Knowledge catalogue
arXiv:2605.23493v1 Announce Type: new Abstract: On-Policy Distillation (OPD) has gained wide attraction as an LLM post-training paradigm due to its effectiveness in improving capabilities without intr
arXiv:2509.26383v5 Announce Type: replace-cross Abstract: Knowledge-graph retrieval-augmented generation (KG-RAG) couples large language models (LLMs) with structured, verifiable knowledge graphs (KGs
arXiv:2404.05108v3 Announce Type: replace-cross Abstract: Gradient estimation is a central challenge in training parameterized quantum circuits (PQCs) for hybrid quantum-classical optimization and lea
arXiv:2605.23323v1 Announce Type: cross Abstract: Entropy coding is widely used in typical learned image compression (LIC) that converts latents into a compact bitstream. However, entropy coding is ty
arXiv:2605.23451v1 Announce Type: new Abstract: Real-world image super-resolution aims to recover high-quality images from complex and unknown real-world degradations. However, existing generative Rea
arXiv:2605.23610v1 Announce Type: cross Abstract: Multi-shot video generation requires maintaining a consistent appearance of recurring entities across shots while remaining faithful to shot-specific
arXiv:2605.23328v1 Announce Type: new Abstract: Emotion Recognition in Conversation is a core component of affective computing, while current resources of sign language emotion datasets primarily focu
arXiv:2605.23189v1 Announce Type: new Abstract: Conformal prediction (CP) gives distribution-free coverage for modern vision and language models, but it is often forced to make a ranking decision from
arXiv:2602.13241v2 Announce Type: replace-cross Abstract: Emergency call-takers form the first operational link in public safety response, handling over 240 million calls annually while facing a susta
arXiv:2605.23096v1 Announce Type: cross Abstract: Fully homomorphic encryption (FHE) enables private inference by evaluating neural networks on encrypted data. In this way, we can delegate the computa
arXiv:2512.22597v2 Announce Type: replace Abstract: Exploring molecular energy landscapes and identifying ground-state conformations are central challenges in computational chemistry. However, generat
arXiv:2605.22883v1 Announce Type: new Abstract: Current AI energy benchmarks measure consumption at the granularity of a single model invocation or training run. For classical single-turn workloads th
arXiv:2512.18363v2 Announce Type: replace Abstract: We propose ESSC-RM, a plug-and-play Enhancing framework for Semantic Scene Completion with a Refinement Module, which can be seamlessly integrated i
arXiv:2605.23324v1 Announce Type: new Abstract: Accurate classification of microscopic blood cells is still a critical task in medical image analysis, where subtle variations and limited data can chal
arXiv:2605.23249v1 Announce Type: cross Abstract: Although deep neural networks (DNNs) achieve high predictive accuracy, their confidence estimates are often unreliable, potentially compromising user
arXiv:2603.07079v2 Announce Type: replace-cross Abstract: On-policy distillation is a promising approach for transferring knowledge between language models, where a student learns from dense token-lev
arXiv:2605.23225v1 Announce Type: cross Abstract: We introduce the problem of entropy equivalence testing for probability distributions, a relaxation of the well-studied closeness testing problem, whe
arXiv:2605.23854v1 Announce Type: new Abstract: Bradley-Terry-Luce (BTL) model estimation is a well-established strategy to rank a collection of items given a dataset of pairwise comparisons. Although
arXiv:2605.23412v1 Announce Type: new Abstract: While social media platforms, such as Twitter, provide a medium for large-scale opinion sharing during news events, it is manually impossible for indivi
arXiv:2605.23897v1 Announce Type: cross Abstract: Multimodal Large Language Models have advanced visual reasoning, yet a purely textual chain of thought remains a bottleneck for questions that require
arXiv:2603.19167v2 Announce Type: replace Abstract: We evaluate Large Language Models (LLMs) in repeated game-theoretic settings to assess whether strategic performance reflects genuine reasoning or r
arXiv:2508.07849v2 Announce Type: replace Abstract: Despite advances in legal NLP, no comprehensive evaluation of Transformer-based models customized for legal tasks (referred to as `legal-specific' m
arXiv:2605.22826v1 Announce Type: cross Abstract: Quantifying the deceptive potential of Large Language Models (LLMs) is critical for AI safety, yet difficult to achieve in uncontrolled environments.
arXiv:2602.11243v2 Announce Type: replace-cross Abstract: Modern LLM-based agents and chat assistants rely on long-term memory frameworks to store reusable knowledge, recall user preferences, and augm
arXiv:2605.22837v1 Announce Type: cross Abstract: Numerous studies have shown that the machine-learning picker PhaseNet produces accurate P and S picks on local earthquake signals, but its performance
arXiv:2605.23271v1 Announce Type: cross Abstract: The rapid evolution of generative video foundation models has propelled the field toward professional-grade cinematic synthesis. To achieve such deman
arXiv:2605.22905v1 Announce Type: new Abstract: Self-evolving agents should not train on examples they cannot justify. Data-free self-evolving search agents offer a scalable route to systems that gene
arXiv:2605.23393v1 Announce Type: cross Abstract: Mechanistic interpretability of transformers requires identifying not just which components matter but how they compose into the computational route t
arXiv:2605.23191v1 Announce Type: new Abstract: Scaling recommendation models is a central challenge in recommender systems. Recently, RankMixer has emerged as an effective solution, operating on a un
arXiv:2605.23118v1 Announce Type: cross Abstract: Tracking tumor lesions across serial CT scans is essential for oncological response assessment. Existing automated methods face a fundamental trade-of
arXiv:2605.23790v1 Announce Type: new Abstract: Saliency prediction has been extensively studied in RGB images and videos as a computational model of human visual attention. In contrast, predicting sa
arXiv:2605.23653v1 Announce Type: new Abstract: Timely and transparent feedback is essential for effective surgical training, yet current assessment remains dependent on expert observation, limiting s
arXiv:2605.22852v1 Announce Type: cross Abstract: The expressive limitations of message-passing Graph Neural Networks (GNNs) have motivated a wide range of more powerful graph learning architectures.
arXiv:2605.22890v1 Announce Type: cross Abstract: Event cameras are well suited for visual odometry under high-speed motion and challenging lighting conditions due to their low latency, high temporal
arXiv:2603.19812v2 Announce Type: replace Abstract: To address this gap, we conduct a Virtual Reality experiment in which pedestrians interact with automated shuttles under varying approach angles (45
arXiv:2605.23163v1 Announce Type: new Abstract: End-to-end autonomous driving via Vision-Language-Action (VLA) models demands a precarious balance between high-fidelity trajectory planning and efficie
arXiv:2605.23428v1 Announce Type: new Abstract: In modern multimedia systems, efficient video processing is critical, especially in resource-constrained environments such as IoT-based camera networks,
arXiv:2605.23215v1 Announce Type: cross Abstract: LLM-based agents for GPU kernel generation are advancing rapidly, yet their progress is fundamentally constrained by the benchmarks they optimize agai
arXiv:2510.08945v3 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) has emerged as a promising paradigm for improving factual accuracy in large language models (LLMs). We introduc
arXiv:2605.22954v1 Announce Type: new Abstract: Multi-center survival prediction can improve robustness and generalizability, yet privacy regulations and institutional governance often prevent pooling
arXiv:2512.12677v2 Announce Type: replace-cross Abstract: We explore efficient strategies to fine-tune decoder-only Large Language Models (LLMs) for downstream text classification under resource const
arXiv:2605.22898v1 Announce Type: new Abstract: Federated learning protocols face a structural trilemma: canonical server-based aggregation~ite{mcmahan2017} creates a single point of failure and gradi
arXiv:2605.23070v1 Announce Type: new Abstract: We propose Flow Mismatching, an unsupervised anomaly detection method that deliberately avoids reconstruction-based paradigms. Instead, we treat flow ma
arXiv:2602.00567v2 Announce Type: replace Abstract: The deployment of quantized neural networks on edge devices, combined with privacy regulations like GDPR, creates an urgent need for machine unlearn
arXiv:2509.12958v2 Announce Type: replace Abstract: Continual Learning (CL) models, while adept at sequential knowledge acquisition, face significant and often overlooked privacy challenges due to acc
arXiv:2605.23218v1 Announce Type: new Abstract: Autonomous agents are moving from tools into a layer of social infrastructure: they browse, purchase, deploy software, manage systems, and increasingly
arXiv:2605.23100v1 Announce Type: new Abstract: Legged robots carry an IMU, but the inertial solution drifts because consumer-grade IMUs are noisy. However, the feet create intermittent contacts with
arXiv:2605.23895v1 Announce Type: new Abstract: Identifying which brain regions represent a visual concept in the human brain is a central challenge in neuroscience. Existing approaches have localized
arXiv:2605.23382v1 Announce Type: new Abstract: Agentic reinforcement learning (Agentic RL) has achieved strong progress in tasks with clear success signals. However, many real-world agent application
arXiv:2605.23899v1 Announce Type: new Abstract: Language agents increasingly improve by reusing skills -- structured procedural artifacts distilled from past experience. In particular, domain-level an
arXiv:2605.22897v1 Announce Type: new Abstract: A persistent challenge in machine learning for scientific applications is jointly achieving prediction and understanding. Statistical models excel on st
arXiv:2605.22848v1 Announce Type: cross Abstract: Global food security depends on predicting crop responses to climate variability, yet process based crop models remain too computationally expensive f
arXiv:2605.22869v1 Announce Type: new Abstract: Both full fine-tuning (Full FT) and parameter-efficient fine-tuning methods such as LoRA introduce weight updates without accounting for the spectral st
arXiv:2605.22868v1 Announce Type: new Abstract: Autonomous systems and smart-industry deployments increasingly split computation across near-sensor, edge, and cloud resources, where tight energy, late
arXiv:2506.14135v5 Announce Type: replace-cross Abstract: Accurate scene perception is critical for vision-based robotic manipulation. Existing approaches typically follow either a Vision-to-Action (V
arXiv:2605.22962v1 Announce Type: new Abstract: Video recordings of child-caregiver interactions enable investigation of attentional dynamics during naturalistic behavior. Such multimodal recording al
arXiv:2605.22882v1 Announce Type: new Abstract: Video world models can generate realistic futures from a single instruction, but they often fail to preserve consistent point-level motion over time. As
arXiv:2605.23078v1 Announce Type: cross Abstract: Mixture-of-Experts Large Language Models (MoE-LLMs) achieve strong performance but incur substantial memory overhead due to massive expert parameters.
arXiv:2605.23304v1 Announce Type: new Abstract: Hazard, as an abstract concept, is typically defined through cognitive-level logical reasoning rather than concrete examples. In contrast, existing haza
arXiv:2605.23159v1 Announce Type: cross Abstract: Generative artificial intelligence (AI) is expected to transform work, but less is known about how firms reorganize labor demand as the technology dif