Search: “models”

arXiv:2605.09995v1 Announce Type: new Abstract: Post-training (via supervised fine-tuning) improves instruction-following, but often induces semantic mode collapse by biasing models toward low-entropy

arXiv:2605.09407v1 Announce Type: new Abstract: Modern object detectors are static, fixed-depth networks optimized for a single operating point, requiring separate models for different deployment scen

arXiv:2605.10876v1 Announce Type: cross Abstract: Recent advances in machine learning and large-scale biological data collections have revived the prospect of building a virtual cell, a computational

arXiv:2511.13415v2 Announce Type: replace-cross Abstract: Visual document retrieval requires understanding heterogeneous and multi-modal content to satisfy implicit information needs. Recent advances

arXiv:2602.09534v2 Announce Type: replace Abstract: Realistic talking-head video generation is critical for virtual avatars, film production, and interactive systems. Current methods struggle with nua

arXiv:2605.10146v1 Announce Type: new Abstract: Large language models (LLMs) increasingly rely on knowledge editing to support knowledge-intensive reasoning, but this flexibility also introduces criti

arXiv:2605.10764v1 Announce Type: cross Abstract: Recent studies show that gradient-based universal image jailbreaks on vision-language models (VLMs) exhibit little or no cross-model transferability,

arXiv:2605.10873v1 Announce Type: cross Abstract: Recovering editable CAD programs from images or 3D observations is central to AI-assisted design, but progress is difficult to measure because existin

arXiv:2605.08325v1 Announce Type: cross Abstract: Many vision datasets now provide segmentation masks in addition to annotated images to support a wide range of tasks. In this work, we propose Class A

arXiv:2512.18880v2 Announce Type: replace-cross Abstract: Accurate estimation of item (question or task) difficulty is critical for educational assessment but suffers from the cold start problem. Whil

arXiv:2605.08556v1 Announce Type: new Abstract: LLMs are increasingly used to make or support high-stakes decisions under uncertainty, where alignment depends not only on factual accuracy but on how m

arXiv:2605.08590v1 Announce Type: cross Abstract: LLMs are increasingly used to explain personal sensing data, translating traces of activity and mood into natural-language accounts of why an anomalou

arXiv:2605.09350v1 Announce Type: new Abstract: Smart-contract exploits have caused billions of USD in cumulative losses, yet audits remain expensive and slow. Automated tools have emerged to close th

arXiv:2506.12090v2 Announce Type: replace Abstract: This paper introduces ChatbotManip, a novel dataset for studying manipulation in Chatbots. It contains simulated generated conversations between a c

arXiv:2605.08730v1 Announce Type: new Abstract: Class-level machine unlearning aims to remove the influence of specified classes while preserving model utility on retained classes. Existing methods ar

arXiv:2605.10510v1 Announce Type: cross Abstract: Biomedical knowledge graphs are increasingly large, dynamic, and multimodal, driven by rapid advances in biotechnology such as high-throughput sequenc

arXiv:2605.09675v1 Announce Type: new Abstract: Clinical reasoning agents based on large language models (LLMs) aim to automate tasks such as intensive care unit (ICU) monitoring and patient state tra

arXiv:2605.10916v1 Announce Type: cross Abstract: Recognition of handwritten Bangla compound characters remains a challenging problem due to complex character structures, large intra-class variation,

arXiv:2605.08112v1 Announce Type: cross Abstract: AI coding agents powered by large language models can read codebases and produce functional code, but they routinely violate team-specific product dec

arXiv:2605.10597v1 Announce Type: cross Abstract: Benchmarks for coding agents increasingly measure source-level software repair, and cybersecurity benchmarks increasingly measure broad capture-the-fl

arXiv:2605.09486v1 Announce Type: cross Abstract: Graph Neural Networks (GNN) and Transformer-based architectures have achieved remarkable progress in graph learning, yet they still struggle to captur

arXiv:2605.10933v1 Announce Type: cross Abstract: While Mixture-of-Experts (MoE) scales model capacity without proportionally increasing computation, its massive total parameter footprint creates sign

arXiv:2605.10569v1 Announce Type: new Abstract: Deep learning has become the dominant approach for creating high capacity, scalable models across diverse data modalities. However, because these models

arXiv:2508.06248v4 Announce Type: replace Abstract: The generalization of deepfake detectors to unseen manipulation techniques remains a challenge for practical deployment. Although many approaches ad

arXiv:2605.09028v1 Announce Type: new Abstract: Machine learning-based Android malware detectors often fail in real-world deployment due to domain shift, where models trained on one data source perfor

arXiv:2605.08237v1 Announce Type: new Abstract: In grokking, a model first fits the training data while test accuracy remains low, and only later begins to generalize. We ask whether this transition c

arXiv:2605.09391v1 Announce Type: new Abstract: It is becoming increasingly necessary to have monitors check for harmful behaviors during language model interactions, but text-only monitoring has not

arXiv:2605.08961v1 Announce Type: new Abstract: We present Dolphin-CN-Dialect, a streaming-capable ASR model with a focus on Chinese and dialect-rich scenarios. Compared to the previous version, Dolph

arXiv:2605.08747v1 Announce Type: new Abstract: Standard embodied evaluations do not independently score whether an agent correctly commits to task completion at episode closure, a capacity we call te

arXiv:2605.09497v1 Announce Type: new Abstract: Vision-language model (VLM) based web agents demonstrate impressive autonomous GUI interaction but remain vulnerable to deceptive interface elements. Ex

arXiv:2605.06644v2 Announce Type: replace Abstract: Fluorescent protein quantum yield (QY) is governed by the mature chromophore and its three-dimensional microenvironment rather than sequence identit

arXiv:2605.09588v1 Announce Type: cross Abstract: Organizations increasingly deploy multiple AI systems across task domains, but selecting a small, high-performing ensemble can require costly model ca

arXiv:2509.21892v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) models typically fix the number of activated experts k at both training and inference. However, real-world deployment

arXiv:2605.10938v1 Announce Type: cross Abstract: Diffusion and flow-based models have become the de facto approaches for generating continuous data, e.g., in domains such as images and videos. Their

arXiv:2505.22919v3 Announce Type: replace Abstract: Existing benchmarks for evaluating the clinical reasoning capabilities of large language models (LLMs) often lack a clear definition of 'clinical re

arXiv:2601.21484v2 Announce Type: replace Abstract: Reinforcement Learning (RL) post-training alignment for language models is effective, but also costly and unstable in practice, owing to its complic

arXiv:2605.10795v1 Announce Type: cross Abstract: Large language models demonstrate remarkable ability in factual recall, yet the fundamental limits of storing and retrieving input--output association

arXiv:2605.10302v1 Announce Type: new Abstract: Existing approaches to controllable generation typically rely on fine-tuning, auxiliary networks, or test-time search. We show that flow matching admits

arXiv:2605.08905v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable success on reasoning benchmarks through Reinforcement Learning with Verifiable Rewards (RLVR), exc

arXiv:2505.05406v2 Announce Type: replace Abstract: News headlines and summaries shape how events are interpreted through selective emphasis and omission, a phenomenon commonly referred to as framing.

arXiv:2605.09925v1 Announce Type: new Abstract: Medical image segmentation is a critical task in computer-aided diagnosis and treatment planning. However, deep learning models often struggle to genera

arXiv:2605.10115v1 Announce Type: new Abstract: Tackling the task of materials generation, we aim to enhance the previously proposed All-atom Diffusion Transformer (ADiT) by introducing SymADiT, a sym

arXiv:2605.10645v1 Announce Type: new Abstract: Data-driven medical AI is traditionally formulated as a discriminative mapping from input X to output Y via a learned function f, which does not general

arXiv:2603.12275v1 Announce Type: cross Abstract: Unlearning knowledge is a pressing and challenging task in Large Language Models (LLMs) because of their unprecedented capability to memorize and dige

arXiv:2605.09408v1 Announce Type: new Abstract: Link prediction (inferring missing or future connections between nodes in a graph) is a fundamental problem in network science with widespread applicati

arXiv:2605.09972v1 Announce Type: cross Abstract: End-to-end autonomous driving has witnessed rapid progress, yet existing benchmarks are increasingly saturated, with state-of-the-art models achieving