Knowledge catalogue
arXiv:2606.19157v2 Announce Type: replace-cross Abstract: AudioLLMs enable speech recognition conditioned on textual prompts such as domain descriptions or entity lists. However, it remains unclear wh
arXiv:2606.24998v1 Announce Type: new Abstract: Language models are running out of high-quality training data, and even aggressively deduplicated corpora retain some amount of repetition. Earlier cont
arXiv:2606.25434v1 Announce Type: new Abstract: Early and scalable detection of mild cognitive impairment (MCI) remains an unresolved clinical challenge. Existing EEG-based screening approaches are co
arXiv:2606.25383v1 Announce Type: new Abstract: As previous research on annotator disagreement in discourse phenomena has shown, understanding text coherence varies considerably from one individual to
arXiv:2606.25083v1 Announce Type: new Abstract: Accurate extended pose estimation (orientation, velocity, and position) for IMU-instrumented articulated rigid-body systems is a key challenge in roboti
arXiv:2606.25270v1 Announce Type: new Abstract: Keystroke dynamics have been explored extensively as a passive digital biomarker for Parkinson's disease (PD), typically by extracting summary statistic
arXiv:2606.25984v1 Announce Type: cross Abstract: Large language models are increasingly deployed as investment research assistants, yet no benchmark tests whether they can accurately reconstruct and
arXiv:2606.24897v1 Announce Type: cross Abstract: Biomedical text mining, scientometrics, and the construction of training corpora for biomedical large language models (LLMs) all assume that the abstr
arXiv:2606.25343v1 Announce Type: new Abstract: Vision Language Models have achieved near-human performance on single-document Visual Question Answering, yet their effectiveness degrades significantly
arXiv:2606.25656v1 Announce Type: new Abstract: As advanced RAG variants like GraphRAG and Agentic RAG emerge, one leading question is when and how to use them. Here, we introduce a framework for diff
arXiv:2606.26009v1 Announce Type: new Abstract: Variational Monte Carlo (VMC) is a central algorithm in electronic structure theory and has gained renewed importance through modern neural-network ansa
It is amazing to me how little the X-verse cares about the rise of authoritarianism in the United States. Is that because most of the “people” here are actually Russian bots? 🤔 More Soviet-style decep
It would be very useful to understand more about the government safety concerns associated with frontier AI releases so we could (a) know what risks everyone will face if/when open source reaches Myth
It’s time for everyone to realize that the fight against data centers has nothing to do with data centers. They have become a proxy for the hate towards AI and the concentration and accumulation of we
arXiv:2606.18394v2 Announce Type: replace Abstract: Speculative decoding (SD) accelerates autoregressive Large Language Models (LLMs) by drafting multiple tokens and verifying them in parallel, but it
Just had a great discussion on dynamic workflows. Rough notes: - applies to a very small set of use cases - think of it as a new paradigm of (test-time compute) TTC - strong for hill-climbing research
arXiv:2606.25298v1 Announce Type: new Abstract: Children are naturally energetic, and during their spontaneous activities, they often encounter potentially dangerous situations, especially when lackin
arXiv:2606.25927v1 Announce Type: cross Abstract: As machine learning models and datasets continue to grow, developing complex models has become increasingly computationally demanding. Knowledge disti
arXiv:2606.25169v1 Announce Type: cross Abstract: Sampling from an unnormalized target by reversing an Ornstein--Uhlenbeck diffusion requires the score of each noise-perturbed marginal. Tweedie's iden
arXiv:2606.25393v1 Announce Type: new Abstract: Hydropower tunnel inspection is critical for infrastructure integrity yet remains inefficient and hazardous using manual methods. We propose FLISP (Fast
arXiv:2606.24982v1 Announce Type: new Abstract: Modeling and sampling from the underlying distribution of asynchronous event sequences are crucial in various real-world applications, including social
arXiv:2506.18414v3 Announce Type: replace Abstract: Melanoma is a highly aggressive skin cancer, making early and accurate diagnosis critical. While deep learning excels in skin lesion classification,
arXiv:2606.25589v1 Announce Type: new Abstract: As graph neural networks (GNNs) become standard tools for critical tasks in circuit design and analysis, their security and privacy risks require carefu
arXiv:2606.26095v1 Announce Type: cross Abstract: Most Vision-Language-Action (VLA) models build on a Vision-Language Model (VLM) backbone by attaching an action module and optimizing the full policy
arXiv:2606.25706v1 Announce Type: new Abstract: High-level humanoid planners often output sparse task-space, low-rate trajectories, whereas whole-body controllers run at high frequency. This creates t
arXiv:2606.24984v1 Announce Type: cross Abstract: Learning representations that remain robust across centuries of variation in handwriting is a key challenge in diachronic representation learning. Tak
arXiv:2606.24966v1 Announce Type: new Abstract: Estimating parameters of dynamical systems from sparse, noisy, and irregularly sampled data is often severely ill-conditioned. When multiple related dat
arXiv:2206.04359v3 Announce Type: replace Abstract: One of the fundamental challenges in the deep learning community is to theoretically understand how well a deep neural network generalizes to unseen
arXiv:2606.25362v1 Announce Type: cross Abstract: Sequential contextual stochastic programs model real-time decision systems in which each time epoch commits to an action under uncertainty whose conse
arXiv:2606.25179v1 Announce Type: new Abstract: Universal quadrupedal locomotion remains limited by the difficulty of integrating perception across diverse robot morphologies. State-of-the-art control
arXiv:2606.26047v1 Announce Type: new Abstract: Robot crowd navigation requires the ability to infer human intentions while accounting for the structural constraints of the environment. Currently, dee
arXiv:2602.14772v2 Announce Type: replace Abstract: The Winner Determination Problem (WDP) in combinatorial auctions is NP-hard, and no existing method reliably predicts which instances will defeat fa
arXiv:2606.25665v1 Announce Type: new Abstract: Domain generalization (DG) aims to learn a model from one or more source domains that generalizes to an unseen target domain without accessing target da
arXiv:2606.17967v2 Announce Type: replace Abstract: Speech foundation models, pre-trained on large corpora of unlabelled speech data, produce general-purpose representations which are useful across ta
arXiv:2606.25659v1 Announce Type: new Abstract: Learning-based Lyapunov Control (LLC) provides formal stability guarantees for nonlinear systems, but its validity relies heavily on accurate system mod
arXiv:2504.09910v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) is a promising technique for applying LLMs to proprietary domains. However, retrieved documents may contain sen
arXiv:2606.25451v1 Announce Type: new Abstract: Estimating token-level advantages in reinforcement learning (RL) for language models remains challenging because scaling up episodic experience collecti
arXiv:2601.02193v2 Announce Type: replace Abstract: We study the extent to which standard machine learning algorithms rely on exchangeability and independence of data by introducing a monotone adversa
arXiv:2606.25312v1 Announce Type: new Abstract: Remote sensing object detection has advanced rapidly with the development of large-scale benchmarks and modern detection architectures. However, existin
arXiv:2606.25402v1 Announce Type: cross Abstract: Large software projects often depend on older versions of libraries, even as APIs continue to evolve across releases. This creates a challenge for LLM
arXiv:2606.25342v1 Announce Type: new Abstract: Lifelong continual learning remains an obstacle on the path to human-like intelligence. Modern transformers show sparks of intelligence with in-context
arXiv:2606.25401v1 Announce Type: cross Abstract: Accurate channel state information (CSI) prediction is essential for proactive beamforming and resource management in 5G massive MIMO systems, yet the
arXiv:2602.05704v2 Announce Type: replace Abstract: Understanding the limitations of gradient methods, and stochastic gradient descent (SGD) in particular, is a central challenge in learning theory. T
arXiv:2606.25437v1 Announce Type: new Abstract: Existing Vision Foundation Model (VFM)-based iterative stereo pipelines under-exploit three information pathways: multi-scale backbone features are coll
LIVE TOMORROW: Replit now has 450+ integrations, and we've made them easier to find and put to work! ⚡ Stripe, Google Docs, Gmail, Figma, Slack, Notion, Airtbable and more! We're going to talk to Jaco
arXiv:2606.25039v1 Announce Type: cross Abstract: Recovering governing Ordinary Differential Equations (ODEs) from data is a central challenge in modeling dynamical systems across scientific domains.
arXiv:2606.25057v1 Announce Type: new Abstract: The rapid growth of scientific submissions has pushed traditional peer review toward its scalability limits, motivating the exploration of large languag
arXiv:2606.24901v1 Announce Type: new Abstract: Continual learning capability is critical for Industrial LLMs, as deployed models must be continuously updated to meet evolving requirements and environ
arXiv:2606.24973v1 Announce Type: new Abstract: We introduce a dataset of 32,534 double-marked real student responses to GCSE mock exams (GCSEs are the UK's national exams, taken at age ~16), spanning
arXiv:2501.18916v2 Announce Type: replace Abstract: Recent work has demonstrated the potential of large language models (LLMs) for program optimization, a key challenge in programming languages. We pr
LLMs are getting better at writing GPU kernels. Multi-GPU kernels are the harder test. At @aiDotEngineer World's Fair, @simran_s_arora will share ParallelKernelBench, an open-source benchmark built fr
arXiv:2602.15438v3 Announce Type: replace Abstract: For a broad family of discriminative models that includes autoregressive language models, identifiability results imply that if two models induce th
arXiv:2606.25396v1 Announce Type: new Abstract: AI companions powered by large language models increasingly interact with cognition-developing users, including children and adolescents, creating risks
Looks like OpenAI’s IPO will be delayed until 2027. • might be a sign that their finances don’t look compelling yet. • gives Anthropic time to go first If they can bump up the valuation by $999.3 bill
Loops, one of the most symbolic tools ever invented, are rescuing generative AI. Neurosymbolic AI is completely dominating. Anthropic engineers just showed how to build agents that can run for days wi
lots of folks prepping talks next week (congrats!). Some thoughts from RLing on thousands of hours of engineer- and researcher- focused talks: - AI generated svgs > AI generated imgs. MAXIMUM 4 ai slo
love that one of my readers made her own, explicit set of public predictions. 🙏 I want to post my predictions for 2ish years horizon. AI stock bubble collapses due to fundamentals not working out with
arXiv:2606.25593v1 Announce Type: new Abstract: We study optimal-policy geometry in structured Markov decision processes. While approximate dynamic programming and reinforcement learning typically app
arXiv:2606.24989v1 Announce Type: new Abstract: Urban flow and air-quality simulations generate high-dimensional datasets describing velocity and pollutant transport across multiple spatial, temporal,
arXiv:2606.25526v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning assumes each agent shares the same reward function and can be trained effectively using the Trust Region