Knowledge catalogue
arXiv:2607.23346v1 Announce Type: new Abstract: Modern deep neural networks are potent catalysts for scientific and industrial impact, yet excessive parameter counts impede deployment in low-compute s
arXiv:2607.23123v1 Announce Type: new Abstract: Existing evaluations of large language models cover knowledge, reasoning, coding, and tool use, but they rarely treat a verifiable deliverable produced
arXiv:2603.20248v2 Announce Type: replace-cross Abstract: AI systems are increasingly entrenched in public governance, yet scholarship lacks formal tools to determine when deviations of public trust i
arXiv:2607.23341v1 Announce Type: cross Abstract: Pretrained deep denoisers can be used to solve a wide range of model-based image reconstruction tasks via Plug-and-Play (PnP) and Regularization-by-De
arXiv:2607.24686v1 Announce Type: cross Abstract: Variational quantum circuits have been central to many proposed near-term applications of quantum computing, but a growing body of evidence suggests t
arXiv:2607.23284v1 Announce Type: new Abstract: Automated sleep staging is increasingly used in large-scale studies to derive sleep-architecture endpoints: total sleep time, REM latency, sleep efficie
arXiv:2607.22649v1 Announce Type: new Abstract: Following complex instructions with multiple explicit constraints remains a fundamental challenge for large language models (LLMs). Existing alignment m
arXiv:2607.22658v1 Announce Type: new Abstract: Speech-to-speech dialogue models increasingly depend on prosody and interactional nuance to convey social intent, yet benchmarks for these cues remain l
arXiv:2607.24191v1 Announce Type: cross Abstract: Conversational stance detection has shifted from static text analysis to dynamic multimodal modeling. However, existing benchmarks exhibit three key l
arXiv:2603.14382v2 Announce Type: replace Abstract: As AI systems are being integrated more rapidly into diverse and complex real-world environments, the ability to perform holistic reasoning over an
arXiv:2607.22798v1 Announce Type: cross Abstract: Computer-use agents are usually improved by strengthening perception: better models for reading a screenshot and choosing where to click. Yet a screen
arXiv:2607.23273v1 Announce Type: cross Abstract: Computational nutrition needs precise ingredient data, but current databases are incomplete, inconsistent, and built for human reference rather than a
arXiv:2607.23840v1 Announce Type: new Abstract: Facial movement and expression are central to face-to-face communication, conveying turn-taking, attention, agreement, and engagement alongside speech.
arXiv:2505.04260v3 Announce Type: replace-cross Abstract: Personalizing LLM responses typically requires users to articulate their preferences through prompting, which can be burdensome at cold start
arXiv:2607.22708v1 Announce Type: new Abstract: Deploying a vision-language model with full UI understanding on end devices has long been trapped between accuracy and efficiency: on one side is the ac
arXiv:2607.24393v1 Announce Type: cross Abstract: Finite-time driving of stochastic systems generates excess dissipation, causing the evolving probability distribution to lag behind the instantaneous
arXiv:2607.24519v1 Announce Type: cross Abstract: Pretrained EEG foundation models are increasingly proposed for clinical decoding, but their transfer across populations and robustness to negative con
arXiv:2607.23045v1 Announce Type: new Abstract: AI is evaluated through knowledge, reasoning and plan generation, yet scientific agency requires reliable physical action and adaptation to evidence. He
arXiv:2607.24009v1 Announce Type: new Abstract: Matricized low-rank approximation via SVD is a standard surrogate for tensor decompositions, but entry-wise reconstruction error fails to capture multiw
arXiv:2607.22725v1 Announce Type: cross Abstract: Data augmentation is routinely used to improve generalization in image classification, but the assumptions underlying standard policies are poorly mat
arXiv:2607.23077v1 Announce Type: new Abstract: Modeling multi-entity temporal data requires capturing dependencies across entities, time, and their interactions. Transformer-based approaches perform
arXiv:2607.22592v1 Announce Type: new Abstract: Graph-based retrieval-augmented generation (GraphRAG) grounds answers in structured knowledge, but current systems extract entities and relationships ex
arXiv:2603.27577v2 Announce Type: replace Abstract: Vision-Language Navigation (VLN) requires an embodied agent to navigate complex environments by following natural language instructions, which typic
arXiv:2607.23046v1 Announce Type: new Abstract: Recent high-resolution Multimodal Large Language Models (MLLMs) generate thousands of visual tokens per input, leading to a visual token explosion that
arXiv:2607.22778v1 Announce Type: cross Abstract: Robust EEG motor imagery decoding remains limited by strong inter-individual variability, making it difficult to identify pipelines that generalize ac
arXiv:2607.24054v1 Announce Type: new Abstract: A correct answer can conceal why an agent succeeded. Once agents change their information state during evaluation, correctness no longer distinguishes i
Super impressed with @huggingface's breakdown of the AI agent autonomous cyber attack from OpenAI, including technical timeline, & interactive replay (AND how they defended against the attack)! Here i
arXiv:2607.24288v1 Announce Type: new Abstract: Quadratic unconstrained binary optimization (QUBO) has emerged as a powerful framework for medical computing problems. Binary decision variables natural
arXiv:2607.24206v1 Announce Type: cross Abstract: The introduction of new technologies, such as surgical robots, is driving the vision of a connected, smart operating room (OR). However, realizing thi
Hi everyone! We’ve just released a major update to the leaderboard! We are expanding beyond Python with a new multilingual slice featuring real-world software engineering tasks across 5 languages. Ope
arXiv:2606.18816v2 Announce Type: replace-cross Abstract: Hybrid brain-computer interfaces (BCIs) that integrate motor imagery (MI) and steady-state visual evoked potentials (SSVEP) provide high-dimen
arXiv:2607.23055v1 Announce Type: new Abstract: Chain-of-thought (CoT) prompting can fail severely on constraint-dense logical reasoning tasks, where unverified errors accumulate silently across steps
arXiv:2607.22563v1 Announce Type: new Abstract: Industrial agent benchmarks require realistic evaluation scenarios that integrate telemetry, failure modes, maintenance records, and domain standards. H
arXiv:2607.23991v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly controlled through system prompts that specify roles, styles, formats, and safety requirements. However,
arXiv:2607.24515v1 Announce Type: new Abstract: The challenge of Machine Translation for low resource languages such as Tamil is primarily caused by the restricted amount of parallel data for these la
arXiv:2509.06278v4 Announce Type: replace Abstract: Table reasoning requires models to jointly perform comprehensive semantic understanding and precise numerical operations. Although recent large lang
arXiv:2607.23976v1 Announce Type: cross Abstract: Appending a two-word confirmation tag to a decision question -- 'Is X the better choice?' versus 'X is the better choice, right?' -- changes whether a
arXiv:2510.12993v3 Announce Type: replace Abstract: Large Language Models (LLMs) can generate highly persuasive disinformation, yet little is known about how effectively they personalise it across lan
arXiv:2607.24359v1 Announce Type: new Abstract: Real-time long-form digital-human generation relies on causal models to extend audio-visual content while preserving subject appearance and audio-video
arXiv:2607.24539v1 Announce Type: new Abstract: Multimodal large language models (LLMs) can combine topology, measurements, and incident text for grid diagnosis, yet answer accuracy does not establish
arXiv:2607.24304v1 Announce Type: cross Abstract: We formulate a statistical physics framework to model a networked stochastic dynamical system exhibiting bistability, driven by additive noise and soc
arXiv:2607.24130v1 Announce Type: cross Abstract: Tabular data is the dominant structured-data modality, and learning table representations has become a core research direction. Table-level embeddings
arXiv:2607.22575v1 Announce Type: new Abstract: Human episodic memory supports the retrieval of experiences that unfold over extended timescales, yet the computational mechanisms underlying this abili
arXiv:2607.24611v1 Announce Type: new Abstract: Deep learning models have achieved state-of-the-art performance in several computer vision tasks. However, they experience severe performance degradatio
arXiv:2607.22697v1 Announce Type: new Abstract: Deployed AI systems are often trained from broad candidate data pools, necessitating data curation towards the deployment test distribution. However, st
arXiv:2607.22674v1 Announce Type: cross Abstract: Tactile graphics are a primary medium for blind and low-vision (BLV) individuals to access non-textual information. However, they are difficult to sca
arXiv:2606.19259v2 Announce Type: replace-cross Abstract: Text-rich images often contain privacy-sensitive, transactional, or decision-relevant information. As recent multimodal image generation model
The administration is very consistent: All aspects of foreign involvement in AI are banned: people (students and employees), hardware and models. And funding is down to a drip and capital allocation i
Michael Nuñez / VentureBeat: The Agentic AI Foundation updates MCP with a fully stateless architecture, a hardened authentication model, a formal 12-month deprecation policy, and more — The Model Cont
The apps you use swap AI models constantly. Doing that without downtime or a bad rollout reaching users is still often manual. We rebuilt that workflow based on what we’ve learned serving more than 40
arXiv:2607.24569v1 Announce Type: cross Abstract: Model-based active flow control requires predictive models that are accurate, stable, and fast enough for real-time optimisation. In controlled wake f
arXiv:2607.22807v1 Announce Type: cross Abstract: Although coding agents are now very effective in a variety of programming languages, this paper first shows that the cost (in tokens) can very signifi
🦔The cost of insuring Big Tech debt against default just hit record highs. Credit default swaps are insurance policies investors buy when they think a company might not pay back what it borrowed. When
arXiv:2607.24063v1 Announce Type: new Abstract: On standard factuality tasks, frontier models now cluster near the top of the scale. The question is therefore shifting from how factual a system is tow
arXiv:2607.23512v1 Announce Type: new Abstract: Offensive language detection models generally suffer performance degradation when deployed across datasets and across languages, yet most existing studi
arXiv:2607.23108v1 Announce Type: new Abstract: While scaling laws for imitation learning have primarily focused on generalization in open-world settings, the relationship between data and precision i
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. OpenAI called the Hugging Face attack unprecedented. But we’ve
arXiv:2607.23050v1 Announce Type: new Abstract: Neural scaling laws describe how loss decreases as models, data, and compute grow, but they do not answer a prior question: for a fixed task, what is th
arXiv:2602.15491v2 Announce Type: replace-cross Abstract: Neural audio codecs (NACs) typically encode the short-term energy (gain) and normalized structure (shape) of speech/audio signals jointly with
arXiv:2509.13196v2 Announce Type: replace Abstract: Over-prompting, a phenomenon where excessive examples in prompts lead to diminished performance in Large Language Models (LLMs), challenges the conv