Search: “models”

NumerosityVLM: A Cognitively Inspired Benchmark for Interpreting Numerosity Representations in Vision-Language Models

arXiv:2608.15425v1 Announce Type: cross Abstract: Vision-language models (VLMs) achieve strong performance on high-level multimodal tasks, yet numerosity perception, a cognitive ability that emerges i

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Research

Steering the Flow: Inverting Face Recognition Models via Gradient-Guided Flow Matching

DGX agent

arXiv:2608.16791v1 Announce Type: cross Abstract: Model Inversion Attacks (MIAs) aim to reconstruct representative training samples of target identities from face recognition models, exposing critical

researcharxiv-cs-ai

18 Aug 2026

The First ChineseBabyLM Challenge: training data-efficient and cognitively plausible language models for Chinese

arXiv:2607.10745v2 Announce Type: replace Abstract: This paper presents the first ChineseBabyLM Challenge, organized as part of NLPCC 2026. The challenge asked participants to train language models fr

UniFed-VLM: Federated Instruction Tuning for Vision-Language Models with Multiple Heterogeneity

arXiv:2608.15516v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have demonstrated strong performance in multimodal understanding and generation. However, fine-tuning of VLMs typically re

What to Forget in Unlearning? Forget Set Curation for Language Models

arXiv:2608.14855v1 Announce Type: new Abstract: Machine unlearning aims to remove targeted data or behaviors from a trained model without retraining from scratch. Yet most evaluations assume that the

Benchmarking data-driven material models on the classic Treloar dataset

arXiv:2608.14063v1 Announce Type: new Abstract: Machine learning is rapidly reshaping constitutive modeling, offers new ways to learn material behavior directly from experimental data, and challenges

Forecast Collapse in Time-Series Foundation Models

arXiv:2608.14106v1 Announce Type: cross Abstract: When forecasting hourly returns for 1,000 US equities, we observe an unexpected phenomenon: predictions become nearly flat and show poor stock ranking

RankT2I: A Submodular Framework for Discovering Interpretable and Diverse Semantics in Text-to-Image Models

arXiv:2608.14226v1 Announce Type: new Abstract: Recent advances in text-to-image (T2I) models have revolutionized the field of image generation and editing. However, identifying semantics that a T2I m

researcharxiv-cs-cv

Safety

The Linear Geometry of Interpretable Tokens: Jailbreaking Attacks and Defenses for Unlearned Diffusion Models

arXiv:2504.21307v3 Announce Type: replace Abstract: Diffusion models excel at generating high-quality images but can memorize and reproduce harmful concepts when prompted. Although fine-tuning methods

safetyarxiv-cs-cv

A Unifying Perspective on Causal World Models: From Observations to Representations to Structure

arXiv:2608.13456v1 Announce Type: new Abstract: World Models (WM) are increasingly seen as a foundation for intelligent agents that can predict, plan, and act beyond their training distribution. In th

researcharxiv-cs-ai

Comparative Analysis of Multilingual Pre-trained Models for Nepali Automatic Speech Recognition

arXiv:2608.12327v1 Announce Type: cross Abstract: Multilingual pretrained models nominally support Nepali, yet no controlled benchmark has compared them under a single fine-tuning protocol. We fine-tu

Concept Drift Detection and Adaptive Retraining of Malware Classification Models

arXiv:2608.13465v1 Announce Type: cross Abstract: Concept drift refers to changes over time in the statistical properties of data, as compared to the data that was used to train a learning model. Mach

researcharxiv-cs-ai

High-dimensional networks and mean squared error for possibly misspecified models

arXiv:2608.13171v1 Announce Type: cross Abstract: To avoid missing important variables and their connections in networks, more and more variables are included in network analysis. Here we show that in

Local Ai

HPSD: Hybrid-Policy Self-Distillation for Text-Image-to-Video Diffusion Models

arXiv:2608.13205v1 Announce Type: new Abstract: Text-Image-to-Video (TI2V) models are an emerging unified architecture, where a single model simultaneously supports text-to-video (T2V) and image-to-vi

local-aiarxiv-cs-cv

StarEmbed: Benchmarking Time Series Foundation Models on Astronomical Observations of Variable Stars

arXiv:2510.06200v4 Announce Type: replace-cross Abstract: Current time series foundation model (TSFM) training corpora largely omit data with certain complexities like irregular temporal sampling. Ast

The Objective Is the Bottleneck: Latent World Models Encode What Their Planners Cannot Use

arXiv:2608.12959v1 Announce Type: cross Abstract: Latent world models are judged by how well they predict, so when planning fails at long horizons the natural reading is that the predictor degrades. O

Unified Multi-Dimensional Benchmark for Complex Graph Reasoning in Large Language Models

arXiv:2608.12391v1 Announce Type: cross Abstract: Graph reasoning provides a promising testbed for evaluating the reasoning ability of large language models (LLMs), as graph instances can be programma

How Can Driving World Models Do Counterfactual Prediction?

arXiv:2608.11601v1 Announce Type: new Abstract: Driving world models are often interpreted as counterfactual simulators for observed driving episodes: given a factual driving log, they are asked what

13 Aug 2026

Test-Time Hallucination Control in Large Vision-Language Models

arXiv:2608.11474v1 Announce Type: new Abstract: Object Hallucination in large vision-language models (LVLMs), where models generate non-factual content about input images, remains a critical barrier t

13 Aug 2026

Applications

Assessing Reliability of BERT-Based Models on Question Answering Tasks

arXiv:2608.10806v1 Announce Type: new Abstract: Reliability estimation of large language models is in many cases as crucial as their accuracy, as reliable models are more trustworthy, robust, and suit

applicationsarxiv-cs-cl

Applications

FACT: Failure-Aware Causal Training for World-Action Models

arXiv:2608.10232v1 Announce Type: cross Abstract: Recent world-action models (WAMs) show that co-training policies with future prediction can provide physical priors for action generation. Building on

applicationsarxiv-cs-ai

From Reasoning Depth to Reasoning Breadth: Evaluating Multi-Point Associative Reasoning in Large Language Models

arXiv:2608.10444v1 Announce Type: cross Abstract: Large language models (LLMs) have made substantial progress on reasoning tasks that require increasingly long and complex inferential chains. This pro

HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models

arXiv:2506.03922v4 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated significant potential to advance a broad range of domains. However, current benchma

Neural Introspection Gating for Adaptive KV-Cache Reuse in Vision-Language-Action Models

arXiv:2608.10824v1 Announce Type: cross Abstract: Vision-Language-Action(VLA) models map camera images and language instructions directly to motor commands through a single autoregressive transformer.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models

arXiv:2608.10471v1 Announce Type: new Abstract: Prompt optimizers automate the search for prompts that improve language-model performance, but existing methods rely on a predefined optimization proced

Safety

SapiensID 2.0: Aligning Human Recognition Foundation Models with Human Perception

arXiv:2608.10497v1 Announce Type: new Abstract: While foundation models have significantly advanced human recognition across diverse modalities, they predominantly rely on static, geometric feature ex

safetyarxiv-cs-cv

Agents

SKILLER: Language-Level Reinforcement Learning for Reusable Skill Extraction in Small Language Models

arXiv:2608.10538v1 Announce Type: new Abstract: Agent skills represent a standardized format for packaging procedural knowledge and domain expertise, serving within agent harness systems as an essenti

agentsarxiv-cs-ai

VisEditBench: Can Vision-Language Models Edit Visualization Code from Multimodal Feedback?

arXiv:2608.10408v1 Announce Type: new Abstract: Vision-language models (VLMs) have shown strong capabilities in generating visualization code from textual or visual specifications. However, real-world

CausalNav: Reliability-Certified Causal World Models for Control under Physical-Parameter Shift

arXiv:2608.07809v1 Announce Type: new Abstract: A world model is only useful for physical AI if it changes what the agent does, and only safe if it declines to do so when it is wrong. We study both ha

CORDA: A Benchmark for Hierarchical Harm-Centric Moral Reasoning in Large Language Models

arXiv:2608.08061v1 Announce Type: new Abstract: The key question in moral judgement is not simply whether someone chooses the 'right' answer, but how they decide what matters most when moral principle

Evaluating Dedicated Monolingual and Joint Multilingual Causal Models for Dravidian Languages

arXiv:2608.07727v1 Announce Type: new Abstract: Dravidian languages, mainly Tamil, Telugu, Kannada, and Malayalam make up only a small part of the data used to train multilingual language models, so i

researcharxiv-cs-cl

Evaluating Generative Time-Series Models on Data with Point Masses

arXiv:2608.09692v1 Announce Type: cross Abstract: Many of the series that generative time-series models are benchmarked on place a large probability mass on a single value --- it does not rain, no rid

Can Language Models Imagine Without Seeing? Ekphrasis: Measuring Visual Creative Ideation in Text-Only LLMs

arXiv:2608.06967v1 Announce Type: new Abstract: Current evaluations do not isolate whether text-only language models can originate visual concepts before image generation. Fluent visual prose can hide

Do Audio Language Models Use Paralinguistic Evidence? Counterfactual Audits for Response Evaluation

arXiv:2608.06718v1 Announce Type: new Abstract: Audio-language models (ALMs) are increasingly used as judges for speech-to-speech systems, but a judge that receives audio may not actually use paraling

Lost in Interpolation: Why Predictive Feedback Fails in Diffusion Language Models

arXiv:2608.06529v1 Announce Type: new Abstract: Soft-masking accelerates the convergence of Masked Diffusion Language Models (MDLMs). Existing formulations build this blend with linear interpolation (

Policy-Masked Private Experts: Auditable and Reversible Capability Access Control in Sparse MoE Models

arXiv:2608.06690v1 Announce Type: cross Abstract: Most language-model access controls regulate behavior while leaving the same computation available to every request. We study a different systems ques

Tutorials

Why Knowing Both Hops Is Not Enough: Understanding Two-Hop Generalization in Language Models

arXiv:2608.07261v1 Announce Type: new Abstract: Large language models (LLMs) can solve complex multi-hop problems yet exhibit puzzling failures on simple two-hop queries: although a model may correctl

tutorialsarxiv-cs-cl

GAUGE: A Measurement-Grounded Benchmark for Physical Fidelity in Simulation Engines and Video World Models

arXiv:2608.05948v1 Announce Type: new Abstract: Physics engines facilitate large-scale training and evaluation for embodied intelligence, while generative video world models are emerging as implicit s

7 Aug 2026

Above-ground Biomass Estimation with Geospatial Foundation Models

arXiv:2608.04792v1 Announce Type: new Abstract: Accurate estimation of Above-Ground Biomass (AGB) from satellite imagery is essential for the large-scale monitoring of carbon stocks, yet it remains a

6 Aug 2026

Enhancing Trustworthy Clinical Diagnosis Decision-Making in Large Language Models via Etiology-Aware Attention Supervision

arXiv:2508.00285v2 Announce Type: replace Abstract: Objective: Large Language Models (LLMs) have demonstrated strong capabilities in medical text understanding and generation. However, their trustwort

6 Aug 2026

Local Ai

Representational separation between unitary and channel quantum generative models via shared classical randomness at shallow depth

arXiv:2608.05110v1 Announce Type: cross Abstract: Near-term quantum hardware limits circuit depth and often imposes geometrically local connectivity for quantum generative models, restricting the outp

local-aiarxiv-cs-ai

6 Aug 2026

AI World Cup 2026: Benchmarking Large Language Models for End-to-End Football Tournament Prediction

arXiv:2608.03416v1 Announce Type: new Abstract: Large language models (LLMs) are now regularly asked to forecast real-world events, but comparisons are often difficult because models receive different

5 Aug 2026

Beyond Accuracy: A Multidimensional Evaluation of Statistical Reasoning in Large Language Models

arXiv:2608.03038v1 Announce Type: new Abstract: Statistical reasoning is multidimensional, yet evaluations of large language models (LLMs) typically emphasize response accuracy while overlooking how m

researcharxiv-cs-cl

5 Aug 2026

Local Ai

SAGE: Semantic Explainability of Attention-Based Survival Models in Computational Pathology

arXiv:2608.02803v1 Announce Type: cross Abstract: Attention-based multiple instance learning (ABMIL) is the predominant approach for slide-level prediction in computational pathology, yet its attentio

local-aiarxiv-cs-ai

5 Aug 2026

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models

arXiv:2601.17883v3 Announce Type: replace-cross Abstract: Electroencephalography (EEG) foundation models (FMs) have recently emerged as a promising paradigm for brain-computer interfaces, aiming to le

Eigenvalues as a Metric for Memory Dynamics in Sequence Models

arXiv:2510.09379v2 Announce Type: replace Abstract: While softmax attention drives state-of-the-art performance in sequence modeling, its quadratic complexity motivates linear alternatives such as sta

researcharxiv-cs-lg

Grounded Semantic Re-Binding for Robust Instruction Generalization in Vision-Language-Action Models

model-releasesarxiv-cs-ro

arXiv:2608.02497v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models excel in robotic manipulation but suffer catastrophic performance drops when canonical instructions are simply parap

Large Causal Models for Temporal Causal Discovery

arXiv:2602.18662v2 Announce Type: replace Abstract: Causal discovery for both cross-sectional and temporal data has traditionally followed a dataset-specific paradigm, where a new model is fitted for