Knowledge catalogue
arXiv:2606.28796v1 Announce Type: new Abstract: Government documents in India are predominantly issued in regional languages such as Marathi, creating substantial accessibility barriers for non-native
arXiv:2606.29471v1 Announce Type: new Abstract: Strictly proper scoring rules identify the true conditional class distribution at population level, but their curvature can alter optimization and finit
arXiv:2606.29861v1 Announce Type: cross Abstract: Visual Object Tracking (VOT) and Moving Object Segmentation (MOS) are two fundamental tasks in computer vision that involve both spatial and temporal
arXiv:2603.08195v2 Announce Type: replace Abstract: Motivation: The rapid growth of biological data has intensified the need for transparent, reproducible, and well-documented computational workflows.
arXiv:2606.29247v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models represent a promising direction for embodied intelligence in surgical robotics. Despite the prevalence of VLA benchm
arXiv:2412.19897v3 Announce Type: replace-cross Abstract: We introduce a local surrogate approach for explainable time-series forecasting. An initially non-interpretable predictive model to improve th
arXiv:2606.29567v1 Announce Type: cross Abstract: LLM-based assistants transmit user queries verbatim to third-party API endpoints that lie outside the user's audit or control. When those queries cont
arXiv:2606.28465v1 Announce Type: cross Abstract: This work examines perturbation generalization in spatial foundation-model embeddings derived from fluorescence microscopy images. Although these mode
arXiv:2603.12703v3 Announce Type: replace Abstract: Video understanding requires models to continuously track and update world state during playback. Although existing benchmarks have advanced video u
arXiv:2606.21401v2 Announce Type: replace-cross Abstract: Agentic AI applications compose multiple model calls and tool executions, creating new scheduling challenges for GPU-CPU clusters. Their infer
arXiv:2511.06090v3 Announce Type: replace-cross Abstract: Optimizing the performance of large-scale software repositories demands expertise in code reasoning and software engineering (SWE) to reduce r
arXiv:2606.30573v1 Announce Type: new Abstract: We introduce SWE-Interact, a new testbed for evaluating coding agents on multi-turn, interactive, user-driven software engineering tasks. Existing front
arXiv:2606.28434v1 Announce Type: cross Abstract: Long-horizon software engineering agents often need to manage lengthy and noisy interaction histories under limited context budgets. Existing memory m
arXiv:2606.29957v1 Announce Type: cross Abstract: Most coding-agent benchmarks are static: an agent receives a complete task description up front and is judged only by its final code. Real coding assi
arXiv:2511.17649v4 Announce Type: replace-cross Abstract: Tangible control interfaces (TCIs), such as appliance panels, remotes, elevators, and embedded GUIs, are a fundamental component of everyday h
arXiv:2606.29171v1 Announce Type: cross Abstract: While existing data attribution methods can identify which training examples build specific mechanistic circuits, they cannot explain how training dat
arXiv:2602.12394v2 Announce Type: replace Abstract: Personalized prompting offers large opportunities for deploying large language models (LLMs) to diverse users, yet existing prompt optimization meth
arXiv:2606.29644v1 Announce Type: new Abstract: Earth system infrastructures relying on satellite-based technologies, such as Global Positioning System (GPS) communications, are affected by ionospheri
arXiv:2606.30147v1 Announce Type: new Abstract: Recent progress in Text-to-Image generation benefits from large-scale Text-Image pairs. However, the scarcity of Text-LiDAR pairs often causes over-smoo
arXiv:2606.30011v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) deployed in real-world systems typically have fixed weights, often leading to degraded performance under distribution shi
arXiv:2606.30109v1 Announce Type: new Abstract: Vision-based tactile sensing converts contact-induced surface deformation into images, enabling robots to infer contact forces and fine surface textures
arXiv:2606.29173v1 Announce Type: new Abstract: Touch resolves the physical-property ambiguity left by vision: exploratory contact recovers shape, texture, compliance, and material, and visuo-haptic o
arXiv:2606.29851v1 Announce Type: new Abstract: Pose Graph Optimization (PGO) is one of the most widely adopted approaches for solving Simultaneous Localization and Mapping (SLAM) problems. However, P
arXiv:2508.12435v2 Announce Type: replace-cross Abstract: While gesture recognition using vision or robot skins is an active research area in Human-Robot Collaboration (HRC), this paper explores deep
arXiv:2606.29089v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models demonstrate impressive reasoning over visual, semantic, and spatial task variations by leveraging large-scale vision
arXiv:2508.07683v2 Announce Type: replace-cross Abstract: Video Temporal Grounding (VTG) aims to localize specific video segments corresponding to natural language queries. While recent Large Vision-L
arXiv:2606.30104v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models aim to learn generalizable representations from large-scale brain recordings. However, the role of tempor
arXiv:2606.29423v1 Announce Type: new Abstract: The importance of gesture recognition has been acknowledged in many domains requiring real-time recognition systems. Two requirements for these are fast
arXiv:2401.11512v2 Announce Type: replace-cross Abstract: Identifying the most suitable variables to represent the state is a fundamental challenge in Reinforcement Learning (RL). These variables must
arXiv:2603.02172v2 Announce Type: replace Abstract: We introduce TerraDiT, a diffusion transformer designed for text-to-satellite image generation with point-based control. Existing controlled satelli
arXiv:2602.02498v2 Announce Type: replace-cross Abstract: Large language models can produce toxic or inappropriate text even for benign inputs, creating risks when deployed at scale. Detoxification is
arXiv:2512.17151v2 Announce Type: replace Abstract: We present a framework for document-centric background generation with multi-page editing and thematic continuity. To ensure text regions remain rea
arXiv:2606.28328v1 Announce Type: cross Abstract: In recent years, text clustering has become a critical technique for applications including intent discovery, topic mining, and recommendation systems
arXiv:2606.29575v1 Announce Type: cross Abstract: Recent advances in speech separation (SS) have led to compact front-end models with small parameter sizes, yet their high computational cost remains a
arXiv:2510.06096v3 Announce Type: replace-cross Abstract: The objectives that Large Language Models (LLMs) implicitly optimize remain dangerously opaque, making trustworthy alignment and auditing a gr
arXiv:2606.29484v1 Announce Type: cross Abstract: Modern deepfake detectors are rarely consumed as bare classifiers. In moderation, provenance, and verification pipelines their output probability is r
arXiv:2606.29278v1 Announce Type: new Abstract: We introduce the Complexity Ceiling Benchmark (CCB), a controlled evaluation of how language-model reasoning decays as the number of required sequential
arXiv:2606.28839v1 Announce Type: new Abstract: We introduce the Contagion Tensor, a measurement framework for quantifying how large language model (LLM) output distributions couple across modalities,
arXiv:2606.29799v1 Announce Type: new Abstract: This project introduces the CRISTAL Method (Coherent Reliable Intentional Synthesis of Truthful Analysis Logic), a neurosymbolic framework for automatin
arXiv:2606.28343v1 Announce Type: cross Abstract: Retrieval-augmented generative agents rely on retrieval for grounding, yet are typically evaluated on a query-by-query basis. This isolates interactio
arXiv:2606.28325v1 Announce Type: cross Abstract: Large language models process the world's writing systems with radical inequality. We constructed the Digital Script Representation Index (DSRI), a se
arXiv:2601.15251v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved impressive proficiency in basic arithmetic, rivaling human-level performance on standard numerical tasks.
arXiv:2606.13079v2 Announce Type: replace-cross Abstract: Nowadays, the autonomous execution of cyberattacks capable of causing substantial real-world harm is widely regarded as one of the critical re
arXiv:2606.30442v1 Announce Type: new Abstract: The Bitter Lesson, which posits that general-purpose methods that scale with computation and data ultimately outperform those with built-in human knowle
arXiv:2606.29832v1 Announce Type: new Abstract: Machine unlearning aims to eliminate the influence of specific data from trained models to safeguard privacy. However, this presents a significant chall
arXiv:2606.30574v1 Announce Type: new Abstract: Many modern generative modeling methods, including diffusion models, normalizing flows, and flow matching, estimate transport maps or plans between dist
arXiv:2606.28843v1 Announce Type: cross Abstract: Fine-tuning a large language model is a ubiquitous method for enhancing its capability on a specific downstream task. However, prior work has shown th
arXiv:2606.29720v1 Announce Type: cross Abstract: Resampling methods such as SMOTE and random under/over-sampling are standard tools for class-imbalanced classification, almost always evaluated by min
arXiv:2603.03305v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to generate executable outputs, JSON objects, and API calls, where a single syntax error ca
arXiv:2606.30561v1 Announce Type: new Abstract: Modern AI evaluation frameworks treat evaluator disagreement as noise to be resolved. In creative domains, professional disagreement reflects genuine di
arXiv:2606.28327v1 Announce Type: cross Abstract: How do retrieval bounds compare between human episodic memory and Retrieval-Augmented Generation (RAG) systems under semantic interference? We present
arXiv:2606.29581v1 Announce Type: cross Abstract: Modern LLM deployments routinely compress models and raise sampling temperature to reduce cost, latency, or repetition, yet safety evaluations usually
arXiv:2606.30206v1 Announce Type: new Abstract: Modern Artificial Intelligence is often framed as limited by its own disembodiment, as if giving it a body would unlock its true potential. We argue to
arXiv:2606.29526v1 Announce Type: new Abstract: Reinforcement learning (RL) has gained growing attention in large language model (LLM) post-training, yet RL training remains fragile and can suffer fro
arXiv:2506.05121v3 Announce Type: replace Abstract: A recent line of research on spoken language assessment (SLA) employs neural models such as BERT and wav2vec 2.0 (W2V) to evaluate speaking proficie
arXiv:2606.29451v1 Announce Type: new Abstract: Self-supervised learning (SSL) pretrained models have become a dominant paradigm for visual representation learning, but they are vulnerable to backdoor
arXiv:2606.28789v1 Announce Type: cross Abstract: Artificial intelligence reaches the land registry not as another tool but as a value chain that turns data into intelligence and intelligence into eco
arXiv:2606.28529v1 Announce Type: cross Abstract: Embodied foundation models have recently been widely used to improve robot generalization and task success rates. Previous works apply lossy efficient
arXiv:2606.29034v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly summarize clinical evidence, where a claim's weight depends on how strongly it is supported. Yet these model
arXiv:2606.30308v1 Announce Type: new Abstract: 4D hand motion reconstruction from egocentric video is bottlenecked by clear limitations of existing methods: image-based pipelines depend on a detector