Knowledge catalogue
arXiv:2605.00276v1 Announce Type: new Abstract: Trip planning for intelligent vehicles increasingly requires selecting optimal routes rather than merely producing feasible itineraries, as interacting
arXiv:2605.00073v1 Announce Type: new Abstract: Decentralized, agentic AI marketplaces are rapidly emerging to support software engineering tasks such as debugging, patch generation, and security audi
arXiv:2605.02651v1 Announce Type: cross Abstract: Scientific peer review increasingly struggles to assess reproducibility at the scale and complexity of modern research output. Evaluating reproducibil
arXiv:2605.01368v1 Announce Type: new Abstract: Human-robot interaction (HRI) has long studied how agents and people coordinate to achieve shared goals. In this work, we formalize and benchmark the no
arXiv:2506.09082v5 Announce Type: replace Abstract: The rise of vision foundation models (VFMs) calls for systematic evaluation. A common approach pairs VFMs with large language models (LLMs) as gener
arXiv:2605.01901v1 Announce Type: new Abstract: Traffic digital twins are powerful tools for advanced traffic management, and most systems are built on static geometric representations. However, these
arXiv:2605.02318v1 Announce Type: cross Abstract: In 2011, Judea Pearl received the Turing Award, considered the Nobel Prize in Computing, for fundamental contributions to artificial intelligence thro
arXiv:2401.04139v4 Announce Type: replace Abstract: Handling class imbalance remains a central challenge in machine learning, particularly in pattern recognition tasks where identifying rare but criti
arXiv:2605.01346v1 Announce Type: new Abstract: Standard selective prediction methods typically estimate uncertainty from the output of a single predictive branch. While effective for general uncertai
arXiv:2605.01011v1 Announce Type: new Abstract: Medical large language model (LLM) evaluations rely on simplified, exam-style benchmarks that rarely reflect the ambiguity of real-world medical inquiri
arXiv:2601.08517v2 Announce Type: replace Abstract: Channel-configuration search, the optimization of layer specifications such as channel widths in deep neural networks, presents a combinatorial chal
arXiv:2509.04470v2 Announce Type: replace Abstract: Autonomous agents executing human instructions must operate reliably even when instructions are incomplete. While recent approaches improve detectio
arXiv:2601.21766v3 Announce Type: replace Abstract: Transformers are arguably the preferred architecture for language generation. In this paper, inspired by continued fractions, we introduce a new fun
arXiv:2605.02647v1 Announce Type: new Abstract: Large language models (LLMs) remain vulnerable to jailbreak attacks that bypass safety alignment and elicit harmful responses. A growing body of work sh
arXiv:2508.12535v3 Announce Type: replace Abstract: Sparse Autoencoders (SAEs) can extract interpretable features from large language models (LLMs) without supervision. However, their effectiveness in
Release: datasette-referrer-policy 0.1 The OpenStreetMap tiles on the Datasette global-power-plants demo weren't displaying correctly. This turned out to be caused by two bugs. The first is that the C
arXiv:2605.01227v1 Announce Type: new Abstract: Quadrupedal locomotion plays a critical role in enabling agile, versatile movement across complex terrains. Understanding and estimating the underlying
arXiv:2605.01799v1 Announce Type: new Abstract: World models have made significant progress in modeling dynamic environments; however, most embodied world models are still restricted to 2D representat
arXiv:2605.02411v1 Announce Type: cross Abstract: A semantic gap separates how users describe tasks from how tools are documented. As API ecosystems scale to tens of thousands of endpoints, static ret
arXiv:2605.02815v1 Announce Type: new Abstract: Text-to-SQL over large analytical databases requires navigating complex schemas, resolving ambiguous queries, and grounding decisions in actual data. Mo
arXiv:2605.01373v1 Announce Type: new Abstract: The iterative denoising paradigm of Diffusion Large Language Models (DLMs) endows them with a distinct advantage in global context modeling. However, cu
arXiv:2605.01131v1 Announce Type: new Abstract: In continual reinforcement learning (CRL), good performance requires never-ending learning, acting, and exploration in a big, partially observable world
arXiv:2605.01656v1 Announce Type: cross Abstract: Human cognition emerges from coordinated spiking dynamics in distributed neural circuits, where information is encoded via both firing rates and preci
arXiv:2605.01299v1 Announce Type: new Abstract: Geometric Algebra (GA) presents challenges to learners due to its highly abstract mathematical structure and complex operational rules, as translating a
arXiv:2603.28767v2 Announce Type: replace Abstract: Recent image generation models have shown strong capabilities in generating high-fidelity and photorealistic images. However, they are fundamentally
arXiv:2605.02376v1 Announce Type: new Abstract: Automated medical report generation, MRG, holds substantial value for alleviating radiologist workload and enhancing diagnostic efficiency. However, mai
arXiv:2605.00845v1 Announce Type: cross Abstract: Knowledge Graph Question Answering (KGQA) has advanced through structured query generation, yet most efforts target RDF/SPARQL, leaving Cypher and pro
arXiv:2605.01627v1 Announce Type: new Abstract: Low-rank decomposition is a compelling approach for compressing large language models, but its effectiveness hinges on selecting which singular-vector b
arXiv:2509.03540v3 Announce Type: replace Abstract: Large Language Models (LLMs) often struggle with producing factually consistent answers due to limitations in their parametric memory. Retrieval-Aug
We’re announcing OS Level Actions for AgentCore Browser. This new capability unblocks these scenarios by exposing direct OS control through the InvokeBrowser API, so agents can interact with content v
arXiv:2412.15251v3 Announce Type: replace Abstract: Multimodal large language models (MLLMs) are effective at capturing the semantics of short video content; however, they often fail to attend to the
arXiv:2605.02392v1 Announce Type: new Abstract: Novelty assessment is a critical yet complex task in the examination process for patent acceptance, requiring examiners to determine whether an inventio
arXiv:2605.01973v1 Announce Type: new Abstract: Conventional LLMs may suffer from corpus heterogeneity and subtle condition changes. While finetuning can create the catastrophe forgetting issue, appli
arXiv:2601.08116v2 Announce Type: replace Abstract: Tropical cyclones are dangerous natural hazards, but their hazard is challenging to quantify directly from historical datasets due to limited datase
arXiv:2510.03599v2 Announce Type: replace Abstract: We present a unified framework for multi-task locomotion and manipulation policy learning grounded in a contact-explicit representation. Instead of
arXiv:2605.01096v1 Announce Type: new Abstract: Reinforcement Learning (RL) has the potential to enable robots with fast, nonlinear, and unstable dynamics to reach the limits of their performance. How
arXiv:2601.01322v2 Announce Type: replace Abstract: Modern Vision-Language Models (VLMs) achieve impressive performance but are limited by the quadratic complexity of self-attention, which prevents th
Release: llm-echo 0.5a0 New -o thinking 1 option to help test against LLM 0.32a0 and higher. This plugin provides a fake model called 'echo' for LLM which doesn't run an LLM at all - it's useful for w
arXiv:2605.01224v1 Announce Type: new Abstract: This paper argues that contemporary multilingual NLP has converged on a fragile and misleading paradigm of incidental multilingualism. Today's LLMs appe
arXiv:2605.01779v1 Announce Type: new Abstract: Vision-language models (VLMs) have shown potential for automated radiology report generation, yet existing approaches rely on global embedding compressi
arXiv:2605.01954v1 Announce Type: cross Abstract: Many sequential decision-making problems exhibit hierarchical structure, where high-level semantic choices constrain downstream actions and feedback i
arXiv:2605.02351v1 Announce Type: new Abstract: Molecular Vibe Coding, a paradigm where chemists interact with LLMs to generate executable programs for molecular tasks, has emerged as a flexible alter
arXiv:2510.08804v3 Announce Type: replace Abstract: We present MOSAIC, a multi-agent Large Language Model (LLM) framework for solving challenging scientific coding tasks. Unlike general-purpose coding
arXiv:2605.02572v1 Announce Type: cross Abstract: Large language models (LLMs) have shown promise as interactive agents that solve tasks through extended sequences of environment interactions. While p
arXiv:2601.03267v2 Announce Type: replace Abstract: This is the system card published alongside the OpenAI GPT-5 launch, August 2025. GPT-5 is a unified system with a smart and fast model that answers
arXiv:2605.01860v1 Announce Type: new Abstract: This paper explores the benefits of computing arborescent trajectories (trajectory-trees) instead of commonly used sequential trajectories for partially
arXiv:2605.02537v1 Announce Type: new Abstract: Autonomous 3D indoor scene synthesis breaks down in non-convex rooms with tightly coupled spatial constraints. Data-driven generators lack topological p
arXiv:2605.01483v1 Announce Type: new Abstract: A hierarchical cross-modal fusion model is proposed for vision-language question answering (VLQA) in industrial robotics, targeting the challenges of se
arXiv:2605.01965v1 Announce Type: new Abstract: A classical vector retrieval problem typically considers a single query embedding vector as input and retrieves the most similar embedding vectors from
arXiv:2605.01831v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback has become the standard paradigm for language model alignment, where reward models directly determine alignme
arXiv:2603.27259v2 Announce Type: replace Abstract: Long video understanding (LVU) remains a core challenge in multimodal learning. Although recent vision-language models (VLMs) have made notable prog
arXiv:2605.01496v1 Announce Type: new Abstract: This report presents the results and findings of the first edition of the Short-Films 20K (SF20K) Competition, held in conjunction with the SLoMO Worksh
arXiv:2605.00043v1 Announce Type: cross Abstract: Big data platforms are widely used in modern enterprises, and an in-production intelligent assistant is increasingly important to help users quickly f
arXiv:2605.00382v2 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed to generate code for human-centered applications where demographic fairness is critical. Howeve
arXiv:2604.06091v2 Announce Type: replace Abstract: Large language model (LLM) agents are increasingly acting as human delegates in multi-agent environments, where a representative agent integrates di
arXiv:2510.13668v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) inference has emerged as a fundamental paradigm, however, variations in output length cause severe workload imbalan
arXiv:2605.01911v1 Announce Type: new Abstract: Purpose: Vision-language models (VLMs) have shown promising performance in surgical visual question answering (VQA). However, existing surgical VQA data
arXiv:2603.29962v3 Announce Type: replace Abstract: Surgical procedures are inherently complex and risky, requiring extensive expertise and constant focus to navigate evolving intraoperative scenes. C
arXiv:2603.20143v2 Announce Type: replace Abstract: Building facade defect inspection is fundamental to structural health monitoring and sustainable urban maintenance, yet it remains a formidable chal
arXiv:2605.02672v1 Announce Type: cross Abstract: The 2026 ACII Dyadic Conversations (ACII-DaiKon) Workshop & Challenge introduces a benchmark for modeling interpersonal affect and social dynamics in