Search: “models”

BARRED: Synthetic Training of Custom Policy Guardrails via Asymmetric Debate

arXiv:2604.25203v1 Announce Type: new Abstract: Deploying guardrails for custom policies remains challenging, as generic safety models fail to capture task-specific requirements, while prompting LLMs

safetyarxiv-cs-cl

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BLASST: Dynamic BLocked Attention Sparsity via Softmax Thresholding

DGX agent

arXiv:2512.12087v3 Announce Type: replace Abstract: The growing demand for long-context inference capabilities in Large Language Models (LLMs) has intensified the computational and memory bottlenecks

model-releasesarxiv-cs-cl

29 Apr 2026

Conditional Flow Matching for Probabilistic Downscaling of Maximum 3-day Snowfall in Alaska

arXiv:2604.25172v1 Announce Type: cross Abstract: Precipitation in complex terrain is governed by orographic processes operating at scales of a few kilometers, yet climate models typically run at reso

researcharxiv-cs-lg

Tutorials

DDA-Thinker: Decoupled Dual-Atomic Reinforcement Learning for Reasoning-Driven Image Editing

arXiv:2604.25477v1 Announce Type: new Abstract: Recent image editing models have achieved strong visual fidelity but often struggle with tasks requiring complex reasoning. To investigate and enhance t

tutorialsarxiv-cs-cv

DiffAdapt: Difficulty-Adaptive Reasoning for Token-Efficient LLM Inference

arXiv:2510.19669v4 Announce Type: replace Abstract: Recent reasoning Large Language Models (LLMs) demonstrate remarkable problem-solving abilities but often generate long thinking traces whose utility

researcharxiv-cs-cl

Doing More With Less: Revisiting the Effectiveness of LLM Pruning for Test-Time Scaling

arXiv:2604.25098v1 Announce Type: cross Abstract: While current Large Language Models (LLMs) exhibit remarkable reasoning capabilities through test-time compute scaling (TTS), their massive parameter

FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices

model-releasesarxiv-cs-lg

arXiv:2604.25421v1 Announce Type: new Abstract: Federated fine-tuning provides a practical route to adapt large language models (LLMs) on edge devices without centralizing private data, yet in mobile

Golden RPG: Confidence-Adaptive Region-Aware Noise for Compositional Text-to-Image Generation

arXiv:2604.25314v1 Announce Type: new Abstract: Compositional text-to-image (T2I) generation requires a model to honour multiple sub-prompts that describe distinct image regions. Recent work shows tha

Improving LLM Predictions via Inter-Layer Structural Encoders

arXiv:2603.22665v2 Announce Type: replace Abstract: The standard practice in Large Language Models (LLMs) is to base predictions on final-layer representations. However, intermediate layers encode com

Limited Linguistic Diversity in Embodied AI Datasets

arXiv:2601.03136v2 Announce Type: replace Abstract: Language plays a critical role in Vision-Language-Action (VLA) models, yet the linguistic characteristics of the datasets used to train and evaluate

researcharxiv-cs-cl

LLM-ReSum: A Framework for LLM Reflective Summarization through Self-Evaluation

arXiv:2604.25665v1 Announce Type: new Abstract: Reliable evaluation of large language model (LLM)-generated summaries remains an open challenge, particularly across heterogeneous domains and document

MICo-150K: A Comprehensive Dataset Advancing Multi-Image Composition

arXiv:2512.07348v2 Announce Type: replace Abstract: In controllable image generation, synthesizing coherent and consistent images from multiple reference inputs, i.e., Multi-Image Composition (MICo),

Quantifying and Mitigating Socially Desirable Responding in LLMs: A Desirability-Matched Graded Forced-Choice Psychometric Study

arXiv:2602.17262v2 Announce Type: replace Abstract: Human self-report questionnaires are increasingly used in NLP to benchmark and audit large language models (LLMs), from persona consistency to safet

RCProb: Probabilistic Rule Extraction for Efficient Simplification of Tree Ensembles

model-releasesarxiv-cs-lg

arXiv:2604.25304v1 Announce Type: new Abstract: Tree ensembles are widely used in industrial machine learning due to their strong predictive performance and efficient training procedures. However, as

Applications

Relational In-Context Learning via Synthetic Pre-training with Structural Prior

arXiv:2603.03805v2 Announce Type: replace Abstract: Relational Databases (RDBs) are the backbone of modern business, yet they lack foundation models comparable to those in text or vision. A key obstac

applicationsarxiv-cs-lg

ReSim: Reliable World Simulation for Autonomous Driving

arXiv:2506.09981v2 Announce Type: replace Abstract: How can we reliably simulate future driving scenarios under a wide range of ego driving behaviors? Recent driving world models, developed exclusivel

safetyarxiv-cs-cv

Sensitivity-Based Tube NMPC for Cooperative Aerial Structures Under Parametric Uncertainty

arXiv:2604.25766v1 Announce Type: new Abstract: This paper presents a sensitivity-based tube Nonlinear Model Predictive Control (NMPC) framework for cooperative aerial chains under bounded parametric

researcharxiv-cs-ro

TopoMamba: Topology-Aware Scanning and Fusion for Segmenting Heterogeneous Medical Visual Media

arXiv:2604.25545v1 Announce Type: new Abstract: Visual state-space models (SSMs) have shown strong potential for medical image segmentation, yet their effectiveness is often limited by two practical i

researcharxiv-cs-cv

When Thoughts Meet Facts: Reusable Reasoning for Long-Context LMs

arXiv:2510.07499v2 Announce Type: replace Abstract: Recent Long-Context Language Models (LCLMs) can process hundreds of thousands of tokens in a single prompt, enabling new opportunities for knowledge

Aligning with Your Own Voice: Self-Corrected Preference Learning for Hallucination Mitigation in LVLMs

arXiv:2604.24395v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) frequently suffer from hallucinations. Existing preference learning-based approaches largely rely on proprietary mo

Analytica: Soft Propositional Reasoning for Robust and Scalable LLM-Driven Analysis

arXiv:2604.23072v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly tasked with complex real-world analysis (e.g., in financial forecasting, scientific discovery), yet t

AsyncShield: A Plug-and-Play Edge Adapter for Asynchronous Cloud-based VLA Navigation

arXiv:2604.24086v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models have been demonstrated possessing strong zero-shot generalization for robot control, their massive parameter

Local Ai

Benchmarking Source-Sensitive Reasoning in Turkish: Humans and LLMs under Evidential Trust Manipulation

arXiv:2604.24665v1 Announce Type: cross Abstract: This paper investigates whether source trustworthiness shapes Turkish evidential morphology and whether large language models (LLMs) track this sensit

local-aiarxiv-cs-ai

Beyond Local vs. External: A Game-Theoretic Framework for Trustworthy Knowledge Acquisition

arXiv:2604.23413v1 Announce Type: new Abstract: Cloud-hosted Large Language Models (LLMs) offer unmatched reasoning capabilities and dynamic knowledge, yet submitting raw queries to these external ser

BIR-Adapter: A parameter-efficient diffusion adapter for blind image restoration

arXiv:2509.06904v3 Announce Type: replace Abstract: We introduce the BIR-Adapter, a parameter-efficient diffusion adapter for blind image restoration. Diffusion-based restoration methods have demonstr

ClawMark: A Living-World Benchmark for Multi-Turn, Multi-Day, Multimodal Coworker Agents

arXiv:2604.23781v1 Announce Type: new Abstract: Language-model agents are increasingly used as persistent coworkers that assist users across multiple working days. During such workflows, the surroundi

Applications

Computational Design and Co-Robotic Fabrication for Material Reuse in Architecture

arXiv:2604.24648v1 Announce Type: new Abstract: Climate change and resource depletion demand a shift from the dominant linear 'take-make-use-dispose' paradigm of construction toward circular, low-wast

applicationsarxiv-cs-ro

Continual Calibration: Coverage Can Collapse Before Accuracy in Lifelong LLM Fine-Tuning

arXiv:2604.23987v1 Announce Type: new Abstract: Continual learning for large language models is typically evaluated through accuracy retention under sequential fine-tuning. We argue that this perspect

researcharxiv-cs-lg

Cortex-Inspired Continual Learning: Unsupervised Instantiation and Recovery of Functional Task Networks

arXiv:2604.24637v1 Announce Type: cross Abstract: Block-sequential continual learning demands that a single model both protect prior solutions from catastrophic forgetting and efficiently infer at inf

Coverage-Based Calibration for Post-Training Quantization via Weighted Set Cover over Outlier Channels

model-releasesarxiv-cs-lg

arXiv:2604.24008v1 Announce Type: new Abstract: Post-Training Quantization (PTQ) compresses large language models to low bit-widths using a small calibration set, and its quality depends strongly on w

DecompKAN: Decomposed Patch-KAN for Long-Term Time Series Forecasting

arXiv:2604.23968v1 Announce Type: cross Abstract: Accurate time series forecasting in scientific domains such as climate modeling, physiological monitoring, and energy systems benefits from both compe

Tutorials

Deep Learning of Solver-Aware Turbulence Closures from Nudged LES Dynamics

arXiv:2604.23874v1 Announce Type: cross Abstract: Deep learning approaches have shown remarkable promise in turbulence closure modeling for large eddy simulations (LES). The differentiable physics par

tutorialsarxiv-cs-lg

Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing

arXiv:2604.24162v1 Announce Type: cross Abstract: Defending against backdoor attacks in large language models remains a critical practical challenge. Existing defenses mitigate these threats but typic

Deploy DINO with Many-to-Many Association

arXiv:2604.23670v1 Announce Type: new Abstract: Motivated by the limited generalization of supervised image matching models to unseen image domains, we explore the zero-shot deployment of DINO feature

DiffuSAM: Diffusion-Based Prompt-Free SAM2 for Few-Shot and Source-Free Medical Image Segmentation

arXiv:2604.24719v1 Announce Type: new Abstract: Segmentation models such as Segment Anything Model (SAM) and SAM2 achieve strong prompt-driven zero-shot performance. However, their training on natural

researcharxiv-cs-cv

EmoTrans: A Benchmark for Understanding, Reasoning, and Predicting Emotion Transitions in Multimodal LLMs

arXiv:2604.23348v1 Announce Type: cross Abstract: Recent multimodal large language models (MLLMs) have shown strong capabilities in perception, reasoning, and generation, and are increasingly used in

Applications

Expert Evaluation of LLM's Open-Ended Legal Reasoning on the Japanese Bar Exam Writing Task

arXiv:2604.23730v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong performance on legal benchmarks, including multiple-choice components of bar exams. However, their capaci

applicationsarxiv-cs-ai

Explanation Quality Assessment as Ranking with Listwise Rewards

arXiv:2604.24176v1 Announce Type: new Abstract: We reformulate explanation quality assessment as a ranking problem rather than a generation problem. Instead of optimizing models to produce a single 'b

FlashOverlap: Minimizing Tail Latency in Communication Overlap for Distributed LLM Training

arXiv:2604.24013v1 Announce Type: cross Abstract: The rapid growth in the size of large language models has necessitated the partitioning of computational workloads across accelerators such as GPUs, T

researcharxiv-cs-cv

Generating Place-Based Compromises Between Two Points of View

arXiv:2604.24536v1 Announce Type: new Abstract: Large Language Models (LLMs) excel academically but struggle with social intelligence tasks, such as creating good compromises. In this paper, we presen

Green Prompting: Characterizing Prompt-driven Energy Costs of LLM Inference

arXiv:2503.10666v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have become widely used across various domains spanning search engines, code generation, and text creation. Howev

researcharxiv-cs-ai

Isotonic Layer: A Unified Framework for Recommendation Calibration and Debiasing

arXiv:2603.06589v2 Announce Type: replace-cross Abstract: Model calibration and debiasing are fundamental yet operationally expensive challenges in large-scale recommendation systems. Existing approac

KLong: Training LLM Agent for Extremely Long-horizon Tasks

arXiv:2602.17547v3 Announce Type: replace Abstract: This paper introduces KLong, an open-source LLM agent trained to solve extremely long-horizon tasks. The principle is to first cold-start the model

MEG-RAG: Quantifying Multi-modal Evidence Grounding for Evidence Selection in RAG

arXiv:2604.24564v1 Announce Type: new Abstract: Multimodal Retrieval-Augmented Generation (MRAG) addresses key limitations of Multimodal Large Language Models (MLLMs), such as hallucination and outdat

MEMCoder: Multi-dimensional Evolving Memory for Private-Library-Oriented Code Generation

arXiv:2604.24222v1 Announce Type: cross Abstract: Large Language Models (LLMs) excel at general code generation, but their performance drops sharply in enterprise settings that rely on internal privat

MetaErr: Towards Predicting Error Patterns in Deep Neural Networks

arXiv:2604.23289v1 Announce Type: cross Abstract: Due to the unprecedented success of deep learning, it has become an integral component in several multimedia computing applications in todays world. U

Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training

arXiv:2506.20332v4 Announce Type: replace Abstract: Vision-language model-based mobile agents have gained the ability to understand complex instructions and mobile screenshots, benefiting from reinfor

MuSS: A Large-Scale Dataset and Cinematic Narrative Benchmark for Multi-Shot Subject-to-Video Generation

arXiv:2604.23789v1 Announce Type: new Abstract: While video foundation models excel at single-shot generation, real-world cinematic storytelling inherently relies on complex multi-shot sequencing. Fur