Search: “models”

Many-Shot CoT-ICL: Making In-Context Learning Truly Learn

arXiv:2605.13511v1 Announce Type: cross Abstract: In-context learning (ICL) adapts large language models (LLMs) to new tasks by conditioning on demonstrations in the prompt without parameter updates.

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Meta observation: DeepSeek is still king of the active-parameter ratio

DGX agent

DeepSeek maintains the highest efficiency in terms of active parameters relative to total model size, outperforming competitors in the ratio of parameters actually used during inference versus total t

model-releasessebastian-raschka--x

14 May 2026

MindVLA-U1: VLA Beats VA with Unified Streaming Architecture for Autonomous Driving

arXiv:2605.12624v1 Announce Type: cross Abstract: Autonomous driving has progressed from modular pipelines toward end-to-end unification, and Vision-Language-Action (VLA) models are a natural extensio

PanoWorld: Towards Spatial Supersensing in 360^irc Panorama World

arXiv:2605.13169v1 Announce Type: cross Abstract: Multimodal large laboratory models (MLLMs) still struggle with spatial understanding under the dominant perspective-image paradigm, which inherits the

Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning

arXiv:2507.01908v3 Announce Type: replace Abstract: Instruction-based image editing (IIE) has advanced rapidly with the success of diffusion models. However, existing efforts primarily focus on simple

Applications

Representing Higher-Order Networks: A Survey of Graph-Based Frameworks

arXiv:2605.12509v1 Announce Type: cross Abstract: Many real-world phenomena are naturally modeled by graphs and networks. However, classical graph models are often limited to pairwise interactions and

applicationsarxiv-cs-ai

Local Ai

Ring-2.6-1T Open sourced today! Soooo looking forward to trying it on Ollama!

Ring-2.6-1T is a trillion-parameter flagship reasoning model designed for real-world complex task scenarios, now available as an open-source model. The model features about 63B activated parameters pe

local-air-ollama

Seg-Agent: Test-Time Multimodal Reasoning for Training-Free Language-Guided Segmentation

arXiv:2605.12953v1 Announce Type: cross Abstract: Language-guided segmentation transcends the scope limitations of traditional semantic segmentation, enabling models to segment arbitrary target region

Tighter Learning Guarantees on Digital Computers via Concentration of Measure on Finite Spaces

arXiv:2402.05576v4 Announce Type: replace Abstract: Machine learning models with inputs in a Euclidean space R^d, when implemented on digital computers, generalize, and their generalization gap conver

Vividh-ASR: A Complexity-Tiered Benchmark and Optimization Dynamics for Robust Indic Speech Recognition

arXiv:2605.13087v1 Announce Type: cross Abstract: Fine-tuning multilingual ASR models like Whisper for low-resource languages often improves read speech but degrades spontaneous audio performance, a p

A compilation of the open-source LoRAs for LTX 2.3 - released in May

model-releasesr-stablediffusion

LTX-2.3 is an open-source video generation model released in January 2026 that supports LoRA fine-tuning for customizing styles, characters, and use cases. The Reddit post compiles available LTX-2.3 m

CAD-feature enhanced machine learning for manufacturing effort estimation on sheet metal bending parts

arXiv:2605.12266v1 Announce Type: new Abstract: Graph-based machine learning has emerged as a promising approach for manufacturability analysis by learning directly from CAD models represented as Boun

Safety

Can Graphs Help Vision SSMs See Better?

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

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

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

Research

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

Research

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

Research

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