Knowledge catalogue
OpenAI is hosed. There is almost no reason to prefer them over Anthropic, they have lost their lead (despite every advantage in the world), they have made commitments far far beyond their means, their
arXiv:2602.15829v2 Announce Type: replace Abstract: The superficial alignment hypothesis (SAH) posits that large language models learn most of their knowledge during pre-training, and that post-traini
arXiv:2606.09434v1 Announce Type: new Abstract: The Fokker-Planck equation (FPE) plays a pivotal role in describing the time evolution of probability density functions (PDFs) for systems governed by s
arXiv:2606.08654v1 Announce Type: new Abstract: In this paper, we propose a perturbation-based conformal prediction framework for uncertainty quantification in operator learning, with a focus on the 2
arXiv:2606.09479v1 Announce Type: new Abstract: Optical Music Recognition (OMR) has seen major progress in model design, with end-to-end methods now capable of recognising notation at all levels of co
arXiv:2606.09585v1 Announce Type: new Abstract: Chain-of-Thought (CoT) improves the performance of Large Language Models (LLMs) and has been extended to Multimodal Large Language Models (MLLMs). More
arXiv:2502.15131v4 Announce Type: replace-cross Abstract: We study the fundamental problem of calibrating a linear binary classifier of the form sigma(hat{w}^op x), where the feature vector x is Gauss
arXiv:2601.23221v2 Announce Type: replace Abstract: As acquiring reliable ground-truth labels is usually costly, or infeasible, crowdsourcing and aggregation of noisy human annotations is the typical
arXiv:2606.07589v1 Announce Type: new Abstract: Sequential filtering pipelines are a common design pattern in large-scale systems, where a large population of items is progressively reduced by a seque
arXiv:2606.09117v1 Announce Type: cross Abstract: While Ising machines serve as advanced physical solvers for the Ising model,enabling applications in combinatorial optimization and neural network tra
arXiv:2602.07345v2 Announce Type: replace Abstract: Distribution Matching Distillation (DMD) is a powerful acceleration paradigm, yet its stability is often compromised in Forbidden Zone, regions wher
arXiv:2606.08783v1 Announce Type: cross Abstract: Orthogonalized momentum updates, as used in Muon-style optimizers, have recently shown strong empirical stability in large-scale deep learning. Howeve
arXiv:2606.09239v1 Announce Type: new Abstract: While visual programming of data analysis workflows has become an important vehicle for the democratization of data science, such systems remain largely
arXiv:2606.08904v1 Announce Type: new Abstract: Macro placement is a fundamental step in modern chip physical design, playing a crucial role in determining the solution quality of high-dimensional com
arXiv:2606.08574v1 Announce Type: cross Abstract: Data pruning (DP), as an oft-stated strategy to alleviate heavy training burdens, reduces the volume of training samples according to a well-defined p
arXiv:2606.08322v1 Announce Type: new Abstract: To characterize the US airline profit cycles from 1995 to 2020, the authors of Renold et al. (2023) combine k-means clustering, principal component anal
arXiv:2606.08046v1 Announce Type: new Abstract: We present OSMGraphCLIP, a CLIP-style geospatial representation model that learns global location embeddings from freely available OpenStreetMap (OSM) d
arXiv:2606.07583v1 Announce Type: cross Abstract: Self-healing smart grids can quickly adjust their network configuration during outages to minimize power disruptions. During an outage, several action
arXiv:2606.07982v1 Announce Type: new Abstract: High-dimensional transient heat diffusion under noisy boundary conditions exposes a fundamental limitation of classical numerical methods: accuracy degr
arXiv:2606.07866v1 Announce Type: new Abstract: Regulatory review of advanced nuclear reactor designs routinely spans more than three years and consumes hundreds of millions of dollars in combined reg
arXiv:2606.08919v1 Announce Type: new Abstract: As LLM agents begin to take real, irreversible actions (shell commands, file edits, deploys), the standard safety pattern is a human-in-the-loop approva
arXiv:2606.08106v1 Announce Type: new Abstract: Self-evolving agents improve by repeatedly proposing changes to their own prompts, skills, or workflows and keeping those that score higher on a small h
arXiv:2511.00934v2 Announce Type: replace-cross Abstract: Signal Temporal Logic (STL) is an expressive language for specifying behaviors of dynamical systems from continuous signals. However, a limita
arXiv:2606.08938v1 Announce Type: cross Abstract: Clinical diagnosis requires flexible use of multiple reasoning paradigms under incomplete patient information. Existing LLM-based medical agents show
arXiv:2606.08414v1 Announce Type: cross Abstract: Diffusion policies have achieved remarkable success in robotic manipulation, yet they often fail to satisfy strict physical constraints required for s
arXiv:2606.08543v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves large language model reasoning but often suffers from rapid policy-entropy collapse, wher
arXiv:2606.07988v1 Announce Type: new Abstract: Large language models (LLMs) increasingly rely on reward models to align their outputs with diverse user preferences. While personalized reward models a
arXiv:2606.07558v1 Announce Type: cross Abstract: Purpose: Digitization projects in the humanities produce vast, heterogeneous archives of historical documents, making manual sorting impractical at sc
arXiv:2606.08935v1 Announce Type: cross Abstract: Representation-based time-series anomaly detection algorithms significantly outperform other methods on diverse anomaly detection tasks. However, we n
arXiv:2605.06582v2 Announce Type: replace Abstract: Many operations on sensory data -- comparison, memory, retrieval, and reasoning -- are naturally expressed over discrete symbolic structures. In lan
arXiv:2606.08795v1 Announce Type: new Abstract: Change detection and scene recognition techniques have been widely applied to Street View Imagery (SVI) to understand changes in scenes across the years
arXiv:2605.25449v2 Announce Type: replace Abstract: Generating complete digital twins from videos requires precise camera control, global scene coverage, and strict spatial-temporal consistency constr
arXiv:2606.08638v1 Announce Type: cross Abstract: Recent research has developed practical, parallelizable first-order methods for large scale linear programming, but performance is highly dependent on
Paramount's chief legal officer accused Netflix of conducting a 'scorched-earth campaign' to derail the merger in a letter to the Department of Justice . The letter claimed Netflix is trying to 'poiso
Parsing a document accurately is one thing. Proving where every value came from is another. When a compliance team reviews an AI extraction, or an auditor needs to sign off on a figure pulled from a f
arXiv:2602.16061v2 Announce Type: replace-cross Abstract: Estimating population quantities such as mean outcomes from user feedback is fundamental to platform evaluation and social science, yet feedba
arXiv:2606.07890v1 Announce Type: new Abstract: Performative prediction studies feedback loops that arise when predictive models are deployed in consequential domains. In these settings, deploying a m
arXiv:2606.07855v1 Announce Type: new Abstract: Path-planning for autonomous vehicles in threat-laden environments is a fundamental challenge because the problem is nonlinear and nonconvex even in sim
arXiv:2606.07549v1 Announce Type: new Abstract: Recent advances in Multimodal Large Language Models (MLLMs) and agent workflows have shown strong promise for computational pathology, yet reliable patc
arXiv:2601.19082v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents that negotiate, coordinate, and act on behalf of users. Whether they coo
arXiv:2606.09348v1 Announce Type: new Abstract: Long-horizon agentic tasks pose a fundamental credit assignment challenge for outcome-base reinforcement learning: trajectory-level rewards verify final
arXiv:2510.20182v2 Announce Type: replace Abstract: Pedestrian simulation traditionally relies on expert-tuned, hand-crafted models that limit scalability and generalization. Meanwhile, large-scale vi
arXiv:2606.08059v1 Announce Type: new Abstract: Humanoid behavior foundation models aim to acquire reusable whole-body control policies from broad human motion priors, enabling a single controller to
arXiv:2606.07661v1 Announce Type: new Abstract: Parsing historical documents with complex, non-standard layouts remains a fundamental bottleneck in large-scale archival digitization. Unlike modern typ
arXiv:2602.04402v3 Announce Type: replace-cross Abstract: Performative predictions influence the very outcomes they aim to forecast. We study performative predictions that affect a sample (e.g., only
arXiv:2606.09038v1 Announce Type: new Abstract: Large Language Models (LLMs) have enabled increasingly personalized interactions by adapting to users' preferences, contexts, and long-term histories. H
arXiv:2606.08458v1 Announce Type: new Abstract: Proactive robot assistance in household environments requires accurate prediction of human activities and object usage under dynamic and noisy condition
arXiv:2606.09756v1 Announce Type: new Abstract: Responses to perturbations are key to understanding physical systems. The ability to contrast such responses by comparing how a system reacts under slig
arXiv:2508.00724v2 Announce Type: replace-cross Abstract: The increasing demand for flexible automation has accelerated the adoption of heterogeneous automated guided vehicles (AGVs). This work invest
arXiv:2606.07559v1 Announce Type: cross Abstract: Fine-tuning a language model on contexts whose correct completion has a near-synonym competitor often fails silently. The cross-entropy loss decreases
arXiv:2606.07698v1 Announce Type: cross Abstract: Graph neural networks (GNNs) applied to drug-drug interaction (DDI) prediction rely exclusively on molecular structure encoded as SMILES-derived graph
arXiv:2606.08132v1 Announce Type: new Abstract: Vision Transformers operate on fixed patch grids, which can introduce phase-dependent instability for dense prediction: changing the patch partition can
arXiv:2406.05335v3 Announce Type: replace-cross Abstract: Generation of text and speech in natural languages can be modeled as a stochastic process. This idea dates back to the seminal work of Markov
arXiv:2503.05169v2 Announce Type: replace Abstract: Applying machine learning to increasingly high-dimensional problems with sparse or biased training data increases the risk that a model is used on i
arXiv:2606.08688v1 Announce Type: cross Abstract: Achieving fully automated, physically plausible 3D motion synthesis is a core objective in graphics and generative AI. However, configuring complex en
arXiv:2606.08655v1 Announce Type: cross Abstract: To perform a wide range of daily tasks, robots need to construct a 3D representation that is semantically rich, physically grounded, and structured en
arXiv:2606.08473v1 Announce Type: new Abstract: False data injection attacks (FDIAs) introducing small measurement perturbations can still cause large deviations in power system state estimation when
arXiv:2606.09640v1 Announce Type: new Abstract: Accurate dynamics models are essential for model-based robotic control, yet nominal Euler--Lagrange models often become inaccurate in the presence of pa
arXiv:2606.08563v1 Announce Type: new Abstract: While global data-driven models excel at predicting continuous atmospheric variables, three-dimensional hydrometeor forecasting remains challenging due
arXiv:2606.09266v1 Announce Type: cross Abstract: Acoustic metamaterial (AMM) inverse design is particularly challenging for broadband target responses due to acoustic dispersion: a structure that mat