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arXiv:2605.11300v1 Announce Type: new Abstract: Vision state space models inherit the efficiency and long-range modeling ability of Mamba-style selective scans. However, their performance depends crit

safetyarxiv-cs-cv

Crash Assessment via Mesh-Based Graph Neural Networks and Physics-Aware Attention

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DGX agent

arXiv:2605.11784v1 Announce Type: cross Abstract: Full-vehicle crash simulations are computationally expensive, limiting their use in iterative design exploration. This work investigates learned hybri

model-releasesarxiv-cs-lg

13 May 2026

Google Named a Leader in the Gartner® Magic Quadrant™ for AI Application Development Platforms: Mid-cycle update

model-releasesgoogle-cloud-ai

May 2026 update: We’ve refreshed this post to reflect our mid-cycle positioning and the evolution of our platform since the report was first published last November. Last fall, Google was recognized a

Keeping Score: Efficiency Improvements in Neural Likelihood Surrogate Training via Score-Augmented Loss Functions

arXiv:2605.12118v1 Announce Type: cross Abstract: For stochastic process models, parameter inference is often severely bottlenecked by computationally expensive likelihood functions. Simulation-based

LatentHDR: Decoupling Exposure from Diffusion via Conditional Latent-to-Latent Mapping for Text/Image-to-Panoramic HDR

arXiv:2605.11115v1 Announce Type: new Abstract: High Dynamic Range (HDR) generation remains challenging for generative models, which are largely limited to low dynamic range outputs. Recent diffusionb

Learning to Foresee: Unveiling the Unlocking Efficiency of On-Policy Distillation

arXiv:2605.11739v1 Announce Type: new Abstract: On-policy distillation (OPD) has emerged as an efficient post-training paradigm for large language models. However, existing studies largely attribute t

Long Story Short: Disentangling Compositionality and Long-Caption Understanding in Contrastive VLMs

arXiv:2509.19207v2 Announce Type: replace Abstract: Contrastive vision-language models (VLMs) have made significant progress in binding visual and textual information, yet understanding long, composit

Measuring Five-Nines Reliability: Sample-Efficient LLM Evaluation in Saturated Benchmarks

arXiv:2605.11209v1 Announce Type: new Abstract: While existing benchmarks demonstrate the near-perfect performance of large language models (LLMs) on various tasks, this apparent saturation often obsc

MedHopQA: A Disease-Centered Multi-Hop Reasoning Benchmark and Evaluation Framework for LLM-Based Biomedical Question Answering

arXiv:2605.12361v1 Announce Type: new Abstract: Evaluating large language models (LLMs) in the biomedical domain requires benchmarks that can distinguish reasoning from pattern matching and remain dis

On Predicting the Post-training Potential of Pre-trained LLMs

arXiv:2605.11978v1 Announce Type: new Abstract: The performance of Large Language Models (LLMs) on downstream tasks is fundamentally constrained by the capabilities acquired during pre-training. Howev

researcharxiv-cs-cl

Overtrained, Not Misaligned

arXiv:2605.12199v1 Announce Type: new Abstract: Emergent misalignment (EM), where fine-tuning on a narrow task (like insecure code) causes broad misalignment across unrelated domains, was first demons

PreScam: A Benchmark for Predicting Scam Progression from Early Conversations

arXiv:2605.12243v1 Announce Type: new Abstract: Conversational scams, such as romance and investment scams, are emerging as a major form of online fraud. Unlike one-shot scam lures such as fake lotter

Scaling Laws and Tradeoffs in Recurrent Networks of Expressive Neurons

arXiv:2605.12049v1 Announce Type: new Abstract: Cortical neurons are complex, multi-timescale processors wired into recurrent circuits, shaped by long evolutionary pressure under stringent biological

Search Your Block Floating Point Scales!

arXiv:2605.12464v1 Announce Type: new Abstract: Quantization has emerged as a standard technique for accelerating inference for generative models by enabling faster low-precision computations and redu

Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification

arXiv:2605.12208v1 Announce Type: cross Abstract: Approximate Bayesian inference typically revolves around computing the posterior parameter distribution. In practice, however, the main object of inte

STAPO: Stabilizing Reinforcement Learning for LLMs by Silencing Rare Spurious Tokens

arXiv:2602.15620v4 Announce Type: replace Abstract: Reinforcement Learning (RL) has significantly improved large language model reasoning, but existing RL fine-tuning methods rely heavily on heuristic

StepCodeReasoner: Aligning Code Reasoning with Stepwise Execution Traces via Reinforcement Learning

arXiv:2605.11922v1 Announce Type: cross Abstract: Existing code reasoning methods primarily supervise final code outputs, ignoring intermediate states, often leading to reward hacking where correct an

AgentPSO: Evolving Agent Reasoning Skill via Multi-agent Particle Swarm Optimization

arXiv:2605.08704v1 Announce Type: new Abstract: Multi-agent reasoning has shown promise for improving the problem-solving ability of large language models by allowing multiple agents to explore divers

AHD Agent: Agentic Reinforcement Learning for Automatic Heuristic Design

arXiv:2605.08756v1 Announce Type: new Abstract: Automatic heuristic design (AHD) has emerged as a promising paradigm for solving NP-hard combinatorial optimization problems (COPs). Recent works show t

AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving

arXiv:2601.01762v2 Announce Type: replace-cross Abstract: Practical autonomous driving requires models that generalize by reasoning through spatial-temporal possibilities to exclude unsafe outcomes. W

AlpsBench: An LLM Personalization Benchmark for Real-Dialogue Memorization and Preference Alignment

arXiv:2603.26680v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) evolve into lifelong AI assistants, LLM personalization has become a critical frontier. However, progress is c

An Elastic Shape Variational Autoencoder for Skeleton Pose Trajectories

arXiv:2605.09231v1 Announce Type: new Abstract: Deep generative models provide flexible frameworks for modeling complex, structured data such as images, videos, 3D objects, and texts. However, when ap

researcharxiv-cs-cv

Can We Trust LLMs for Mental Health Screening? Consistency, ASR Robustness, and Evidence Faithfulness

arXiv:2605.09634v1 Announce Type: new Abstract: LLMs can estimate Hospital Anxiety and Depression Scale (HADS) scores from speech in a zero-shot manner, but clinical deployment requires reliability ac

Capacity-Aware Inference: Mitigating the Straggler Effect in Mixture of Experts

arXiv:2503.05066v5 Announce Type: replace-cross Abstract: The Mixture of Experts (MoE) is an effective architecture for scaling large language models by leveraging sparse expert activation to balance

Causal Dimensionality of Transformer Representations: Measurement, Scaling, and Layer Structure

arXiv:2605.08740v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) decompose transformer residual streams into interpretable feature dictionaries, yet the relationship between SAE width and

ChartDiff: A Large-Scale Benchmark for Comprehending Pairs of Charts

arXiv:2603.28902v2 Announce Type: replace Abstract: Charts are central to analytical reasoning, yet existing benchmarks for chart understanding focus almost exclusively on single-chart interpretation

CLR-voyance: Reinforcing Open-Ended Reasoning for Inpatient Clinical Decision Support with Outcome-Aware Rubrics

arXiv:2605.09584v1 Announce Type: cross Abstract: Inpatient clinical reasoning is a sequential decision under partial observability: the clinician sees the admission so far and must choose the next ac

CoLVR: Enhancing Exploratory Latent Visual Reasoning via Contrastive Optimization

arXiv:2605.08802v1 Announce Type: new Abstract: Due to the potential for exploratory reasoning of Latent Visual Reasoning, recent works tend to enable MLLMs (Multimodal Large Language Models) to perfo

Coordinates of Capability: A Unified MTMM-Geometric Framework for LLM Evaluation

arXiv:2605.08522v1 Announce Type: new Abstract: The evaluation of Large Language Models (LLMs) faces a critical challenge in construct validity, where fragmented benchmarks and ad hoc metrics frequent

Defense effectiveness across architectural layers: a mechanistic evaluation of persistent memory attacks on stateful LLM agents

arXiv:2605.08442v1 Announce Type: cross Abstract: Persistent memory attacks against LLM agents achieve high attack success rates against open-source models. In these attacks, malicious instructions in

researcharxiv-cs-ai

DiffATS: Diffusion in Aligned Tensor Space

arXiv:2605.09275v1 Announce Type: new Abstract: Direct diffusion modeling of high-resolution spatiotemporal fields is computationally challenging. Parameter-efficient primitives address this by repres

Different Prompts, Different Ranks: Prompt-aware Dynamic Rank Selection for SVD-based LLM Compression

arXiv:2605.08568v1 Announce Type: new Abstract: Large language models (LLMs) have rapidly grown in scale, creating substantial memory and computational costs that hinder efficient deployment. Singular

DSGBench: A Diverse Strategic Game Benchmark for Evaluating LLM-based Agents in Complex Decision-Making Environments

arXiv:2503.06047v2 Announce Type: replace Abstract: Large language model (LLM)-based agents are increasingly applied to complex strategic environments that demand long-horizon reasoning, multi-agent i

EdgeFlowerTune: Evaluating Federated LLM Fine-Tuning Under Realistic Edge System Constraints

arXiv:2605.08636v1 Announce Type: new Abstract: Federated fine-tuning offers a promising paradigm for adapting large language models (LLMs) on edge devices by leveraging the rich, diverse, and continu

Efficient Evaluation of LLM Performance with Statistical Guarantees

arXiv:2601.20251v3 Announce Type: replace-cross Abstract: Exhaustively evaluating many large language models (LLMs) on a large suite of benchmarks is expensive. We cast benchmarking as finite-populati

Efficient Neural Architectures for Real-Time ECG Interpretation on Limited Hardware

arXiv:2605.09848v1 Announce Type: new Abstract: Electrocardiogram (ECG) interpretation is essential for diagnosing a wide range of cardiac abnormalities. While deep learning has shown strong potential

EMO: Pretraining Mixture of Experts for Emergent Modularity

arXiv:2605.06663v2 Announce Type: replace Abstract: Large language models are typically deployed as monolithic systems, requiring the full model even when applications need only a narrow subset of cap

researcharxiv-cs-cl

Fashion Florence: Fine-Tuning Florence-2 for Structured Fashion Attribute Extraction

arXiv:2605.09827v1 Announce Type: cross Abstract: We present Fashion Florence, a Florence-2 vision-language model fine-tuned with LoRA to extract structured fashion attributes from clothing images. Gi

Feature Rivalry in Sparse Autoencoder Representations: A Mechanistic Study of Uncertainty-Driven Feature Competition in LLMs

arXiv:2605.08149v1 Announce Type: cross Abstract: Sparse Autoencoders (SAEs) decompose large language model representations into interpretable features, but how these features interact under uncertain

Fin-Bias: Comprehensive Evaluation for LLM Decision-Making under human bias in Finance Domain

arXiv:2605.09106v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in financial contexts, raising critical concerns about reliability, alignment, and susceptibility

FRACTAL: SSM with Fractional Recurrent Architecture for Computational Temporal Analysis of Long Sequences

arXiv:2605.08833v1 Announce Type: new Abstract: Effective sequence modeling fundamentally requires balancing the retention of unbounded history with the high-resolution detection of abrupt short-term

Generating Leakage-Free Benchmarks for Robust RAG Evaluation

arXiv:2605.08838v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) is widely used to augment large language models (LLMs) with external knowledge. However, many benchmark datasets,

Geometry-free prediction of inertial lift forces in microfluidic devices using deep learning

arXiv:2605.08109v1 Announce Type: new Abstract: Inertial microfluidic devices (IMDs) offer low-cost, high-throughput alternative techniques for many traditional particle- (or cell-) manipulation tasks

Grounded or Guessing? LVLM Confidence Estimation via Blind-Image Contrastive Ranking

arXiv:2605.10893v1 Announce Type: new Abstract: Large vision-language models suffer from visual ungroundedness: they can produce a fluent, confident, and even correct response driven entirely by langu

HS-FNO: History-Space Fourier Neural Operator for Non-Markovian Partial Differential Equations

arXiv:2605.09523v1 Announce Type: new Abstract: Neural operators provide fast surrogate models for time-dependent partial differential equations, but their standard autoregressive use usually assumes

In-Context Fixation: When Demonstrated Labels Override Semantics in Few-Shot Classification

arXiv:2605.08295v1 Announce Type: cross Abstract: While random demonstration labels barely hurt in-context learning (Min et al., 2022), we show that homogeneous labels--even semantically valid ones--c

Applications

JUST-DUB-IT: Video Dubbing via Joint Audio-Visual Diffusion

arXiv:2601.22143v2 Announce Type: replace-cross Abstract: Audio-Visual Foundation Models, which are pretrained to jointly generate sound and visual content, have recently shown an unprecedented abilit

applicationsarxiv-cs-cv

LEVI: Stronger Search Architectures Can Substitute for Larger LLMs in Evolutionary Search

arXiv:2605.09764v1 Announce Type: cross Abstract: LLM-guided evolutionary methods such as AlphaEvolve have proven effective in domains like math, systems research, and algorithmic discovery, but their