Search: “models”

MIRROR: Learning from the Other View for Multi-Modal Reasoning

arXiv:2607.21552v1 Announce Type: new Abstract: Unlike large language models (LLMs) that exhibit strong reasoning capabilities, vision-language models (VLMs) struggle with visual reasoning, even on ge

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MO-GRPO: Mitigating Reward Hacking of Group Relative Policy Optimization on Multi-Objective Problems

DGX agent

arXiv:2509.22047v3 Announce Type: replace Abstract: Group Relative Policy Optimization (GRPO) has been shown to be an effective algorithm when an accurate reward model is available. However, such a hi

model-releasesarxiv-cs-lg

24 Jul 2026

More Is Not More: What Matters for Diversity in LLM Opinions?

arXiv:2607.20429v1 Announce Type: cross Abstract: Large language models are increasingly used to simulate diverse human opinions in open-ended tasks such as synthetic surveys, focus group modeling, an

One More Turn, Less Regret: A Regret-Based Multi-Turn Benchmark for LLMs' Clarification Policies

arXiv:2607.21143v1 Announce Type: cross Abstract: Ambiguous user requests make clarification a sequential decision problem for conversational LLM assistants: they must decide whether to ask, what to a

Tutorials

Probabilistic Physics-Aware Machine Learning Predictions of Electric Truck Energy Consumption with Field Data

arXiv:2607.19054v2 Announce Type: replace Abstract: In this work, we incorporate first principle physics into the construction of data-driven methods by considering a model that accounts for the diffe

tutorialsarxiv-cs-lg

Probabilistic Residual Learning for Online Recommendations

arXiv:2607.20863v1 Announce Type: cross Abstract: Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and items. As a res

Same Dangerous Objective, Opposite Advice: Direct Exposure versus Multi-Agent Mediation

arXiv:2607.21518v1 Announce Type: new Abstract: Even a current high-capability LLM can appear safer when shown a dangerous objective directly than when other agents transform and relay its direction.

Semi-Supervised Text-Attributed Graph Distillation

arXiv:2607.20477v1 Announce Type: new Abstract: {em Text-Attributed Graphs} (TAGs) have emerged as an expressive data model for integrating graph topology with rich textual semantics. Existing represe

Surprisal Theory is Tautological (without Rational Grounding)

arXiv:2607.21574v1 Announce Type: new Abstract: Surprisal theory holds that the human processing difficulty of a linguistic unit in context is an affine function of its surprisal under some language m

researcharxiv-cs-cl

The Weight of Silence: A Causal Case for Weights Over the Scratchpad in Latent Chess Reasoning

arXiv:2607.20952v1 Announce Type: cross Abstract: Latent, or silent, reasoning lets language models carry out intermediate computation in continuous vector space instead of words, and is widely assume

researcharxiv-cs-cl

Towards an Automated Test of LLM Security Knowledge

arXiv:2607.18496v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for a range of software, hardware and human-centered security tasks. Consequently, LLM perf

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls

arXiv:2607.21381v1 Announce Type: new Abstract: Instance-level explanations aim to reveal the rationale behind a model's decisions for a specific graph. Previous methods explain graph neural networks

ViSTR-Bench: Can MLLMs Reason from Continuous Visual Cues in Dynamic Scenes?

arXiv:2607.20868v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable success across diverse expert-level tasks, but they still struggle with fundamental ab

WaveformQA: Benchmarking LLM Temporal Reasoning on Digital Waveforms

arXiv:2607.20638v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated strong capabilities in code generation and reasoning, yet their ability to perform temporal reasoning ove

When Trivia Is Not Trivial: Everyday Knowledge Failures in Multilingual LLMs

model-releasesarxiv-cs-cl

arXiv:2607.21445v1 Announce Type: new Abstract: Quiz rooms, trivia nights, and quiz shows challenge human knowledge across a wide range of topics, from canonical facts to everyday culture. In this pap

WhereEdit: Mask-aware Local Latent Editing for One-Step Image Editing

arXiv:2607.20883v1 Announce Type: new Abstract: Recent one-step text-to-image (T2I) models enable efficient image synthesis and provide new opportunities for real-time image editing. However, existing

Aligned Stable Inpainting: Mitigating Unwanted Object Insertion and Preserving Color Consistency

arXiv:2601.15368v3 Announce Type: replace Abstract: Generative image inpainting can produce realistic results even with large, irregular masks, but existing methods still suffer from two common proble

Code-in-the-Loop Forensics: Agentic Tool Use for Image Forgery Detection

arXiv:2512.16300v3 Announce Type: replace Abstract: Existing image forgery detection (IFD) methods either exploit low-level, semantics-agnostic artifacts or rely on multimodal large language models (M

Current Injection Spiking Neural Network for Infrared and Visible Image Fusion

arXiv:2607.19879v1 Announce Type: new Abstract: Infrared and visible image fusion (IVIF) integrates the complementary information of two modalities into a single image with richer scene content. While

Local Ai

Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images

arXiv:2607.20087v1 Announce Type: new Abstract: Aims: Cardiovascular magnetic resonance (CMR) imaging enables non-invasive assessment of myocardial structure, function, and pathology, but requires sub

local-aiarxiv-cs-cv

Enhancing LLMs for Identifying and Prioritizing Important Medical Jargons from Electronic Health Record Notes Utilizing Data Augmentation: A Comparative Study

model-releasesarxiv-cs-cl

arXiv:2502.16022v3 Announce Type: replace Abstract: OpenNotes gives patients access to their EHR notes, but dense medical jargon limits comprehension. We evaluate closed-source and open-source LLMs fo

Fluid-SDF: Ultra-Lightweight and Editable Implicit Shape Representation via Differentiable Primitives

arXiv:2607.18646v1 Announce Type: new Abstract: Implicit Neural Representations (INRs) have become the standard for continuous 2D shape modeling, but they suffer from black-box uneditability, vulnerab

GATE-3D: Geometry-Aware Test-time Adaptive Reranking for Open-Set 3D Shape Retrieval

arXiv:2607.19111v1 Announce Type: new Abstract: Large pretrained vision models have substantially improved appearance-based 3D shape retrieval, but they still confuse shapes that look similar while di

Geospatial Diffusion-based Evolution Synthesis (GeoDES) for Storm-Centered Weather Augmentation

arXiv:2607.19522v1 Announce Type: new Abstract: While machine learning-based weather models hold significant promise, they struggle to predict the detailed structure of large-scale weather systems suc

researcharxiv-cs-lg

Tutorials

Look Less, Think Faster: Joint Token-Compute Adaptation for Multimodal LLMs

arXiv:2607.20357v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have recently demonstrated strong performance across vision-language tasks. However, their high inference cost,

tutorialsarxiv-cs-cv

Safety

Membership Inference Attacks for Unseen Classes

arXiv:2506.06488v3 Announce Type: replace Abstract: A key tool in developing safe AI models is data auditing, i.e., using statistical tools to determine whether harmful content may have been used in t

safetyarxiv-cs-lg

Safety

Norm or Direction? Decoding Vision Mambas for High-Resolution Vision

arXiv:2607.18625v1 Announce Type: new Abstract: Vision Mamba models replace quadratic self-attention with linear complexity selective state space models (SSMs), emerging as efficient visual backbones.

safetyarxiv-cs-cv

Self Gradient Forcing: Native Long Video Extrapolation

arXiv:2607.20368v1 Announce Type: new Abstract: Recent autoregressive video diffusion methods are increasingly built upon Self Forcing, where the student is trained on histories produced by its own ro

Stateful Guardrails for Multi-Turn LLM Systems: A Conversational Risk Accumulation Framework

arXiv:2607.19361v1 Announce Type: cross Abstract: Most safety guardrails for large language models (LLMs) evaluate each prompt-response pair in isolation, which misses failures that arise only over a

Taming the Security-Energy Paradox: A Green AI Approach to Optimized Android Malware Detection

arXiv:2607.20003v1 Announce Type: cross Abstract: An increase in advanced Android malware requires the use of deep learning models, which can run on Android devices. But there is a trade-off between s

The Chronos Vulnerability: A Taxonomy of Temporal Persistence and Memory-Based Deception in Agentic AI

arXiv:2607.19433v1 Announce Type: new Abstract: The transition from stateless generative models in artificial intelligence to stateful, autonomous agents represents an architectural evolution that, wh

Trace: A Taxonomy-Guided Environment for Multidomain Visual Reasoning

arXiv:2607.19790v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has substantially improved language-model reasoning, yet its extension to vision-language models r

researcharxiv-cs-cv

Trusting What You Cannot See: Auditable Fine-Tuning and Inference for Proprietary AI

arXiv:2603.07466v2 Announce Type: replace-cross Abstract: Cloud-based infrastructure has become the dominant platform for deploying large models, particularly large language models (LLMs). Fine-tuning

researcharxiv-cs-lg

Safety

Trustworthy Privacy-Preserving Multimodal Federated Learning for Personalised Breast Cancer Prediction

arXiv:2607.19532v1 Announce Type: cross Abstract: Federated learning has emerged as a potential solution to privacy concerns associated with using sensitive health data for training predictive models,

safetyarxiv-cs-ai

Unified Prediction and Planning via Conflict-Aware Disjoint Parameter Training

model-releasesarxiv-cs-ro

arXiv:2607.19971v1 Announce Type: new Abstract: Accurate motion prediction of surrounding agents and safe motion planning are two closely coupled key tasks for social robot navigation in crowded envir

A plug-and-play approach with fast uncertainty quantification for weak lensing mass mapping

model-releasesarxiv-cs-lg

arXiv:2603.22006v2 Announce Type: replace-cross Abstract: Upcoming stage-IV surveys such as Euclid and Rubin will deliver vast amounts of high-precision data, opening new opportunities to constrain co

Beyond Description: Cognitively Benchmarking Fine-Grained Action for Embodied Agents

arXiv:2511.18685v4 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) show promising results as decision-making engines for embodied agents operating in complex, physical enviro

Agents

Boogu-Image-0.1: Boosting Open-Source Unified Multimodal Understanding and Generation

arXiv:2607.13125v1 Announce Type: cross Abstract: We introduce Boogu-Image-0.1, an open-source unified multimodal understanding and generation model family, comprising Base, Turbo, Edit, and Edit-Turb

agentsarxiv-cs-ai

Data-Efficient Adaptation of LLMs via Attention Head Reweighting

arXiv:2607.13425v1 Announce Type: cross Abstract: Learning effectively from limited data is critical in domains like security where labeled examples are scarce. Large language models (LLMs) have demon

Local Ai

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch

arXiv:2607.13960v1 Announce Type: new Abstract: World Action Models (WAMs) improve robot policy learning by jointly modeling actions and future visual observations, using future scene evolution as den

local-aiarxiv-cs-ro

Implementations of Quantum and Classical Topology-Aligned Architectures for Molecular Property Prediction

model-releasesarxiv-cs-lg

arXiv:2607.13737v1 Announce Type: new Abstract: For low-data and resource-constrained regimes typical of quantum chemistry, parameter-efficient learning is a key objective. Here, we propose a topology

LessonBench-V1: A Benchmark Dataset for Evaluating AI Lesson Generation Agents

arXiv:2607.13041v1 Announce Type: cross Abstract: Large Language Model (LLM) based AI educational content generation systems are increasingly being developed, yet no standardised benchmark exists to s

Mono-Z Dark Matter Search with Neural Spline Flows Using CMS Run 2015D Open Data

model-releasesarxiv-cs-lg

arXiv:2607.13771v1 Announce Type: new Abstract: We report a search for dark matter (DM) produced in association with a leptonically decaying (Z) boson at (sqrt{s}=13) TeV using CMS Run 2015D open data

NeMo: Needle in a Montage for Video-Language Understanding

arXiv:2509.24563v3 Announce Type: replace Abstract: Recent advances in video large language models (VideoLLMs) call for new evaluation protocols and benchmarks for video-language understanding. Inspir

Safeguard-Conditioned Uplift: Measuring Utility-Risk Frontiers for Dual-Use Biology Assistants

arXiv:2607.13039v1 Announce Type: cross Abstract: Safety evaluations for dual-use biology assistants often measure base-model capability, refusal behavior, or jailbreak success. These metrics miss a d

STOCKTAKE: Measuring the Gap Between Perception and Action in LLM Agents with a Fair Oracle

arXiv:2607.13618v1 Announce Type: new Abstract: LLM agents are increasingly evaluated on multi-week decision tasks in which the state that drives cost is never directly observed. On such tasks the fin

Traffic-Aware Randomized Smoothing for LLM-Based Network Intrusion Detection

arXiv:2607.13801v1 Announce Type: cross Abstract: Large language model (LLM)-based intrusion detection systems (IDS) are increasingly studied for security monitoring, yet their robustness against feas

WaterMoE: Expert-Routing-based Watermarking for High Fidelity and Efficiency

arXiv:2607.13099v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable success but raise growing concerns about content provenance and misuse, motivating the need for