Search: “models”

Functional Emotion Without Character: Large Language Models, Aristotelian Disposition, and the Limits of Behavioral Alignment

arXiv:2609.22362v1 Announce Type: new Abstract: Debates about whether artificial systems can feel are often forced between two unsatisfactory positions: behavioral equivalence is treated as sufficient

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Research

INTCORT: Training-Free Spatial Reasoning Enhancement for Vision-Language Models via Input Transformations and Confidence Routing

DGX agent

arXiv:2609.24813v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have demonstrated remarkable capabilities in multimodal tasks, yet they still exhibit poor ability in spatial reasoning. E

researcharxiv-cs-cv

22 Sept 2026

Large language models in medical time series analysis

arXiv:2609.22262v1 Announce Type: cross Abstract: Medical time series (MedTS), including electrocardiograms (ECG), electroencephalograms (EEG), photoplethysmography (PPG), and vital-sign recordings, a

applicationsarxiv-cs-lg

Model Releases

Monotone-Constrained Diffusion Models for Long-Horizon Production Forecasting

model-releasesarxiv-cs-lg

arXiv:2609.22643v1 Announce Type: new Abstract: Forecasting a long horizon from only the first observations of a sequence is ill-posed: many trajectories are consistent with the same short history. We

Local Ai

Pharmacokinetic State Space Models for Unbiased Prediction of Haemodynamic Collapse

arXiv:2609.24338v1 Announce Type: new Abstract: An Intraoperative Hypotension (IOH) event is a frequent complication during administration of general anaesthesia with serious downstream consequences,

local-aiarxiv-cs-lg

Rethinking Pivot Programming Languages in Code Language Models

arXiv:2609.22988v1 Announce Type: new Abstract: Multilingual code language models transfer skills across programming languages (PLs), but whether any PL occupies a privileged pivot position remains co

Token Signatures of Code: Comparing Coding Behaviors Across Large Language Models

arXiv:2609.22097v1 Announce Type: new Abstract: The evaluation of large language models (LLMs) on coding tasks has primarily focused on performance metrics such as pass@k. As LLMs continue to advance,

When Cosine Similarity Fails to Reflect Linearly Accessible Structure in Dialogue Models

arXiv:2609.22522v1 Announce Type: new Abstract: Cosine similarity is widely used to analyze transformer representations, implicitly assuming that similarity reflects task-relevant structure. We study

A Generative Grammar Underlying the Voynich Manuscript, the Pastiche Hypothesis: Evidence from Large Language Models

arXiv:2609.20835v1 Announce Type: new Abstract: Background: The Voynich Manuscript is a fifteenth-century codex written in an unknown script whose content remains undeciphered. Previous studies sugges

DiaVLo: Diagnosing Behaviours of Vision-Language Models

arXiv:2609.22008v1 Announce Type: cross Abstract: Vision-language models (VLMs) rely on storing and transferring appropriate information across their sub-components. Verifying that the VLMs exhibit de

Model Releases

Learning to Move Cities: Deep Meta-Models and Reinforcement Policies for Calibration and Control in Urban Networks

model-releasesarxiv-cs-lg

arXiv:2609.21945v1 Announce Type: new Abstract: Urban transportation networks present complex optimization challenges spanning calibration of high-fidelity simulators and real-time operational control

LogicTrack: Auditing Reasoning Trajectories of Large Language Models with Formal Logic Solvers

arXiv:2609.21492v1 Announce Type: new Abstract: Chain-of-Thought (CoT) reasoning has been shown to improve the performance of large language models (LLMs), yet existing optimization methods largely re

Hardware

RBS-Attention: Radius-Bounded Sparse Prefill for Long-Context Large Language Models

arXiv:2609.20971v1 Announce Type: new Abstract: Long-context large language model inference is increasingly limited by prefill, where dense self-attention processes the entire prompt before generation

hardwarearxiv-cs-ai

Recursive Language Models Generalize Out of Domain

arXiv:2609.20831v1 Announce Type: new Abstract: We study when limiting what a language model can see improves learning. We compare standard CoT, the more general learner that reads the full trace, wit

Rewarding Efficient Reasoning Improves Abstention on Underspecified Tasks in Reasoning Models

arXiv:2609.20846v1 Announce Type: new Abstract: While modern large reasoning models (LRMs) excel at providing correct answers in many tasks, we provide additional evidence for the observation that the

CleanVideo: Adaptive Concept Erasure for Text-to-Video Diffusion Models

arXiv:2609.20267v1 Announce Type: cross Abstract: Concept erasure aims to selectively eliminate undesired visual semantics from pre-trained generative models without compromising their general utility

Compositional Reasoning in Language Models under Reinforcement Learning Post-Training

arXiv:2609.19465v1 Announce Type: new Abstract: Compositional reasoning is critical for real-world problem solving: since training data is necessarily limited, models must generalize by composing lear

MoWAM: Explicit Future Motion Prediction for Efficient World Action Models

arXiv:2609.20709v1 Announce Type: new Abstract: World Action Models (WAMs) improve robot policy learning by incorporating future dynamics, yet explicitly generating future videos at inference introduc

safetyarxiv-cs-ro

Tutorials

Parallelism, critical windows, and separations among diffusion language models

arXiv:2609.20539v1 Announce Type: new Abstract: A popular selling point of diffusion large language models (dLLMs) is their capacity for parallelism: the ability to generate sequences of text far more

tutorialsarxiv-cs-lg

Queries Knew More Than We Thought: Uncovering Latent Knowledge in Segmentation Models

arXiv:2609.20283v1 Announce Type: new Abstract: Modern segmenters often fail after the expensive computation has already been done: a useful mask is present among the model's query-conditioned candida

researcharxiv-cs-cv

Hardware

Recovering Aggressively Pruned Vision-Language-Action Models with Offline Hidden-State Distillation

arXiv:2609.19579v1 Announce Type: new Abstract: Vision-language-action (VLA) models let robots follow language instructions, but their language backbones of several billion parameters are the main obs

hardwarearxiv-cs-ro

Local Ai

SCGFM-ART: Amortized Relational Transport for Structure-Centric Graph Foundation Models

arXiv:2609.20419v1 Announce Type: cross Abstract: Graph foundation models (GFMs) aim to learn transferable representations across severely heterogeneous graph domains. However, severe domain shifts in

local-aiarxiv-cs-ai

Watermarking Diffusion Language Models

arXiv:2509.24368v3 Announce Type: replace-cross Abstract: We introduce the first watermark tailored for diffusion language models (DLMs), an emergent LLM paradigm able to generate tokens in arbitrary

A Probe Shift Is Not a Fairness Fix: The Limits of Representation Steering in Speech Models

arXiv:2609.18533v1 Announce Type: new Abstract: Automatic speech recognition (ASR) systems exhibit unequal error rates across speaker groups, motivating interventions on their internal representations

Extracting Probabilistic Knowledge from Large Language Models for Bayesian Network Parameterization

arXiv:2505.15918v3 Announce Type: replace-cross Abstract: In this work, we evaluate the potential of Large Language Models (LLMs) in building Bayesian Networks (BNs) by approximating domain expert pri

From a River in Gilead to the Inference Distributions of Large Language Models: Covert Dialect Bias and Linguistic Profiling at Scale

arXiv:2609.18068v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in high-stakes domains such as housing screening. While alignment techniques mitigate explicit

safetyarxiv-cs-ai

The Limits of BPE Tokenization in Polish: Segmentation-Flexional Forms, Grammatical Anchoring, and First-Person Stability in Inflectional Language Models

arXiv:2609.17553v1 Announce Type: new Abstract: This article analyzes BPE tokenization in Polish as a test case for the limits of statistical segmentation in an inflectional language. It asks whether

Visual Input and Its Framing Affect Attribute-based Descriptions Produced by Large Vision-Language Models

arXiv:2609.18345v1 Announce Type: new Abstract: Large vision-language models (LVLMs) are commonly used with only a single text prompt as the input, or plus an image. In this paper, we demonstrate that

researcharxiv-cs-cv

Cross-Domain Inference for Human Localization: Applying Wi-Fi RSSI Data to CSI-Trained Models

arXiv:2609.17204v1 Announce Type: cross Abstract: Wi-Fi signal data can be used to compromise the privacy of individuals. While many existing approaches rely on Channel State Information (CSI), collec

Efficient Quantization-Aware Distillation with Cross-Modal Alignment for Edge Vision-Language Models

arXiv:2609.16689v1 Announce Type: new Abstract: Large-scale vision-language models (VLM) such as CLIP enable strong open-vocabulary reasoning, yet deploying these capabilities on resource-constrained

safetyarxiv-cs-cv

Extremely coarse learning objectives induce human-aligned representations in AI vision models

arXiv:2605.05556v2 Announce Type: replace Abstract: Artificial neural networks trained on visual tasks develop internal representations resembling those of the primate visual system, a discovery that

safetyarxiv-cs-cv

Generative models for simulation based filtering: Formulations and Empirical Comparisons

arXiv:2609.16317v1 Announce Type: new Abstract: This letter presents a unified formulation and a controlled numerical comparison of generative-model approaches to the nonlinear filtering problem. Unde

Quantifying Organizational Environmental Action from Web Data and Large Language Models

arXiv:2609.16627v1 Announce Type: new Abstract: Quantifying organizational environmental action from publicly available web content remains a challenging environmental data science problem because rel

TecoPrompt: Temporal-Conservative Prompt Learning for Vision-Language Models

arXiv:2609.16858v1 Announce Type: new Abstract: Prompt learning adapts vision-language models, such as CLIP, by adjusting a small set of context tokens. However, under few-shot supervision, even moder

safetyarxiv-cs-cv

Are Gradient Boosting Models Suitable for Intermittent Demand Forecasting?

arXiv:2609.14718v1 Announce Type: new Abstract: Demand forecasting is critical in modern industry, offering opportunities to reduce costs and gain competitive advantage through improved inventory mana

CHAI for LLMs: Improving Code-Mixed Translation in Large Language Models through Reinforcement Learning with AI Feedback

arXiv:2411.09073v4 Announce Type: replace-cross Abstract: Large language models (LLMs) show strong performance across many tasks but remain weak at understanding code-mixed (CM) language. Despite this

CRITICS - Critical Science Without Borders: Language Models to Promote Critical Thinking in Science Education

arXiv:2609.13942v1 Announce Type: cross Abstract: The CRITICS project addresses science accessibility and literacy by converging advanced Machine Translation (MT) based on Large Language Models (LLMs)

Design and Embedded Validation of Compact ML Models for Affective Touch Classification in a Soft Interactive Companion

arXiv:2607.16196v2 Announce Type: replace Abstract: Soft plush companions provide a safe and intuitive platform for affective human-robot interaction, but their deformable structure and distributed ta

Exact Recovery by Neighborhood Smoothing in Directed Stochastic Block Models

arXiv:2601.16427v3 Announce Type: replace-cross Abstract: We study exact community recovery in sparse directed stochastic block models using neighborhood smoothing of connection-probability profiles.

Exploring the Potential of Diffusion Large Language Models in Code Generation

arXiv:2509.11252v3 Announce Type: replace-cross Abstract: LLMs have become the mainstream approaches to code generation. Existing LLMs mainly employ autoregressive generation, i.e. generating code tok

Human-Grounded Calibration for Long-Text Image-Text Congruence in Vision-Language Models

arXiv:2609.15640v1 Announce Type: cross Abstract: Long-text image--text congruence scoring is increasingly important for vision-language systems that must evaluate whether detailed textual description

Improving Mathematical Reasoning Capabilities in Large Language Models via Reasoning Process Error Classification

arXiv:2609.15145v1 Announce Type: new Abstract: The reasoning ability of large language models (LLMs) is a critical factor for practical LLM-based applications. To investigate the current reasoning ca

Impute-EM: Native Mixed-State Diffusion Models for Heterogeneous Data Imputation

arXiv:2609.15284v1 Announce Type: new Abstract: Missing values are ubiquitous in heterogeneous data mining, where numerical, categorical, and binary variables often coexist. Many imputation methods, e

Breaking the Vision-Action Shortcut: Latent Interface Training for Generalizable Robotics Foundation Models

arXiv:2609.12641v1 Announce Type: new Abstract: Robot foundation models achieve strong in-distribution performance but often degrade under visual distribution shifts. When learning to generate actions

applicationsarxiv-cs-ro

DropVLA: An Action-Level Backdoor Attack on Vision-Language-Action Models

arXiv:2510.10932v5 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models map multimodal perception and language instructions to executable robot actions, making them particularly

safetyarxiv-cs-ro

DWMP: Leveraging Dual World Models for Humanoid Obstacle Traversal

arXiv:2609.12347v1 Announce Type: new Abstract: Humanoid robots must traverse cluttered obstacle fields using onboard proprioceptive and visual observations, yet existing methods usually process multi

safetyarxiv-cs-ro

Local Ai

On-Device Language Models for Privacy-Preserving Stress Prediction: A Multimodal Evaluation on Mobile Health

arXiv:2609.11961v1 Announce Type: new Abstract: Stress is a pervasive determinant of mental health and a key target for mobile health interventions. On-device language models (ODLMs) offer privacy-pre

local-aiarxiv-cs-lg

Sampling via Decision-Flow: Training-Free Extraction of Improved Latent Reasoning Paths in Large Language Models

arXiv:2609.12317v1 Announce Type: new Abstract: A central question in LLM reasoning is whether reinforcement learning (RL) instills genuinely new capabilities or merely reshapes how existing knowledge