Knowledge catalogue
arXiv:2511.06894v3 Announce Type: replace-cross Abstract: Reconstruction-based methods are a dominant paradigm in time series anomaly detection (TSAD), however, their near-universal reliance on Mean S
arXiv:2605.10462v1 Announce Type: new Abstract: Formalisation is the process of writing system requirements in a formal language. These requirements mostly originate in Natural Language. In the field
arXiv:2601.23087v3 Announce Type: replace Abstract: Learning long-horizon robotic manipulation requires jointly achieving expressive behavior modeling, real-time inference, and stable execution, which
arXiv:2605.08735v1 Announce Type: new Abstract: Recent 'Thinking with Video' approaches use Video Generation Models (VGMs) for visual reasoning by producing temporally coherent Chain-of-Frames as reas
arXiv:2605.10528v1 Announce Type: cross Abstract: We investigate the emergent collective dynamics of LLM-based multi-agent systems on a 2D square lattice and present a model-agnostic statistical-physi
arXiv:2601.16836v3 Announce Type: replace-cross Abstract: Text-to-image (T2I) models have advanced considerably in generating high-quality images from textual descriptions. However, their ability to a
arXiv:2605.08802v1 Announce Type: new Abstract: Due to the potential for exploratory reasoning of Latent Visual Reasoning, recent works tend to enable MLLMs (Multimodal Large Language Models) to perfo
arXiv:2605.10005v1 Announce Type: cross Abstract: In this paper, we describe early work on a specification inference tool for the Move Prover that combines a weakest-precondition (WP) analysis over Mo
arXiv:2605.08750v1 Announce Type: cross Abstract: Natural language is widely used to describe, prompt, and control audio systems, but rarely serves as the representation carrying audio itself. We intr
arXiv:2605.08171v1 Announce Type: cross Abstract: Background and motivation. The Communication Dynamics (CD) framework, introduced in two earlier papers for atomic-energy prediction and field-induced
arXiv:2605.08885v1 Announce Type: new Abstract: SO(3) equivariant graph neural networks have become the dominant paradigm for atomistic foundation models, achieving high accuracy and data efficiency b
arXiv:2605.10673v1 Announce Type: new Abstract: Low-bit forward evaluation is an attractive route to memory-efficient zeroth-order (ZO) adaptation: the optimizer needs only scalar losses, and the mode
arXiv:2512.00175v3 Announce Type: replace-cross Abstract: Identifying causal effects in the presence of unmeasured variables is a fundamental challenge in causal inference, for which proxy variable me
arXiv:2511.07055v3 Announce Type: replace Abstract: High-stakes decisions informed by decision support systems require explicit evidence. While prior work focuses on short sufficient evidence, regulat
arXiv:2605.10123v1 Announce Type: new Abstract: Complex-valued Transformers have largely inherited softmax attention from real-valued architectures. However, row-normalised token competition is not ne
arXiv:2605.10787v1 Announce Type: new Abstract: Current LLM agents are proficient at calling isolated APIs but struggle with the 'last mile' of commercial software automation. In real-world scenarios,
arXiv:2605.10642v1 Announce Type: new Abstract: Pretrained diffusion models provide powerful learned priors, but in scientific sampling the target distribution often depends on physical context that i
arXiv:2508.04660v2 Announce Type: replace Abstract: Group Relative Policy Optimization (GRPO) has proven to be an effective tool for post-training language models (LMs). However, AI systems are increa
arXiv:2605.08810v1 Announce Type: cross Abstract: We propose Compressed Video Aggregator (CVA), a lightweight micro-video recommendation module that decouples video information from preference learnin
arXiv:2509.14234v3 Announce Type: replace Abstract: Where do learning signals come from when there is no ground truth in post-training? We show that inference compute itself can serve as supervision.
arXiv:2605.10875v1 Announce Type: cross Abstract: Efficient LLM inference research has largely focused on reducing the cost of each decoding step (e.g., using quantization, pruning, or sparse attentio
arXiv:2605.08261v1 Announce Type: cross Abstract: Evaluating Computer Use Agents (CUAs) on interactive environments is fraught with methodological pitfalls that the field has yet to systematically add
arXiv:2601.21164v3 Announce Type: replace Abstract: Plane Geometry Problem Solving (PGPS) is a multimodal reasoning task that aims to solve a plane geometric problem based on a geometric diagram and p
arXiv:2605.09855v1 Announce Type: new Abstract: Federated learning (FL) enables training large language models (LLMs) without sharing raw data, but adapting LLMs under strict data isolation and non-II
arXiv:2605.10847v1 Announce Type: new Abstract: Anomaly detection methods can be very useful in identifying unusual or interesting patterns in data. A recently proposed conditional anomaly detection f
arXiv:2605.10916v1 Announce Type: cross Abstract: Recognition of handwritten Bangla compound characters remains a challenging problem due to complex character structures, large intra-class variation,
arXiv:2605.09760v1 Announce Type: new Abstract: A reliable resume-job matching system helps a company find suitable candidates from a pool of resumes and helps a job seeker find relevant jobs from a l
arXiv:2605.09688v1 Announce Type: new Abstract: Feedforward 3D Gaussian Splatting (3DGS) often struggles in trajectory-based sparse-view driving scenes. Existing Gaussian repair methods mainly target
arXiv:2605.10721v1 Announce Type: cross Abstract: Artificial intelligence safety research focuses on aligning individual language models with human values, yet deployed AI systems increasingly operate
arXiv:2605.10533v1 Announce Type: new Abstract: In causal inference, confounders are variables that influence both treatment decisions and outcomes. However, unlike as in randomized clinical trials, t
arXiv:2605.10793v1 Announce Type: new Abstract: Large language models (LLMs) are costly to deploy due to their large memory footprint and high inference cost. Weight-activation quantization can reduce
arXiv:2511.09493v2 Announce Type: replace Abstract: Motivated by undetectable risks in generative AI, we study a general robust aggregation problem: how to aggregate several probability distributions
arXiv:2605.10516v1 Announce Type: new Abstract: This paper establishes a rigorous measurement science for AI agent reliability, providing a foundational framework for quantifying consistency under sem
arXiv:2512.03706v2 Announce Type: replace-cross Abstract: Coarse graining (CG) is an important task for efficient modeling and simulation of complex multi-scale systems, such as the conformational dyn
arXiv:2605.09869v1 Announce Type: cross Abstract: Zero-shot object navigation has advanced rapidly with open-vocabulary detectors, image--text models, and language-guided exploration. However, even af
arXiv:2605.09968v1 Announce Type: new Abstract: Every adaptive learning system must alternate between two operations: consolidating what it already knows and expanding into new evidence. We propose Co
arXiv:2605.09085v1 Announce Type: new Abstract: Density estimation is a central primitive in probabilistic modeling, yet continuous, discrete, and mixed-variable domains are often treated by separate
arXiv:2605.08804v1 Announce Type: new Abstract: Reinforcement learning combined with imitation learning has significantly advanced biomimetic quadrupedal locomotion. However, scaling these frameworks
arXiv:2510.11491v3 Announce Type: replace-cross Abstract: Safe reinforcement learning (RL) seeks to mitigate unsafe behaviors that arise from exploration during training by reducing constraint violati
arXiv:2602.08606v2 Announce Type: replace-cross Abstract: Motivated by applications in conditional sampling, given a probability measure mu and a diffeomorphism phi, we consider the problem of simulta
arXiv:2605.09045v1 Announce Type: new Abstract: Agentic frameworks are the software layer through which AI agents act in the world. Existing safety methods intervene on the model and therefore remain
arXiv:2605.08112v1 Announce Type: cross Abstract: AI coding agents powered by large language models can read codebases and produce functional code, but they routinely violate team-specific product dec
arXiv:2602.02350v2 Announce Type: replace Abstract: Multi-Agent Discussion (MAD) has garnered increasing attention very recently, where multiple LLM instances collaboratively solve problems via struct
arXiv:2605.09112v1 Announce Type: cross Abstract: Selecting a coherent sequence or subset of elements is a fundamental problem in structured prediction, arising in tasks such as detection, trajectory
arXiv:2605.09998v1 Announce Type: cross Abstract: Coding harnesses such as Claude Code and OpenHands wrap foundation models with tools, memory, and planning, but no equivalent exists for embodied agen
arXiv:2605.08539v1 Announce Type: cross Abstract: Inductive biases influence the behavior and performance of sequential models. In this work, we study an underexplored inductive bias in sequential mod
arXiv:2605.09867v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit a strong capacity for in-context learning: Given labeled examples, they can generate good predictions without par
arXiv:2602.07209v2 Announce Type: replace Abstract: Localization and mapping of an environment are crucial tasks for any robot operating in unstructured environments. Time-of-flight (ToF) sensors (e.g
arXiv:2605.09216v1 Announce Type: new Abstract: Predicting the shape of tendon driven continuum robots (TDCRs) at steady state from actuation remains challenging due to continuous deformation, complex
arXiv:2605.08781v1 Announce Type: new Abstract: AI-assisted bridge defect inspection often produces bounding boxes with crude geometry or raster masks that are costly to store, transmit, and reuse. Th
arXiv:2605.08561v1 Announce Type: cross Abstract: Density estimation and reliable prediction regions for outputs are crucial in supervised and unsupervised learning. While conformal prediction effecti
arXiv:2510.06637v3 Announce Type: replace-cross Abstract: Despite advances in test-time scaling and diffusion finetuning, guidance for Auto-Regressive Diffusion Models (ARDMs) remains underexplored. W
arXiv:2605.08727v1 Announce Type: cross Abstract: Learned image compression (LIC) integrates deep neural networks (DNNs) to map high-dimensional images into compact latent representations, reducing re
arXiv:2605.10585v1 Announce Type: new Abstract: Multi-objective reinforcement learning (MORL) allows a user to express preference over outcomes in terms of the relative importance of the objectives, b
arXiv:2605.08856v1 Announce Type: new Abstract: Autoregressive neural simulators now match classical solvers on short-horizon prediction of physical systems, yet their accuracy degrades rapidly when r
arXiv:2605.08352v1 Announce Type: new Abstract: A convergence analysis is developed for the regularized Newton method for training neural networks (NNs) in the overparameterized limit. As the number o
arXiv:2605.08522v1 Announce Type: new Abstract: The evaluation of Large Language Models (LLMs) faces a critical challenge in construct validity, where fragmented benchmarks and ad hoc metrics frequent
arXiv:2605.08681v1 Announce Type: cross Abstract: We study solving large-scale fixed-point equation (x^star=ar F(x^star)) with decomposition. Standard strict decomposition assigns each agent a disjoin
arXiv:2602.05902v2 Announce Type: replace-cross Abstract: Post-training quantization (PTQ) enables efficient deployment of large language models by mapping pretrained weights to low-bit formats withou
arXiv:2605.09253v1 Announce Type: cross Abstract: While recent work in Reinforcement Learning with Verifiable Rewards (RLVR) has shown that a small subset of critical tokens disproportionately drives