structural data management and data mining. However, the issue of privacy leakage in distributed caus
arXiv:2606.04823v1 Announce Type: new Abstract: Large language models (LLMs) are fluent on open-ended tasks, yet in agentic settings, where a system must plan, use tools, and act over extended horizon
arXiv:2606.04739v1 Announce Type: cross Abstract: Large language models (LLMs) have shown strong potential for automated software vulnerability detection, particularly in retrieval-augmented generatio
Sometimes you need to start over. But that decision is hard. @llama_index had to make that call: they built one of the most popular AI frameworks in the world, but saw the agent harness and frontier l
arXiv:2606.04317v1 Announce Type: cross Abstract: Deep neural networks are increasingly deployed across heterogeneous and partially untrusted environments, where models are distributed through cloud s
arXiv:2603.20304v2 Announce Type: replace Abstract: As generative AI advances, global governance frameworks increasingly mandate verifiable content provenance. However, existing watermarking technique
arXiv:2606.04101v1 Announce Type: cross Abstract: Large-scale expert parallelism (EP) is becoming pivotal for training and serving frontier MoE models, but it also amplifies device-level expert load i
arXiv:2606.03609v1 Announce Type: cross Abstract: Embodied agents that navigate cities rely on world models that predict how their surroundings will change as they move. But for navigation, what matte
arXiv:2606.03381v1 Announce Type: cross Abstract: Ensuring the protection of Artificial Intelligence (AI) models deployed in military Command and Control (C2) systems and critical infrastructure is es
arXiv:2602.06842v2 Announce Type: replace-cross Abstract: Deep learning-based hybrid iterative methods (DL-HIMs) integrate classical numerical solvers with neural operators, utilizing their complement
arXiv:2606.03103v1 Announce Type: new Abstract: Real-world professional desktop workflows in specialized creative and engineering software unfold over long horizons and often require human-in-the-loop