Search: “models”

How Do LLM-Generated Texts Impact Term-Based Retrieval Models?

arXiv:2508.17715v2 Announce Type: replace-cross Abstract: As more content generated by large language models (LLMs) floods into the Internet, information retrieval (IR) systems now face the challenge

safetyarxiv-cs-cl

Learning Intrinsic Dynamics in Latent World Models

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

DGX agent

arXiv:2610.21612v1 Announce Type: new Abstract: Learning intrinsic dynamics---the relationship between actions and their consequences---is essential for world models. However, visual variations unrela

safetyarxiv-cs-ai

16 Oct 2026

Pathology foundation models for training-free case retrieval in salivary gland tumors

arXiv:2610.22087v1 Announce Type: new Abstract: In pathology, pattern recognition relies on a frame of reference built through extensive training and experience, which may still fall short when encoun

Stabilizing the Modality Gap: Null-Space Projected Continual Learning for Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2507.19839v2 Announce Type: replace-cross Abstract: The modality gap is intrinsic to multimodal alignment, where embeddings from different modalities remain partially separated in a shared repre

Tool-Augmented Large Language Models for Reliable Medical Calculation from Patient Notes

model-releasesarxiv-cs-ai

arXiv:2610.21477v1 Announce Type: new Abstract: Medical calculators translate raw patient observations into clinically actionable values and scores. However, applying these calculators directly to fre

Until we solve the alignment problem at its source, AI models will continue to optimize for the wrong goals, and increasingly impactful real…

Until we solve the alignment problem at its source, AI models will continue to optimize for the wrong goals, and increasingly impactful real-world consequences may occur, beyond the ones described her

safetyyoshua-bengio--x

Velox: Spatial Memory Makes World Models Efficient

arXiv:2610.22311v1 Announce Type: new Abstract: Video world models turn actions and camera motion into long visual rollouts, making video a natural interface for interactive simulation. Such interacti

safetyarxiv-cs-cv

Anticipatory Evaluation of Language Models: Predicting LLM Benchmark Scores from Descriptions

model-releasesarxiv-cs-cl

arXiv:2509.20645v4 Announce Type: replace Abstract: Modern ML research increasingly faces an evaluation bottleneck: building benchmarks and running experiments is costly, slowing iteration. We ask how

Articulation Upper Bounds for Language Models

arXiv:2610.19721v1 Announce Type: cross Abstract: Language captures only a subset of the perceptions, intuitions and intents that form meaning between speakers. What, then, are the limits of language?

researcharxiv-cs-cl

Atomic-WAM: Execution-Grounded Atomic-Motion Reasoning for World Action Models

arXiv:2610.20157v1 Announce Type: new Abstract: World Action Models (WAMs) typically adapt video foundation models to robotic control through joint video and action prediction. However, task-level ins

researcharxiv-cs-ro

Encounter-Driven City-Scale Human Mobility Generation with Large Language Models

arXiv:2610.17672v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong potential for human mobility generation by modeling individual routines, activities, and mobility prefere

researcharxiv-cs-ai

Evaluating Large Language Models for Multilingual Abstractive Summarization: A Comprehensive Study of English and Bengali

model-releasesarxiv-cs-cl

arXiv:2610.19003v1 Announce Type: new Abstract: Abstractive summarization remains challenging across languages, where linguistic quality and factual consistency may diverge, particularly in low-resour

From Copying to Composition: How Language Models Develop In-Context Learning

arXiv:2610.19113v1 Announce Type: new Abstract: In-context learning (ICL) allows language models to adapt their behavior from instructions and demonstrations in a prompt, but accuracy alone does not r

researcharxiv-cs-cl

GLoD-LVSM: Learning Geometry-Grounded Continuous-LoD Scene Representations for Large View Synthesis Models

arXiv:2610.20254v1 Announce Type: new Abstract: Large view synthesis models (LVSMs) reconstruct scenes from sparse views and render novel views quickly, but they incur unnecessary storage and renderin

tutorialsarxiv-cs-cv

Monitoring latent reasoning in looped language models

arXiv:2610.18127v1 Announce Type: new Abstract: Looped language models (LMs) reuse one block of layers several times per token generation, promising more latent computation at no additional storage co

safetyarxiv-cs-ai

One-shot Human Video-Guided Adaptation for Vision-Language-Action Models

arXiv:2610.19746v1 Announce Type: new Abstract: Vision-language-action (VLA) models have recently achieved remarkable progress by leveraging large-scale robot datasets and foundation models for genera

safetyarxiv-cs-ro

Order-Consistent Latent Geometry for Reliable Short-Horizon Planning in World Models

arXiv:2610.18570v1 Announce Type: new Abstract: Latent planners often rank candidate action sequences by terminal Euclidean distance to a goal latent. Exact agreement with transition distance would ma

researcharxiv-cs-ai

Subgraph-Aware Training of Language Models for Knowledge Graph Completion Using Structure-Aware Contrastive Learning

arXiv:2407.12703v5 Announce Type: cross Abstract: Fine-tuning pre-trained language models (PLMs) has recently shown a potential to improve knowledge graph completion (KGC). However, most PLM-based met

safetyarxiv-cs-ai

To Reason but Not to Stop: Understanding and Recovering Pruned Large Reasoning Models

arXiv:2610.19934v1 Announce Type: new Abstract: Weight pruning can degrade the accuracy of large reasoning models (LRMs), yet it remains unclear whether it damages reasoning itself. We find that much

researcharxiv-cs-cl

Training Language Models to Fail Gracefully

arXiv:2610.20401v1 Announce Type: new Abstract: Reinforcement learning makes language models better at solving tasks, but the same mechanism also rewards guessing. When success is the only way to earn

Local Ai

A Decade of PhotoMappers: A Longitudinal Study of Crowdsourced Disaster Photos Geolocalization with Vision-Language Models and Geospatial Reasoning

arXiv:2610.15403v1 Announce Type: cross Abstract: Crowdsourced disaster imagery provides timely, ground-level observations when conventional disaster mapping is delayed or incomplete, yet its geograph

local-aiarxiv-cs-cv

ANTE: Anticipate, Then Execute -- Learning Internal World Models for Coding Agents from Verified Beliefs

arXiv:2610.15368v1 Announce Type: new Abstract: Rapid advances in large language models have made software engineering agents capable of tackling complex coding tasks. Within a containerized environme

CatPortrait: Taming Video Generative Models for Portrait Animation via In-context Conditioning

arXiv:2610.14957v1 Announce Type: new Abstract: Recent video generative models have shown strong capability in synthesizing high-quality and temporally coherent videos. Yet, adapting them for controll

Characterizing and Mitigating Distributional Bias in Physics-Informed Generative Models

arXiv:2610.16582v1 Announce Type: new Abstract: Physics-informed generative models augment a data-fitting objective with residual penalties that encourage physical constraints to hold in generated sam

Data Free Training of Diffusion Models for Lattice Field Theories

model-releasesarxiv-cs-lg

arXiv:2610.18863v1 Announce Type: cross Abstract: Sampling equilibrium field configurations remains a major computational bottleneck in lattice field theory, motivating the development of generative s

Does Pretraining Teach Chemical Language Models Chemistry?

arXiv:2610.18651v1 Announce Type: new Abstract: Chemical language models (CLMs) are pretrained on large corpora of molecules with the aim of learning representations that transfer to diverse downstrea

tutorialsarxiv-cs-lg

DVTR-KD: Distilling Vision-Language Models via Intermediate Visual Token Reconstruction

arXiv:2610.17853v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved strong performance on diverse vision-language tasks, but their computational demands limit deploy

Graph Metanetworks for Transformer Based Language Models

arXiv:2610.18471v1 Announce Type: new Abstract: Weight-space learning treats the weights of neural networks as data, processed by other models called metanetworks. A key challenge is that different we

Applications

HGA-Quant: Heterogeneity-Aware Quantization for AutoRegressive Visual Generation Models

arXiv:2610.15297v1 Announce Type: new Abstract: Recently, AutoRegressive Visual Generation (ARVG) models have emerged as a competitive paradigm for high-quality image synthesis. However, their substan

applicationsarxiv-cs-cv

IND-Eval: Aligning Evaluation and Simulation for Industrial Time Series Foundation Models

model-releasesarxiv-cs-lg

arXiv:2610.16652v1 Announce Type: new Abstract: We introduce IND-Eval, a public benchmark that organizes industrial time series across domains into source-specific forecasting tasks. Relative to estab

Learning from fracture: Teaching language models to design through physical experience

arXiv:2610.17804v1 Announce Type: cross Abstract: How can language models learn to optimize physical designs through experience? We introduce FracLearn, an experience-abstraction-generalization framew

tutorialsarxiv-cs-lg

MOSAIC: Mode- and Support-Aware Inference-Time Control of Robot Diffusion Models

arXiv:2610.17001v1 Announce Type: new Abstract: Generative policies such as diffusion models provide expressive data-driven priors for robot behavior, yet the situations a robot faces at execution tim

safetyarxiv-cs-ro

Multi-Stream Residual Computation in Diffusion Language Models

arXiv:2610.18407v1 Announce Type: new Abstract: Diffusion language models reconstruct masked tokens in parallel, offering an alternative to autoregressive generation. Yet their Transformer backbones t

tutorialsarxiv-cs-lg

Naga: Vedic Encoding for Deep State Space Models

arXiv:2511.13510v2 Announce Type: replace Abstract: This paper presents Naga, a deep State Space Model (SSM) encoding approach inspired by structural concepts from Vedic mathematics. The proposed meth

Probing the Geometry of Color Representations in Multilingual Language Models

arXiv:2610.18331v1 Announce Type: cross Abstract: Do multilingual language models learn a universal internal geometry for color, and is it perceptually grounded or primarily linguistic? We decompose c

Radial Tail Repair: Post-Training Correction for Diffusion and Flow Models

arXiv:2610.16851v1 Announce Type: new Abstract: Diffusion and flow-matching models may struggle to accurately represent distributional tails, especially for heavy-tailed data. Existing approaches ofte

SharedSAE: a unified sparse dictionary across u-net blocks and denoising time for diffusion models

arXiv:2610.13997v1 Announce Type: new Abstract: Concept representations in diffusion models drift across network depth and denoising time, yet existing sparse autoencoders offer only per-block or per-

A Distributional Framework for Behavioral Characterization and Risk Management of Large Language Models

arXiv:2610.12718v1 Announce Type: cross Abstract: Large language models are stochastic by nature. Repeated executions under the same experimental conditions may produce different responses. We propose

AI Models Can Provably Hide Arbitrary Capabilities

arXiv:2610.13928v1 Announce Type: new Abstract: Capability evaluations aim to surface dangerous model behaviors before deployment, but their reliability depends on the assumption that hidden capabilit

AutoIndexer: Flexible-Order Decoding by Training Causal Models on Chains of Edits

arXiv:2610.12545v1 Announce Type: cross Abstract: Autoregressive decoding is less suited to non-causally ordered data, e.g., cross-references or future dependencies, since their outputs cannot be revi

researcharxiv-cs-ai

DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models

arXiv:2610.12572v1 Announce Type: new Abstract: Online reinforcement learning (RL) fine-tunes diffusion models with black-box rewards, but reverse-process methods such as Flow-GRPO are limited in effi

Diversity-Aware Ensemble of Quantum-Classical Models via Combinatorial Fusion

arXiv:2610.13808v1 Announce Type: cross Abstract: Variational quantum circuits (VQCs) are highly sensitive to circuit architecture, yet most quantum machine learning pipelines select a single best des

Applications

Enhancing Legal Reasoning in Pre-trained Language Models via Knowledge Graph-Guided Multi-Task Pre-training

arXiv:2610.13722v1 Announce Type: new Abstract: Legal language understanding requires reasoning over semi-structured regulatory documents, yet standard pre-training objectives do not explicitly target

applicationsarxiv-cs-cl

Industry

How Flo Health built safer, more useful open models with Databricks AI Runtime

Flo Health leveraged Databricks AI Runtime to create open‑source language models that prioritize both safety and practical utility. By integrating Databricks’ scalable training infrastructure with rob

industrydatabricks

Agents

JANUS: Enhancing Long-Horizon Autonomous Research with Co-Evolving World Models

arXiv:2610.13194v1 Announce Type: new Abstract: Long-horizon autonomous research has shown promise in accelerating scientific progress through sustained cycles of hypothesis generation, implementation

agentsarxiv-cs-ai

Local Ai

Probabilistic Ship Trajectory Forecasting using Transformer Models on AIS Data

arXiv:2610.13557v1 Announce Type: new Abstract: Anticipation of vessel trajectories is an essential task when navigating at sea. This applies to both human navigators and autonomous surface vessels. T

local-aiarxiv-cs-lg

Hardware

RAMP: Recurrent Adaptive Mixed Precision for Efficient Prefill in Recurrent-Depth Language Models

arXiv:2610.13041v1 Announce Type: new Abstract: Recurrent-depth language models improve inference-time reasoning by repeatedly applying a shared network to refine internal representations, but the add

hardwarearxiv-cs-cl

Hardware

Sampling-based Kinodynamic Planning with World Models

arXiv:2610.12910v1 Announce Type: new Abstract: Visual world models predict the consequences of actions from images, but planning with them is typically done by optimizers such as CEM and MPPI. Howeve

hardwarearxiv-cs-ro