Knowledge catalogue
arXiv:2601.21971v2 Announce Type: replace-cross Abstract: Imitation learning has achieved remarkable success in robotic manipulation, yet its application to surgical robotics remains challenging due t
arXiv:2605.08190v1 Announce Type: new Abstract: Autonomous systems increasingly rely on machine-learning (ML) components for safety-critical tasks such as perception and control in autonomous vehicles
arXiv:2605.09424v1 Announce Type: new Abstract: Generative modelling is a demanding test of foundation models, because it requires robust, holistic representation learning for a given data modality, r
arXiv:2505.11604v5 Announce Type: replace Abstract: Editing presentation slides is a frequent yet tedious task, ranging from creative layout design to repetitive text maintenance. While recent GUI-bas
arXiv:2605.08440v1 Announce Type: cross Abstract: Adversarial purification with diffusion models seeks to project adversarial examples back toward the data manifold, but balancing semantic preservatio
arXiv:2605.10247v1 Announce Type: new Abstract: Using Large Language Models (LLMs) to process graph-structured data is an active research area, yet current state-of-the-art approaches typically rely o
arXiv:2512.13618v3 Announce Type: replace Abstract: Representing continuous time is a critical and under-explored challenge in modeling temporal event sequences with large language models (LLMs). Vari
arXiv:2605.08427v1 Announce Type: new Abstract: Self-play red team is an established approach to improving AI safety in which different instances of the same model play attacker and defender roles in
arXiv:2605.10698v1 Announce Type: cross Abstract: Multi-agent systems (MAS) assume that collaborating inherently improves Large Language Model (LLM) reasoning. We challenge this by demonstrating that
arXiv:2502.01237v3 Announce Type: replace Abstract: Direct Alignment Algorithms (DAAs) simplify LLM alignment by directly optimizing policies, bypassing reward modeling and RL. While DAAs differ in th
arXiv:2605.08737v1 Announce Type: cross Abstract: On-policy distillation (OPD) is widely used for LLM post-training. When pushed with a reward-extrapolation coefficient lambda > 1, the student can lif
arXiv:2605.09844v1 Announce Type: new Abstract: The Metacognitive Probe is an exploratory five-task, 15-slot diagnostic that decomposes an LLM's confidence behaviour into five behaviourally-distinct d
arXiv:2511.02623v2 Announce Type: replace Abstract: Post-training alignment of large language models (LLMs) relies on large-scale human annotations guided by policy specifications that change over tim
arXiv:2605.08323v1 Announce Type: cross Abstract: Communication is fundamental to sustaining reciprocity and cooperation in strategic interactions. We identify and formulate the influence attribution
arXiv:2605.08116v1 Announce Type: cross Abstract: Recent work on text diffusion models offers a promising alternative to autoregressive generation, but controlling their safety remains underexplored.
arXiv:2605.08749v1 Announce Type: new Abstract: We present the Wristband Gaussian Loss, a deterministic batch loss for Gaussianizing point embeddings without sampling, KL terms, or iterative transport
thoughts after doing a bunch of synthetic data gen for eval + environment building - LLMs are incredible projections of the world bundled into a set of weights - but doing targeted extraction of certa
arXiv:2605.10563v1 Announce Type: cross Abstract: Threat detection in Natural Language Processing lacks consistent definitions and standardized benchmarks, and is often conflated with broader phenomen
arXiv:2605.10038v1 Announce Type: new Abstract: Time series analysis underpins forecasting, monitoring, and decision making in domains such as finance and weather, where solving a task often requires
arXiv:2507.09788v2 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLM) have led to a new class of autonomous agents, renewing and expanding interest in the area. LLM-
arXiv:2605.09104v1 Announce Type: new Abstract: As LLM agents evolve, tokens have emerged as the core economic primitives of Agentic AI. However, their exponential consumption introduces severe comput
arXiv:2510.20036v2 Announce Type: replace Abstract: Large language model (LLM) agents rely on external tools to solve complex tasks, but real-world toolsets often contain redundant tools with overlapp
arXiv:2605.10091v1 Announce Type: new Abstract: Many modern datasets mix points, edges, regions, groups, objects, events, hyperedges, and relations. Yet neural architectures often force such data into
arXiv:2605.10425v1 Announce Type: cross Abstract: AI systems can now cheaply generate plausible scientific artifacts such as papers, reviews, and surveys. This creates a risk of epistemic pollution in
arXiv:2605.10772v1 Announce Type: cross Abstract: Large language-vision models (LLVM), such as OpenAI's ChatGPT and GPT-4, have gained prominence as powerful tools for analyzing text and imagery. The
arXiv:2605.10257v1 Announce Type: new Abstract: Managing disruptions in railway traffic management is a major challenge. Rising traffic density and infrastructure limits increase complexity, making th
arXiv:2605.09272v1 Announce Type: new Abstract: The practice of medicine relies not only upon skillful dialogue but also on the nuanced exchange and interpretation of rich auditory and visual cues bet
arXiv:2605.08129v1 Announce Type: new Abstract: Unified multimodal understanding and generation models enable richer human-AI interaction. Yet jointly customizing a character's persona, dialogue style
arXiv:2605.09294v1 Announce Type: cross Abstract: We propose Representational Effective Theory (RET), a framework for describing large language model computation in terms of learned macrostates rather
arXiv:2605.09965v1 Announce Type: new Abstract: The real world unfolds along a single set of physics laws, yet human intelligence demonstrates a remarkable capacity to generalize experiences from this
arXiv:2605.09670v1 Announce Type: cross Abstract: Teleoperation systems are fundamentally limited by communication latency, which degrades situational awareness and control performance. Predictive dis
arXiv:2409.13107v3 Announce Type: replace Abstract: Large language model-based (LLM) agents are emerging as a powerful enabler of robust embodied intelligence due to their capability of planning compl
arXiv:2605.08128v1 Announce Type: cross Abstract: Gene Regulatory Network (GRN) inference is essential for understanding complex cellular mechanisms, rendered tractable through single-cell transcripto
arXiv:2605.08741v1 Announce Type: new Abstract: Inference-time harnesses substantially improve large language models on complex reasoning tasks. However, the intrinsic capabilities of the underlying m
arXiv:2605.09734v1 Announce Type: cross Abstract: Most language-model training data shows final artifacts, not the process that produced them. We study a tractable version of this question in tool use
arXiv:2605.10705v1 Announce Type: new Abstract: Transmissive scenes are ubiquitous in daily life, yet reconstructing and rendering them remains highly challenging due to the inherent entanglement betw
arXiv:2605.08278v1 Announce Type: cross Abstract: GNNs have become a standard tool for learning on relational data, yet they remain highly vulnerable to backdoor attacks. Prior defenses often depend o
arXiv:2605.09276v1 Announce Type: cross Abstract: Spiking transformers have shown strong potential for neuromorphic vision, yet their token processing across multiple spiking steps still introduces su
arXiv:2603.29002v2 Announce Type: replace-cross Abstract: Modern large language models (LLMs) increasingly depends on efficient long-context processing and generation mechanisms, including sparse atte
arXiv:2605.08168v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models offer a promising path to generalist robot control, but their inference latency causes observation staleness when
arXiv:2511.12878v4 Announce Type: replace Abstract: Forecasting how human hands move in egocentric views is critical for applications like augmented reality and human-robot policy transfer. Recently,
arXiv:2605.05831v2 Announce Type: replace Abstract: The communication of scientific knowledge has become increasingly multimodal, spanning text, visuals, and speech through materials such as research
arXiv:2605.09692v1 Announce Type: new Abstract: Unpredictable behavior is often taken as evidence of control, yet stochastic dispersion and structured action control need not coincide. This paper test
arXiv:2605.10850v1 Announce Type: new Abstract: Self-verification, re-invoking the same vision language model (VLM) in a fresh context to check its own generated answer, is increasingly used as a defa
arXiv:2605.09677v1 Announce Type: new Abstract: Reliable displacement measurement is fundamental for structural health monitoring and digital engineering workflows, as it provides direct structural re
arXiv:2603.13224v2 Announce Type: replace-cross Abstract: Vision-to-code tasks require models to reconstruct structured visual inputs, such as charts, tables, and SVGs, into executable or structured r
arXiv:2605.09908v1 Announce Type: cross Abstract: Current approaches to detecting depression and anxiety from speech primarily rely on machine learning techniques that utilize hand-engineered paraling
arXiv:2605.08146v1 Announce Type: cross Abstract: Multi-model learning has attracted great attention in visual-text tasks. However, visual-tabular data, which plays a pivotal role in high-stakes domai
arXiv:2605.08201v1 Announce Type: cross Abstract: Neurosymbolic systems promise to combine deep neural network's (DNN) processing of raw sensor inputs with few-shot performance of symbolic artificial
arXiv:2605.08137v1 Announce Type: cross Abstract: Weight pruning is widely advocated for deploying Large Language Models on resource-constrained IoT and edge devices, yet its impact on model fairness
April brings Foundry Local GA for local AI development, GPT-5.5 model support with Tier 5 and Tier 6 default quota in Microsoft Foundry, new tracing paths for Microsoft Agent Framework and hosted agen
arXiv:2605.08318v1 Announce Type: cross Abstract: We study the problem of architecture selection for deep learning models trained to solve partial differential equations (PDEs), asking when transforme
arXiv:2605.10236v1 Announce Type: cross Abstract: Modern off-policy reinforcement learning algorithms often rely on simple uniform replay sampling and it remains unclear when and why non-uniform repla
arXiv:2605.08234v1 Announce Type: cross Abstract: Long-context LLM inference is bottlenecked by the memory and bandwidth cost of reading large KV caches during decoding. KV compression reduces this co
arXiv:2602.09789v3 Announce Type: replace Abstract: Scaling up model parameters has long been a prevalent training paradigm driven by the assumption that larger models yield superior generation capabi
arXiv:2605.10176v1 Announce Type: cross Abstract: Natural language interfaces to structured databases are becoming increasingly common, largely due to advances in large language models (LLMs) that ena
arXiv:2605.10171v1 Announce Type: cross Abstract: Scientific peer reviews frequently contain conflicting expert judgments, and the increasing scale of conference submissions makes it challenging for A
arXiv:2512.08875v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have recently demonstrated remarkable performance in generating high-quality tabular synthetic data. In practice,
arXiv:2603.10051v2 Announce Type: replace-cross Abstract: Self-supervised masked modeling shows promise for encrypted traffic classification by masking and reconstructing raw bytes. Yet recent work re
arXiv:2605.05611v2 Announce Type: replace-cross Abstract: In this paper, we present X-Voice, a 0.4B multilingual zero-shot voice cloning model that clones arbitrary voices and enables everyone to spea