Search: “models”

Robust Non-Clairvoyant Scheduling with Classification Models

arXiv:2610.01343v1 Announce Type: new Abstract: We study the classical single-machine scheduling problem of minimizing the sum of completion times of jobs in a non-clairvoyant setting, where the proce

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

STEER: Reducing Inference Cost in Relational Foundation Models through Semantically Informed Sampling

DGX agent

arXiv:2610.00907v1 Announce Type: cross Abstract: Relational foundation models (RFMs) are pretrained once on a collection of relational databases and prediction tasks, and then applied zero-shot to pr

researcharxiv-cs-lg

2 Oct 2026

Temporally-Resolved Token Attribution Reveals the Generation Dynamics of Diffusion Language Models

arXiv:2610.01177v1 Announce Type: cross Abstract: This work presents Diffusion Layer Integrated Gradients (DLIG), a token attribution method for diffusion language models (DLMs) that extends Integrate

Verbalized and Internal Probabilities Are Coupled in Large Language Models

arXiv:2610.00827v1 Announce Type: cross Abstract: Large language models carry an internal notion of uncertainty in their sampling distribution, i.e., the probabilities they place on generating one ans

When Do Causal World Models Help Modular LLM Agents

arXiv:2610.00012v1 Announce Type: new Abstract: LLM agents increasingly act through modular systems, such as order, payment, inventory, and shipment services, where actions in one module change which

local-aiarxiv-cs-ai

Colorectal Cancer Segmentation with Adaptive Augmentation and Multi-Resolution Ensemble Models

arXiv:2609.38419v1 Announce Type: cross Abstract: Colorectal cancer (CRC) is the second most deadly and third most common cancer, and the leading cause of death among gastrointestinal cancers. Early d

safetyarxiv-cs-ai

Agents

Comparative study of adapting pre-trained models for driving behavior video captioning

arXiv:2609.39542v1 Announce Type: new Abstract: This report examines and compares some of the many fine tuning and prompting methods existing, applying them within the domain of autonomous driving. Th

agentsarxiv-cs-cv

Diagnosing On-Policy Self-Distillation for Reasoning Language Models

arXiv:2609.39118v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) has attracted growing interest as a promising approach to improve the reasoning ability of language models. Without e

safetyarxiv-cs-cl

Inferring Causal Relations between Two Sequences of Events with Language Models

arXiv:2609.39406v1 Announce Type: new Abstract: Causal AI is a branch of Artificial Intelligence which helps understand and reason about cause and effect relationships, not just patterns or correlatio

Learning Where to Look: Anatomical Grounding and Guided Attention for Cardiac MRI Vision-Language Models

arXiv:2609.39899v1 Announce Type: new Abstract: Cardiac magnetic resonance imaging (CMR) enables assessment of cardiac anatomy, ventricular function, and myocardial tissue characteristics. Clinicians

local-aiarxiv-cs-cv

On the (In)effectiveness of AMR Augmentation for Large Language Models

arXiv:2609.40121v1 Announce Type: cross Abstract: While Abstract Meaning Representation (AMR) has historically improved performance on a range of NLP tasks, the benefit---or lack thereof---of AMR augm

Residuals Are Not Enough: Limits of Physics-Informed Pre-Training for Scientific Foundation Models

arXiv:2503.19081v2 Announce Type: replace Abstract: Scientific foundation models (SciFMs) aim to learn generalizable representations of physical systems governed by partial differential equations (PDE

tutorialsarxiv-cs-lg

Superficial Reflection or Genuine Thought? A Fine-Grained Cognitive Analysis of Large Reasoning Models

arXiv:2512.00729v2 Announce Type: replace Abstract: Motivated by the observed human-like behaviours in Large Reasoning Models (LRMs), this paper introduces a comprehensive taxonomy to characterise ato

Benchmarking graph-based models for in-silico toxicity prediction in drug discovery

arXiv:2609.37555v1 Announce Type: new Abstract: Drug discovery is a costly and high-risk process, where toxicity-related failures remain a major cause of attrition in both preclinical and clinical sta

tutorialsarxiv-cs-lg

Model Releases

Can Multimodal Large Language Models Generate and Detect Multimodal Social Media Fake News?

model-releasesarxiv-cs-ai

arXiv:2609.35809v1 Announce Type: cross Abstract: The rapid advancement of generative AI raises concerns about the misuse of Multimodal LLMs (MLLMs) for large-scale disinformation campaigns on social

Improved Distributional Diffusion Models

arXiv:2609.37147v1 Announce Type: new Abstract: Distributional Diffusion Models (DDMs) replace the standard mean-prediction denoiser with a distributional denoiser trained via a scoring rule objective

Lost in Translation: Measuring the Effect of Non-Native English on End User Performance of Large Language Models

arXiv:2609.36214v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used by people whose first language is not English, yet these users have been shown to receive systematica

researcharxiv-cs-cl

Lucid Dreaming for World Models: Learning to Doubt Imagination and Decide by Trust

arXiv:2609.37156v1 Announce Type: cross Abstract: World models enable agents to learn and plan in imagination, but predictions beyond their experience can become unreliable and mislead decisions. Exis

safetyarxiv-cs-ai

OTROPE: Optimal Transport-based Robust Off-policy Evaluation for Large Language Models

arXiv:2609.36264v1 Announce Type: new Abstract: Reliable evaluation of large language models (LLMs) is essential for their development and deployment, yet is often costly, risky, and difficult to perf

safetyarxiv-cs-ai

Riemannian Splat Regression Models for Learning Time Fields on Arbitrary Riemannian Manifolds

arXiv:2609.36561v1 Announce Type: new Abstract: Motion planning on arbitrary Riemannian manifolds is an important and difficult problem that frustrates typical planning methods for Euclidean spaces. I

tutorialsarxiv-cs-ro

Model Releases

World2Motion: Turning Video World Models into 3D Human Motion Generators

model-releasesarxiv-cs-cv

arXiv:2609.37004v1 Announce Type: new Abstract: We present World2Motion, a framework that generates scene-aware 3D human motion and corresponding video from a single image and a text prompt. While exi

Accurate Sampling from Diffusion Models

arXiv:2609.29902v1 Announce Type: cross Abstract: A new proposal called DM-SMC (Diffusion Model - Sequential Monte Carlo) is investigated, which samples ensembles defined in terms of an action, using

Beyond One Epoch: Uncertainty-Weighted Sensitivity Regularization for Recommendation Models

arXiv:2609.34083v1 Announce Type: new Abstract: Recommendation models with sparse embeddings and a shared consumer often exhibit the one-epoch phenomenon: a second epoch lowers training loss while sha

Can Protein-Derived Knowledge Improve Pathology Foundation Models?

arXiv:2609.33178v1 Announce Type: new Abstract: Molecularly guided pathology foundation models (PFMs) exploit transcriptomic or proteomic information to enrich whole-slide image (WSI) representations,

Model Releases

Can Tabular Foundation Models Amortize Statistical Inference?

model-releasesarxiv-cs-lg

arXiv:2609.33114v1 Announce Type: cross Abstract: For decades, statistical inference has largely been developed one problem at a time. Given a scientific target, such as a treatment effect or a regres

Correcting the Dropout-LayerNorm Expectation Gap Improves Protein Structure Models

arXiv:2609.32062v1 Announce Type: cross Abstract: Although E[dropout(x)] = x, here we show that E[LayerNorm(dropout(x))] is not equal to LayerNorm(x). Accordingly, the pattern of a Dropout layer follo

Do Emotion Concepts Generalize Across Sources, Modalities, and Architectures in Vision-Language Models?

arXiv:2609.34742v1 Announce Type: new Abstract: Recent studies suggest that large language models encode emotion concepts as structured internal representations, but most existing work focuses on text

Drug-Target Interaction Prediction via Hierarchical Sequential Cross-Attention over Chemical and Protein Language Models

arXiv:2609.34921v1 Announce Type: new Abstract: Predicting Drug-Target Interactions~(DTIs) is a central task in computational drug discovery, with direct applications in virtual screening, drug repurp

Dynamic Manipulation with World-Action Models via Counterfactual Planning

arXiv:2609.33172v1 Announce Type: cross Abstract: World-Action models (WAMs) trained on static demonstrations often fail to manipulate moving targets even when they possess the required manipulation s

safetyarxiv-cs-cv

From Constitutions to Control: Interpretable Rewards for Aligning Language Models

arXiv:2609.33086v1 Announce Type: new Abstract: Current approaches to aligning language models often make it hard to know what behavior is being rewarded or to change that reward in a targeted way. In

From Perception to Integration: Revisiting the Internal Dynamics of Reasoning in Vision-Language Models

arXiv:2609.34809v1 Announce Type: new Abstract: Vision-language models (VLMs) can answer simple visual questions, but often struggle when one question requires several visual judgments. We study this

FutureDuet: Decoupling Observation Access from Future Supervision in World Action Models

arXiv:2609.34362v1 Announce Type: new Abstract: World Action Models (WAMs) augment robot action generation with future visual supervision. Existing WAMs commonly fuse main and wrist observations into

local-aiarxiv-cs-ro

Graph World Models for Constrained Epidemic Policy Planning

arXiv:2609.35545v1 Announce Type: new Abstract: Epidemic policy planning often requires coordination between geographical regions, taking into account mobility-driven spillovers and how to make use of

Hardware

Greenpixie's AI Token Methodology: Assessing the Energy, Water and CO_2ext{-eq} Impact of AI Tokens for Open and Closed Weight Models

arXiv:2609.33965v1 Announce Type: cross Abstract: We describe a methodology for estimating the per-token energy cost of cloud-hosted large language model (LLM) inference, separating between input (pre

hardwarearxiv-cs-lg

Learning Native Reflection in Unified Models with Interleaved Reinforcement Learning

arXiv:2609.35767v1 Announce Type: new Abstract: Unified multimodal models can both look at and render images, so in principle they can repair their own generations: diagnose what an image gets wrong,

Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting

arXiv:2607.17511v2 Announce Type: replace Abstract: Large Time Series Foundation Models (TSFMs) demonstrate strong zero-shot forecasting capabilities across diverse domains. However, their application

Agents

MA-JEPA: Joint-Embedding World Models for Multi-Agent Reinforcement Learning

arXiv:2609.33563v1 Announce Type: new Abstract: World models improve sample efficiency by training policies on imagined trajectories, but their usefulness depends on learning representations that capt

agentsarxiv-cs-lg

On the Pitfalls of Verbalized Confidence Priors for Calibrating Large Reasoning Models

arXiv:2609.32470v1 Announce Type: new Abstract: Large reasoning models (LRMs) often suffer from overconfidence when expressing their uncertainty. Confidence-aware reinforcement learning (RL) offers a

Position Aware Layer Queries for Test Time Training in Vision Language Models

arXiv:2609.34021v1 Announce Type: new Abstract: Test-Time Training (TTT) adapts models to incoming test samples (e.g. out-of-distribution, (OOD)) when conventional fine-tuning is infeasible. Existing

safetyarxiv-cs-cv

Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Tasks

arXiv:2609.31862v1 Announce Type: cross Abstract: Reconstruction and simulation at a collider experiment are long chains of specialised algorithms, each tuned to a single step. We explore how one mode

REALM: Regime-Switching, Explainable, and Activation-Induced Linear Models

arXiv:2609.32141v1 Announce Type: new Abstract: Deep ReLU networks are piecewise-affine mappings that partition the input space into cells, each characterized by a distinct activation pattern. This st

Reduce, Then Encode: Multiscale Volumetric Reduction for 2D Foundation Models in Brain MRI

arXiv:2609.35405v1 Announce Type: new Abstract: Pretrained 2D foundation models offer a practical alternative to dedicated 3D pretraining for brain structural magnetic resonance imaging (sMRI), but th

SCOPD: Sparse-Context On-Policy Self-Distillation for Efficient Vision-Language Models

arXiv:2609.34044v1 Announce Type: new Abstract: Reasoning vision-language models (VLMs) process images and videos as long sequences of visual tokens, making inference expensive. Training-free token pr

safetyarxiv-cs-cv

Simple Diffusion Language Models Are More Effective Few-Step Generators Than Reported

arXiv:2609.33947v1 Announce Type: cross Abstract: Diffusion language models (DLMs) promise fast parallel generation, yet high-quality samples often require large number of refinement steps, which dimi

UniCache: Task- and Type-Aware KV Cache Compression for Unified Multimodal Models

arXiv:2609.32831v1 Announce Type: new Abstract: Unified multimodal models combine understanding, generation, and editing within a single network, offering a promising foundation for versatile multimod

Weighting Schedules Govern What and When Score-Based Generative Models Learn from Multimodal Data

arXiv:2609.35322v1 Announce Type: new Abstract: Score-based generative models generate new samples by integrating a time-dependent drift that carries Gaussian noise onto the target distribution. In pr

tutorialsarxiv-cs-lg

What does FFN compression change downstream? Same-state causal restoration in diffusion language models

arXiv:2609.31685v1 Announce Type: new Abstract: Diffusion language models (DLMs) enable flexible, parallel generation, but their iterative denoising remains computationally expensive, motivating incre

What Should We Freeze? Guarded Freezing: Connectivity Shapes the Fine-Tuning of Pretrained Models

arXiv:2609.32124v1 Announce Type: new Abstract: When adapting pre-trained models through fine-tuning, freezing weights alone might not preserve performance, as updates elsewhere can change the inputs