Search: “models”

DecompressionLM: Deterministic, Diagnostic, and Zero-Shot Concept Graph Extraction from Language Models

arXiv:2602.00377v3 Announce Type: replace Abstract: Existing knowledge probing methods rely on pre-defined queries, limiting extraction to known concepts. We introduce DecompressionLM, a stateless fra

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Model Releases

Does Reasoning Improve Psychological Depth in Large Language Models? It Depends on Who's Judging

DGX agent

arXiv:2609.13773v1 Announce Type: cross Abstract: LLM-as-a-Judge evaluators are increasingly used to score open-ended generation, yet a judge's correlation with human ratings on its development set ma

model-releasesarxiv-cs-cl

15 Sept 2026

Dynamic Semantic Compression for Efficient Latent-Space Inference in Large Language Models

arXiv:2609.15338v1 Announce Type: cross Abstract: Large Language Models (LLMs) primarily perform inference at the token level, resulting in substantial memory overhead and compromised computational ef

Applications

How Well Do Pseudo-Rigid Body Models Capture Real Plants? In-Field Validation of Simulated Blueberry Canes

arXiv:2609.13627v1 Announce Type: new Abstract: Many labor-intensive tasks in fruit production such as pruning require physically interacting with the plant (e.g., pushing, pulling, bending limbs, etc

applicationsarxiv-cs-ro

Sampling headroom is not selection gain: a compute-value audit of test-time scaling for video world models

arXiv:2609.13257v1 Announce Type: cross Abstract: Test-time scaling (TTS) can improve generation only when additional compute produces better candidates and the system can reliably identify them. This

Vision-Language Models for Criterion-Level Grading of Handwritten Examinations in Outcome-Based Education

model-releasesarxiv-cs-cv

arXiv:2609.14284v1 Announce Type: new Abstract: Criterion-level grading connects examination performance to learning outcomes, but manual marking introduces workload and variation between markers. Thi

Certifying Concept Unlearning in Text-to-Image Diffusion Models

arXiv:2609.12163v1 Announce Type: new Abstract: Existing evaluations of concept unlearning in text-to-image (T2I) diffusion models primarily rely on attack success rates obtained through automated adv

safetyarxiv-cs-lg

14 Sept 2026

ProactiveBench: Can Streaming Video Models Really Interact Like Humans?

arXiv:2609.12658v1 Announce Type: new Abstract: Streaming video understanding requires models to process continuous multimodal input while maintaining temporal context. Existing evaluations are predom

researcharxiv-cs-lg

14 Sept 2026

Routing by Reasoning Need: Trajectory-Aware Decoding Control for Diffusion Vision-Language Models

arXiv:2609.11315v1 Announce Type: new Abstract: Diffusion vision-language models generate answers through iterative refinement, exposing intermediate answer trajectories that can be inspected and cont

12 Sept 2026

Agents

AI Economist Agent: An Agentic Framework for Evidence-Based Economic and Financial Analysis with RAG, Knowledge Graphs, and Large Language Models

arXiv:2606.20041v2 Announce Type: replace-cross Abstract: We propose an AI economist agent for economic and financial scenario analysis. Scenario design often requires analysts to assess emerging risk

agentsarxiv-cs-lg

11 Sept 2026

Automated multi-class wound assessment using dedicated instance segmentation models for boundary detection and classification

arXiv:2603.27325v2 Announce Type: replace Abstract: Accurate wound classification (WC) and boundary segmentation are essential for guiding clinical decisions in chronic and acute wound management. How

11 Sept 2026

Distribution-aware Language Neuron Identification in Multilingual Large Language Models

arXiv:2609.10993v1 Announce Type: new Abstract: Multilingual large language models (mLLMs) contain a small fraction of feed-forward neurons that are sensitive to particular languages, commonly termed

11 Sept 2026

A Statistical Approach to Estimating Sample Size of Machine Learning Models

arXiv:2609.09547v1 Announce Type: new Abstract: Sample size determination for machine learning (ML) prediction models is challenging because conventional power analysis typically requires the predicto

tutorialsarxiv-cs-lg

AdaCultureSafe: Adaptive Cultural Safety Grounded by Cultural Knowledge in Large Language Models

arXiv:2603.08275v2 Announce Type: replace-cross Abstract: With the global proliferation of Large Language Models (LLMs), cultural safety, defined as the ability to generate respectful and appropriate

An Evolutionary Framework for Automatic Optimization Benchmark Generation via Large Language Models

model-releasesarxiv-cs-ai

arXiv:2601.12723v3 Announce Type: replace-cross Abstract: Optimization benchmarks play a fundamental role in assessing algorithm performance; however, existing artificial benchmarks often fail to capt

Large Language Models Transform Organic Synthesis From Reaction Prediction to Automation

arXiv:2508.05427v2 Announce Type: replace Abstract: Large language models (LLMs) are beginning to reshape how organic-synthesis workflows are represented, queried, planned, and connected to experiment

Noesis: Deterministic-First Retrieval with Two-Tier Context Hydration for Factuality-Critical Queries on Small Local Models

arXiv:2609.07663v1 Announce Type: cross Abstract: A wrong number is worse than no answer. Across factuality-critical domains -- audience metrics, scheduling and rights in media; dosages and lab values

Performance of Clinical AI System and Physicians and Frontier Language Models in primary care diagnostics

model-releasesarxiv-cs-ai

arXiv:2609.09070v1 Announce Type: cross Abstract: Clinical AI evaluation should encompass diagnosis and management after adaptive information gathering. We compared Doctorina, eight physicians and fou

World-Time Compute with Verified Code World Models

arXiv:2609.09163v1 Announce Type: new Abstract: LLMs generalize across a domain only after seeing many real, labeled examples, which most domains lack. We study a way to manufacture it cheaply. When a

researcharxiv-cs-lg

ActionSplice: In-Flight Action Editing for Interactive World Models

arXiv:2609.08230v1 Announce Type: new Abstract: Chunk-autoregressive video world models typically condition each generated chunk on one action. An action received during sampling must therefore wait f

Artificial Foveated Perception for Mitigating Shortcut Learning in Robotic Foundation Models

arXiv:2607.10655v2 Announce Type: replace Abstract: Robotic foundation models still need task-specific fine-tuning before deployment, and the fine-tuned policies often break under modest changes in sc

safetyarxiv-cs-ro

ComVLA: Communication-Aware Split Inference for VLA Models in 6G-Connected Robotics

model-releasesarxiv-cs-ro

arXiv:2609.07838v1 Announce Type: new Abstract: Connected robotics is an emerging 6G application where mobile robots follow natural-language instructions to manipulate physical objects. The Vision-Lan

CONDUIT: A Unified Residual-Stream Restoration Framework for KV Cache Reuse in Vision-Language Models

arXiv:2609.05821v1 Announce Type: cross Abstract: Vision-language models (VLMs) often answer new questions about recurring visual content, where reusing the key-value (KV) cache can avoid re-encoding

safetyarxiv-cs-cv

Applications

ContactWorld: What Representations Matter in Vision-Tactile World Models for Contact-Rich Manipulation

arXiv:2606.13877v2 Announce Type: replace Abstract: Contact-rich manipulation requires world models to capture complex interaction dynamics from heterogeneous visual and tactile observations, yet the

applicationsarxiv-cs-ro

Decomposition-Guided Diffusion Language Models for Inertial Confinement Fusion Prediction

arXiv:2609.07756v1 Announce Type: new Abstract: Inertial confinement fusion (ICF) is a leading pathway toward clean energy, but each shot at the National Ignition Facility costs on the order of one mi

Denoising Models Develop Human-Like Perceptual Illusion Representations Across Architectures

arXiv:2607.17138v2 Announce Type: replace Abstract: Deep neural networks trained on natural images are shown to produce outputs consistent with human observers for brightness illusions. While this phe

Diagnosing and Dynamically Filtering Occupancy World Models for Active Mapping

arXiv:2609.06820v1 Announce Type: cross Abstract: Active mapping requires a robot to select camera viewpoints that efficiently reconstruct an unknown 3D scene. To reason about unobserved regions, rece

Diffuse2Seg: Diffusion Models Can Segment Anything Without Supervision

arXiv:2609.06491v1 Announce Type: new Abstract: Open-world entity segmentation aims to predict masks for arbitrary objects across domains and at multiple granularities, from parts to whole objects. In

EntropyPrune: Matrix Entropy Guided Visual Token Pruning for Multimodal Large Language Models

arXiv:2602.17196v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) incur substantial inference cost due to the processing of hundreds of visual tokens per image. Although tok

Foundation Models for Generalizable Semantic and Goal-Oriented Communication

arXiv:2609.07853v1 Announce Type: new Abstract: Semantic and goal-oriented communication is increasingly studied for 6G, but generalization beyond seen data remains a key weakness under tight rate bud

tutorialsarxiv-cs-lg

KDFlow: A User-Friendly and Efficient Knowledge Distillation Framework for Large Language Models

arXiv:2603.01875v4 Announce Type: replace-cross Abstract: Knowledge distillation (KD) is widely used to compress and post-train large language models (LLMs), yet many existing frameworks execute teach

safetyarxiv-cs-lg

Learning Multi-Index Models with Hyper-Kernel Ridge Regression

arXiv:2510.02532v2 Announce Type: replace-cross Abstract: Deep neural networks excel in high-dimensional problems, outperforming models such as kernel methods, which suffer from the curse of dimension

tutorialsarxiv-cs-lg

MI-PEFT: Mixture-of-Experts Integrated Parameter-Efficient Fine-Tuning Protein Language Models Improves Acidophilic Proteins Classification

arXiv:2609.08059v1 Announce Type: cross Abstract: Acidophilic proteins that remain stable and functional under highly acidic conditions, are important for industrial biocatalysis, acid-related bioproc

Observability conditions for neural state-space models with eigenvalues and their roots of unity

arXiv:2504.15758v3 Announce Type: replace Abstract: We operate through the lens of ordinary differential equations and control theory to study the concept of observability in the context of neural sta

RePro: Training Language Models to Faithfully Recycle the Web for Pretraining

arXiv:2510.10681v2 Announce Type: replace-cross Abstract: High-quality data is a cornerstone of large language model (LLM) pretraining, yet its growth has not kept pace with the needs of frontier mode

researcharxiv-cs-lg

Separating Capability from Confidence: Grounded Dual-State Calibration for GRPO-Trained Medical Vision-Language Models

arXiv:2609.06419v1 Announce Type: new Abstract: Medical vision-language models (VLMs) require confidence that reflects both answer correctness and patient-specific visual evidence. Recent GRPO-based m

SimpleMemVLA: A Simple but Effective Native-Video Memory for Vision-Language-Action Models

arXiv:2609.05533v1 Announce Type: new Abstract: Long-horizon manipulation is partially observable: the information needed to choose the next action may appear only in observations from minutes earlier

safetyarxiv-cs-cv

STAR-Pro: Stage-Wise Token Adaptive Reduction with Progressive Refinement for Efficient Large Vision-Language Models

arXiv:2609.05916v1 Announce Type: new Abstract: Large vision-language models (LVLMs) achieve strong multimodal understanding, but the hundreds to thousands of visual tokens they process impose substan

Sustained Performance and Energy Accounting for Nonlinear Forecasting Across Classical and Simulated Quantum Models

arXiv:2510.25183v2 Announce Type: replace-cross Abstract: Energy-efficient AI should be evaluated across the full application pipeline, not only by lowest error or shortest training time. We study thi

The Role of Uncertainty in Assessing the Fairness of Machine Learning Models

arXiv:2609.07959v1 Announce Type: cross Abstract: Machine learning models are widely used in clinical applications, social media, law enforcement and critical infrastructure. Verifying whether their o

safetyarxiv-cs-lg

Understanding Cross-Modal Contributions in Continual Vision-Language Models: A Theoretical Perspective

arXiv:2606.14883v2 Announce Type: replace Abstract: Continual vision-language models are commonly addressed through sequential fine-tuning; however, although this paradigm enables adaptation to new en

Cross-Task Generalization Between Understanding and Generation in Unified Vision-Language Models: A Controlled Study

arXiv:2505.23043v2 Announce Type: replace-cross Abstract: Unified vision-language models (VLMs) aim to support both visual understanding and generation within a single framework, but it remains unclea

DART: Depth-as-Target Pretraining for Surgical Vision Foundation Models

arXiv:2609.04555v1 Announce Type: new Abstract: Vision foundation models (VFMs) are valuable in data-scarce domains such as surgery, where a single pretrained backbone can provide rich representations

tutorialsarxiv-cs-cv

Data-Driven Discovery of Composition-Dependent Constitutive Models for Hyperelasticity and Viscoelasticity of Digital Materials

arXiv:2609.04541v1 Announce Type: new Abstract: Digital materials fabricated by multi-material 3D printing are designed as controlled mixtures of stiff and compliant constituents, yielding effective r

Agents

Diffusion Language Models for Mobile Edge Agentic AI: Foundations, Applications, and Challenges

arXiv:2609.04778v1 Announce Type: new Abstract: Diffusion language models (DLMs) offer a non-autoregressive alternative for mobile edge agentic artificial intelligence (AI) by refining tokens through

agentsarxiv-cs-ai

Do Androids Dream of Unseen Puppeteers? Probing for a Conspiracy Tendencies in Large Language Models

arXiv:2511.03699v2 Announce Type: replace Abstract: We investigate whether Large Language Models (LLMs) exhibit conspiratorial tendencies, whether they display socio-demographic biases in this domain,

Evaluating Large Language Models for Forced Outage Risk Prediction: Benefits and Comparison to Machine Learning

arXiv:2609.04272v1 Announce Type: cross Abstract: This study examines the ability of large language models (LLMs) to predict the risk of weather-related forced outages in the distribution grid in a ze

LLM-Assisted Behavioural and Scenario Augmentation for Agent-Based Energy Adoption Models

arXiv:2609.04866v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) create opportunities to enrich simulation-based energy policy analysis, particularly by supporting struc