Search: “models”

A unifying Bayesian framework for adversarial robustness

arXiv:2510.09288v2 Announce Type: replace-cross Abstract: The vulnerability of machine learning models to adversarial attacks remains a critical societal security challenge. Traditional defenses, such

researcharxiv-cs-lg

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ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition

DGX agent

arXiv:2601.19919v2 Announce Type: replace-cross Abstract: Knowledge distillation (KD) is one of the most effective paradigms for compressing large-scale foundation models into deployable architectures

researcharxiv-cs-ai

2 Jun 2026

Attention mechanisms and transfer learning for robust peach leaf damage classification under domain shift

arXiv:2606.02045v1 Announce Type: cross Abstract: Artificial intelligence provides a practical framework for crop damage assessment from imagery data, supporting early decision-making in agricultural

AXIOM: A Trust-First Neuro-Symbolic Execution Architecture for Verifiable Mathematical Reasoning

arXiv:2606.00671v1 Announce Type: new Abstract: We present AXIOM, a trust-first neuro-symbolic execution architecture for natural-language mathematical reasoning. In AXIOM, the language model function

Better with Experience: Self-Evolving LLM Agents for Evidence-Grounded Health Community Notes

arXiv:2606.02215v1 Announce Type: new Abstract: Large Language Model (LLM)-augmented Community Notes offer a scalable path for timely, evidence-grounded correction of health misinformation on social p

Beyond Rigid: Benchmarking Non-Rigid Video Editing

arXiv:2601.18340v2 Announce Type: replace Abstract: As video generation models are increasingly expected to manipulate physical dynamics, there is a growing need to move evaluation beyond appearance f

Beyond Scalar Rewards: Dense Feedback for LLM Policy Synthesis in Sequential Social Dilemmas

arXiv:2603.19453v2 Announce Type: replace Abstract: We study LLM policy synthesis: using a language model to iteratively generate programmatic agent policies for multi-agent environments. Rather than

CART: Context-Anchored Recurrent Transformer -- A Parameter-Efficient Architecture with Learned Stability

arXiv:2606.01495v1 Announce Type: cross Abstract: We present CART (Context-Anchored Recurrent Transformer), a parameter-efficient language model that reuses a single shared core block R times across d

Claudini: Autoresearch Discovers State-of-the-Art Adversarial Attack Algorithms for LLMs

arXiv:2603.24511v2 Announce Type: replace-cross Abstract: We show that AI agents are capable of discovering novel algorithms for adversarial attacks against LLMs, advancing the state of the art on whi

Continuous Reasoning for Vision-Language-Action

arXiv:2606.00229v1 Announce Type: cross Abstract: Natural language is a powerful reasoning medium for language and vision-language models, but it is mismatched to the granularity of continuous control

ContinuousBench: Can Differentially Private Synthetic Text Improve Capabilities?

arXiv:2606.01849v1 Announce Type: cross Abstract: Differentially private (DP) text synthesis promises to unlock sensitive corpora for model training, but it remains unclear whether DP synthetic data t

CRAM: Centroid-Routing and Adaptive MoE for Multimodal Continual Instruction Tuning

arXiv:2606.02502v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) unify heterogeneous vision-language tasks under a shared generative framework via instruction tuning, yet real-

CRMA: A Spectrally-Bounded Backbone for Modular Continual Fine-Tuning of LLMs

arXiv:2606.00382v1 Announce Type: new Abstract: Sequential fine-tuning of large language models forces a choice: let the shared substrate keep learning and accept catastrophic forgetting, or freeze it

Cross-Generational Transfer of Adversarial Attacks Reveals Non-Monotonic Safety Alignment in LLMs

arXiv:2606.00813v1 Announce Type: cross Abstract: Safety alignment in LLMs does not improve monotonically across model generations. Studying four generations of Google's Gemma family (7B-31B) with qua

CV-Arena: An Open Benchmark for Instructional Computer Vision Problem Solving with Human-AI Collaborative Preferences

arXiv:2606.00931v1 Announce Type: cross Abstract: Instruction-guided image editing is becoming a general interface for visual work, yet existing benchmarks still focus largely on narrow appearance edi

Data Collection for Training Quality-Control AI in Carpet Manufacturing

arXiv:2606.01023v1 Announce Type: cross Abstract: Visual inspection remains the dominant quality-control practice in woven and tufted carpet production, yet it is slow, subjective, and inconsistent at

DenseMLLM: Standard Multimodal LLMs for Dense Prediction

arXiv:2602.14134v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in high-level visual understanding. However, extending the

Safety

Dialectics of Alignment: Harnessing Unsafe Knowledge for Dynamic Safety Routing

arXiv:2606.00686v1 Announce Type: new Abstract: The prevailing paradigm in large language model (LLM) alignment operates via erasure, filtering unsafe data or training models to strictly refuse harmfu

safetyarxiv-cs-lg

Safety

DOT-MoE: Differentiable Optimal Transport for MoEfication

arXiv:2606.01666v1 Announce Type: cross Abstract: The scaling of Large Language Models (LLMs) has driven significant performance gains but created substantial challenges in inference efficiency. While

safetyarxiv-cs-ai

Dr. DocBench: A Comprehensive Benchmark for Expert-Level and Difficult Document Parsing

arXiv:2606.01393v1 Announce Type: cross Abstract: Document parsing and recognition are fundamental capabilities for vision-language models (VLMs) and document processing systems. However, existing Opt

Echo State Networks for Time Series Forecasting: Hyperparameter Sweep and Benchmarking

arXiv:2602.03912v4 Announce Type: replace Abstract: This paper investigates the performance of Echo State Networks (ESNs) for univariate forecasting of monthly and quarterly time series from the M4 Fo

eMoT: evolving Memory-of-Thought via Symbolic Anchoring and Memory Corrosion

arXiv:2606.02054v1 Announce Type: new Abstract: While Large Language Models (LLMs) achieve impressive performance on multi-step reasoning tasks, their reliability is persistently hindered by critical

Applications

Enhancing BiGRU with a KAN Block for Legal Document Classification and Summarization

arXiv:2606.00116v1 Announce Type: cross Abstract: This study introduces a novel architecture of KAN-based BiGRU model for the task of classification and summarization of legal documents in a low-resou

applicationsarxiv-cs-ai

EuraGovExam: A Multilingual Multimodal Benchmark from Real-World Civil Service Exams

arXiv:2603.27223v2 Announce Type: replace-cross Abstract: We present EuraGovExam, a multilingual and multimodal benchmark sourced from real-world civil service examinations across five representative

FLaG: Fine-Grained Latent Grouping for Hallucination Detection

arXiv:2606.00301v1 Announce Type: new Abstract: Hallucinations in large language models (LLMs) arise from heterogeneous failure mechanisms, making reliable detection difficult for any single global un

researcharxiv-cs-lg

Flow Matching for Convective-Scale Precipitation Downscaling

arXiv:2606.00281v1 Announce Type: cross Abstract: Generative machine learning is an increasingly important complement to dynamical downscaling for producing high-resolution precipitation projections,

From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures

arXiv:2602.04861v2 Announce Type: replace-cross Abstract: Machine Learning Interatomic Potentials (MLIPs) sometimes fail to reproduce the physical smoothness of the quantum potential energy surface (P

From Outliers to Errors: Auditing Pali-to-English LLM Translations with Multi-Reference Adjudication

arXiv:2606.01136v1 Announce Type: new Abstract: Single-score translation metrics can conflate legitimate variation with error, a problem especially acute for classical languages where multiple defensi

GABI: Geometry-Aware Boundary Integration for Spacecraft Segmentation

arXiv:2606.00886v1 Announce Type: new Abstract: Accurate segmentation is crucial for autonomous spacecraft, as it directly affects downstream tasks related to 3D situational awareness. The harsh illum

GateKD: Confidence-Gated Closed-Loop Distillation for Robust Reasoning

arXiv:2605.13136v2 Announce Type: replace Abstract: Distilling multi-step reasoning abilities from large language models (LLMs) into compact student models remains challenging due to noisy rationales,

researcharxiv-cs-cl

Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures

arXiv:2510.24081v2 Announce Type: replace Abstract: To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and

Hierarchical Online Prompt Mutation with Dual-Loop Feedback for Guardrailed Evidence Document Generation: A Production-Evaluation Case Study

arXiv:2606.01472v1 Announce Type: cross Abstract: High-stakes production document-generation systems require language models to be adaptive, evidence-grounded, and auditable. We present HOPM, a hierar

HomeFlow: A Data Flywheel for Smart Home Agent Training with Verifiable Simulation

arXiv:2606.01230v1 Announce Type: new Abstract: Large language model agents are moving beyond text-only interaction toward physical-world control, with smart homes as a representative domain. Real dom

Hybrid Imbalanced Regression Through Unified Data-Level and Algorithm-Level Balancing

arXiv:2606.01221v1 Announce Type: cross Abstract: Imbalanced learning is a critical challenge in machine learning, where underrepresented target values can bias models and degrade prediction performan

Ideas in Inference-time Scaling can Benefit Generative Pre-training Algorithms

arXiv:2503.07154v3 Announce Type: replace-cross Abstract: Generative pre-training is often framed through a false dichotomy between autoregressive models for discrete signals and diffusion models for

InsightVQA: High-Dimensional Emotion-Cognitive Visual Question Answering Benchmark

arXiv:2606.02171v1 Announce Type: new Abstract: Visual emotion understanding requires models not only to recognize emotional states, but also to why they arise and perform higher-level cognitive reaso

LALE: Lightweight-Transformer Architecture for Land-Cover Estimation

arXiv:2606.02092v1 Announce Type: cross Abstract: Semantic segmentation of remote sensing imagery requires models that capture both global context and local detail under tight computational budgets. P

LaSR: Context-Aware Speech Recognition via Latent Reasoning

arXiv:2606.00507v1 Announce Type: new Abstract: Recent advances in Speech Large Language Models (Speech LLMs) have significantly enhanced spoken language understanding and reasoning. However, their co

Agents

Latent Collaboration in Multi-Agent Systems

arXiv:2511.20639v3 Announce Type: replace-cross Abstract: Multi-agent systems (MAS) extend large language models (LLMs) from independent single-model reasoning to coordinative system-level intelligenc

agentsarxiv-cs-ai

LeAP: Learnable Adaptive Permutation for Feature Selection in Heterogeneous and Sparse Recommender Systems

arXiv:2606.01111v1 Announce Type: new Abstract: Modern industrial recommender systems rely on thousands of heterogeneous features -- ranging from low-dimensional scalars (e.g., statistical value) to h

Learning from Saturated Data: Signals Beyond Correctness for LLM Training

arXiv:2606.01436v1 Announce Type: new Abstract: The growing capabilities of large language models (LLMs) have led to the saturation of many benchmarks and training datasets used to improve them. Motiv

researcharxiv-cs-cl

LLM Consortium for Software Design Refinement: A Controlled Experiment on Multi-Agent Collaboration Topologies

arXiv:2606.01490v1 Announce Type: cross Abstract: We present a controlled experiment evaluating 12 multi-agent LLM collaboration topologies for software architecture design. Using a 2imes2imes2 factor

LLM4Cov: Execution-Aware Agentic Learning for High-coverage Testbench Generation

arXiv:2602.16953v3 Announce Type: replace Abstract: Execution-aware LLM agents offer a promising paradigm for learning from tool feedback, but such feedback can be expensive and slow to obtain, making

LLMs for Cardiovascular Risk Prediction from Structured Clinical Data

arXiv:2606.00031v1 Announce Type: cross Abstract: Coronary artery disease (CAD) remains one of the leading causes of death globally, highlighting the need for reliable predictive systems to support ea

Make a Video Call with LLM: A Measurement Campaign over Six Mainstream Apps

arXiv:2510.00481v2 Announce Type: replace-cross Abstract: In 2025, Large Language Model (LLM) services have launched a new feature -- AI video chat -- allowing users to interact with AI agents via rea

Med-Scout: Curing MLLMs' Geometric Blindness in Medical Perception via Geometry-Aware RL Post-Training

arXiv:2601.23220v2 Announce Type: replace-cross Abstract: Despite recent Multimodal Large Language Models (MLLMs)' linguistic prowess in medical diagnosis, we find even state-of-the-art MLLMs suffer f

Applications

MineDraft: A Framework for Batch Parallel Speculative Decoding

arXiv:2603.18016v2 Announce Type: replace-cross Abstract: Speculative decoding (SD) accelerates large language model inference by using a smaller draft model to propose draft tokens that are subsequen

applicationsarxiv-cs-ai

MixerSENet: A Lightweight Framework for Efficient Hyperspectral Image Classification

arXiv:2606.01700v1 Announce Type: new Abstract: In this paper, a novel framework, MixerSENet, is introduced for hyperspectral image (HSI) classification, designed to address the challenges of computat