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arXiv:2605.09287v1 Announce Type: new Abstract: Large Language Model (LLM)-based search agents trained with reinforcement learning (RL) have significantly improved the performance of knowledge-intensi

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Position: Stop Evaluating AI with Human Tests, Develop Principled, AI-specific Tests instead

DGX agent

arXiv:2507.23009v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved remarkable results on a range of standardized tests originally designed to assess human cognitive a

model-releasesarxiv-cs-ai

12 May 2026

Prediction Bottlenecks Don't Discover Causal Structure (But Here's What They Actually Do)

arXiv:2605.09169v1 Announce Type: cross Abstract: A Mamba state-space model trained only for next-step prediction appears to recover Granger-causal structure through a simple readout S = |W_{out} W_{i

PRIM: Meta-Learned Bayesian Root Cause Analysis

arXiv:2605.08786v1 Announce Type: new Abstract: Root cause analysis (RCA) in complex systems is challenging due to error propagation across multiple variables, the need for structural causal knowledge

Quantifying Concentration Phenomena of Mean-Field Transformers in the Low-Temperature Regime

arXiv:2605.10931v1 Announce Type: cross Abstract: Transformers with self-attention modules as their core components have become an integral architecture in modern large language and foundation models.

Re^2Math: Benchmarking Theorem Retrieval in Research-Level Mathematics

arXiv:2605.09012v1 Announce Type: new Abstract: Large language models are increasingly capable at closed-world mathematical reasoning, but research assistance also requires source-grounded use of the

Rennala MVR: Improved Time Complexity for Parallel Stochastic Optimization via Momentum-Based Variance Reduction

arXiv:2605.08871v1 Announce Type: cross Abstract: Large-scale machine learning models are trained on clusters of machines that exhibit heterogeneous performance due to hardware variability, network de

Rethinking Agentic Search with Pi-Serini: Is Lexical Retrieval Sufficient?

arXiv:2605.10848v1 Announce Type: cross Abstract: Does a lexical retriever suffice as large language models (LLMs) become more capable in an agentic loop? This question naturally arises when building

Retrieve-then-Steer: Online Success Memory for Test-Time Adaptation of Generative VLAs

arXiv:2605.10094v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models show strong potential for general-purpose robotic manipulation, yet their closed-loop reliability often degrades u

RubricRefine: Improving Tool-Use Agent Reliability with Training-Free Pre-Execution Refinement

arXiv:2605.09730v1 Announce Type: new Abstract: Iterative self-refinement is a popular inference-time reliability technique, but its effectiveness in code-mode tool use depends heavily on the structur

Scam2Prompt: A Scalable Framework for Auditing Malicious Scam Endpoints in Production LLMs

arXiv:2509.02372v3 Announce Type: replace-cross Abstract: Large Language Models have become critical to modern software development, but their reliance on uncurated web-scale datasets for training int

Self-ReSET: Learning to Self-Recover from Unsafe Reasoning Trajectories

arXiv:2605.08936v1 Announce Type: new Abstract: Large Reasoning Models possess remarkable capabilities for self-correction in general domain; however, they frequently struggle to recover from unsafe r

safetyarxiv-cs-ai

Separate First, Fuse Later: Mitigating Cross-Modal Interference in Audio-Visual LLMs Reasoning with Modality-Specific Chain-of-Thought

arXiv:2605.09906v1 Announce Type: new Abstract: Audio and vision provide complementary evidence for audio-visual question answering, yet current audio-visual large language models may suffer from cros

Simultaneous Long-tailed Recognition and Multi-modal Fusion for Highly Imbalanced Multi-modal Data

arXiv:2605.10498v1 Announce Type: cross Abstract: Long-tailed distributions in class-imbalanced data present a fundamental challenge for deep learning models, which tend to be biased toward majority c

Skill-R1: Agent Skill Evolution via Reinforcement Learning

arXiv:2605.09359v1 Announce Type: cross Abstract: Agentic large language models often rely on skills, reusable natural language procedures that guide planning, action, and tool use. In practice, skill

safetyarxiv-cs-ai

SmartEval: A Benchmark for Evaluating LLM-Generated Smart Contracts from Natural Language Specifications

arXiv:2605.09610v1 Announce Type: cross Abstract: We introduce SmartEval, a benchmark for systematically evaluating the quality of Solidity smart contracts generated by large language models (LLMs) fr

Source or It Didn't Happen: A Multi-Agent Framework for Citation Hallucination Detection

arXiv:2605.08583v1 Announce Type: new Abstract: Large language models are increasingly used in scientific writing, yet they can fabricate citation-shaped references that appear plausible but fail bibl

Supersampling Stable Diffusion and More: An Approach for Interpolating Neural Networks Using Common Interpolation Methods

arXiv:2605.08698v1 Announce Type: new Abstract: Stable Diffusion (SD) has evolved DDPM (Denoising Diffusion Probabilistic Model) based image generation significantly by denoising in latent space inste

researcharxiv-cs-cv

Task-Aware Calibration: Provably Optimal Decoding in LLMs

arXiv:2605.10202v1 Announce Type: cross Abstract: LLM decoding often relies on the model's predictive distribution to generate an output. Consequently, misalignment with respect to the true generating

researcharxiv-cs-cl

Tutorials

The finite expression method for turbulent dynamics with high-order moment recovery

arXiv:2605.10687v1 Announce Type: new Abstract: Turbulent dynamical systems are characterized by nonlinear interactions and stochastic effects that generate coupled statistical quantities, such as non

tutorialsarxiv-cs-lg

The Value of Mechanistic Priors in Sequential Decision Making

arXiv:2605.10018v1 Announce Type: new Abstract: Hybrid mechanistic models, physical priors with learned residuals, promise to reduce the data required for good decisions, but have no computable criter

safetyarxiv-cs-lg

TINS: Test-time ID-prototype-separated Negative Semantics Learning for OOD Detection

arXiv:2605.10756v1 Announce Type: new Abstract: Vision-language models enable OOD detection by comparing image alignment with ID labels and negative semantics. Existing negative-label-based methods ma

TRACER: Verifiable Generative Provenance for Multimodal Tool-Using Agents

arXiv:2605.09934v1 Announce Type: new Abstract: Multimodal large language models increasingly solve vision-centric tasks by calling external tools for visual inspection, OCR, retrieval, calculation, a

Training with Harnesses: On-Policy Harness Self-Distillation for Complex Reasoning

arXiv:2605.08741v1 Announce Type: new Abstract: Inference-time harnesses substantially improve large language models on complex reasoning tasks. However, the intrinsic capabilities of the underlying m

safetyarxiv-cs-cl

TrajPrism: A Multi-Task Benchmark for Language-Grounded Urban Trajectory Understanding

arXiv:2605.10782v1 Announce Type: new Abstract: Urban mobility is naturally expressed both as trajectories in space and as natural-language descriptions of travel intent, constraints, and preferences.

Applications

TSNN: A Non-parametric and Interpretable Framework for Traffic Time Series Forecasting

arXiv:2605.09208v1 Announce Type: new Abstract: Although many complex models were proposed to analyze time series data, some studies have demonstrated remarkable performance with simpler structures. A

applicationsarxiv-cs-lg

Uni-Synergy: Bridging Understanding and Generation for Personalized Reasoning via Co-operative Reinforcement Learning

arXiv:2605.10445v1 Announce Type: new Abstract: Unified Multimodal Models (UMMs) excel in general tasks but struggle to bridge the gap between personalized understanding and generation. Prior works la

Unifying Scientific Communication: Fine-Grained Correspondence Across Scientific Media

arXiv:2605.05831v2 Announce Type: replace Abstract: The communication of scientific knowledge has become increasingly multimodal, spanning text, visuals, and speech through materials such as research

Urban-ImageNet: A Large-Scale Multi-Modal Dataset and Evaluation Framework for Urban Space Perception

arXiv:2605.09936v1 Announce Type: new Abstract: We present Urban-ImageNet, a large-scale multi-modal dataset and evaluation benchmark for urban space perception from user-generated social media imager

WATCH: Wide-Area Archaeological Site Tracking for Change Detection

arXiv:2605.08160v1 Announce Type: cross Abstract: Monitoring archaeological sites at scale is vital for protecting cultural heritage, yet pinpointing when disturbances occur remains difficult because

What Concepts Lie Within? Detecting and Suppressing Risky Content in Diffusion Transformers

arXiv:2605.10180v1 Announce Type: new Abstract: The rise of text-to-image (T2I) models has increasingly raised concerns regarding the generation of risky content, such as sexual, violent, and copyrigh

researcharxiv-cs-cv

What Parameter Golf taught us about AI-assisted research

Parameter Golf brought together 1,000+ participants and 2,000+ submissions to explore AI-assisted machine learning research, coding agents, quantization, and novel model design under strict constraint

model-releasesopenai

What’s new in Microsoft Foundry | April 2026

model-releasesmicrosoft-foundry

April brings Foundry Local GA for local AI development, GPT-5.5 model support with Tier 5 and Tier 6 default quota in Microsoft Foundry, new tracing paths for Microsoft Agent Framework and hosted agen

When Adaptation Fails: A Gradient-Based Diagnosis of Collapsed Gating in Vision-Language Prompt Learning

arXiv:2605.09549v1 Announce Type: new Abstract: Adaptive prompting mechanisms have been proposed to enhance vision-language models by dynamically tailoring prompts to inputs. However, in frozen few-sh

When Tables Leak: Attacking String Memorization in LLM-Based Tabular Data Generation

arXiv:2512.08875v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have recently demonstrated remarkable performance in generating high-quality tabular synthetic data. In practice,

researcharxiv-cs-ai

Why Retrying Fails: Context Contamination in LLM Agent Pipelines

arXiv:2605.08563v1 Announce Type: new Abstract: When an LLM agent fails a multi-step tool-augmented task and retries, the failed attempt typically remains in its context window -- contaminating the ne

2.5-D Decomposition for LLM-Based Spatial Construction

arXiv:2605.07066v1 Announce Type: new Abstract: Autonomous systems that build structures from natural-language instructions need reliable spatial reasoning, yet large language models (LLMs) make syste

Ask Patients with Patience: Enabling LLMs for Human-Centric Medical Dialogue with Grounded Reasoning

arXiv:2502.07143v3 Announce Type: replace Abstract: The severe shortage of medical doctors limits access to timely and reliable healthcare, leaving millions underserved. Large language models (LLMs) o

Beyond Factor Aggregation: Gauge-Aware Low-Rank Server Representations for Federated LoRA

arXiv:2605.06733v1 Announce Type: cross Abstract: Federated LoRA enables parameter-efficient adaptation of large language models under decentralized data and limited client resources.However, directly

Beyond Factual Accuracy: Evaluating Global Reasoning Integrity in RAG Systems with LogicScore

arXiv:2601.15050v4 Announce Type: replace Abstract: Current evaluation methods for Retrieval Augmented Generation (RAG) suffer from extit{factual myopia}: they relentlessly emphasize factual accuracy

BRIDGE: Background Routing and Isolated Discrete Gating for Coarse-Mask Local Editing

arXiv:2605.07846v1 Announce Type: new Abstract: Coarse-mask local image editing asks a model to modify a user-indicated region while preserving the surrounding scene. In practice, however, rough masks

Can Agents Price a Reaction? Evaluating LLMs on Chemical Cost Reasoning

arXiv:2605.07251v1 Announce Type: new Abstract: Large Language Models (LLMs) have become increasingly capable as tool-using agents, with benchmarks spanning diverse general agentic tasks. Yet rigorous

ChartREG++: Towards Benchmarking and Improving Chart Referring Expression Grounding under Diverse referring clues and Multi-Target Referring

arXiv:2605.07415v1 Announce Type: cross Abstract: Referring expression grounding is a core problem in visual grounding and is widely used as a diagnostic of spatial grounding and reasoning in vision a

Applications

Christoffel-DPS: Optimal sensor placement in diffusion posterior sampling for arbitrary distributions

arXiv:2605.06861v1 Announce Type: new Abstract: State estimation is a critical task in scientific, engineering and control applications. Since the reliability of reconstructions depends on the number

applicationsarxiv-cs-lg

Contrast-X: A Multi-Modal Contrast Image Synthesis Benchmark and Universal Modality Flow Matching

arXiv:2601.15884v2 Announce Type: replace Abstract: Contrast-enhanced imaging is central to oncologic diagnosis, but contrast agents can be contraindicated for many of the patients who need them most.

CSMCIR: CoT-Enhanced Symmetric Alignment with Memory Bank for Composed Image Retrieval

arXiv:2601.03728v3 Announce Type: replace-cross Abstract: Composed Image Retrieval (CIR) enables users to search for target images using both a reference image and manipulation text, offering substant

CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios

arXiv:2605.07830v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents in offensive cybersecurity. In this paper, we reveal an interesting phenom

Excluding the Target Domain Improves Extrapolation: Deconfounded Hierarchical Physics Constraints

arXiv:2605.07485v1 Announce Type: cross Abstract: Extrapolation to out-of-distribution conditions is a fundamental challenge for physics-constrained deep generative models. Existing methods apply phys