Knowledge catalogue
arXiv:2605.25188v1 Announce Type: new Abstract: Multi-agent LLM systems improve reasoning by combining outputs from multiple agents, but interaction-heavy methods can introduce error propagation and h
arXiv:2409.20473v3 Announce Type: replace Abstract: Tactile sensing is critical for learning-based dexterous manipulation, yet principled guidelines for sensor placement remain largely absent. While d
arXiv:2605.25221v1 Announce Type: cross Abstract: Reservoir computing typically relies on large, randomly generated reservoirs, enabling simple, often linear readouts. Over the past two decades, most
arXiv:2605.24860v1 Announce Type: cross Abstract: Advanced driver assistance systems (ADAS) play an important role in modern automotive intelligence, significantly enhancing vehicle safety and stabili
arXiv:2605.25393v1 Announce Type: new Abstract: Large Language Models (LLMs) and Multimodal LLMs (MLLMs) have demonstrated immense potential in autonomous driving (AD) by offering human-like reasoning
arXiv:2602.18640v2 Announce Type: replace Abstract: Modern large-scale ranking systems operate within a sophisticated landscape of competing objectives, operational constraints, and evolving product r
arXiv:2605.25605v1 Announce Type: cross Abstract: In the past decade, numerous studies have applied deep neural networks (DNNs) to decode auditory attention (AAD) from Electroencephalogram (EEG) signa
arXiv:2605.24454v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong performance in the legal domain, demonstrating notable potential in Legal Question Answering (LQA). Howev
arXiv:2605.24748v1 Announce Type: cross Abstract: Understanding and forecasting the geoeffectiveness of a coronal mass ejection (CME) is crucial for protecting infrastructure in the near-Earth space e
arXiv:2605.24548v1 Announce Type: new Abstract: Time series driven by unobserved latent states frequently exhibit abrupt jump discontinuities whose timing and magnitude cannot be predicted from observ
arXiv:2510.08350v3 Announce Type: replace-cross Abstract: Objective: Enteral nutrition (EN) delivery in the ICU remains suboptimal due to limited personalization and uncertainty regarding appropriate
arXiv:2605.25527v1 Announce Type: new Abstract: This paper studies reinforcement learning for high-frequency trading on limit order books by pairing an Order-Flow-based state model with policy-gradien
arXiv:2605.25749v1 Announce Type: cross Abstract: In multi-stage recommender systems, reranking optimizes overall utility by capturing intra-list contextual dependencies, yet its central challenge lie
arXiv:2410.01648v2 Announce Type: replace Abstract: The increasing availability of sensitive textual data has created an urgent need for robust de-identification methods that enable compliant data sha
arXiv:2601.16091v2 Announce Type: replace-cross Abstract: Clustering is a fundamental problem, aiming to partition a set of elements, like agents or data points, into clusters such that elements in th
arXiv:2510.11296v3 Announce Type: replace-cross Abstract: Recent approaches for vision-language models (VLMs) have shown remarkable success in achieving fast downstream adaptation. When applied to rea
arXiv:2605.24539v1 Announce Type: new Abstract: Agent harness evolution improves frozen language-model agents by modifying the executable structures around them. We study this paradigm as a form of sa
arXiv:2605.24632v1 Announce Type: cross Abstract: Recent demonstrations of large language models producing candidate and confirmed vulnerabilities in production software have renewed the narrative tha
arXiv:2605.25997v1 Announce Type: new Abstract: Benchmarks increasingly guide deployment, procurement and scientific screening, yet a score supports only the response it records, not necessarily the d
arXiv:2403.06636v2 Announce Type: replace Abstract: In recent years, multimodal locomotion capabilities have enabled robots to maneuver in both terrestrial and aerial domains. However, most of these r
arXiv:2508.11872v3 Announce Type: replace-cross Abstract: Traditional syllabi often function as static reference documents rather than engaging introductions to a course. In practical teaching, we obs
arXiv:2605.24136v1 Announce Type: cross Abstract: We study the problem of identifying dynamically distinct basins of attraction in high dimensional time-homogeneous Markov processes using only traject
arXiv:2605.25603v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning improves the problem-solving ability of large language models (LLMs), but generated reasoning traces may not faithfully
arXiv:2605.24001v1 Announce Type: cross Abstract: Recent advances in one-step text-to-image generation have enabled real-time synthesis with remarkable efficiency and quality. Previous reinforcement l
arXiv:2605.25452v1 Announce Type: cross Abstract: Graph Neural Networks (GNN) are currently the most popular approach for learning and prediction on graph-structured data and are deployed in various f
arXiv:2605.23975v1 Announce Type: new Abstract: Audio large language models (Audio LLMs) exhibit systematic failures in transcribing code-switching speech despite strong multilingual capabilities. Foc
arXiv:2605.25189v1 Announce Type: cross Abstract: Reward hacking arises when a model improves a proxy reward by exploiting shortcuts rather than solving the intended task. We study this failure mode t
arXiv:2603.18444v2 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as an effective post-training paradigm for improving the reasoning capabilit
arXiv:2605.24310v1 Announce Type: new Abstract: Lexical gaps are words that do not exist in certain languages. They pose challenges for building multilingual lexical resources, for machine translation
arXiv:2605.26087v1 Announce Type: cross Abstract: Frontier LLMs now perform strongly across a wide range of physics evaluations, but it is hard to disentangle genuine reasoning from recall of establis
arXiv:2602.05961v2 Announce Type: replace Abstract: Sampling from a distribution p(x) propto e^{-E(x)} known up to a normalising constant is an important and challenging problem in statistics. Recent
arXiv:2605.24639v1 Announce Type: cross Abstract: With the widespread application of drones in recent years, object detection of aerial images has attracted increasing attention, especially open-vocab
arXiv:2605.24808v1 Announce Type: cross Abstract: Confounding bias is a key challenge in causal effect estimation from observational data. Double Machine Learning (DML) addresses this issue by estimat
arXiv:2605.24375v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown great ability in generating executable code from natural language, opening the possibility of automatically cons
arXiv:2605.24344v1 Announce Type: new Abstract: Research on harmful meme detection has garnered significant attention, resulting in the development of numerous datasets and methods. However, progress
arXiv:2512.19097v3 Announce Type: replace-cross Abstract: Intracranial EEG (iEEG) provides direct, millisecond-scale recordings of human neural activity, but reusable representation learning is diffic
arXiv:2601.21670v3 Announce Type: replace-cross Abstract: Multimodal fusion is often treated as an optimization-balancing problem, where training signals are adjusted to prevent one modality from domi
arXiv:2605.24799v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated strong capabilities across a wide range of vision language tasks. However, when applied to
arXiv:2603.17044v2 Announce Type: replace-cross Abstract: Unified multimodal models share a language model backbone for both understanding and generating images. Can DPO align both capabilities simult
arXiv:2605.23910v1 Announce Type: cross Abstract: Information fusion is used widely to improve document classification by the integration of multiple data sources (multimodal) or representations (mult
arXiv:2605.25924v1 Announce Type: new Abstract: Recent automated essay scoring (AES) studies increasingly use pretrained transformer models, but these models are usually pretrained on general-domain E
arXiv:2605.25681v1 Announce Type: cross Abstract: Designing a single molecule that modulates two targets is a promising strategy for polypharmacology, but it remains substantially harder than standard
arXiv:2011.10396v3 Announce Type: replace-cross Abstract: Multi-view clustering has been applied in many real-world applications where original data often contain noises. Some graph-based multi-view c
arXiv:2605.25781v1 Announce Type: new Abstract: Evaluating structured-information extraction from historical documents at scale requires high-precision ground-truth annotations, yet traditional manual
arXiv:2605.24761v1 Announce Type: cross Abstract: We propose Drift-Resistant Navigation World Model, a generative model that mitigates both perceptual drift and geometric drift in conventional rollout
arXiv:2605.24841v1 Announce Type: new Abstract: Property-conditional molecular generation should produce valid, diverse molecules while responding to continuous target values at low sampling cost. We
arXiv:2605.24267v1 Announce Type: new Abstract: Human conversation relies heavily on conversational implicature, in which speakers convey meanings that are suggested rather than explicitly stated. Alt
arXiv:2605.23939v1 Announce Type: new Abstract: Web agents require both high-level reasoning (for task decomposition) and low-level interactions (for page elements manipulation) to conduct different t
arXiv:2601.21726v2 Announce Type: replace Abstract: Deep time series models are vulnerable to noisy data ubiquitous in real-world applications. Existing robustness strategies either prune data or rely
arXiv:2605.26038v1 Announce Type: cross Abstract: Lightweight vision-language models perform competitively on standard benchmarks yet fail systematically in dense-scene reasoning, where multiple objec
arXiv:2605.24885v1 Announce Type: new Abstract: Counterfactual story rewriting is a natural language processing task that requires updating an existing story to reflect a chosen alternative event, yet
arXiv:2605.24794v1 Announce Type: cross Abstract: Reinforcement learning (RL) has emerged as an effective paradigm for improving the reasoning capability of vision-language models (VLMs). However, RL-
arXiv:2605.25604v1 Announce Type: new Abstract: Reinforcement Learning has become a standard paradigm for aligning Large Language Models with human intent and task requirements. While Group Relative P
arXiv:2603.28716v2 Announce Type: replace Abstract: Agentic RL can benefit substantially from reusable experience, yet existing skill-based methods mainly extract trajectory-level guidance and often l
arXiv:2605.24924v1 Announce Type: new Abstract: Diffusion models excel at generating diverse and multimodal trajectories for robotic planning, yet their iterative denoising process introduces latency
arXiv:2505.18979v2 Announce Type: replace Abstract: Text-to-image (T2I) models can generate not-safe-for-work (NSFW) content, motivating multi-stage safety pipelines with both text and image filters.
arXiv:2509.12196v2 Announce Type: replace-cross Abstract: Standard attention mechanisms in transformers employ static token representations that remain unchanged across all pair-wise computations in e
arXiv:2601.21601v2 Announce Type: replace-cross Abstract: Neural networks adapt through first-order parameter updates, yet it remains unclear whether such updates preserve logical coherence. We invest
arXiv:2512.11941v2 Announce Type: replace-cross Abstract: Zero-shot skeleton-based action recognition (ZS-SAR) is fundamentally constrained by prevailing approaches that rely on aligning skeleton feat
arXiv:2605.06415v2 Announce Type: replace-cross Abstract: We introduce E = T*H/(O+B), a dimensionless control parameter that predicts whether Mixture-of-Experts (MoE) models will develop a healthy exp