Knowledge catalogue
arXiv:2610.19785v1 Announce Type: new Abstract: Crash Moment Localization aims to localize the major physical impact that interrupts controlled flight in an onboard first-person-view (FPV) video recor
arXiv:2610.20363v1 Announce Type: new Abstract: Neural operators learn mappings between function spaces, enabling fast surrogate solutions to partial differential equations. Despite remarkable archite
arXiv:2610.19564v1 Announce Type: new Abstract: A spatial answer depends on the reference frame specified in language and its supporting visual evidence. Item accuracy alone obscures whether correctne
arXiv:2610.15236v1 Announce Type: cross Abstract: Condensed matter stands to gain profoundly from widespread adoption of artificial intelligence (AI). However, its current means of data organization i
arXiv:2610.18126v1 Announce Type: new Abstract: Freshness shapes an LLM decision at the moment it is made; the action it commits to is latent, carried out later, in a changing world. We therefore intr
arXiv:2606.17648v3 Announce Type: replace Abstract: Standard accuracy metrics cannot explain why LLMs handle variable tracking but fail on semantically equivalent loops. We study an internal lifecycle
arXiv:2610.16498v1 Announce Type: cross Abstract: Generative Social Simulation (GSS) leverages Large Language Models (LLMs) to create sophisticated personas that define agents with demographics, emoti
arXiv:2608.00672v2 Announce Type: replace-cross Abstract: Security Operations Centers (SOCs) process large volumes of security events, requiring analysts to accurately detect and assess ongoing cybera
arXiv:2610.17243v1 Announce Type: cross Abstract: The original EneMonIE proposal describes a Web-based semantic system for continuously monitoring energy trends through targeted information extraction
arXiv:2610.19113v1 Announce Type: new Abstract: In-context learning (ICL) allows language models to adapt their behavior from instructions and demonstrations in a prompt, but accuracy alone does not r
arXiv:2610.19235v1 Announce Type: cross Abstract: Automatic stuttering systems primarily detect dysfluencies after they have occurred, limiting their use for anticipatory intervention. We address this
arXiv:2610.19287v1 Announce Type: cross Abstract: Compute cost in automated essay scoring (AES) is usually treated as a technical efficiency issue rather than a measurement design concern. Yet deploym
arXiv:2610.14625v1 Announce Type: new Abstract: Recursive self-improvement (RSI) offers a route to AI systems that participate in their own continued development. Recent methods allow language agents
arXiv:2610.19420v1 Announce Type: cross Abstract: Simulated environments enable scalable training data generation for embodied agents, but more data does not necessarily provide what a particular agen
arXiv:2610.19773v1 Announce Type: new Abstract: Score-based generative models driven by alpha-stable L{evy processes have been proposed as an alternative to Brownian diffusion models, with empirical e
arXiv:2610.20396v1 Announce Type: new Abstract: When does a human correction become useful learning, and what establishes better segmentation at matched human effort? This critical narrative review fo
arXiv:2506.23101v2 Announce Type: replace-cross Abstract: Ensuring the trustworthiness of Multimodal Large Language Models (MLLMs) requires rigorous evaluation of gender bias, particularly in socially
arXiv:2610.20286v1 Announce Type: new Abstract: The Platonic Representation Hypothesis (PRH) holds that increasingly capable models converge toward a common representation. However, recent studies hav
arXiv:2610.19164v1 Announce Type: new Abstract: Mixed methods research offers established strategies for integrating qualitative and quantitative evidence, but less guidance for integrating two comput
arXiv:2610.18674v1 Announce Type: cross Abstract: Large language models rely on prompt context to perform in-context learning, follow formats, and adapt to local evidence. This ability, however, also
arXiv:2610.20112v1 Announce Type: new Abstract: Prior work on implicit bias shows that gradient descent selects the Frobenius max-margin direction on linearly separable problems, whereas Fan et al. (2
arXiv:2610.15620v1 Announce Type: new Abstract: Multimodal Chain-of-Thought (MCoT) substantially enhances the complex reasoning capabilities of Multimodal Large Language Models (MLLMs) by explicitly m
arXiv:2610.20370v1 Announce Type: new Abstract: Hate speech has become increasingly prevalent on social media, raising concerns about its detection, interpretation, and moderation. Persian, a relative
arXiv:2610.20417v1 Announce Type: new Abstract: Audits of large language model (LLM) extraction from full-length documents report severe unreliability, leaving two questions open: what the failures ac
arXiv:2610.14428v1 Announce Type: new Abstract: Enterprise workflow automation typically requires retrieving a complete and accurate set of relevant information from large operational document corpora
arXiv:2610.19890v1 Announce Type: new Abstract: Majority Voting (MV) is a simple and widely used strategy which improves the reasoning accuracy of Large Language Models (LLMs) by allocating additional
arXiv:2610.19250v1 Announce Type: new Abstract: Reduced-order models (ROMs) make model predictive control (MPC) of high-dimensional nonlinear systems tractable, but the resulting modeling error can le
arXiv:2610.19391v1 Announce Type: new Abstract: We study continuous global optimization problems in which the objective function cannot be directly evaluated; rather, we observe only which of two sele
arXiv:2610.19687v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) improves reasoning in language models, but scalar outcomes provide coarse supervision over respo
arXiv:2610.20264v1 Announce Type: new Abstract: Client-level fair federated learning, in the form of q-fair federated learning (q-FFL), agnostic federated learning, or tilted empirical risk minimizati
arXiv:2610.16776v1 Announce Type: new Abstract: Game-Creation-Agents (GCA) turns a natural-language request into a runnable Phaser 4 browser game. The pipeline first builds a multimodal game design do
arXiv:2610.15082v1 Announce Type: cross Abstract: As text-to-speech (TTS) systems continue to advance, provenance tracking and security verification of synthesized speech have become increasingly impo
arXiv:2610.20162v1 Announce Type: new Abstract: Language models often produce semantically similar answers to open-ended questions, even when many plausible alternatives exist. Generating useful alter
arXiv:2610.14754v1 Announce Type: new Abstract: Hybrid language models interleave Gated DeltaNet (GDN) layers, whose recurrent memory has a fixed size, with full-attention layers, whose key-value (KV)
arXiv:2610.20053v1 Announce Type: new Abstract: Regression models used in real-world applications are often challenged by noise, outliers, data heterogeneity, and asymmetric error characteristics, whi
arXiv:2610.20073v1 Announce Type: new Abstract: Accurate nuclei segmentation underpins quantitative computational pathology, yet remains challenging due to morphological variability, weak boundaries,
arXiv:2601.06795v4 Announce Type: replace Abstract: Automated Theorem Proving (ATP) is a fundamental AI challenge, requiring machine-verifiable proofs in formal languages like Lean to evaluate reasoni
arXiv:2610.17401v1 Announce Type: new Abstract: Incomplete field-of-view (FOV) diffusion MRI (dMRI) acquisitions compromise whole-brain white matter analysis by excluding brain regions from tractograp
arXiv:2610.19211v1 Announce Type: cross Abstract: While causal discovery methods have matured for complete data, such algorithms for handling missing values remain limited. We establish general suffic
arXiv:2604.06138v2 Announce Type: replace-cross Abstract: Long-context audio reasoning is underserved in both training data and evaluation. Existing benchmarks target short-context tasks, and the open
arXiv:2112.09646v2 Announce Type: replace Abstract: The generation of discontinuous distributions is a difficult task for most known frameworks such as generative autoencoders and generative adversari
arXiv:2610.20533v1 Announce Type: cross Abstract: We introduce an approach for self-supervised representation learning designed to be used as a tokenizer for generative modeling. Our objective supervi
arXiv:2610.19726v1 Announce Type: new Abstract: We present a Generative World Simulation framework that couples persistent shared world state with continuous first-person audiovisual generation. The f
arXiv:2610.19114v1 Announce Type: new Abstract: Activation steering controls large language models at inference time, but its effectiveness depends on the choice of reference data. We introduce genesi
arXiv:2603.14426v2 Announce Type: replace Abstract: Existing text-to-video retrieval benchmarks often permit matching from static appearance, making it difficult to determine whether models capture st
arXiv:2610.20055v1 Announce Type: new Abstract: The rapid advancement of large vision language models (LVLMs) has made geolocation inference from images highly accurate and accessible. This poses seri
arXiv:2603.01502v2 Announce Type: replace Abstract: End-to-end speech large language models (LLMs) often answer the same question less accurately from speech than from text, even when the spoken conte
arXiv:2610.19570v1 Announce Type: cross Abstract: Quantifying the diversity of a set of objects is a fundamental problem in machine learning and statistics. Mironov and Prokhorenkova (2025) recently s
arXiv:2610.16769v1 Announce Type: new Abstract: Geospatial reasoning links VLMs to real-world action and decision making. Reliable reasoning requires accurate spatial information and effective spatial
arXiv:2610.20592v1 Announce Type: new Abstract: Existing geometry-aware Multimodal Large Language Models (MLLMs) increasingly rely on external 3D encoders to provide structural priors, but executing t
arXiv:2610.15129v1 Announce Type: new Abstract: Human social perception is incremental: we infer intent before an utterance is complete, revise our interpretation as new cues arrive, and attend to spe
arXiv:2610.17378v1 Announce Type: new Abstract: AI agents now browse and buy on people's behalf, and benchmarks measure how well the agents perform. Far less is known about the other side of the trans
arXiv:2610.20220v1 Announce Type: new Abstract: Autoregressive generation underlies real-time, causal video synthesis and is the backbone of video world models. Today's dominant approach distills a bi
arXiv:2610.16998v1 Announce Type: new Abstract: Deep search enables language agents to solve complex information-seeking tasks through iterative web exploration. However, developing robust open-source
arXiv:2412.08016v3 Announce Type: replace Abstract: Standard deep learning architectures used for classification generate label predictions with a projection head and softmax activation function. Alth
arXiv:2610.20254v1 Announce Type: new Abstract: Large view synthesis models (LVSMs) reconstruct scenes from sparse views and render novel views quickly, but they incur unnecessary storage and renderin
arXiv:2610.15750v1 Announce Type: new Abstract: Google Earth AI is a family of geospatial AI models, together with an agent that orchestrates them. We present this agent, Google Earth AI's Geospatial
arXiv:2610.17002v1 Announce Type: cross Abstract: Large language models are trained on collections that may include scholarly works, while authors generally lack work-level information about whether a
arXiv:2610.19043v1 Announce Type: cross Abstract: Because its binary optimization landscape contains exponentially many local optima, MAX-CUT is highly sensitive to initialization. We study Particle P
arXiv:2610.20502v1 Announce Type: new Abstract: Longitudinal electronic health records (EHRs) can reveal disease-progression structure, while useful forecasting requires leakage-controlled discovery,