Search: “models”

Sparse Autoencoder Decomposition of Clinical Sequence Model Representations: Feature Complexity, Task Specialisation, and Mortality Prediction

arXiv:2605.04072v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) have been applied to large language models and protein language models, but not systematically to electronic health record

7 May 2026

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How Language Models Process Negation

DGX agent

arXiv:2605.03052v1 Announce Type: new Abstract: We study how Large Language Models (LLMs) process negation mechanistically. First, we establish that even though open-weight models often provide wrong

model-releasesarxiv-cs-cl

6 May 2026

Applications

Re-Key-Free, Risky-Free: Adaptable Model Usage Control

arXiv:2511.18772v2 Announce Type: replace-cross Abstract: Deep neural networks (DNNs) have become valuable intellectual property of model owners, due to the substantial resources required for their de

applicationsarxiv-cs-ai

6 May 2026

Causal2Vec: Improving Decoder-only LLMs as Embedding Models through a Contextual Token

arXiv:2507.23386v3 Announce Type: replace Abstract: Decoder-only large language models (LLMs) have been increasingly adopted to build embedding models for diverse tasks. To overcome the inherent limit

CP-SynC: Multi-Agent Zero-Shot Constraint Modeling in MiniZinc with Synthesized Checkers

arXiv:2605.01675v1 Announce Type: cross Abstract: Constraint Programming (CP) is a powerful paradigm for solving combinatorial problems, yet translating natural language problem descriptions into exec

Developing a Strong Pre-Trained Base Model for Plant Leaf Disease Classification

arXiv:2605.01283v1 Announce Type: new Abstract: Plants, crops and their yields are essential to our very existence, but diseases and pests cause large losses every year. As such it is vital to ensure

Model Merging: Foundations and Algorithms

model-releasesarxiv-cs-lg

arXiv:2605.01580v1 Announce Type: new Abstract: Modern deep learning usually treats models as separate artifacts: trained independently, specialized for particular purposes, and replaced when improved

RMGAP: Benchmarking the Generalization of Reward Models across Diverse Preferences

arXiv:2605.01831v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback has become the standard paradigm for language model alignment, where reward models directly determine alignme

Rethinking LLM Ensembling from the Perspective of Mixture Models

arXiv:2605.00419v1 Announce Type: cross Abstract: Model ensembling is a well-established technique for improving the performance of machine learning models. Conventionally, this involves averaging the

4 May 2026

HealthBench Professional: Evaluating Large Language Models on Real Clinician Chats

arXiv:2604.27470v1 Announce Type: new Abstract: Millions of clinicians use ChatGPT to support clinical care, but evaluations of the most common use cases in model-clinician conversations are limited.

Agents

Modeling Clinical Concern Trajectories in Language Model Agents

arXiv:2604.27872v1 Announce Type: new Abstract: Large language model (LLM) agents deployed in clinical settings often exhibit abrupt, threshold-driven behavior, offering little visibility into accumul

agentsarxiv-cs-ai

Models Recall What They Violate: Constraint Adherence in Multi-Turn LLM Ideation

arXiv:2604.28031v1 Announce Type: new Abstract: When researchers iteratively refine ideas with large language models, do the models preserve fidelity to the original objective? We introduce DriftBench

What Suppresses Nash Equilibrium Play in Large Language Models? Mechanistic Evidence and Causal Control

arXiv:2604.27167v1 Announce Type: cross Abstract: LLM agents are known to deviate from Nash equilibria in strategic interactions, but nobody has looked inside the model to understand why, or asked whe

A Comparative Study in Surgical AI: Datasets, Foundation Models, and Barriers to Med-AGI

arXiv:2603.27341v2 Announce Type: replace-cross Abstract: Recent Artificial Intelligence (AI) models have matched or exceeded human experts in several benchmarks of biomedical task performance, but su

Benchmarking Layout-Guided Diffusion Models through Unified Semantic-Spatial Evaluation in Closed and Open Settings

arXiv:2604.25358v1 Announce Type: new Abstract: Evaluating layout-guided text-to-image generative models requires assessing both semantic alignment with textual prompts and spatial fidelity to prescri

Tutorials

Diffusion Model for Manifold Data: Score Decomposition, Curvature, and Statistical Complexity

arXiv:2603.20645v2 Announce Type: replace Abstract: Diffusion models have become a leading framework in generative modeling, yet their theoretical understanding -- especially for high-dimensional data

tutorialsarxiv-cs-lg

How Fast Should a Model Commit to Supervision? Training Reasoning Models on the Tsallis Loss Continuum

arXiv:2604.25907v1 Announce Type: new Abstract: Adapting reasoning models to new tasks during post-training with only output-level supervision stalls under reinforcement learning from verifiable rewar

safetyarxiv-cs-lg

Marco-MoE: Open Multilingual Mixture-of-Expert Language Models with Efficient Upcycling

arXiv:2604.25578v1 Announce Type: new Abstract: We present Marco-MoE, a suite of fully open multilingual sparse Mixture-of-Experts (MoE) models. Marco-MoE features a highly sparse design in which only

Personalization Toolkit: Training Free Personalization of Large Vision Language Models

arXiv:2502.02452v4 Announce Type: replace Abstract: Personalization of Large Vision-Language Models (LVLMs) involves customizing models to recognize specific users or object instances and to generate

RISE: Self-Improving Robot Policy with Compositional World Model

arXiv:2602.11075v2 Announce Type: replace Abstract: Despite the sustained scaling on model capacity and data acquisition, Vision-Language-Action (VLA) models remain brittle in contact-rich and dynamic

safetyarxiv-cs-ro

Why Does Reinforcement Learning Generalize? A Feature-Level Mechanistic Study of Post-Training in Large Language Models

arXiv:2604.25011v1 Announce Type: new Abstract: Reinforcement learning (RL)-based post-training often improves the reasoning performance of large language models (LLMs) beyond the training domain, whi

A systematic evaluation of vision-language models for observational astronomical reasoning tasks

arXiv:2604.24589v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly proposed as general-purpose tools for scientific data interpretation, yet their reliability on real astro

Applications

Au-M-ol: A Unified Model for Medical Audio and Language Understanding

arXiv:2604.23284v1 Announce Type: cross Abstract: In this work, we present Au-M-ol, a novel multimodal architecture that extends Large Language Models (LLMs) with audio processing. It is designed to i

applicationsarxiv-cs-ai

Channel Adaptation for EEG Foundation Models: A Systematic Benchmark Across Architectures, Tasks, and Training Regimes

model-releasesarxiv-cs-lg

arXiv:2604.23091v1 Announce Type: new Abstract: Scaling EEG foundation models requires pooling data across heterogeneous electrode montages, a prerequisite both for larger pretraining corpora and for

Energy-Aware Routing to Large Reasoning Models

arXiv:2601.00823v2 Announce Type: replace Abstract: Large reasoning models (LRMs) have heterogeneous inference energy costs based on which model is used and how much it reasons. To reduce energy, it i

Learning an Image Editing Model without Image Editing Pairs

arXiv:2510.14978v2 Announce Type: replace Abstract: Recent image editing models have achieved impressive results while following natural language editing instructions, but they rely on supervised fine

researcharxiv-cs-cv

Measuring Temporal Linguistic Emergence in Diffusion Language Models

arXiv:2604.23235v1 Announce Type: new Abstract: Diffusion language models expose an explicit denoising trajectory, making it possible to ask when different kinds of information become measurable durin

Applications

MetaEarth3D: Unlocking World-scale 3D Generation with Spatially Scalable Generative Modeling

arXiv:2604.22828v1 Announce Type: cross Abstract: Recent generative AI models have achieved remarkable breakthroughs in language and visual understanding. However, although these models can generate r

applicationsarxiv-cs-ai

On Emergent Social World Models -- Evidence for Functional Integration of Theory of Mind and Pragmatic Reasoning in Language Models

arXiv:2602.10298v2 Announce Type: replace Abstract: This paper investigates whether LMs recruit shared computational mechanisms for general Theory of Mind (ToM) and language-specific pragmatic reasoni

On the Reasoning Abilities of Masked Diffusion Language Models

arXiv:2510.13117v3 Announce Type: replace-cross Abstract: Masked diffusion models (MDMs) for text offer a compelling alternative to traditional autoregressive language models. Parallel generation make

The Randomness Floor: Measuring Intrinsic Non-Randomness in Language Model Token Distributions

arXiv:2604.22771v1 Announce Type: cross Abstract: Language models cannot be random. This paper introduces Entropic Deviation (ED), the normalised KL divergence between a model's token distribution and

Local Ai

Differentiable Filtering for Learning Hidden Markov Models

arXiv:2511.10571v2 Announce Type: replace Abstract: Hidden Markov Models (HMMs) are fundamental for modeling sequential data, yet learning their parameters from observations remains challenging. Class

local-aiarxiv-cs-lg

27 Apr 2026

Language Specific Knowledge: Do Models Know Better in X than in English?

arXiv:2505.14990v3 Announce Type: replace Abstract: Often, multilingual language models are trained with the objective to map semantically similar content (in different languages) in the same latent s

27 Apr 2026

When LoRA Betrays: Backdooring Text-to-Image Models by Masquerading as Benign Adapters

arXiv:2602.21977v4 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has emerged as a leading technique for efficiently fine-tuning text-to-image diffusion models, and its widespread adoptio

researcharxiv-cs-cv

27 Apr 2026

ChessArena: A Chess Testbed for Evaluating Strategic Reasoning Capabilities of Large Language Models

arXiv:2509.24239v4 Announce Type: replace-cross Abstract: Recent large language models (LLMs) have shown strong reasoning capabilities. However, a critical question remains: do these models possess ge

Hi-WM: Human-in-the-World-Model for Scalable Robot Post-Training

arXiv:2604.21741v1 Announce Type: new Abstract: Post-training is essential for turning pretrained generalist robot policies into reliable task-specific controllers, but existing human-in-the-loop pipe

safetyarxiv-cs-ro

Pretrain Where? Investigating How Pretraining Data Diversity Impacts Geospatial Foundation Model Performance

arXiv:2604.21104v1 Announce Type: new Abstract: New geospatial foundation models introduce a new model architecture and pretraining dataset, often sampled using different notions of data diversity. Pe

Revealing Geography-Driven Signals in Zone-Level Claim Frequency Models: An Empirical Study using Environmental and Visual Predictors

model-releasesarxiv-cs-lg

arXiv:2604.21893v1 Announce Type: cross Abstract: Geographic context is often consider relevant to motor insurance risk, yet public actuarial datasets provide limited location identifiers, constrainin

VLA-Forget: Vision-Language-Action Unlearning for Embodied Foundation Models

arXiv:2604.03956v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models are emerging as embodied foundation models for robotic manipulation, but their deployment introduces a new

safetyarxiv-cs-ai

LayerTracer: A Joint Task-Particle and Vulnerable-Layer Analysis framework for Arbitrary Large Language Model Architectures

arXiv:2604.20556v1 Announce Type: cross Abstract: Currently, Large Language Models (LLMs) feature a diversified architectural landscape, including traditional Transformer, GateDeltaNet, and Mamba. How

23 Apr 2026

AlignCultura: Towards Culturally Aligned Large Language Models?

arXiv:2604.19016v1 Announce Type: new Abstract: Cultural alignment in Large Language Models (LLMs) is essential for producing contextually aware, respectful, and trustworthy outputs. Without it, model

Local Ai

Automated Energy-Aware Time-Series Model Deployment on Embedded FPGAs for Resilient Combined Sewer Overflow Management

arXiv:2508.13905v2 Announce Type: replace Abstract: Extreme weather events, intensified by climate change, increasingly challenge aging combined sewer systems, raising the risk of untreated wastewater

local-aiarxiv-cs-lg

Dynamic Model Routing and Cascading for Efficient LLM Inference: A Survey

arXiv:2603.04445v2 Announce Type: replace-cross Abstract: The rapid growth of large language models (LLMs) with diverse capabilities, costs, and domains has created a critical need for intelligent mod

Fine-Tuning Small Reasoning Models for Quantum Field Theory

arXiv:2604.18936v1 Announce Type: cross Abstract: Despite the growing application of Large Language Models (LLMs) to theoretical physics, there is little academic exploration into how domain-specific

How Far Are Video Models from True Multimodal Reasoning?

arXiv:2604.19193v1 Announce Type: new Abstract: Despite remarkable progress toward general-purpose video models, a critical question remains unanswered: how far are these models from achieving true mu

LLM-as-Judge Framework for Evaluating Tone-Induced Hallucination in Vision-Language Models

arXiv:2604.18803v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are increasingly deployed in settings where reliable visual grounding carries operational consequences, yet their behavi

Multilingual Language Models Encode Script Over Linguistic Structure

arXiv:2604.05090v2 Announce Type: replace Abstract: Multilingual language models (LMs) organize representations for typologically and orthographically diverse languages into a shared parameter space,

QSLM: A Performance- and Memory-aware Quantization Framework with Tiered Search Strategy for Spike-driven Language Models

arXiv:2601.00679v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have been emerging as prominent AI models for solving many natural language tasks due to their high performance (