Knowledge catalogue
arXiv:2608.26334v1 Announce Type: new Abstract: Automated theorem proving offers a natural foundation for recursive self-improvement in scientific discovery. However, existing neural provers do not fu
arXiv:2608.27392v1 Announce Type: cross Abstract: Camera-derived remote photoplethysmography (rPPG) is commonly validated through endpoint accuracy, but endpoint performance does not establish whether
arXiv:2508.05600v3 Announce Type: replace Abstract: Backdoor poisoning attacks are a threat to machine learning models that are trained on data collected from untrusted sources; these attacks enable a
arXiv:2608.26233v1 Announce Type: new Abstract: Extreme compression of deep neural networks, up to full binarization, dramatically reduces memory footprint and arithmetic complexity, facilitating depl
arXiv:2608.27358v1 Announce Type: cross Abstract: Influential public discourse shapes public beliefs and can also mislead, not only through what is stated, but also through how information is framed,
arXiv:2608.26216v1 Announce Type: cross Abstract: The deployment of advanced, AI-enabled control algorithms in tokamak experiments requires robust integration with existing plasma control system (PCS)
arXiv:2608.27036v1 Announce Type: new Abstract: Large Language Models (LLMs) can be trained to perform chain-of-thoughts reasoning in order to improve the reliability of their responses. In this work,
arXiv:2608.26671v1 Announce Type: new Abstract: Long autoregressive video generation faces a fundamental memory challenge: with a finite attention window, a model must decide which information from an
arXiv:2608.26120v1 Announce Type: new Abstract: Large Language Models (LLMs) are probabilistic models, typically defined by an autoregressive factorization. While recent work has begun to study richer
arXiv:2608.27407v1 Announce Type: new Abstract: Estimation of Human-Object Interactions in 3D (3D HOI) is a fundamental problem in 3D computer vision with applications in AR/VR, robotics, and embodied
arXiv:2608.27291v1 Announce Type: cross Abstract: Music recommendation relies primarily on two signals: user-item interactions, which fail in the cold-start regime, and intrinsic musical content, avai
arXiv:2402.09900v4 Announce Type: replace-cross Abstract: Memory models such as Recurrent Neural Networks (RNNs) and Transformers address Partially Observable Markov Decision Processes (POMDPs) by map
arXiv:2608.26694v1 Announce Type: new Abstract: Detecting AI-generated text (AIGT) remains challenging because existing approaches rely on token-level statistical signals or independent stylometric fe
arXiv:2608.26622v1 Announce Type: new Abstract: Achieving stable, sustained grasping with soft robotic hands remains a fundamental challenge. Compliance enables safe and adaptive contact, yet the intr
arXiv:2608.27020v1 Announce Type: cross Abstract: Hidden coordinates are not uniquely determined by a language model's input--output function, so representation-derived measurements should be invarian
arXiv:2608.27035v1 Announce Type: new Abstract: Korean constituency parsing raises a representational challenge because the terminal units of a phrase-structure tree do not straightforwardly correspon
arXiv:2608.26732v1 Announce Type: cross Abstract: Graph neural networks (GNNs) are typically conceptualized as message-passing neural networks, yet it remains unclear why neighborhood aggregation reli
arXiv:2608.26794v1 Announce Type: new Abstract: Scaling video generation to long durations reveals a critical bottleneck: current models lack robust long-term memory. This deficiency can be studied al
arXiv:2608.26649v1 Announce Type: new Abstract: Objective: Model-based closed-loop neural stimulation holds promise for therapeutic applications ranging from Parkinson's disease to sensory restoration
arXiv:2608.26956v1 Announce Type: new Abstract: Reward models play an essential role in aligning visual generative models, yet most existing visual reward models use a single scalar score or rely on f
arXiv:2608.26160v1 Announce Type: new Abstract: Nowadays semantic models provide limited support for representing distributed AI workflows and their execution across heterogeneous edge, fog, and cloud
arXiv:2608.26958v1 Announce Type: cross Abstract: Scaling model-generated data is usually viewed as improving distillation: more examples should increase coverage, reduce noise, and produce stronger s
arXiv:2608.26697v1 Announce Type: new Abstract: Synthetic speech provides scalable supervision for automatic speech recognition (ASR), but its benefit depends on the selected texts, reference speech,
arXiv:2608.27265v1 Announce Type: new Abstract: Latent chain-of-thought models move intermediate reasoning from emitted text into continuous states, improving compactness but hiding the causal object.
arXiv:2608.26748v1 Announce Type: new Abstract: Diffusion models can be used to generate spatiotemporal signals of physical phenomena, such as time-series images of fluid dynamics. However, a major li
arXiv:2509.06147v2 Announce Type: replace-cross Abstract: Ranking and selection (R&S) seeks to identify the alternative with the best mean performance from a finite collection of simulated alternative
arXiv:2608.26659v1 Announce Type: new Abstract: Recent progress in offline reinforcement learning (RL) has been driven by expressive generative actors such as diffusion and flow-matching policies, whi
arXiv:2608.27048v1 Announce Type: new Abstract: Silent speech recognition (SSR) provides an alternative communication pathway in the absence of audible speech. However, conventional approaches are lim
arXiv:2608.27364v1 Announce Type: new Abstract: We study how sophistication in generative AI (genAI) use varies among the back-office workforce of a large firm. Using proprietary data, we observe 713,
arXiv:2603.09215v2 Announce Type: replace Abstract: Interleaved spoken language models (SLMs) alternately generate text and speech tokens, but decoding at full transformer depth for every step becomes
arXiv:2608.27366v1 Announce Type: cross Abstract: Signals are short textual or visual snippets displayed on the eBay View-Item (VI) page, providing additional, contextual information for users about t
arXiv:2608.27225v1 Announce Type: cross Abstract: Effective human-robot collaboration in industrial settings requires robots to understand human intentions and assist with task planning, reducing work
arXiv:2608.26191v1 Announce Type: new Abstract: Incident risk prediction from longitudinal electronic health records (EHRs) is challenging because relevant signals are multimodal, weak in isolation, a
arXiv:2608.26374v1 Announce Type: new Abstract: Diffusion language models (DLMs) generate text by iteratively denoising masked sequences, but standard decoding either fixes the sequence length or reli
arXiv:2608.26511v1 Announce Type: new Abstract: Large language models often exhibit sycophancy, revising their answers to align with users when users push back. Such answer flips, however, can arise f
arXiv:2608.26836v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable proficiency in natural language understanding, yet they struggle with strict multi-step reason
arXiv:2608.27076v1 Announce Type: new Abstract: Algorithmic trading now represents a market exceeding $20 billion, where even marginal gains in signal robustness can translate into economically signif
arXiv:2608.27186v1 Announce Type: new Abstract: Soft actuators enable dexterous and compliant interaction, but closed-loop task-space control remains challenging due to strong nonlinearities, distribu
arXiv:2608.26613v1 Announce Type: new Abstract: This study provides a comprehensive benchmarking of conventional machine learning (ML), ensemble learning, deep neural networks, recurrent architectures
arXiv:2608.26970v1 Announce Type: cross Abstract: Place names provide persistent records of how landscapes have been described and organised, but their quantitative reuse requires explicit separation
arXiv:2608.27175v1 Announce Type: new Abstract: Many Earth Observation applications need land-use/land-cover maps that are both precise and frequently updated, yet the strongest Earth Observation foun
arXiv:2608.26821v1 Announce Type: new Abstract: Vision-language-action (VLA) models leverage pretrained vision-language representations for robot control, yet simply adding historical frames does not
arXiv:2608.26978v1 Announce Type: cross Abstract: Landscapes are named, but whether names retain measurable environmental information beyond broad geographic structure is rarely tested. We analysed 3,
arXiv:2608.26902v1 Announce Type: new Abstract: Streaming autoregressive video models generate long videos chunk by chunk, using historical memory to maintain consistency. Existing methods typically e
arXiv:2608.26624v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation (OVSS) aims to segment image regions corresponding to arbitrary text queries. Although the Segment Anything Model
arXiv:2606.22179v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as black-box classifiers in pipelines that automate confident decisions and route uncertain o
arXiv:2507.22893v3 Announce Type: replace-cross Abstract: Contemporary human-AI interaction research overlooks how AI systems fundamentally reshape human cognition pre-consciously, a critical blind sp
arXiv:2608.26148v1 Announce Type: new Abstract: Depression affects millions worldwide, yet diagnosis relies on subjective self-reports that may miss authentic behavior. This paper presents an approach
arXiv:2608.26112v1 Announce Type: new Abstract: Speculative decoding accelerates large language model inference through a draft-then-verify paradigm. Building on this, tree-structured methods improve
arXiv:2511.22273v2 Announce Type: replace-cross Abstract: Selecting the best alternative from a finite set is the central objective of ranking and selection (R&S) and best arm identification (BAI). Tr
arXiv:2602.22712v2 Announce Type: replace Abstract: Small target detection in UAV imagery faces significant challenges such as scale variations, dense distribution, and the dominance of small targets.
arXiv:2608.27372v1 Announce Type: cross Abstract: We establish a sharp phase transition for fitting random vectors by an ellipsoid. The random vectors have independent subgaussian coordinates with mea
arXiv:2608.26165v1 Announce Type: cross Abstract: Automated creativity assessment has been a long standing challenge, with traditional methods often being resource intensive or lacking practical accur
arXiv:2510.25548v3 Announce Type: replace Abstract: In task and motion planning, high-level task planning is done over an abstraction of the world to enable efficient search in long-horizon robotics p
arXiv:2608.26146v1 Announce Type: new Abstract: We present Vagdhenu, a vrutta (meter) aware shloka-to-chant system for Sanskrit: a text-to-speech system that maps a metrical verse to its chanted paray
arXiv:2608.26495v1 Announce Type: new Abstract: Multimodal queries can require different types of reasoning. Some can be answered via perceptual reasoning, extracting information directly from the vis
arXiv:2608.26998v1 Announce Type: new Abstract: Intracranial electroencephalography (iEEG) provides temporally precise and spatially specific access to neural activity from focal and deep brain region
arXiv:2608.26580v1 Announce Type: cross Abstract: Diffusion multimodal large language models (dMLLMs) have recently emerged as a new decoding paradigm for multimodal generation. Starting from a fully
arXiv:2510.25732v2 Announce Type: replace-cross Abstract: Unlearning in large language models (LLMs) is usually evaluated as whether an 'unlearned' fact can be recovered. We instead ask whether a fact
arXiv:2608.27187v1 Announce Type: new Abstract: Peer effects are difficult to estimate when interaction graphs evolve because pre-assignment network history, dynamic peer exposure, and post-assignment