Search: “models”

Ablating Safety: Mechanisms for Removing Alignment in Language Models for Security Applications

arXiv:2605.17413v1 Announce Type: cross Abstract: Safety-aligned language models often refuse cybersecurity requests whose wording resembles misuse, even when the task is authorized and defensive. Thi

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Agents

Baba in Wonderland: Online Self-Supervised Dynamics Discovery for Executable World Models

DGX agent

arXiv:2605.16725v1 Announce Type: new Abstract: Executable world models can be read, edited, executed, and reused for planning, but only if the program captures the environment's transition law rather

agentsarxiv-cs-ai

19 May 2026

BacktestBench: Benchmarking Large Language Models for Automated Quantitative Strategy Backtesting

model-releasesarxiv-cs-ai

arXiv:2605.17937v1 Announce Type: cross Abstract: Quantitative backtesting is essential for evaluating trading strategies but remains hampered by high technical barriers and limited scalability. While

Buffer-Parameterized Machine Learning Surrogate Models for Cross-Technology Signal Integrity Analysis and Optimization

arXiv:2605.18170v1 Announce Type: cross Abstract: Signal integrity (SI) analysis in printed circuit board (PCB) interconnects faces increasing complexity due to diverse integrated circuit (IC) buffer

applicationsarxiv-cs-lg

ChemVA: Advancing Large Language Models on Chemical Reaction Diagrams Understanding

arXiv:2605.17214v1 Announce Type: new Abstract: While Large Language Models (LLMs) have revolutionized scientific text processing, they exhibit a significant capability gap when interpreting chemical

Concepts Worth Having: Refining VLM-Guided Concept Bottleneck Models with Minimal Annotations

arXiv:2605.16405v1 Announce Type: new Abstract: Concept-bottleneck models (CBMs) are neural classifiers that compute predictions from high-level concepts extracted from the input. CBMs ensure stakehol

CounterCount: A Diagnostic Framework for Counting Bias in Vision Language Models

arXiv:2605.17826v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) excel at multimodal reasoning, yet it remains unclear whether their answers are grounded in visual evidence or driven by

Dual-Rate Diffusion: Accelerating diffusion models with an interleaved heavy-light network

arXiv:2605.18190v1 Announce Type: cross Abstract: Diffusion models achieve state-of-the-art generative performance but suffer from high computational costs during inference due to the repeated evaluat

DyGRO-VLA: Cross-Task Scaling of Vision-Language-Action Models via Dynamic Grouped Residual Optimization

arXiv:2605.17486v1 Announce Type: cross Abstract: Recent progress in Reinforcement Learning (RL) provides a principled approach to optimizing Vision-Language-Action (VLA) models, facilitating a shift

safetyarxiv-cs-lg

Agents

Dynamic Generation of Multi-LLM Agents Communication Topologies with Graph Diffusion Models

arXiv:2510.07799v2 Announce Type: replace-cross Abstract: The efficiency of multi-agent systems driven by large language models (LLMs) largely hinges on their communication topology. However, designin

agentsarxiv-cs-ai

Empirical evaluation of Time Series Foundation Models for Day-ahead and Imbalance Electricity Price Forecasting in Belgium

arXiv:2605.17045v1 Announce Type: cross Abstract: Recent advances in Time Series Foundation Models (TSFMs) promise zero-shot forecasting capabilities with minimal task-specific training. While these m

Generating Physically Consistent Molecules with Energy-Based Models

arXiv:2605.18381v1 Announce Type: new Abstract: Molecules in equilibrium follow a Boltzmann distribution, making the underlying energy landscape a physically grounded modeling objective. However, such

safetyarxiv-cs-lg

Learning Lifted Action Models from Traces with Minimal Information About Actions and States

arXiv:2605.18627v1 Announce Type: new Abstract: It has been recently shown that lifted STRIPS models can be learned correctly and efficiently from action traces alone; i.e., applicable action sequence

LURE: Latent Space Unblocking for Multi-Concept Reawakening in Diffusion Models

arXiv:2601.14330v2 Announce Type: replace Abstract: Concept erasure aims to suppress sensitive content in diffusion models, but recent studies show that erased concepts can still be reawakened, reveal

New Wide-Net-Casting Jailbreak Attacks Risk Large Models

arXiv:2605.17128v1 Announce Type: cross Abstract: Jailbreak attacks on large models have drawn growing attention due to their close ties to societal safety. This work identifies a practical yet unexpl

OCCAM: Open-set Causal Concept explAnation and Ontology induction for black-box vision Models

arXiv:2605.18481v1 Announce Type: new Abstract: Interpreting the decisions of deep image classifiers remains challenging, particularly in black-box settings where model internals are inaccessible. We

OmniSelect: Dynamic Modality-Aware Token Compression for Efficient Omni-modal Large Language Models

arXiv:2605.18041v1 Announce Type: new Abstract: Omnimodal large language models (OmniLLMs) have recently gained increasing attention for unified audio-video understanding. However, processing long mul

Agents

Position: Universal Time Series Foundation Models Rest on a Category Error

arXiv:2602.05287v2 Announce Type: replace Abstract: This position paper argues that the pursuit of 'Universal Foundation Models for Time Series' rests on a fundamental category error, mistaking a stru

agentsarxiv-cs-ai

What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?

arXiv:2512.24497v3 Announce Type: replace Abstract: A long-standing challenge in AI is to develop agents capable of solving a wide range of physical tasks and generalizing to new, unseen tasks and env

applicationsarxiv-cs-ai

When Marginals Match but Structure Fails: Covariance Fidelity in Generative Models

arXiv:2603.17041v2 Announce Type: replace-cross Abstract: Generative models are increasingly deployed as substitutes for real data in downstream scientific workflows, yet standard evaluation criteria

Controllable Molecular Generative Foundation Models

arXiv:2605.15354v1 Announce Type: new Abstract: Despite the success of foundation models in language and vision, molecular graph generation still lacks a unified framework for heterogeneous design tas

safetyarxiv-cs-lg

18 May 2026

Tutorials

Health-Conditioned Vision-Language-Action Models for Malfunction-Aware Robot Control

arXiv:2605.16056v1 Announce Type: new Abstract: Research on Vision Language Action (VLA) models has been increasing rapidly in recent years. Although some of them focus on detecting, preventing, and r

tutorialsarxiv-cs-ro

18 May 2026

Neutral-Reference Prompting for Vision-Language Models

arXiv:2605.15615v1 Announce Type: new Abstract: Efficient transfer learning of vision-language models (VLMs) commonly suffers from a Base-New Trade-off (BNT): improving performance on unseen (new) cla

safetyarxiv-cs-cv

18 May 2026

A Hormone-inspired Emotion Layer for Transformer language models (HELT)

arXiv:2605.13858v1 Announce Type: cross Abstract: Large Language Models have demonstrated remarkable capabilities in generating contextually relevant and grammatically correct text. However, they fund

researcharxiv-cs-cl

Causal Foundation Models with Continuous Treatments

arXiv:2605.15133v1 Announce Type: new Abstract: Causal inference, estimating causal effects from observational data, is a fundamental tool in many disciplines. Of particular importance across a variet

Conditional Attribute Estimation with Autoregressive Sequence Models

arXiv:2605.14004v1 Announce Type: new Abstract: Generative models are often trained with a next-token prediction objective, yet many downstream applications require the ability to estimate or control

Generalizing Score-based generative models for Heavy-tailed Distributions

arXiv:2603.00772v2 Announce Type: replace-cross Abstract: Score-based generative models (SGMs) have achieved remarkable empirical success, motivating their application to a broad range of data distrib

Merging Methods for Multilingual Knowledge Editing for Large Language Models: An Empirical Odyssey

model-releasesarxiv-cs-cl

arXiv:2605.13919v1 Announce Type: new Abstract: Multilingual knowledge editing (MKE) remains challenging because language-specific edits interfere with one another, even when locate-then-edit methods

Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons

arXiv:2603.02115v2 Announce Type: replace-cross Abstract: General-purpose robot reward models are typically trained to predict absolute task progress from expert demonstrations, providing only local,

TopoPrimer: The Missing Topological Context in Forecasting Models

arXiv:2605.15035v1 Announce Type: new Abstract: We introduce TopoPrimer, a framework that makes the global topological structure of the series population an explicit input to any forecasting model. To

Towards Fine-Grained and Verifiable Concept Bottleneck Models

arXiv:2605.14210v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) offer interpretable alternatives to black-box predictors by introducing human-relatable concepts before the final out

Correct Answers from Sound Reasoning: Verifiable Process Supervision for Language Models

arXiv:2605.12519v1 Announce Type: cross Abstract: Training language models to produce both correct answers and sound reasoning remains an open challenge. Reinforcement learning with verifiable rewards

Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models

arXiv:2605.13540v1 Announce Type: cross Abstract: Dynamic graphs are ubiquitous in real-world systems, and building generalizable dynamic Graph Foundation Models has become a frontier in graph learnin

applicationsarxiv-cs-ai

Efficient Generative Prediction for EHR Foundation Models: The SCOPE and REACH Estimators

arXiv:2602.03730v2 Announce Type: replace-cross Abstract: Generative foundation models trained on tokenized electronic health record (EHR) timelines show promise for clinical outcome prediction via Mo

applicationsarxiv-cs-lg

KamonBench: A Grammar-Based Dataset for Evaluating Compositional Factor Recovery in Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2605.13322v1 Announce Type: new Abstract: Kamon (family crests) are an important part of Japanese culture and a natural test case for compositional visual recognition: each crest combines a smal

Large Language Models for Agentic NetOps and AIOps: Architectures, Evaluation, and Safety

arXiv:2605.12729v1 Announce Type: cross Abstract: Large language models are increasingly being used to support network operations (NetOps) and artificial intelligence for IT operations (AIOps), includ

Plan for Speed: Dilated Scheduling for Masked Diffusion Language Models

arXiv:2506.19037v4 Announce Type: replace-cross Abstract: Masked diffusion language models (MDLMs) promise fast, non-autoregressive text generation, yet existing samplers, which pick tokens to unmask

Predict-Project-Renoise: Sampling Diffusion Models under Hard Constraints

arXiv:2601.21033v2 Announce Type: replace Abstract: Diffusion models cannot enforce hard constraints, yet applications in the physical sciences demand exact satisfaction of conservation laws, boundary

Topo-R1: Detecting Topological Anomalies via Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2603.13054v2 Announce Type: replace Abstract: Topology is critical in tubular structures such as blood vessels, nerve fibers, and road networks, where connectivity and loop structure govern down

Training Large Language Models to Predict Clinical Events

model-releasesarxiv-cs-ai

arXiv:2605.12817v1 Announce Type: cross Abstract: Longitudinal clinical notes contain rich evidence of how patients evolve over time, but converting this signal into training supervision for clinical

Uncovering Symmetry Transfer in Large Language Models via Layer-Peeled Optimization

arXiv:2605.12756v1 Announce Type: cross Abstract: Large language models (LLMs) are pretrained by minimizing the cross-entropy loss for next-token prediction. In this paper, we study whether this optim

Hardware

BOOST: BOttleneck-Optimized Scalable Training Framework for Low-Rank Large Language Models

arXiv:2512.12131v2 Announce Type: replace Abstract: The scale of transformer model pre-training is constrained by the increasing computation and communication cost. Low-rank bottleneck architectures o

hardwarearxiv-cs-lg

Can Nano Banana 2 Replace Traditional Image Restoration Models? An Evaluation of Its Performance on Image Restoration Tasks

arXiv:2604.03061v2 Announce Type: replace Abstract: Recent advances in generative AI raise the question of whether general-purpose image editing models can serve as unified solutions for image restora

Combining On-Policy Optimization and Distillation for Long-Context Reasoning in Large Language Models

arXiv:2605.12227v1 Announce Type: new Abstract: Adapting large language models (LLMs) to long-context tasks requires post-training methods that remain accurate and coherent over thousands of tokens. E

safetyarxiv-cs-cl

Dynamic Execution Commitment of Vision-Language-Action Models

arXiv:2605.11567v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models predominantly adopt action chunking, i.e., predicting and committing to a short horizon of consecutive low-level act

applicationsarxiv-cs-cv

Is Monotonic Sampling Necessary in Diffusion Models?

arXiv:2605.11773v1 Announce Type: new Abstract: Diffusion models generate samples by iteratively denoising a Gaussian prior, traversing a sequence of noise levels that, in every published sampler, dec

ORCE: Order-Aware Alignment of Verbalized Confidence in Large Language Models

arXiv:2605.12446v1 Announce Type: cross Abstract: Large language models (LLMs) often produce answers with high certainty even when they are incorrect, making reliable confidence estimation essential f

safetyarxiv-cs-cl

Overparametrized models with posterior drift

arXiv:2506.23619v2 Announce Type: replace-cross Abstract: This paper investigates the impact of posterior drift on out-of-sample forecasting accuracy in overparametrized machine learning models. We do