Knowledge catalogue
arXiv:2610.06170v2 Announce Type: replace Abstract: Biomedical large language model (LLM) evaluation requires auditable assessment of narrow, evolving, source-grounded subspecialty knowledge. Multiple
arXiv:2610.19252v1 Announce Type: cross Abstract: Fairness audits with several sensitive attributes are often performed one coordinate at a time. This can miss purely intersectional disparities: a sco
Jeffrey Kampman / Tom's Hardware: Nvidia's RTX Spark Superchip in the Surface Laptop Ultra outperforms Apple's M5 Max MacBook Pro by ~25% in agentic AI completion benchmarks using Qwen models — Laying
arXiv:2610.02022v2 Announce Type: replace Abstract: AI-based ideation systems are often evaluated on the novelty of their ideas, and that judgment is increasingly delegated to large language models. S
arXiv:2610.19123v1 Announce Type: new Abstract: Generating novel molecules with desired properties is a central challenge in drug discovery. Although foundational work focused on de novo generation, p
arXiv:2606.18060v2 Announce Type: replace Abstract: As Large Language Model based agents enter autonomous scientific research, their ability to resist pseudoscience becomes increasingly important. Oth
arXiv:2601.08689v3 Announce Type: replace Abstract: Large Language Models (LLMs) have shown strong capabilities in finance-oriented question answering, yet their evaluation on professional quantitativ
arXiv:2610.19385v1 Announce Type: new Abstract: Large Language Model (LLM) unlearning is often evaluated primarily by target suppression and clean retain utility. These metrics can miss a local failur
arXiv:2610.15642v1 Announce Type: new Abstract: Under a limited context budget, a long-term agent must recall not merely memories that are individually relevant, but evidence that is collectively suff
arXiv:2510.23160v2 Announce Type: replace Abstract: Data selection for supervised fine-tuning (SFT) often discards low-scoring instruction examples that may retain useful supervision. We propose a rul
arXiv:2610.19246v1 Announce Type: new Abstract: Equivariant structure-prediction pipelines alternate coordinate updates with the construction of a k-nearest-neighbour (kNN) graph whose membership is a
arXiv:2610.20243v1 Announce Type: new Abstract: Anomaly detection is a fundamental task for time-series analytics, with important implications for the downstream performance of many applications. Desp
arXiv:2610.20174v1 Announce Type: cross Abstract: Reinforcement Learning on Mixed-domain preference data (Mix-RL) has become the mainstream approach for training general-purpose Generative Reward Mode
arXiv:2610.17820v1 Announce Type: cross Abstract: TypeSafe AI's Jev is a new kind of safety judge. It generates no text and no label: it answers questions the developer defines in advance and returns
arXiv:2610.20457v1 Announce Type: new Abstract: Co-scientists and agentic scientific claim verifiers are increasingly used to assess claims across the sciences. Natural-language scientific claims, wri
arXiv:2610.20563v1 Announce Type: cross Abstract: Automated research with LLM agents has shown promising results in various domains. Through iterative refinement and scored verifiers, LLM agents are c
arXiv:2610.20221v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed across languages, yet their safety and reasoning capabilities remain predominantly evaluated in E
arXiv:2610.17436v1 Announce Type: new Abstract: Large language models (LLMs) on mobile devices can act as agents that call external software tools to complete complex tasks, but the major challenge of
arXiv:2610.14815v1 Announce Type: cross Abstract: Industrial IoT sensors produce continuous, high-volume data streams that predictive maintenance needs to analyse in real time. Cloud-centric architect
arXiv:2610.20136v1 Announce Type: new Abstract: Vision-language models (VLMs) are moving onto edge devices, but little is known about how much energy they draw on single-board computers with NPU or GP
arXiv:2610.19366v1 Announce Type: cross Abstract: Multimodal models and agents have emerged to enable AI systems to learn from and interact with the real world more like humans by integrating informat
arXiv:2610.14209v1 Announce Type: new Abstract: The most accurate agents executing user-provided tasks are driven by large, cloud-hosted models, which solve them by reading the private data of the use
arXiv:2507.13638v4 Announce Type: replace-cross Abstract: A grand challenge in modern neuroscience is bridging the gap between the microscale neural circuits and cognitive functions. While extensive k
arXiv:2610.19187v1 Announce Type: cross Abstract: Full waveform inversion (FWI) is a powerful tool for high-resolution subsurface model reconstruction, but it remains highly nonlinear and ill-posed, e
arXiv:2610.16636v1 Announce Type: cross Abstract: State space models provide efficient linear-complexity processing for long sequences, but their performance frequently degrades as context length grow
arXiv:2610.18933v1 Announce Type: cross Abstract: Modern coding agents are fundamentally reshaping the role of human input for computational research: they write code, launch in silico experiments, an
arXiv:2610.14984v1 Announce Type: cross Abstract: Generative AI makes options cheap and shifts the bottleneck of creative work from generating options to screening them. We argue that AI's value depen
arXiv:2610.20490v1 Announce Type: new Abstract: Certifiable optimization provides global optimality guarantees for non-convex state estimation in robotics. However, under severe physical sensing degen
arXiv:2610.15055v1 Announce Type: new Abstract: Self-evolving agents are usually scored by endpoint Gain: held-out success after an update minus success before. That difference can credit a frozen res
arXiv:2512.10309v3 Announce Type: replace-cross Abstract: Chemical reaction networks are widely used to model stochastic dynamics in biophysical processes. Their exact probabilistic description via th
arXiv:2610.16887v1 Announce Type: new Abstract: Transparency in AI-supported decision systems is widely required but poorly defined. Existing approaches treat transparency as a property of systems or
arXiv:2610.19536v1 Announce Type: new Abstract: Typed decision models are language models that answer a question with a fixed set of possible answers by returning a probability for each answer instead
arXiv:2610.14939v1 Announce Type: new Abstract: GRPO-style reinforcement learning has advanced vision-language reasoning, but its sequence-level rewards provide limited credit assignment for individua
arXiv:2511.09568v3 Announce Type: replace-cross Abstract: Diffusion models show promise for 3D molecular generation, but face a fundamental trade-off between sampling efficiency and conformational acc
arXiv:2603.13509v2 Announce Type: replace-cross Abstract: Differential-algebraic equations (DAEs) arise in power networks, chemical processes, and multibody systems, where algebraic constraints encode
arXiv:2610.19424v1 Announce Type: new Abstract: LLM system capabilities depend not only on model parameters but also on the executable harnesses that organize information acquisition, tool use, reason
arXiv:2606.22936v2 Announce Type: replace Abstract: Tool-using LLM agents make early choices about what to search for, which evidence to trust, and what to do next. A wrong early choice can steer ever
arXiv:2610.16039v1 Announce Type: cross Abstract: Multi-agent LLM systems (MALS) introduce a privacy-leakage problem that conventional sensitivity-based defenses miss, since information disclosed to o
arXiv:2610.20342v1 Announce Type: new Abstract: Missing data is a pervasive challenge in machine learning, often degrading predictive performance and complicating model deployment. Most existing appro
arXiv:2610.18108v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as judges to predict which of two candidate answers a human evaluator would prefer, providing a scala
arXiv:2610.15666v1 Announce Type: new Abstract: Converting natural-language instructions into executable action plans conditioned on a scene image is a core capability for embodied human-robot interac
arXiv:2610.15723v1 Announce Type: cross Abstract: We study adaptive confidence sets for mean vectors of multivariate Gaussians with identity covariance under Huber contamination when the contamination
arXiv:2603.17712v1 Announce Type: cross Abstract: Zero-Shot Object Navigation (ZSON) in unknown multi-floor environments presents a significant challenge. Recent methods, mostly based on semantic valu
arXiv:2610.17832v1 Announce Type: new Abstract: Materials and chemical optimization must choose the next synthesis, characterization, or high-fidelity simulation from sequential observations under a l
arXiv:2610.14063v1 Announce Type: new Abstract: LLM program-search agents are usually judged by the score that drives their search. We study LLM-driven search over Python exercise policies for Bermuda
arXiv:2610.16330v1 Announce Type: new Abstract: Feed-forward transformer models have driven remarkable progress in 3D vision,yet their quadratic complexity renders them impractical for long video sequ
arXiv:2610.18161v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated strong zero-shot reasoning across diverse domains, but their potential for Human-Object Inter
arXiv:2610.17802v1 Announce Type: cross Abstract: Robot manipulation has traditionally relied on end-effector-centric interactions. In contrast, humans routinely exploit whole-arm contacts to improve
arXiv:2610.15077v1 Announce Type: new Abstract: A brain tumor is an unusual growth of cells in the brain that can be cancerous or not, affecting normal brain function and causing serious risk to healt
arXiv:2610.14746v1 Announce Type: cross Abstract: World Action Models (WAMs) commonly couple video and action experts through dense, layer-wise Mixture-of-Transformers (MoT) attention, exposing action
arXiv:2610.14647v1 Announce Type: new Abstract: Synthetic UAV imagery can expand pre-deployment test evidence when task-specific Real data are scarce, but visual plausibility or semantic label alignme
arXiv:2610.14226v1 Announce Type: new Abstract: Purpose : To develop and evaluate CAPTOR, a deep-learning vision-language foundation model for oncologic chest-abdomen-pelvis (CAP) CT interpretation. M
arXiv:2610.14856v1 Announce Type: new Abstract: This paper studies the problem of recovering motion from monocular video, even when the target skeleton topology differs from the subject observed in th
arXiv:2610.14957v1 Announce Type: new Abstract: Recent video generative models have shown strong capability in synthesizing high-quality and temporally coherent videos. Yet, adapting them for controll
arXiv:2610.14413v1 Announce Type: new Abstract: Computational coarse-graining aims to replace a sequence of updates with a cheaper module while preserving task performance. We present a continuation-r
arXiv:2610.14941v1 Announce Type: new Abstract: Diffusion-based policies have recently emerged as a powerful paradigm for offline reinforcement learning, but their reliance on multi-step denoising int
arXiv:2610.14342v1 Announce Type: new Abstract: We present ContinuousCloth, a continuous-time, physics-informed neural solver for deformable cloth dynamics that enforces a spring--mass ODE within a ne
arXiv:2610.17098v1 Announce Type: new Abstract: The incorporation of footage from other sources has long been a concern from a copyright perspective, as such usage can harm the interests of rights hol
arXiv:2610.18194v1 Announce Type: new Abstract: Robotic manipulation systems commonly learn object affordances from human-annotated datasets, which reflect interaction possibilities for a human hand r
arXiv:2610.16956v1 Announce Type: new Abstract: Historical document analysis is crucial for cultural heritage preservation and low-resource language digitization. However, real historical documents of