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Adversarial Fine-tuning of Compressed Neural Networks for Joint Improvement of Robustness and Efficiency

model-releasesarxiv-cs-lg

arXiv:2403.09441v2 Announce Type: replace Abstract: As deep learning (DL) models are increasingly being integrated into our everyday lives, ensuring their safety by making them robust against adversar

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AdvJudge-Zero: Binary Decision Flips in LLM-as-a-Judge via Adversarial Control Tokens

DGX agent

arXiv:2512.17375v2 Announce Type: replace-cross Abstract: LLM-as-a-Judge systems supply the reward signal in modern RLHF and RLVR pipelines, but their binary verdict reduces to a single linear readout

model-releasesarxiv-cs-cl

28 May 2026

ADWIN: Adaptive Windows for Horizon-Aware On-Policy Distillation

arXiv:2605.28396v1 Announce Type: cross Abstract: On-policy distillation (OPD) transfers reasoning behavior by training a student on teacher feedback along student-generated trajectories, but standard

safetyarxiv-cs-ai

Affective Music Recommendation: A Rollout-Based World Model for Offline Preference Optimization

arXiv:2605.28810v1 Announce Type: new Abstract: Functional music applications, from consumer focus and sleep aids to clinical interventions, share a distinctive recommendation problem: success is defi

safetyarxiv-cs-lg

AgensFlow: A Coordination-Policy Substrate for Multi-Agent Systems

arXiv:2605.27466v1 Announce Type: cross Abstract: Multi-agent systems built on large language models (LLMs) require many coordination choices that are difficult to fix a priori: which skill protocol t

safetyarxiv-cs-ai

Agent Explorative Policy Optimization for Multimodal Agentic Reasoning

arXiv:2605.28774v1 Announce Type: new Abstract: Vision-language models with extended reasoning succeed on complex problems, but many real-world problems require external tools that internal reasoning

safetyarxiv-cs-cl

Agentic Active Omni-Modal Perception for Multi-Hop Audio-Visual Reasoning

arXiv:2605.28192v1 Announce Type: new Abstract: Multi-hop audio-visual reasoning remains challenging for Omni-LLMs, as relevant evidence is often sparse, temporally dispersed, and distributed across b

Agentic Language-to-Objective Synthesis for Optofluidic Assembly

arXiv:2605.27643v1 Announce Type: new Abstract: Light-based advanced manufacturing increasingly requires programmable, closed-loop tools that translate human design intent into executable operations a

agentsarxiv-cs-ro

Agentic Literacy Debt: A Structural Problem the AI Literacy Field Has Not Yet Named

arXiv:2605.27396v1 Announce Type: cross Abstract: Autonomous AI agents now plan, decide, and act on behalf of users across healthcare, financial services, and workplace contexts, often without step-by

Agentic Separation Logic Specification Synthesis

arXiv:2605.27531v1 Announce Type: cross Abstract: Specification synthesis, the task of automatically inferring formal specifications from program implementations and natural language, is important for

Agents that Matter: Optimizing Multi-Agent LLMs via Removal-Based Attribution

arXiv:2605.27621v1 Announce Type: cross Abstract: As multi-agent systems (MAS) become increasingly complex, identifying the contributions of individual agents is critical for system optimization. Howe

safetyarxiv-cs-cl

Agyn: An Open-Source Platform for AI Agents with Scalable On-Demand Execution, Agent Definition as a Code, and Zero-Trust Access

arXiv:2605.27575v1 Announce Type: new Abstract: As organizations move toward production deployments of AI agents, which execute non-deterministic workflows, maintain stateful sessions, and often opera

Applications

AI in the Workplace: The Impact of AI on Perceived Job Decency and Meaningfulness

arXiv:2605.28680v1 Announce Type: cross Abstract: The proliferation of Artificial Intelligence (AI) in workplaces is transforming how we work. While existing research on human-AI collaboration at work

applicationsarxiv-cs-ai

AI Research Agents Narrow Scientific Exploration

arXiv:2605.27905v1 Announce Type: new Abstract: AI research agents can now generate research ideas, design experiments, run code, and draft papers, raising the possibility of large-scale AI-assisted s

agentsarxiv-cs-cl

AI, Take the Wheel: What Drives Delegation and Trust in Human-Computer Cooperative Question Answering?

arXiv:2605.28255v1 Announce Type: new Abstract: AI systems are fallible, and humans can make mistakes in deciding whether to trust AI over their own judgment. Thus, improving human-AI collaboration re

safetyarxiv-cs-ai

AIBuildAI-2: A Knowledge-Enhanced Agent for Automatically Building AI Models

arXiv:2605.27873v1 Announce Type: new Abstract: AI models underpin data-centric applications from image and text processing to scientific discovery in biology, physics, and chemistry. Yet developing t

Aligning Language Model Benchmarks with Pairwise Preferences

arXiv:2602.02898v2 Announce Type: replace Abstract: Language model benchmarks are pervasive and computationally-efficient proxies for real-world performance. However, many recent works find that bench

Tutorials

Aligning LLMs with Human Uncertainty: A Beta-Bernoulli Calibrator for LLM Forecasting

arXiv:2605.27668v1 Announce Type: cross Abstract: Probabilistic forecasting estimates the likelihood of uncertain future events. To improve LLM forecasting, existing methods typically learn from binar

tutorialsarxiv-cs-ai

AlphaForgeBench: Benchmarking End-to-End Trading Strategy Design with Large Language Models

arXiv:2602.18481v2 Announce Type: replace-cross Abstract: The rapid advancement of Large Language Models (LLMs) has led to a surge of financial benchmarks, evolving from static knowledge evaluation to

AlphaTransit: Learning to Design City-scale Transit Routes

arXiv:2605.28730v1 Announce Type: new Abstract: Designing a transit network requires many sequential route extension decisions, but their quality is often visible only after the full network is assemb

Alterbute: Editing Intrinsic Attributes of Objects in Images

arXiv:2601.10714v2 Announce Type: replace Abstract: We introduce Alterbute, a diffusion-based method for editing an object's intrinsic attributes in an image. We allow changing color, texture, materia

researcharxiv-cs-cv

An analytic theory of convolutional neural network inverse problems solvers

arXiv:2601.10334v2 Announce Type: replace Abstract: Supervised convolutional neural networks (CNNs) are widely used to solve imaging inverse problems, achieving state-of-the-art performance in numerou

researcharxiv-cs-cv

An Empirical Audit of k-NAF Budget Accounting for Anchored Decoding

arXiv:2605.28001v1 Announce Type: new Abstract: We empirically audit the k-NAF budget-accounting mechanism in Anchored Decoding using (i) a fixed, class-stratified workload (approximately 8,500 random

researcharxiv-cs-ai

An Empirical Study on Variance-based MC Dropout Uncertainty-Error Correlation in 2D Brain Tumor Segmentation

arXiv:2510.15541v2 Announce Type: replace-cross Abstract: Accurate brain tumor segmentation from MRI is vital for diagnosis and treatment planning. Although Monte Carlo (MC) Dropout is widely used to

researcharxiv-cs-cv

An Enhanced Large Neighborhood Search Approach for the Capacitated Facility Location Problem with Incompatible Customers

arXiv:2605.28337v1 Announce Type: new Abstract: A new variant of the classic capacitated facility location problem, which considers incompatibilities between customers, has recently been introduced in

Local Ai

An Evolutionary Approach for Designing Stable and Highly Expressible Low-Immunogenicity Therapeutic mRNA Sequences

arXiv:2605.27986v1 Announce Type: new Abstract: Messenger RNA (mRNA) sequences as therapeutics require optimized design to ensure efficient translation, structural stability, and minimal immunogenicit

local-aiarxiv-cs-cl

An LLM-Based Assistance System for Intuitive and Flexible Capability-Based Planning

arXiv:2605.28666v1 Announce Type: new Abstract: In modern industry, dynamic environments and the complexity of modular and reconfigurable resources require automated planning of process sequences. Cap

An Operator-Based Approach to STL

arXiv:2605.28092v1 Announce Type: new Abstract: Signal Temporal Logic (STL), has recently seen extensive development, owing to its rich expressivenes for autonomous planning and control. Nevertheless,

agentsarxiv-cs-ro

Applications

Analyzing Cancer Patients' Experiences with Embedding-based Topic Modeling and LLMs

arXiv:2601.12154v2 Announce Type: replace Abstract: This study investigates the use of neural topic modeling and LLMs to uncover meaningful themes from patient storytelling data, to offer insights tha

applicationsarxiv-cs-cl

Analyzing Quality-Latency-Resource Trade-offs in a Technical Documentation RAG Assistant Using LoRA Adaptation

arXiv:2605.28222v1 Announce Type: new Abstract: We study quality-latency-resource trade-offs in a documentation-grounded retrieval-augmented generation (RAG) system that uses Low-Rank Adaptation (LoRA

AndroidDaily: A Verifiable Benchmark for Mobile GUI Agents on Real-World Closed-Source Applications

model-releasesarxiv-cs-cv

arXiv:2605.27761v1 Announce Type: new Abstract: The rapid development of GUI foundation models and mobile GUI agents has spurred numerous evaluation benchmarks, yet most rely on simulated environments

Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization

arXiv:2605.28428v1 Announce Type: cross Abstract: Detecting subtle visual anomalies in images remains challenging, particularly when only normal samples are available a priori. Such unsupervised anoma

researcharxiv-cs-ai

AOE: Exhaustive Out-of-Distribution Detection via Recalibrating Outlier Labels

arXiv:2605.28021v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world and safety-critical scenarios, where test inputs ma

safetyarxiv-cs-lg

Apple Intelligence Foundation Language Models

arXiv:2407.21075v2 Announce Type: replace Abstract: We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficie

Applications of temporal graph learning for predicting the dynamics of biological systems

arXiv:2605.28659v1 Announce Type: new Abstract: Biological foundation models have shown strong performance in single-cell representation learning by applying transformer architectures directly to gene

researcharxiv-cs-lg

Applications

Architecture-driven Shift: towards a lightweight selector for capturing the trends of logit shift

arXiv:2605.27469v1 Announce Type: cross Abstract: Continual Learning (CL) is a practical paradigm to utilize power of deep pre-trained neural networks, but which pre-trained model has a better ability

applicationsarxiv-cs-ai

Are Large Pre-trained Vision Language Models Effective Construction Safety Inspectors?

model-releasesarxiv-cs-cv

arXiv:2508.11011v2 Announce Type: replace Abstract: Construction safety inspections typically involve a human inspector identifying safety concerns on-site. With the rise of powerful Vision Language M

Are We Truly Innovating? A Qualitative and Quantitative Study of Originality in AI Research Papers

arXiv:2602.06054v3 Announce Type: replace Abstract: Assessing originality in AI research is arguably the most consequential yet least reliable step in peer review. Reviewer judgments of originality re

researcharxiv-cs-cl

AREA: Attribute Extraction and Aggregation for CLIP-Based Class-Incremental Learning

arXiv:2605.28809v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) is important in building real-world learning systems. In CLIP-based CIL, the model performs classification by comparing

safetyarxiv-cs-cv

Argument Quality Assessment with Large Language Models: A Pairwise Bradley-Terry Approach

arXiv:2605.28313v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in tasks related to reasoning and judgment. However, assessing the quality of arg

Ariel-ML: Computing Parallelization with Embedded Rust for Neural Networks on Heterogeneous Multi-core Microcontrollers

model-releasesarxiv-cs-lg

arXiv:2512.09800v2 Announce Type: replace Abstract: Low-power microcontroller (MCU) hardware is currently evolving from single-core architectures to predominantly multi-core architectures. In parallel

Artemis: Structured Visual Reasoning for Perception Policy Learning

arXiv:2512.01988v2 Announce Type: replace Abstract: Recent reinforcement-learning frameworks for visual perception policy usually incorporate intermediate reasoning chains expressed in natural languag

safetyarxiv-cs-cv

Ask Now, Use Later: Benchmarking the Proactivity Gap in Long-Lived LLM Agents

arXiv:2605.28108v1 Announce Type: new Abstract: A long-lived LLM agent, such as OpenClaw, earns its value by acting on a user's preferences and constraints across sessions, not just the current reques

Asking Is Not Enough: Protocol Sensitivity in LLM Confidence Calibration

arXiv:2605.27752v1 Announce Type: new Abstract: LLM confidence calibration is often evaluated by comparing two signals: token-probability scores and verbalized confidence. These signals are sometimes

researcharxiv-cs-ai

AssertLLM2: A Comprehensive LLM Benchmark for Assertion Generation from Design Specifications

arXiv:2605.27472v1 Announce Type: cross Abstract: Assertion-based verification (ABV) is a cornerstone of modern hardware design, yet manually translating design intent into formal SystemVerilog Assert

Assessing Factual Music Comprehension in Large Audio Language Models

arXiv:2511.05550v2 Announce Type: replace-cross Abstract: Large audio language models (LALMs) leverage multimodal representations to generate open-ended answers to natural language queries about audio

ASTRA: Communication-Efficient Acceleration for Multi-Device Transformer Inference

arXiv:2505.19342v2 Announce Type: replace-cross Abstract: Multi-device inference can reduce Transformer latency by parallelizing computation. However, existing methods require high inter-device bandwi

Asynchronous Remote Sensing Time-Series Fusion for Cloud Removal and Anytime Reconstruction

model-releasesarxiv-cs-cv

arXiv:2605.27726v1 Announce Type: new Abstract: Frequent cloud cover severely limits the usability of Sentinel-2 (S2) optical time series for Earth surface monitoring. Sentinel-1 (S1) SAR provides all