Knowledge catalogue
arXiv:2510.19225v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become essential for unlocking advanced reasoning capabilities in large language models (LLMs). RL workflows i
arXiv:2604.07753v1 Announce Type: cross Abstract: Empowering Large Multimodal Models (LMMs) with image generation often leads to catastrophic forgetting in understanding tasks due to severe gradient c
arXiv:2604.07916v1 Announce Type: new Abstract: Referring Expression Segmentation (RES) aims to segment image regions described by natural-language expressions, serving as a bridge between vision and
arXiv:2604.07801v1 Announce Type: new Abstract: Large language models are trained and evaluated on quantitative reasoning tasks written in clean, emotionally neutral language. However, real-world quer
arXiv:2604.07267v1 Announce Type: cross Abstract: Gaussian process (GP) regression is a widely used non-parametric modeling tool, but its cubic complexity in the training size limits its use on mass
arXiv:2601.21463v2 Announce Type: replace-cross Abstract: Existing speech editing detection (SED) datasets are predominantly constructed using manual splicing or limited editing operations, resulting
arXiv:2510.23642v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have recently enabled coding agents capable of generating, executing, and revising visualization code. However, e
arXiv:2604.07634v1 Announce Type: new Abstract: Streaming vision-language models (VLMs) continuously generate responses given an instruction prompt and an online stream of input frames. This is a core
arXiv:2602.22220v2 Announce Type: replace-cross Abstract: Quotation recommendation aims to enrich writing by suggesting quotes that complement a given context, yet existing systems mostly optimize sur
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arXiv:2604.07834v1 Announce Type: new Abstract: This paper presents an LLM-driven approach for constructing diverse social media datasets to measure and compare loneliness in the caregiver and non-car
FlowInOne is a vision-centric multimodal image generation framework that reformulates multimodal generation as a purely visual flow, converting all inputs into visual prompts and enabling a clean i...
OpenAI's Child Safety Blueprint, released in April 2026, is a policy framework aimed at combating the rise of AI-enabled child sexual exploitation by combining legal, operational, and technical app...
Vultr has upgraded its Identity and Access Management (IAM) framework by replacing legacy ACLs with fine-grained, policy-driven access controls, enabling organizations to assign roles, manage permi...
Nous Research's **Hermes Agent** is an open-source agentic framework by NousResearch that delivers significantly improved reliability when using GPT-5.x or Codex (OpenAI Codex subscription) models ...
arXiv:2610.20630v1 Announce Type: new Abstract: The development of Financial Natural Language Processing (NLP) systems for Brazilian Portuguese is hindered by the scarcity of domain-specific datasets.
The drumbeat over how safe artificial intelligence is — or more to the point, isn’t — just keeps louder. In the wake of weeks of incidents involving rogue agents, with likely more to come, more govern
arXiv:2606.17090v2 Announce Type: replace-cross Abstract: ANEForge is a Python package that programs the Apple Neural Engine (ANE), the fixed-function neural accelerator on every recent Apple device,
arXiv:2610.21882v1 Announce Type: cross Abstract: Embodied spatial intelligence requires more than visual recognition: models must localize entities, reason about their relations, and express this kno
arXiv:2610.22277v1 Announce Type: cross Abstract: When robots are deployed in the real-world, they must interact with other agents and accomplish their own goals while adhering to legal constraints. T
arXiv:2610.21586v1 Announce Type: cross Abstract: Indoor service robots often enter unmapped buildings whose layouts and task constraints are already familiar to users. We address this human-familiar,
arXiv:2610.21095v1 Announce Type: new Abstract: World and action modeling are closely related, yet their reciprocal benefits under unified causal training remain unclear. We introduce OneWM, a simple
arXiv:2610.20868v1 Announce Type: new Abstract: Biological age (BA) captures inter-individual variation in physiological aging beyond chronological age (CA). Although machine-learning models have adva
arXiv:2610.21364v1 Announce Type: new Abstract: One-shot Font Generation (OFG) aims to synthesize a target glyph by transferring the stylistic characteristics of a single reference glyph while preserv
arXiv:2610.20877v1 Announce Type: cross Abstract: Early detection of mild cognitive impairment remains a critical bottleneck in dementia care, demanding scalable tools capable of capturing subtle beha
arXiv:2610.21825v1 Announce Type: cross Abstract: Aggressive quantization of neural networks can expose activation functions as bottlenecks for efficient hardware acceleration. Implementing quantized
arXiv:2610.22032v1 Announce Type: cross Abstract: Standard Bayesian experimental design (BED) methods assume the experimenter alone will act on the data, choosing the best action given their own belie
arXiv:2610.22340v1 Announce Type: cross Abstract: How can robots autonomously collect the contact-rich experience needed to learn dexterous manipulation? We present DSP: Dexterity from supervised play
arXiv:2610.22367v1 Announce Type: cross Abstract: Significant efforts are currently being directed toward the implementation of large-scale quantum information processors, but realizing useful quantum
arXiv:2610.21281v1 Announce Type: new Abstract: Root-cause analysis (RCA) in large-scale distributed systems is challenging because a single symptom can cross service, data, configuration, compute, an
arXiv:2603.20804v2 Announce Type: replace Abstract: Vision-Language Navigation (VLN) systems are fundamentally constrained by partial observability, as an agent can only accumulate knowledge from loca
arXiv:2610.20893v1 Announce Type: cross Abstract: Estimating causal effects on right-censored time-to-event outcomes is challenging under unmeasured confounding. Proximal causal inference addresses un
arXiv:2603.15270v3 Announce Type: replace-cross Abstract: Medical coding, which assigns standardized ICD codes to clinical documents, is a fundamental yet challenging task in modern healthcare. It req
arXiv:2610.21452v1 Announce Type: cross Abstract: Human face-to-face conversation requires coordinating speech, gestures and expressions across both speaking and listening. We present ELLSA 2, an end-
arXiv:2510.26309v2 Announce Type: replace Abstract: Regulatory compliance is an important requirement in many industrial and administrative settings. Yet compliance assessment remains difficult becaus
arXiv:2509.16998v2 Announce Type: replace Abstract: Design for Robotic Assembly (DfRA) remains largely dependent on manual planning and heuristic simulation, limiting scalability and robustness in com
arXiv:2610.21411v1 Announce Type: new Abstract: Multi-agent LLM systems rely on generative models for expert execution, workflow planning, and artifact review. Yet many coordination operations require
arXiv:2610.21414v1 Announce Type: new Abstract: Hierarchical forecasting exploits aggregation structures that expose multiple views of the same underlying spatiotemporal forecasting problem, while ens
arXiv:2606.13220v2 Announce Type: replace Abstract: Large language models (LLMs) are used for technical troubleshooting, where problem descriptions are often incomplete and multiple causes can explain
arXiv:2607.18253v2 Announce Type: replace Abstract: Large language model (LLM) query routers typically assign each query to a suitable model that balances response accuracy and monetary cost. Subseque
arXiv:2507.18903v2 Announce Type: replace-cross Abstract: Causal identification asks whether a causal target can be recovered from the observational distribution in an idealized infinite-data setting.
arXiv:2610.20881v1 Announce Type: new Abstract: While foundation models and vision transformers (ViTs) have redefined visual representations, current active learning (AL) strategies for these backbone
arXiv:2610.21764v1 Announce Type: new Abstract: A fixed discount factor in Q-learning imposes a single planning horizon throughout training, despite the differing demands of early exploration and long
arXiv:2610.21344v1 Announce Type: new Abstract: Resting-state functional connectivity is widely used in machine learning-based classification of neuropsychiatric disorders. Quantum machine learning mo
arXiv:2606.09449v2 Announce Type: replace Abstract: Autoformalization (AF) requires both prover acceptance and fidelity to an informal source, but prover acceptance alone cannot establish fidelity, an
arXiv:2610.21640v1 Announce Type: new Abstract: Reinforcement learning (RL) has been a critical paradigm for improving large language model (LLM) reasoning and agentic capabilities, but its efficiency
arXiv:2505.15737v3 Announce Type: replace Abstract: Reconstructing high-fidelity underwater scenes remains a challenging task due to light absorption, scattering, and limited visibility inherent in aq
arXiv:2610.22225v1 Announce Type: new Abstract: Propulsive drones are a promising new technology for generating high-resolution terrain models of the Moon. In future lunar missions, propulsive drones
arXiv:2610.20959v1 Announce Type: cross Abstract: Answering questions over heterogeneous data means combining evidence from files, databases, documents and the web. Data agents automate this, but thei
arXiv:2610.22120v1 Announce Type: new Abstract: Knowing the layout of the physical world matters for many applications, yet the sensors we rely on for it (e.g., cameras and LiDAR) can fail in darkness
arXiv:2610.21082v1 Announce Type: new Abstract: Large language model (LLM) agents can keep scientific experiments running for months with little human supervision, but reliable autonomous research als
arXiv:2610.21393v1 Announce Type: new Abstract: Vehicle Routing Problems (VRPs) are fundamental to many real-world applications. Recent advances in large language models (LLMs) have spurred progress i
arXiv:2610.21440v1 Announce Type: new Abstract: In automatic speech recognition (ASR), transcription errors with similar lexical or semantic discrepancies can have markedly different consequences for
arXiv:2610.21913v1 Announce Type: new Abstract: Continuous physical human-robot interaction requires robots to rapidly adapt their control behaviour without interrupting task execution. However, exist
arXiv:2610.20906v1 Announce Type: cross Abstract: Dementia affects millions of older adults worldwide, yet early detection remains difficult outside clinical settings. Traditional models primarily rel
arXiv:2610.21845v1 Announce Type: new Abstract: Implicit Neural Representations (INRs) have recently gained popularity for their ability to model functions using simple and lightweight networks. These
arXiv:2610.21646v1 Announce Type: new Abstract: With the rise of large language models, agentic systems are becoming increasingly autonomous, as they write code, call tools, revise prompts, and propos
arXiv:2610.21908v1 Announce Type: new Abstract: Many modern learning systems are used not to select a single best action, but to sample diverse high-quality outputs. Examples include entropy-regulariz
arXiv:2602.23959v2 Announce Type: replace Abstract: Recent MLLMs increasingly rely on visual chain-of-thought to perform region-grounded reasoning over images. However, existing approaches ground regi
arXiv:2610.21477v1 Announce Type: new Abstract: Medical calculators translate raw patient observations into clinically actionable values and scores. However, applying these calculators directly to fre