Search: “models”

Informed Masking: Structure-Aware Perturbation for Reinforcement Learning in Diffusion Large Language Models

arXiv:2609.25927v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have emerged as an efficient alternative to autoregressive models, yet aligning them via Reinforcement Learning

local-aiarxiv-cs-cl

23 Sept 2026

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Research

On the Lexical Superstition of Large Language Models for Code Comprehension: Re-evaluation on Code of Low Lexical Quality

DGX agent

arXiv:2609.26388v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have made them widely used for code-related tasks. Identifier names are statistically informative in n

researcharxiv-cs-cl

23 Sept 2026

Spoken Language Models that Think Aloud

arXiv:2609.26488v1 Announce Type: new Abstract: While Chain-of-Thought (CoT) reasoning has improved the capability of language models, directly applying it to Spoken Language Models (SLMs) may introdu

23 Sept 2026

Applications

StrataVLA: Hierarchical and Efficient 3D Geometric Grounding for Vision-Language-Action Models

arXiv:2609.26071v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models inherit strong semantic priors from large-scale vision-language pretraining, yet remain limited in robotic manipulat

applicationsarxiv-cs-ro

23 Sept 2026

Assessing Adversarial Robustness of Latent Reasoning Models

arXiv:2609.22228v1 Announce Type: new Abstract: Large language models increasingly rely on long chain-of-thought (CoT) trajectories for complex reasoning, but autoregressive generation brings substant

Comparative Analysis of State-of-the-Art Foundation Models for Sleep Analysis Under Channel Reduction

arXiv:2609.22105v1 Announce Type: cross Abstract: Automatic sleep staging from polysomnography (PSG) is a well-studied task, but PSG itself is expensive, clinic-based, and burdensome to manually score

researcharxiv-cs-lg

Correlation-Aware Structured Pruning for Large Language Models

arXiv:2609.22131v1 Announce Type: new Abstract: Structured pruning is a promising approach for reducing the substantial inference costs of Large Language Models (LLMs) while maintaining hardware effic

Do Language Models Know Their Own Constraints?

model-releasesarxiv-cs-cl

arXiv:2609.22151v1 Announce Type: new Abstract: We ask whether behavioral constraints acquired through post training remain explicitly reportable. Using constrained recipe generation as a testbed, fiv

ECP-Bench: Benchmarking and Learning Entertainment Content Promotion with Foundation Models

model-releasesarxiv-cs-lg

arXiv:2609.22150v1 Announce Type: cross Abstract: Content promotion spans a broad set of skills, from understanding content to forecasting its market reception. However, LLMs' ability to support such

Eraser: Jailbreaking Defense in Large Language Models via Unlearning Harmful Knowledge

arXiv:2404.05880v3 Announce Type: replace Abstract: Jailbreaking attacks can enable Large Language Models (LLMs) to bypass the safeguard and generate harmful content. Existing jailbreaking defense met

Kev: tiny Jev-like decision models (0.8B/4B/9B) on Qwen3.5 you can train and run locally - the 9B fits a 32GB Mac

Been playing with judge models in my eval pipeline, so this landed at the right time. Kev is a small family of Jev-architecture decision models (0.8B, 4B, 9B) built on Qwen3.5, Apache-2.0, with traini

local-air-localllama

Leveraging Industrial Foundation Models at the Edge of Particle Physics Detectors via Distillation Learning and Hardware Co-design

arXiv:2609.23385v1 Announce Type: cross Abstract: Data acquisition (DAQ) systems at future particle physics experiments stand to benefit from the extremes of AI/ML development: large-scale foundation

researcharxiv-cs-lg

Measuring Behavioural Signatures of Large Language Models through Psychometric Profiling

arXiv:2609.22934v1 Announce Type: new Abstract: Large language models (LLMs) increasingly mediate human decisions and communication, yet their behavioural regularities remain difficult to characterize

Memory vs. Context? Influential Factors of Factual Recall in Language Models

arXiv:2609.24238v1 Announce Type: new Abstract: We reproduce and stress-test the work of Yu et al. (2023), who characterize how language models (LMs) arbitrate between memorized knowledge and contradi

Observing and Controlling Features in Vision-Language-Action Models

arXiv:2603.05487v2 Announce Type: replace Abstract: Vision-Language-Action models (VLAs) have shown remarkable progress towards embodied intelligence. While their architecture partially resembles that

safetyarxiv-cs-ro

Replay-Gated Neural Execution: Decoupling Persistent Behavioral Specifications from Neural Realizations in Frozen Language Models

model-releasesarxiv-cs-cl

arXiv:2609.22243v1 Announce Type: new Abstract: Input-conditioned neural interventions raise a runtime question: what persists when one behavioral specification admits multiple actions whose validity

Safe Learning for Contact-Rich Robot Tasks: A Survey from Classical Learning-Based Methods to Safe Foundation Models

arXiv:2512.11908v3 Announce Type: replace Abstract: Contact-rich tasks pose significant challenges for robotic systems due to inherent uncertainty, complex dynamics, and the high risk of damage during

safetyarxiv-cs-ro

Toward Personalized Sleep Guidance from Wearable Data Using Language Models

arXiv:2609.22463v1 Announce Type: new Abstract: Sleep monitoring using wearable data has shown promise for personal health, yet large language model (LLM)-based summarization and question answering re

local-aiarxiv-cs-cl

Applications

Why Do Video Diffusion Models Violate Physics? Unveiling the Flaws in Attention Mechanisms

arXiv:2609.23658v1 Announce Type: new Abstract: Despite impressive visual quality, state-of-the-art video diffusion models often generate content that violates real-world physical laws. While existing

applicationsarxiv-cs-cv

Hiding in Plain Sight: A Diffusion-based Mitigation of Geolocation Privacy Leakage in Vision-Language Models

arXiv:2609.21363v1 Announce Type: new Abstract: Multimodal large reasoning models (MLRMs) have demonstrated remarkable capabilities in complex visual understanding. However, this very power introduces

21 Sept 2026

Robust Mixture Models for Algorithmic Fairness Under Latent Heterogeneity

arXiv:2509.17411v2 Announce Type: replace-cross Abstract: Machine learning models optimized for average performance can perform poorly on vulnerable subpopulations. Existing approaches often rely on g

safetyarxiv-cs-lg

21 Sept 2026

WM-VS: Progress-Aligned World Models for Closed-Loop Visual Servoing

model-releasesarxiv-cs-cv

arXiv:2609.20892v1 Announce Type: cross Abstract: Closed-loop visual servoing requires predictions that indicate whether an action reduces task error, not only whether the action is plausible. We call

21 Sept 2026

Tutorials

Ambient Dataloops: Generative Models for Dataset Refinement

arXiv:2601.15417v2 Announce Type: replace-cross Abstract: We propose Ambient Dataloops, an iterative framework for refining datasets that makes it easier for diffusion models to learn the underlying d

tutorialsarxiv-cs-ai

Can Vision-Language Models Judge Olympic Diving? From Reasoning to Scores in Zero-Shot Action Quality Assessment

arXiv:2609.19354v1 Announce Type: cross Abstract: Automated action quality assessment (AQA) in Olympic sports remains a challenging task due to the complexity of human motion and the subjectivity inhe

Effective and Efficient Threat Hunting with Small Language Models

arXiv:2512.06660v3 Announce Type: replace-cross Abstract: Analysts in Security Operations Centers query massive telemetry streams using Kusto Query Language (KQL), but writing correct KQL demands spec

Feeling Terrain Before Crossing: World Models for Off-Road Navigation

arXiv:2609.19863v1 Announce Type: cross Abstract: Navigation world models plan by foresight, predicting the future that each candidate action sequence produces and selecting the best, rather than mapp

Geopolitical Divisions Across Languages in Large Language Models

arXiv:2609.20005v1 Announce Type: new Abstract: People increasingly turn to AI chatbots for news and explanations of world events. But do they receive the same political answers when they ask in diffe

Summarization Bias: The Directional Collapse of Objective Projection into Told-Mode Labels in Large Language Models --- A Conceptual Framework and Registered Test Protocol

arXiv:2609.20712v1 Announce Type: new Abstract: This paper introduces and operationalizes summarization bias: a proposed systematic tendency of large language models (LLMs) to represent narrative mean

When2Think: Learning Difficulty-Aware Length Control for Efficient Hybrid Reasoning Models

arXiv:2609.19671v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) achieve strong performance on complex tasks but exhibit systematic inefficiency: they often overthink easy problems and un

ActionPiece: Rethinking Action Tokenization for Autoregressive Vision-Language-Action Models

arXiv:2609.18487v1 Announce Type: cross Abstract: Action tokenizers play a central role in autoregressive vision-language-action (VLA) models, determining both the targets for policy training and the

safetyarxiv-cs-ai

Applications

Causal-History Test-Time Scaling for Failure Recovery in Autoregressive World-Action Models

arXiv:2609.18016v1 Announce Type: new Abstract: World-action models (WAMs) have emerged as a promising paradigm for robot manipulation by jointly modeling future visual dynamics and robot actions. How

applicationsarxiv-cs-ro

Large Language Models Versus Physicians in Traditional Chinese Medicine: A Real-World Clinical Case Evaluation

arXiv:2609.17544v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly being explored for clinical applications, yet their assessment for real-world traditional Chinese medicine

Not All Patches Are Equally Forgettable: Spatially Localized Domain Unlearning in Vision-Language Models

arXiv:2609.17790v1 Announce Type: new Abstract: Pre-trained vision-language models (VLMs) exhibit strong cross-domain recognition performance even without additional training. However, this robustness

researcharxiv-cs-cv

Planning or Improvisation? Stress-Testing the Poetry Planning Site on Open Models and Open Cross-Layer Transcoders

model-releasesarxiv-cs-cl

arXiv:2609.18440v1 Announce Type: new Abstract: Lindsey et al. (2025) report that Claude 3.5 Haiku plans rhymes: features for candidate rhyme words are active on the newline before a line is written,

Industry

Arcee AI, which develops open-weight models in the US, raised a Series B at a 1B pre-money valuation; a source says Arcee raised at least 150M (Allie Garfinkle/Fortune)

Allie Garfinkle / Fortune: Arcee AI, which develops open-weight models in the US, raised a Series B at a 1B pre-money valuation; a source says Arcee raised at least 150M — In 2025, Mark McQuade took a

industrytechmeme

Challenges of Auditing: Variability in Outputs of Large Language Models for Health

arXiv:2609.16590v1 Announce Type: new Abstract: People increasingly use frontier AI models for health advice, but via different access modes (e.g., ChatGPT, ChatGPT Health, APIs) with varying settings

Efficient Text-to-Image Generation: An Adaptive Step Schedule Controller for Diffusion Models

arXiv:2609.16572v1 Announce Type: cross Abstract: Text-to-image diffusion models often use a fixed number of denoising steps, balancing time costs and image quality. However, the optimal number of ste

Optical-Flow Wingbeat Counting in MuJoCo: A Comparison of Convolutional, Spiking, and Attention-Based Temporal Models

arXiv:2609.17308v1 Announce Type: new Abstract: Visual monitoring of flapping-wing vehicles requires distinguishing individual wingbeats from motion strength and average frequency. This paper presents

researcharxiv-cs-cv

Semantic-Spatial Agreement Verification for Mitigating Object Hallucination in Multimodal Large Language Models

arXiv:2609.17269v1 Announce Type: new Abstract: Multimodal large language models generate natural-language responses from visual inputs, yet may mention objects absent from an image. In medication ass

World-Action Models for Robot Learning and Control: A Survey

arXiv:2609.16074v1 Announce Type: cross Abstract: Robots operating in open environments act under partial observability, physical constraints, and dynamic task contexts. Beyond mapping observations an

A deep dive into on-device and data center inference for robots, including a primer on robot models, deployments, supply chains, the 'network wall', and more (SemiAnalysis)

SemiAnalysis: A deep dive into on-device and data center inference for robots, including a primer on robot models, deployments, supply chains, the “network wall”, and more — Robot Models, Silicon & DR

local-aitechmeme

Beyond Quacking: Deep Integration of Language Models and RAG into DuckDB

arXiv:2504.01157v1 Announce Type: cross Abstract: Knowledge-intensive analytical applications retrieve context from both structured tabular data and unstructured, text-free documents for effective dec

Beyond Safe Answers: Segment-Aware Listwise Alignment for Reasoning Safety in Large Reasoning Models

arXiv:2609.15517v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) pose a dual-surface safety challenge: both intermediate reasoning traces and final answers can contain harmful content. Ex

safetyarxiv-cs-ai

CAL-MOS: Bridging Layers with Adapters for Robust MOS Prediction Across Speech Foundation Models

arXiv:2609.14956v1 Announce Type: cross Abstract: Speech Quality Assessment (SQA) is essential for modern speech technologies, and recent non-intrusive SQA predictors increasingly rely on Speech Found

From Vision to Harvest: Benchmarking Vision-Language Models for Multi-Arm Robotic Fruit Harvesting

model-releasesarxiv-cs-cv

arXiv:2609.13606v1 Announce Type: cross Abstract: Multi-arm robotic harvesting offers a promising path to improve harvesting efficiency and reduce reliance on manual labor. However, practical deployme

Rethinking Correctness for Uncertainty Estimation in Clinical Prediction with Vision-Language Models

arXiv:2609.15180v1 Announce Type: new Abstract: Vision-language models are increasingly explored for clinical prediction from electronic health records and medical images, where identifying unreliable

researcharxiv-cs-lg

Temporal Self-Distillation: Faster Inference in Discrete Diffusion Language Models

arXiv:2609.15177v1 Announce Type: new Abstract: Diffusion language models (dLLMs) promise fast inference by generating multiple tokens in parallel, but suffer severe performance degradation when paral

safetyarxiv-cs-lg

Thought without systematicity? Evaluating reasoning models on rule induction tasks

arXiv:2609.13948v1 Announce Type: cross Abstract: A central tenet of human cognition is systematicity, the principle that understanding one concept is inherently tied to understanding close variations