Knowledge catalogue
arXiv:2604.15959v1 Announce Type: new Abstract: Multi-objective Bayesian optimization (MOBO) provides a principled framework for optimizing expensive black-box functions with multiple objectives. Howe
arXiv:2509.17292v3 Announce Type: replace-cross Abstract: Cognitive distortions have been closely linked to mental health disorders, yet their automatic detection remains challenging due to contextual
arXiv:2604.16232v1 Announce Type: cross Abstract: Understanding natural and engineered systems often relies on symbolic formulations, such as differential equations, which provide interpretability and
arXiv:2509.21623v2 Announce Type: replace-cross Abstract: The expanding long-context capabilities of large language models are constrained by a significant memory bottleneck: the key-value (KV) cache
arXiv:2604.16108v1 Announce Type: new Abstract: Speech-Driven Facial Animation (SDFA) has gained significant attention due to its applications in movies, video games, and virtual reality. However, mos
arXiv:2604.15728v1 Announce Type: cross Abstract: Large language model (LLM) routing has emerged as a critical strategy to balance model performance and cost-efficiency by dynamically selecting servic
Qiskit v2.4 represents the latest release of IBM's open-source quantum computing framework, introducing new features and improvements to the software development kit for quantum programming. The relea
arXiv:2604.16299v1 Announce Type: new Abstract: We introduce LaviGen, a framework that repurposes 3D generative models for 3D layout generation. Unlike previous methods that infer object layouts from
arXiv:2603.27312v2 Announce Type: replace Abstract: Maximum entropy (MaxEnt) modelling provides a principled framework for generating synthetic populations from aggregate census data, without access t
arXiv:2603.03188v2 Announce Type: replace-cross Abstract: We introduce a novel framework for uncertainty quantification in clustering that combines martingale posterior distributions with density-base
arXiv:2604.16117v1 Announce Type: cross Abstract: Practice and extensive exercises are essential in programming education. Intelligent tutoring systems (ITSs) are a viable option to provide individual
arXiv:2604.16263v1 Announce Type: new Abstract: Coordinating multi-robot systems (MRS) to search in unknown environments is particularly challenging for tasks that require semantic reasoning beyond ge
arXiv:2604.15735v1 Announce Type: cross Abstract: Fine-grained image retrieval via hand-drawn sketches or textual descriptions remains a critical challenge due to inherent modality gaps. While hand-dr
arXiv:2604.15862v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has recently redefined the paradigm of 3D reconstruction, striking an unprecedented balance between visual fidelity and com
arXiv:2604.16070v1 Announce Type: new Abstract: We present TableSeq, an image-only, end-to-end framework for joint table structure recognition, content recognition, and cell localization. The model fo
arXiv:2604.15702v1 Announce Type: new Abstract: We introduce a cross-domain behavioural assay of monitoring-control coupling in LLMs, grounded in the Nelson and Narens (1990) metacognitive framework a
You can use ToolSimulator, an LLM-powered tool simulation framework within Strands Evals, to thoroughly and safely test AI agents that rely on external tools, at scale. Instead of risking live API cal
arXiv:2604.15507v1 Announce Type: new Abstract: Planning safe trajectories under model uncertainty is a fundamental challenge. Robust planning ensures safety by considering worst-case realizations, ye
arXiv:2604.15375v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant improvement in RTL code generation. Despite the advances, the generated code is often riddled with
arXiv:2411.10446v3 Announce Type: replace-cross Abstract: Recent progress in vision-language models (VLMs) has opened new possibilities for robot task planning, but these models often produce incorrec
arXiv:2510.08480v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have demonstrated remarkable potential in bridging visual and textual reasoning, yet their reliance on text
arXiv:2603.13966v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models are increasingly evaluated across multiple simulation benchmarks, yet adding each benchmark to an evaluation pip
arXiv:2604.15764v1 Announce Type: cross Abstract: Early-exit neural networks enable adaptive computation by allowing confident predictions to exit at intermediate layers, achieving 2-8imes inference s
arXiv:2604.15343v1 Announce Type: cross Abstract: We report a detailed autoethnographic case study of a single-subject who deliberately constructed and operated a multi-modal prompt-engineering system
This post discusses the integration of LangChain's Deep Agents framework into Toad, noting that the implementation functioned without requiring significant modifications or troubleshooting. The entry
Harrison Chase demonstrates creating a custom CLI agent using LangChain's deep agent functionality, highlighting the ease and simplicity of building autonomous agents with modern frameworks. The post
Nous Research announced integration or collaboration between Hermes (their AI agent framework) and Ollama (a local LLM runtime platform), enabling users to run Hermes agents with locally-hosted langua
Seedance 2.0, an update to the Seedance tool within the ComfyUI framework, is now available with enhanced 1080p resolution capability, providing sharper and higher-quality output compared to previous
arXiv:2510.13829v3 Announce Type: replace Abstract: As large language models (LLMs) continue to advance rapidly, reliable governance tools have become critical. Publicly verifiable watermarking is par
arXiv:2604.14603v1 Announce Type: cross Abstract: The fundamental limit of natural signal compression has traditionally been characterized by classical rate-distortion (RD) theory through the tradeoff
arXiv:2604.13812v1 Announce Type: new Abstract: Quantum circuit optimization is a central task in Quantum Computing, as current Noisy Intermediate Scale Quantum devices suffer from error propagation t
arXiv:2604.14362v1 Announce Type: new Abstract: Large language models still struggle with reliable long-term conversational memory: simply enlarging context windows or applying naive retrieval often i
arXiv:2601.06559v2 Announce Type: replace Abstract: Grounding events in videos serves as a fundamental capability in video analysis. While Vision Language Models (VLMs) are increasingly employed for t
arXiv:2505.10846v3 Announce Type: replace Abstract: This paper presents AutoRAN, the first framework to automate the hijacking of internal safety reasoning in large reasoning models (LRMs). At its cor
b8829 is a release build of llama.cpp , a C/C++ framework for running large language models locally. Notable in this release was a significant size difference compared to the preceding build b8828 , t
arXiv:2604.15090v1 Announce Type: new Abstract: Any-Time Person Re-identification (AT-ReID) necessitates the robust retrieval of target individuals under arbitrary conditions, encompassing both modali
arXiv:2604.15124v1 Announce Type: new Abstract: Continuous glucose monitoring (CGM) is central to diabetes care, but explaining CGM patterns clearly and empathetically remains time-intensive. Evidence
arXiv:2604.13102v1 Announce Type: cross Abstract: Enterprise software organizations face an escalating challenge in maintaining the integrity, security, and freshness of codebases that span hundreds o
arXiv:2604.14259v1 Announce Type: cross Abstract: Functional magnetic resonance imaging (fMRI) is widely used for studying and diagnosing brain disorders, with functional connectivity (FC) matrices pr
arXiv:2604.15302v1 Announce Type: cross Abstract: LLM-as-judge frameworks are increasingly used for automatic NLG evaluation, yet their per-instance reliability remains poorly understood. We present a
arXiv:2604.14621v1 Announce Type: cross Abstract: Conformal prediction (CP) has attracted broad attention as a simple and flexible framework for uncertainty quantification through prediction sets. In
arXiv:2411.00361v4 Announce Type: replace Abstract: Hierarchical reinforcement learning (HRL) enables agents to solve complex, long-horizon tasks by decomposing them into manageable sub-tasks. However
arXiv:2602.12389v3 Announce Type: replace-cross Abstract: Temporal knowledge graph (TKG) forecasting requires predicting future facts by jointly modeling structural dependencies within each snapshot a
arXiv:2604.14727v1 Announce Type: new Abstract: To quantify the geometric expressivity of transformers, we introduce a tropical geometry framework to characterize their exact spatial partitioning capa
arXiv:2506.23334v3 Announce Type: replace-cross Abstract: Federated learning enables collaborative training of deep learning models across institutions without sharing sensitive patient data. However,
arXiv:2512.22897v3 Announce Type: replace Abstract: Spectral clustering has emerged as one of the most effective clustering algorithms due to its superior performance. However, most existing models ar
arXiv:2604.15037v1 Announce Type: cross Abstract: Recent advancements in LLM agents are gradually shifting from reactive, text-based paradigms toward proactive, multimodal interaction. However, existi
arXiv:2604.14575v1 Announce Type: new Abstract: Data-driven operations management often relies on parameters estimated from costly human-generated labels. Recent advances in large language models (LLM
arXiv:2604.14800v1 Announce Type: cross Abstract: Objective. Standard Magnetic Resonance Imaging (MRI) reconstruction pipelines discard phase information captured during acquisition, despite evidence
arXiv:2604.14268v1 Announce Type: new Abstract: We introduce HY-World 2.0, a multi-modal world model framework that advances our prior project HY-World 1.0. HY-World 2.0 accommodates diverse input mod
arXiv:2604.14240v1 Announce Type: cross Abstract: The simulation of complex systems increasingly relies on sophisticated but fundamentally opaque computational black-box simulators. Surrogate models p
arXiv:2604.14034v1 Announce Type: cross Abstract: Recent advances in Generative Artificial Intelligence, particularly Large Language Models (LLMs), have stimulated growing interest in automating or as
arXiv:2604.13348v1 Announce Type: new Abstract: We introduce CONCORD, a privacy-aware asynchronous assistant-to-assistant (A2A) framework that leverages collaboration between proactive speech-based AI
arXiv:2604.15174v1 Announce Type: new Abstract: Despite recent advances in state space models (SSMs) such as Mamba across various sequence domains, research on their standalone capacity for time serie
arXiv:2604.15190v1 Announce Type: cross Abstract: Simulating group-level user behavior enables scalable counterfactual evaluation of merchant strategies without costly online experiments. However, bui
arXiv:2604.14158v1 Announce Type: new Abstract: Current evaluations of long-term memory in LLMs are fundamentally static. By fixating on simple retrieval and short-context inference, they neglect the
arXiv:2604.14424v1 Announce Type: new Abstract: Most practical engineering design problems involve nonlinear spatio-temporal dynamical systems. Multi-physics simulations are often performed to capture
arXiv:2604.13385v1 Announce Type: cross Abstract: Open-pit mine scheduling is a complex real world optimization problem that involves uncertain economic values and dynamically changing resource capaci
arXiv:2604.14338v1 Announce Type: new Abstract: We introduce path-sampled integrated gradients (PS-IG), a framework that generalizes feature attribution by computing the expected value over baselines
arXiv:2511.19204v3 Announce Type: replace Abstract: We present a sampling-based model predictive control (MPC) framework that enables emergent locomotion without relying on handcrafted gait patterns o