Search: “models”

Parameter-Efficient Adapter Tuning for Tabular-Image Multimodal Learning

arXiv:2606.11682v1 Announce Type: new Abstract: Tabular-image multimodal learning aims to improve predictive modeling by jointly using structured tabular attributes and visual data. Although pretraine

Model Releases

ParseFixer: An Agentic Framework for Document Parsing via Selective Multimodal Correction

DGX agent

arXiv:2606.11977v1 Announce Type: new Abstract: In this report, we present our third-place solution for the DataMFM Challenge Track 1: Document Parsing. This track requires models to recover structure

model-releasesarxiv-cs-cv

11 Jun 2026

Phi-Actor-Critic: Steering General-Sum Games to Pareto-Efficient Correlated Equilibria

arXiv:2606.11284v1 Announce Type: cross Abstract: Real-world multi-agent systems, from traffic coordination to resource allocation, are often modeled as general-sum games where individual incentives c

Robustness of Mixtures of Experts to Feature Noise

arXiv:2601.14792v2 Announce Type: replace Abstract: Despite their practical success, it remains unclear why Mixture of Experts (MoE) models can outperform dense networks beyond sheer parameter scaling

SAFER-Nav: Enhancing Safety for Visual Robot Navigation via Segmentation-Aware Fine-Tuning

arXiv:2606.11636v1 Announce Type: new Abstract: Vision-based navigation models, particularly foundation models, generate viable trajectories from RGB observations alone. However, even state-of-the-art

safetyarxiv-cs-ro

SAGE: Answer-Conditioned Uncertainty Targets for Verbal Uncertainty Alignment

arXiv:2606.11512v1 Announce Type: new Abstract: Large language models increasingly express uncertainty through natural-language statements, yet these expressions often fail to reflect the model's samp

safetyarxiv-cs-cl

Steering the Noise: Turning Random Perturbations into Effective Descent for Memory-Efficient LLM Fine-Tuning

arXiv:2601.04710v2 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) achieves strong performance but is often limited by the memory overhead of backpropagation. Zeroth-order (Z

Agents

The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes

arXiv:2606.11470v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved strong performance across natural language processing tasks, yet reliable reasoning remains an open challenge

agentsarxiv-cs-cl

Using Explainability as a Training-Time Reliability Signal for Efficient ECG Classification

arXiv:2606.12252v1 Announce Type: cross Abstract: Training deep neural networks for clinical time-series analysis is computationally demanding, yet many healthcare settings lack the resources required

VL-DINO: Leveraging CLIP Vision-Language Knowledge for Open-Vocabulary Object Detectio

arXiv:2606.11546v1 Announce Type: new Abstract: Vision-language models like CLIP can provide rich semantic priors for open-vocabulary object detection. However, jointly integrating both textual and vi

Weighted Random Dot Product Graphs

arXiv:2505.03649v4 Announce Type: replace-cross Abstract: Modeling of intricate relational patterns has become a cornerstone of contemporary statistical research and related data science fields. Netwo

researcharxiv-cs-lg

3SPO: State-Score-Supervised Policy Optimization for LLM Agents

arXiv:2606.09961v1 Announce Type: cross Abstract: Training large language models (LLMs) as autonomous agents via reinforcement learning (RL) has enabled frontier models to achieve superhuman performan

safetyarxiv-cs-ai

5% > 100%: Flatness Preference is All You Need for Multimodal Parameter-Efficient Fine-Tuning

arXiv:2606.10488v1 Announce Type: new Abstract: Parameter-Efficient Fine-Tuning (PEFT) methods provide a streamlined and efficient tool for adapting large models to domain-specific multimodal downstre

A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications

arXiv:2410.15595v4 Announce Type: replace Abstract: With the rapid advancement of large language models (LLMs), aligning policy models with human preferences has become increasingly critical. Direct P

safetyarxiv-cs-ai

A Constrained Natural-Language Interface for Variational Multi-Physics Finite Element Simulations in FEniCS

arXiv:2606.10928v1 Announce Type: cross Abstract: Large language models can reduce the manual effort required to set up finite element simulations, but they introduce reliability risks when generated

A History-Aware Visually Grounded Critic for Computer Use Agents

arXiv:2606.11078v1 Announce Type: new Abstract: Various test-time interventions for Computer Use Agents (CUAs), including critic models, have been developed to improve performance through pre-executio

ABC-Bench: An Agentic Bio-Capabilities Benchmark for Biosecurity

arXiv:2606.11150v1 Announce Type: new Abstract: Large language models (LLMs) are rapidly acquiring capabilities relevant to biological research, from literature synthesis to interpretation of experime

[AINews] Anthropic Claude Fable 5 — Mythos but Safe, with Controversial Terms

model-releaseslatent-space

This article from Latent Space discusses Anthropic's Claude Fable 5 model, examining its capabilities in handling creative and mythological content while maintaining safety guardrails, along with cove

An Industrial-Scale Insurance LLM Achieving Verifiable Domain Mastery and Hallucination Control without Competence Trade-offs

arXiv:2603.14463v2 Announce Type: replace Abstract: Adapting Large Language Models (LLMs) to high-stakes vertical domains like insurance presents a significant challenge: scenarios demand strict adher

BadRobot: Jailbreaking Embodied LLM Agents in the Physical World

arXiv:2407.20242v5 Announce Type: replace-cross Abstract: Embodied AI represents systems where AI is integrated into physical entities. Large Language Model (LLM), which exhibits powerful language und

Beyond Memorization: Distinguishing Between Pattern-Based and Epistemic Reasoning in LLMs Using Epistemic Puzzles

arXiv:2603.21350v2 Announce Type: replace Abstract: Epistemic reasoning requires agents to infer the state of the world from partial observations and information about other agents' knowledge. Prior w

Blurry Window Attention

arXiv:2606.09862v1 Announce Type: cross Abstract: The Softmax Attention operation in Transformer language models has a quadratic complexity in the sequence length and a growing state size in the form

researcharxiv-cs-ai

CoTAL: Human-in-the-Loop Prompt Engineering for Generalizable Formative Assessment Scoring and Feedback

arXiv:2504.02323v4 Announce Type: replace Abstract: Large language models (LLMs) have created new opportunities to assist teachers and support student learning. While researchers have explored various

Data-Driven Dynamic Assortment in Online Platforms: Learning about Two Sides

arXiv:2606.11118v1 Announce Type: new Abstract: We study a dynamic assortment problem on a two-sided service platform with incomplete information and heterogeneous customers in a discrete-time setting

DB-3DME: From Dataset to Benchmark for Human-aligned Automatic 3D Mesh Evaluation

arXiv:2606.10142v1 Announce Type: new Abstract: Recent advances in 3D generation have led to substantial improvements in realism, controllability, and efficiency, yet the evaluation of 3D assets remai

Do VLMs Reason Like Engineers? A Benchmark and a Stage-wise Evaluation

arXiv:2606.10833v1 Announce Type: new Abstract: Vision-Language Models (VLMs) demonstrate strong performance on general multimodal reasoning benchmarks, yet their ability to perform engineering reason

Dual-Branch Gated Fusion for Open-Set Audio Deepfake Source Tracing

arXiv:2606.10223v1 Announce Type: cross Abstract: Attributing a synthetic utterance to its originating system remains an open challenge: closed-set models fail to reject unseen synthesizers and produc

Enabling Progressive Whole-slide Image Analysis with Multi-scale Pyramidal Network

arXiv:2602.01951v2 Announce Type: replace Abstract: Multiple-instance Learning (MIL) is commonly used for computational pathology (CPath), where multi-scale features are essential for capturing both f

Fisher-Guided Progressive Parameter Selection for Adaptive Fine-Tuning

arXiv:2606.10196v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) aims to adapt pretrained models with a small trainable parameter subset, however, most existing methods choose

Flaws in the LLM Automation Narrative

arXiv:2606.11166v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly described as performing at the level of human experts on knowledge economy tasks. These claims are prima

GhazalBench: Evaluating LLM Understanding and Canonical Surface-Form Access in Persian Ghazals

arXiv:2603.09979v2 Announce Type: replace Abstract: Persian poetry plays an active role in Iranian cultural practice, where verses by canonical poets such as Hafez are frequently quoted, paraphrased,

GRID: Scaling Task-Agnostic Inference in Continual Prompt Tuning

arXiv:2507.14725v4 Announce Type: replace-cross Abstract: Prompt-based continual learning (CL) offers a parameter-efficient way to adapt large language models (LLMs) across task sequences. However, ex

LakeQA: An Exploratory QA Benchmark over a Million-Scale Data Lake

arXiv:2606.10460v1 Announce Type: cross Abstract: Recent large language models (LLMs) have shown rapid progress in reading-based question answering (QA), where evidence is explicitly provided or can b

Machine Learning Methods for Studying Latent Neural Activity Dynamics

arXiv:2606.10530v1 Announce Type: cross Abstract: Recent developments in brain recording are driving a demand for machine learning tools capable of decoding the latent structure of large populations o

researcharxiv-cs-ai

MemVenom: Triggered Poisoning of Multimodal Memories in Web Agents

arXiv:2606.10742v1 Announce Type: cross Abstract: External memory has become a core component of modern web agents, enabling long-horizon reasoning through the retrieval of past experiences. However,

Microsoft restricts Claude Fable for employees over data retention concerns

model-releasesthe-verge-ai

Anthropic released Claude Fable, its first Mythos-class AI model, yesterday and it's already causing concerns inside Microsoft. Sources tell me that Microsoft is limiting the use of Claude Fable 5 for

Mixtures of Neural Operators Reduce Active Complexity in Operator Learning

arXiv:2404.09101v3 Announce Type: replace-cross Abstract: Operator-learning systems are not governed solely by total parameter count; for one query, the relevant bottleneck can be the model that must

Nonlinear Estimator: Dual Bayesian Affine Estimators for Parameter Learning

arXiv:2606.10111v1 Announce Type: new Abstract: This paper presents a nonlinear parameter estimator for Wiener-type state-space models obtained as a fixed-point architecture that couples two affine mi

NVIDIA Accelerates Google DeepMind’s DiffusionGemma for Local AI

model-releasesnvidia-blog

Today, Google DeepMind released DiffusionGemma — an experimental open model built for exceptionally fast text generation. NVIDIA has optimized DiffusionGemma to run even faster across NVIDIA GeForce R

Hardware

One-Step Residual Shifting Diffusion for Image Super-Resolution via Distillation

arXiv:2503.13358v5 Announce Type: replace Abstract: Diffusion models for super-resolution (SR) produce high-quality visual results but require expensive computational costs. Despite the development of

hardwarearxiv-cs-cv

Online Self-Training for Co-Adaptation in Hierarchical Diffusion Policies

model-releasesarxiv-cs-ro

arXiv:2603.05291v2 Announce Type: replace Abstract: Hierarchical policies decompose language-conditioned long-horizon robotic manipulation into a high-level planner and a low-level controller. However

Applications

Privacy-Preserving Credit Risk Prediction with Alternative Data

arXiv:2606.10333v1 Announce Type: new Abstract: Credit risk prediction is a critical problem in the consumer credit industry. Traditionally, financial institutions construct credit risk prediction mod

applicationsarxiv-cs-lg

Quality Is Not a Safety Proxy Under Quantization

arXiv:2606.10154v1 Announce Type: new Abstract: Quantized checkpoints are often screened first with quality metrics and only later, if at all, with direct safety tests. This paper audits that shortcut

REAL: A Reasoning-Enhanced Graph Framework for Long-Term Memory Management of LLMs

arXiv:2606.10694v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly expected to interact with users over long time horizons. However, due to their finite context window, LLMs

RobotEQ: Transitioning from Passive Intelligence to Active Intelligence in Embodied AI

model-releasesarxiv-cs-ro

arXiv:2605.06234v2 Announce Type: replace Abstract: Embodied AI is a prominent research topic in both academia and industry. Current research centers on completing tasks based on explicit user instruc

SPACR: Single-Pass Adaptive Training of Uncertainty-Aware Conformal Regressors

arXiv:2606.10734v1 Announce Type: new Abstract: Conformal Prediction (CP) provides robust uncertainty guarantees for predictive models, but is typically applied post hoc, which misaligns model trainin

researcharxiv-cs-lg

Stop Early, Spend Less: Hidden-State Probes as a Practical Recipe for Streaming Moderation of LLM Outputs

arXiv:2606.10487v1 Announce Type: cross Abstract: Deploying large language models in user-facing systems requires efficient output safety filtering. Existing approaches typically rely on a separate mo

safetyarxiv-cs-ai

Structure from Reasoning, Numbers from Search: On-Premise Open LLMs as Structural Priors for Coupled MIMO Controller Tuning

arXiv:2606.11015v1 Announce Type: new Abstract: Tuning controllers for strongly coupled multi-input multi-output (MIMO) industrial processes is hard: decentralized classical auto-tuning ignores loop i