Knowledge catalogue
arXiv:2606.28190v1 Announce Type: cross Abstract: This study analyzes Sri Lankan migration and remittances over 32 years (1994-2025). Using a 384-month harmonized dataset, we apply exploratory data an
arXiv:2606.16974v3 Announce Type: replace Abstract: The reproducibility crisis has directed the AI research community toward improving documentation practices. Several studies have identified methodol
arXiv:2606.28013v1 Announce Type: new Abstract: Headline type-correctness (TC%) of LLM autoformalization has climbed from sim53% to sim76% in two years, yet this scalar conceals which errors each meth
arXiv:2606.27711v1 Announce Type: cross Abstract: We introduce a neural network-based framework for learning time series estimators through a process we term decision-theoretic pretraining. Analysts s
arXiv:2606.27739v1 Announce Type: new Abstract: Process reward models (PRMs) enhance the reasoning capabilities of large language models (LLMs) by providing fine-grained feedback, yet training PRMs ty
arXiv:2606.27905v1 Announce Type: new Abstract: Multi-exposure fusion (MEF) brings the dynamic range of conventional cameras closer to that of human vision, producing images with rich scene content. G
arXiv:2606.27736v1 Announce Type: new Abstract: The rapid spread of fake news poses increasing threats to information ecosystems, especially as AI-generated misinformation under Generative Engine Opti
arXiv:2606.28061v1 Announce Type: cross Abstract: Large language models (LLMs) have increasingly moved from standalone text generation systems to agents that invoke external tools, access environments
arXiv:2606.28149v1 Announce Type: cross Abstract: In-context segmentation (ICS) requires a model to segment target regions in a query image using only a few reference images and their corresponding ma
arXiv:2606.28277v1 Announce Type: cross Abstract: Artificial intelligence is driving a revolution in scientific discovery, accelerating everything from hypothesis generation to mathematical theorem pr
arXiv:2511.20196v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) can inadvertently memorize privacy-sensitive information during training. While existing unlearning methods
arXiv:2606.27406v1 Announce Type: cross Abstract: Software engineering, whether performed by humans or by AI agents, requires reasoning about how software behaves. We call the internal model that supp
arXiv:2606.27757v1 Announce Type: new Abstract: Large language models (LLMs) have attracted widespread attention from academia and industry, yet their deployment raises critical security concerns rega
arXiv:2412.20802v3 Announce Type: replace-cross Abstract: Recommender systems are widely used in the digital landscape to match users with content fitting their preferences. However, growing concerns
arXiv:2606.28217v1 Announce Type: cross Abstract: We propose a framework for reward allocation in fully delegated AI cooperatives where humans are represented by agents that contribute data and partic
arXiv:2606.27981v1 Announce Type: new Abstract: We introduce a new, contextual, multilingual dataset called ToxiREX: Toxic REasoning in ConteXt. The dataset consists of threads of Reddit comments and
arXiv:2509.17932v2 Announce Type: replace Abstract: Large language models (LLMs) are prone to generating factually incorrect content, motivating methods for assessing truthfulness from internal model
arXiv:2510.00705v3 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) often struggle with fine-grained perception, such as identifying small objects in high-resolution images or
arXiv:2606.27609v1 Announce Type: new Abstract: Physical or operational constraints often impose communications limitations on autonomous agents. Such limitations complicate monitoring or multiagent c
arXiv:2606.28133v1 Announce Type: cross Abstract: We study whether we can learn novel manipulation skills from human actions to a bi-manual robot with parallel grippers. Human action data is cheap, ab
arXiv:2506.10355v2 Announce Type: replace Abstract: Many real-world applications collect data in a streaming environment, where learning tasks are encountered sequentially. This necessitates continual
arXiv:2606.27909v1 Announce Type: cross Abstract: Theory-of-mind evaluations of large language models typically use dyadic social-deduction games, where every observable cue points to a single hidden
arXiv:2606.27505v1 Announce Type: new Abstract: AI generated images are proliferating across the Internet. While some are used for entertainment, others are weaponized for fraud and social engineering
arXiv:2606.27777v1 Announce Type: new Abstract: Abdominal ultrasound is indispensable for rapid, noninvasive trauma triage. However, interpreting the subtle dynamic cues embedded in continuous scannin
arXiv:2512.23075v5 Announce Type: replace-cross Abstract: Policy gradient methods for Large Language Models optimize a policy pi_heta via a surrogate objective computed from samples of a rollout polic
arXiv:2603.11229v2 Announce Type: replace-cross Abstract: Machine learning forecast systems are moving beyond point predictions to full predictive distributions for future outcomes y conditional on co
arXiv:2606.27678v1 Announce Type: new Abstract: Cross-domain diagnosis remains a major challenge in cervical cell pathology due to pronounced domain shifts across institutions and the subtle visual di
arXiv:2606.27939v1 Announce Type: cross Abstract: Protein language models are standard priors for biological sequence generation, but steering them toward explicit distributional design targets remain
arXiv:2509.21785v2 Announce Type: replace-cross Abstract: Discretizing raw features into bucketized attribute representations is a popular step before sharing a dataset. It is, however, evident that t
arXiv:2605.25042v2 Announce Type: replace Abstract: Existing score-based methods for inverse problems often resort to approximate minimization of the KL divergence between the inversion distribution a
arXiv:2606.27554v1 Announce Type: new Abstract: Camera-based perception systems for autonomous driving are typically developed and evaluated using fixed sensor rigs, while real-world vehicle fleets ex
arXiv:2606.27947v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) describe artworks with remarkable fluency, yet the visual reasoning behind their outputs remains opaque. When a
arXiv:2606.27780v1 Announce Type: new Abstract: World models are often used for planning by rolling learned dynamics forward. Many planning environments, however, are not vectors or images; they are g
arXiv:2606.27438v1 Announce Type: new Abstract: Since its initial release in 2020, Darts has become a widely used open-source Python library for time series analysis. A series of foundation models hav
arXiv:2606.28237v1 Announce Type: new Abstract: Quadruped robots have achieved remarkable locomotion, yet their behavioral repertoire remains confined to a few gaits--far from the expressive, companio
arXiv:2606.27832v1 Announce Type: new Abstract: Statistical adversarial detection (SAD) treats detection as a two-sample test. Given a reference set of clean examples (CEs) and a batch of queries, pot
arXiv:2606.27941v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) provide useful decompositions of Transformer residual streams, but their learned features are usually named post hoc rather
arXiv:2606.27926v1 Announce Type: new Abstract: Geometry Problem Solving have increasingly adopt the neuro-symbolic paradigm, combining neural intuition with symbolic rigor. However, current framework
arXiv:2606.28301v1 Announce Type: new Abstract: Inference-time scaling is a promising paradigm to improve generative models, especially when outputs must satisfy structural constraints or optimize dow
arXiv:2606.27828v1 Announce Type: new Abstract: Recent interest in multimodal large language models (MLLMs) raises a central question: can they reason over dynamic visual evidence rather than merely r
arXiv:2606.28273v1 Announce Type: new Abstract: Vision-language models must reconcile visual evidence with memorized world knowledge when the two conflict. How they resolve this conflict shapes the re
arXiv:2606.27646v1 Announce Type: new Abstract: Conventional machine-vision pipelines typically rely on high-quality optics that produce clean, human-interpretable images, and optical design has there
arXiv:2507.17455v2 Announce Type: replace Abstract: Geo-localization from a single image at planet scale (essentially an advanced or extreme version of the kidnapped robot problem) is a fundamental an
arXiv:2606.28320v1 Announce Type: new Abstract: Scaling imitation learning requires large datasets, yet human teleoperation inevitably produces mixed-quality demonstrations containing hesitations and
arXiv:2606.27841v1 Announce Type: cross Abstract: The widespread adoption of Artificial Intelligence (AI) has led to increasing concerns about energy consumption, yet there is a lack of standardized m
arXiv:2505.05517v3 Announce Type: replace Abstract: Functional grasping is essential for enabling dexterous multi-finger robot hands to manipulate objects effectively. Prior work largely focuses on po
arXiv:2606.27698v1 Announce Type: cross Abstract: Automatic Speech Recognition systems are notoriously both sensitive to adversarial and benign perturbations. While this has been repeatedly demonstrat
arXiv:2606.27443v1 Announce Type: new Abstract: Personality prompting shapes how large language models communicate, yet whether these behavioral shifts affect objective task outcomes remains under-exp
arXiv:2606.27929v1 Announce Type: new Abstract: Embodied AI is increasingly becoming agentic, shifting robots from perception--control pipelines towards closed-loop systems that can retrieve context,
arXiv:2606.28117v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become the standard tool for parameter-efficient fine-tuning of large pretrained models. When applied sequentially across
arXiv:2606.27669v1 Announce Type: new Abstract: Search agents powered by large language models (LLMs) are increasingly used to solve complex information-seeking tasks, requiring multi-step retrieval a
arXiv:2602.10179v2 Announce Type: replace-cross Abstract: Recent advances in large image editing models have shifted the paradigm from text-driven instructions to vision-prompt editing, where user int
arXiv:2606.28308v1 Announce Type: cross Abstract: Many two-player zero-sum games admit not a unique Nash equilibrium but a convex set of them: a polytope of profiles that all share the minimax value V
arXiv:2606.18142v3 Announce Type: replace Abstract: AI agents are moving from advisors to actors, booking travel, planning menus, and running procurement on behalf of users. Existing benchmarks for AI
arXiv:2606.27632v1 Announce Type: new Abstract: As large language models are increasingly deployed in real-world systems, safety failures can still lead to harmful outputs and dangerous misuse. We arg
arXiv:2606.27708v1 Announce Type: new Abstract: Adapting a foundation vision-language encoder to a specialized retrieval task creates a fundamental tradeoff: gains on the target distribution come at t
arXiv:2606.26465v1 Announce Type: cross Abstract: Spatial pattern matching is the process of matching query entities and constraints with database entities and relations. It has many applications, inc
arXiv:2512.04238v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly integrated into clinical workflows. However, existing benchmarks primarily assess performance on comm
arXiv:2606.26467v1 Announce Type: new Abstract: We introduce TabPFN-CFM, a causal foundation model that can handle multiple causal problems. TabPFN-CFM predicts both causal structure and outcomes from
arXiv:2606.26616v1 Announce Type: new Abstract: Bearing-odometry-based cooperative localization has attracted increasing research interest due to its minimal infrastructure requirements, low communica