Search: “models”

Understanding Task Transfer in Vision-Language Models

arXiv:2511.18787v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) perform well on multimodal benchmarks but lag behind humans and specialized models on visual perception tasks like dep

tutorialsarxiv-cs-cv

10 Apr 2026

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AutoWorldModel-Bench: A State-Centric Benchmark for Automated World-Model Research

DGX agent

arXiv:2608.11216v1 Announce Type: new Abstract: World modeling is an unsettled field: architectures, training objectives, and state representations interact in complex ways, and no single recipe domin

model-releasesarxiv-cs-ai

13 Aug 2026

How to Spend Your Oracle Budget: Practical Guidance for Protein Structure Prediction Models

arXiv:2608.12192v1 Announce Type: new Abstract: Foundation models for protein structure prediction remain unreliable on certain targets. External oracles can flag and correct these failures, but biolo

13 Aug 2026

StellaVLA: In-Context Structured Demonstration for Generalizable Vision-Language-Action Models

model-releasesarxiv-cs-ro

arXiv:2608.11671v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models can follow instructions and manipulate objects, but their performance often collapses out of distribution (OOD), whe

13 Aug 2026

Attention-Path Fragility as an Uncertainty Signal in Large Language Models

arXiv:2608.11138v1 Announce Type: cross Abstract: We propose that a model's uncertainty about a token is reflected not only in the breadth of its output distribution but also in whether a confident pr

CurveFP: Rational-Radix Logarithmic Datatypes with Closed Products for Language Models

arXiv:2608.10010v1 Announce Type: new Abstract: Low-precision datatypes reduce language-model cost, but most formats optimize scalar fidelity while leaving the arithmetic induced by their products unc

Generator-Guided Inverse Sampling for Levy-Driven Generative Models

arXiv:2608.10384v1 Announce Type: new Abstract: This paper studies inverse sampling for Levy-driven generative models from the perspective of Markov generators. Unlike conventional diffusion models, L

researcharxiv-cs-lg

Mixture-of-Experts-based Entropy Model for Learned Image Compression

arXiv:2608.10947v1 Announce Type: new Abstract: Learned image compression has seen significant progress in recent years with the development of end-to-end learned models that achieve better compressio

researcharxiv-cs-cv

MRIComp4Flow: Compression of 3D Brain MRI for Training Multi-Modal Generative Models

arXiv:2608.10291v1 Announce Type: cross Abstract: Large-scale multi-modal MRI datasets impose substantial storage and I/O costs, limiting the training of 3D generative models on commodity infrastructu

tutorialsarxiv-cs-ai

Safety

Never Stop Speaking: a Denial-of-Service Attack on End-to-End Speech Language Models

arXiv:2608.10405v1 Announce Type: cross Abstract: Many studies have shown that specially crafted inputs can induce large language models (LLMs) to generate excessively long outputs, resulting in signi

safetyarxiv-cs-ai

PRMU: A Corpus-Free Benchmark for Person-Centric Knowledge Unlearning in Multimodal Large Language Models

arXiv:2608.11149v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in storing and recalling rich person-related knowledge, raising incre

SPIEval: Evaluating Large Language Models as Mobile Assistants over Scattered Personal Information

arXiv:2608.10692v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as mobile assistants, where a key challenge is leveraging personal information scattered across

When Visual Signals Mislead: A Mechanistic Study of Attribute Hallucination in Vision-Language Models

arXiv:2608.11024v1 Announce Type: new Abstract: Attribute hallucination---where vision-language models (VLMs) correctly identify an object but mischaracterize its properties---is prevalent yet mechani

A continually expandable foundation model for brain MRI

arXiv:2608.08319v1 Announce Type: new Abstract: Brain magnetic resonance imaging (MRI) is central to neuroscience and clinical assessment, but models are commonly developed for individual diseases, po

researcharxiv-cs-cv

CMU-Drive and V2V-VLA: Cooperative Multi-agent Unified Driving with Reasoning Benchmark and Vehicle-to-Vehicle Vision-Language-Action Models

arXiv:2608.07621v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have recently achieved impressive performance for end-to-end autonomous driving, yet existing approaches are primari

Distilling CT Foundation Models into Editable Concept Bottlenecks for Lung Nodule Malignancy Prediction

arXiv:2608.07857v1 Announce Type: cross Abstract: Foundation models provide transferable CT representations, but predictions based directly on these embeddings are difficult to interpret. We developed

researcharxiv-cs-ai

From Evaluated Models to Evaluation Aids: A Multi-Evidence Study of LLM-Based Difficulty Calibration for Programming Examinations

arXiv:2608.07523v1 Announce Type: cross Abstract: Difficulty differences across parallel-class programming examinations affect the fairness of course assessment. This study repositions large language

LegoLM: Structured Weight Sharing for Large Language Models

arXiv:2608.08652v1 Announce Type: cross Abstract: We present LegoLM{}, a structured weight-sharing compression framework for large language models grounded in a systematic study of why global weight s

Memorization Dynamics in Knowledge Distillation for Language Models

arXiv:2601.15394v2 Announce Type: replace Abstract: Knowledge Distillation (KD) is increasingly adopted to transfer capabilities from large language models to smaller ones, offering significant improv

OpenMHC: Accelerating the Science of Wearable Foundation Models

arXiv:2607.16235v3 Announce Type: replace-cross Abstract: Mobile and wearable devices offer an unprecedented opportunity for continuous, passive health monitoring and active health coaching. However,

Prompt engineering does not universally improve Large Language Model performance across clinical decision-making tasks

arXiv:2512.22966v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated promise in medical knowledge assessments, yet their practical utility in real-world clinical decision

Reducing Pretraining-Generation Mismatch in Diffusion Language Models

arXiv:2608.09424v1 Announce Type: new Abstract: Autoregressive language models align training and use: generation conditions on a clean prompt, and training predicts future tokens from clean left cont

Scaling Inherently Interpretable Language Models

arXiv:2608.07594v1 Announce Type: cross Abstract: Interpretability is often treated as a tax on capability: language models are trained as opaque systems, then explained after the fact, with methods w

researcharxiv-cs-ai

SpikeWorld: Fast-State Adaptation for Frozen Spiking World Models

arXiv:2608.07712v1 Announce Type: cross Abstract: A predictive model receives a self-supervised signal whenever the consequence of an action is observed. Using that signal after deployment is difficul

Thinking Hard, Not Smart: Reasoning Models Fail to Ration Test-Time Compute Across Questions

arXiv:2608.07968v1 Announce Type: cross Abstract: Reasoning language models increasingly use test-time compute to improve performance, but existing evaluations typically study this compute one questio

tutorialsarxiv-cs-ai

Thinking vs. NoThinking: Towards Interpreting Reasoning Mechanisms of Large Language Models via Sparse Autoencoders

arXiv:2608.08168v1 Announce Type: new Abstract: While Large Language Models (LLMs) employing Chain-of-Thought (CoT) exhibit superior reasoning capabilities, the neural mechanisms distinguishing this e

TrustRoboReward: Preference-Ordered Isotonic Score Editing for Multi-Paradigm Robot Reward Models

arXiv:2608.08491v1 Announce Type: new Abstract: Reward models are a bottleneck for reinforcement learning in embodied AI. Long-horizon robotic manipulation requires scalable vision feedback beyond han

Unified Hallucination Fuzzing for Multimodal Large Language Models

arXiv:2608.07525v1 Announce Type: cross Abstract: Hallucination remains a persistent challenge for Multimodal Large Language Models (MLLMs), severely limiting their reliability in high-stakes applicat

Unsure but Certain: Uncovering the Representation-Confidence Gap in Diffusion Language Models

arXiv:2608.08791v1 Announce Type: new Abstract: Diffusion language models use broad context to create text, suggesting they might handle input noise better than standard models. Testing reveals this i

researcharxiv-cs-cl

VectraYX-Vision-1B: A Sub-2B Spanish/LATAM Cybersecurity Vision-Language Model with Structured Visual Reasoning and Native Tool Use

arXiv:2608.08477v1 Announce Type: new Abstract: We present VectraYX-Vision-1B, a sub-2B vision-language model (VLM) for Spanish/LATAM cybersecurity imagery, coupling a frozen SigLIP-so400m encoder to

Safety

Vid2WAM: Distilling Video Diffusion Priors into World Action Models

arXiv:2608.08558v1 Announce Type: new Abstract: World Action Models (WAMs) improve robot policy learning by jointly modeling future visual dynamics and actions. However, their scalability and generali

safetyarxiv-cs-ro

CoDAT: Collaborative Dual-Attention Transformer with Low-Cost Temporal Modeling for Efficient Edge Action Recognition

arXiv:2608.06691v1 Announce Type: new Abstract: Real-time human action recognition on Internet-of-Things (IoT) edge devices requires models that capture rich spatio-temporal cues within strict latency

CrossTracer: Cross-Embodiment Navigation via VLA Model Reasoning and Trace Residuals Adapting

model-releasesarxiv-cs-ro

arXiv:2608.06688v1 Announce Type: new Abstract: Vision-language-action (VLA) models provide strong semantic priors for robot navigation, but they often ignore embodiment-specific mobility constraints.

EchoVLA: Robotic Vision-Language-Action Model with Synergistic Declarative Memory for Mobile Manipulation

model-releasesarxiv-cs-ro

arXiv:2511.18112v3 Announce Type: replace Abstract: Recent progress in Vision-Language-Action (VLA) models has enabled embodied agents to interpret multimodal instructions and perform complex tasks. H

Game-Theoretic Inverse Reinforcement Learning for Modeling Competitive Human Driving: A Cut-in Prediction Study

arXiv:2608.06445v1 Announce Type: cross Abstract: Capturing the strategic decision-making inherent in competitive human driving is critical for autonomous vehicle safety and traffic simulation. This s

Geo-Spatial Concept Probing of Large Language Models: Abstraction, Compositionality, and Grounding

arXiv:2608.07353v1 Announce Type: cross Abstract: Understanding concepts is fundamental to generalization. Despite their impressive performance on a wide range of tasks, Large Language Models (LLMs) s

GraphVerse: A Comprehensive Visual Graph Reasoning Benchmark for Multimodal Large Language Models

arXiv:2608.06769v1 Announce Type: new Abstract: Recent Multimodal Large Language Models (MLLMs) have achieved remarkable progress across diverse vision-language tasks, creating an urgent need for more

LoRAScan: Detecting Backdoor Prompts in Low-Rank Adapters for Large Language Models via Down-Projection Activation Spikes

arXiv:2608.06795v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) enables efficient specialization and distribution of large language models through compact adapters. However, untrusted ada

tutorialsarxiv-cs-ai

Pathryoshka: Compressing Pathology Foundation Models via Multi-Teacher Knowledge Distillation with Nested Embeddings

arXiv:2511.23204v2 Announce Type: replace Abstract: Pathology foundation models (FMs) have driven significant progress in computational pathology. However, these high-performing models can easily exce

researcharxiv-cs-cv

Symbolic Graphics Programming with Large Language Models

arXiv:2509.05208v2 Announce Type: replace Abstract: Large language models (LLMs) excel at program synthesis, yet their ability to produce symbolic graphics programs (SGPs) that render into precise vis

Towards Multi-Label Graph Foundation Models: from Single-Vector Representation Learning to Multi-Semantic Basis Learning

arXiv:2608.06394v1 Announce Type: new Abstract: Multi-label node classification is an important yet challenging task in graph learning, where nodes exhibit multiple semantics simultaneously. Existing

researcharxiv-cs-ai

Big, Bright, or Invisible: A Frozen-Feature Benchmark of 3D CT Foundation Models

arXiv:2608.05960v1 Announce Type: cross Abstract: Routine CT interpretation is inherently comprehensive, capturing incidental findings across the entire scan volume. 3D CT foundation models could assi

Human-Like Anaphor Resolution in Large Language Models

arXiv:2608.05630v1 Announce Type: new Abstract: Anaphors are expressions that refer to other expressions, called antecedents. The process of connecting the two is called resolution. Cognitive science

KV-Skill: Forging Expertise in the Model's Native Language

arXiv:2608.05475v1 Announce Type: new Abstract: Task knowledge is commonly stored either as text in the prompt or as an update to model weights. Text is modular but must be interpreted on every use, w

MASS: Multiplayer World Models with Authoritative Shared State

arXiv:2608.06257v1 Announce Type: new Abstract: Current video world models struggle in multiplayer environments because they entangle world state with view-dependent visual latents, leading to redunda

MetaboLLM: a metabolomics-specialized large language model for biochemical knowledge integration and predictive metabolite graph construction

arXiv:2608.06253v1 Announce Type: new Abstract: Metabolomics knowledge is distributed across heterogeneous resources and remains difficult to translate into predictive representations. We developed Me

One Leak Away: How Pretrained Model Exposure Amplifies Jailbreak Risks in Finetuned LLMs

arXiv:2512.14751v3 Announce Type: replace-cross Abstract: Finetuning pretrained large language models (LLMs) has become the standard paradigm for developing downstream applications. However, its secur

Scaffold-Mediated Post-Training: Co-Evolving Model Parameters and Procedural Scaffold Graphs

arXiv:2608.05156v1 Announce Type: new Abstract: Post-training of large language models optimizes only parameters, while inference-time procedural scaffolds are typically designed independently of para