Search: “models”

Towards reconstructing experimental sparse-view X-ray CT data with diffusion models

arXiv:2602.12755v2 Announce Type: replace Abstract: Diffusion-based image generators are promising priors for ill-posed inverse problems like sparse-view X-ray Computed Tomography (CT). As most studie

applicationsarxiv-cs-cv

23 Apr 2026

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

Agent-GWO: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models

DGX agent

arXiv:2604.18612v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated strong capabilities in complex reasoning tasks, while recent prompting strategies such as Chain-of-Thou

model-releasesarxiv-cs-ai

22 Apr 2026

All of the AI models have preferred names. If you asked Claude 4.5 for a software developer, you are going to get Marcus Chen. Wizards are m…

model-releasesethan-mollick--x

All of the AI models have preferred names. If you asked Claude 4.5 for a software developer, you are going to get Marcus Chen. Wizards are mostly named Aldric. Space pilots are Kira from Claude, Mara

Beyond Linear Probes: Dynamic Safety Monitoring for Language Models

arXiv:2509.26238v4 Announce Type: replace Abstract: Monitoring large language models' (LLMs) activations is an effective way to detect harmful requests before they lead to unsafe outputs. However, tra

safetyarxiv-cs-lg

Bridging the High-Frequency Data Gap: A Millisecond-Resolution Network Dataset for Advancing Time Series Foundation Models

arXiv:2603.16497v2 Announce Type: replace-cross Abstract: Time series foundation models (TSFMs) require diverse, real-world datasets to adapt across varying domains and temporal frequencies. However,

applicationsarxiv-cs-ai

CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark

model-releasesarxiv-cs-ai

arXiv:2505.16968v4 Announce Type: replace-cross Abstract: Cross-architecture GPU code transpilation is essential for unlocking low-level hardware portability, yet no scalable solution exists. We intro

Concept Inconsistency in Dermoscopic Concept Bottleneck Models: A Rough-Set Analysis of the Derm7pt Dataset

arXiv:2604.19323v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) route predictions exclusively through a clinically grounded concept layer, binding interpretability to concept-label

Diff-SBSR: Learning Multimodal Feature-Enhanced Diffusion Models for Zero-Shot Sketch-Based 3D Shape Retrieval

arXiv:2604.19135v1 Announce Type: new Abstract: This paper presents the first exploration of text-to-image diffusion models for zero-shot sketch-based 3D shape retrieval (ZS-SBSR). Existing sketch-bas

safetyarxiv-cs-cv

DUALVISION: RGB-Infrared Multimodal Large Language Models for Robust Visual Reasoning

arXiv:2604.18829v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved impressive performance on visual perception and reasoning tasks with RGB imagery, yet they remain

FairTree: Subgroup Fairness Auditing of Machine Learning Models with Bias-Variance Decomposition

arXiv:2604.19357v1 Announce Type: new Abstract: The evaluation of machine learning models typically relies mainly on performance metrics based on loss functions, which risk to overlook changes in perf

safetyarxiv-cs-lg

Hierarchically Robust Zero-shot Vision-language Models

arXiv:2604.18867v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) can perform zero-shot classification but are susceptible to adversarial attacks. While robust fine-tuning improves their

safetyarxiv-cs-ai

How to Teach Large Multimodal Models New Skills

arXiv:2510.08564v2 Announce Type: replace Abstract: How can we teach large multimodal models (LMMs) new skills without erasing prior abilities? We study sequential fine-tuning on five target skills wh

safetyarxiv-cs-ai

InHabit: Leveraging Image Foundation Models for Scalable 3D Human Placement

arXiv:2604.19673v1 Announce Type: new Abstract: Training embodied agents to understand 3D scenes as humans do requires large-scale data of people meaningfully interacting with diverse environments, ye

applicationsarxiv-cs-cv

Hardware

LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation

arXiv:2604.19167v1 Announce Type: cross Abstract: Deploying large language models (LLMs) in resource-constrained environments is hindered by heavy computational and memory requirements. We present LBL

hardwarearxiv-cs-ai

OLLM: Options-based Large Language Models

model-releasesarxiv-cs-ai

arXiv:2604.19087v1 Announce Type: new Abstract: We introduce Options LLM (OLLM), a simple, general method that replaces the single next-token prediction of standard LLMs with a extit{set of learned op

OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models

arXiv:2502.16161v2 Announce Type: replace-cross Abstract: Visually-situated text parsing (VsTP) has recently seen notable advancements, driven by the growing demand for automated document understandin

researcharxiv-cs-cl

Once Correct, Still Wrong: Counterfactual Hallucination in Multilingual Vision-Language Models

arXiv:2602.05437v2 Announce Type: replace Abstract: Vision-language models (VLMs) can achieve high accuracy while still accepting culturally plausible but visually incorrect interpretations. Existing

Presumably GPT-imagegen-2 (aka ChatGPT Images 2.0 aka gpt-image-2) works as a tool which the models generate prompts for? I wish we could se…

model-releasessimon-willison--x

Presumably GPT-imagegen-2 (aka ChatGPT Images 2.0 aka gpt-image-2) works as a tool which the models generate prompts for? I wish we could see those prompts, like back in the DALL-E 3 days https://simo

Q-Mask: Query-driven Causal Masks for Text Anchoring in OCR-Oriented Vision-Language Models

arXiv:2604.00161v2 Announce Type: replace Abstract: Optical Character Recognition (OCR) is increasingly regarded as a foundational capability for modern vision-language models (VLMs), enabling them no

Local Ai

R^2-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction

arXiv:2604.18995v1 Announce Type: cross Abstract: Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to autoregressive generation by enabling parallel token prediction. Ho

local-aiarxiv-cs-ai

Agents

Rethinking Scale: Deployment Trade-offs of Small Language Models under Agent Paradigms

arXiv:2604.19299v1 Announce Type: cross Abstract: Despite the impressive capabilities of large language models, their substantial computational costs, latency, and privacy risks hinder their widesprea

agentsarxiv-cs-ai

Safety-Critical Contextual Control via Online Riemannian Optimization with World Models

arXiv:2604.19639v1 Announce Type: cross Abstract: Modern world models are becoming too complex to admit explicit dynamical descriptions. We study safety-critical contextual control, where a Planner mu

safetyarxiv-cs-ai

TEMPO: Scaling Test-time Training for Large Reasoning Models

arXiv:2604.19295v1 Announce Type: new Abstract: Test-time training (TTT) adapts model parameters on unlabeled test instances during inference time, which continuously extends capabilities beyond the r

safetyarxiv-cs-lg

An LLM-Guided Query-Aware Inference System for GNN Models on Large Knowledge Graphs

arXiv:2603.04545v2 Announce Type: replace Abstract: Efficient inference for graph neural networks (GNNs) on large knowledge graphs (KGs) is essential for many real-world applications. GNN inference qu

applicationsarxiv-cs-lg

BEFT: Bias-Efficient Fine-Tuning of Language Models in Low-Data Regimes

arXiv:2509.15974v2 Announce Type: replace Abstract: Fine-tuning the bias terms of large language models (LLMs) has the potential to achieve unprecedented parameter efficiency while maintaining competi

Closing the Modality Reasoning Gap for Speech Large Language Models

arXiv:2601.05543v2 Announce Type: replace Abstract: Although Speech Large Language Models have achieved notable progress, a substantial modality reasoning gap remains: their reasoning performance on s

ConMeZO: Adaptive Descent-Direction Sampling for Gradient-Free Finetuning of Large Language Models

model-releasesarxiv-cs-lg

arXiv:2511.02757v2 Announce Type: replace Abstract: Zeroth-order or derivative-free optimization (MeZO) is an attractive strategy for finetuning large language models (LLMs) because it eliminates the

Employing General-Purpose and Biomedical Large Language Models with Advanced Prompt Engineering for Pharmacoepidemiologic Study Design

arXiv:2604.17988v1 Announce Type: new Abstract: Background: The potential of large language models (LLMs) to automate and support pharmacoepidemiologic study design is an emerging area of interest, ye

Erasing Thousands of Concepts: Towards Scalable and Practical Concept Erasure for Text-to-Image Diffusion Models

arXiv:2604.16481v1 Announce Type: new Abstract: Large-scale text-to-image (T2I) diffusion models deliver remarkable visual fidelity but pose safety risks due to their capacity to reproduce undesirable

safetyarxiv-cs-cv

ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection

arXiv:2410.04509v3 Announce Type: replace Abstract: As the field of Multimodal Large Language Models (MLLMs) continues to evolve, their potential to revolutionize artificial intelligence is particular

Establishing a Scale for Kullback-Leibler Divergence in Language Models Across Various Settings

arXiv:2505.15353v3 Announce Type: replace Abstract: Log-likelihood vectors define a common space for comparing language models as probability distributions, enabling unified comparisons across heterog

researcharxiv-cs-cl

GaLa: Hypergraph-Guided Visual Language Models for Procedural Planning

arXiv:2604.17241v1 Announce Type: new Abstract: Implicit spatial relations and deep semantic structures encoded in object attributes are crucial for procedural planning in embodied AI systems. However

researcharxiv-cs-ro

How Language Models Conflate Logical Validity with Plausibility: A Representational Analysis of Content Effects

arXiv:2510.06700v3 Announce Type: replace Abstract: Both humans and large language models (LLMs) exhibit content effects: biases in which the plausibility of the semantic content of a reasoning proble

How Should We Enhance the Safety of Large Reasoning Models: An Empirical Study

arXiv:2505.15404v2 Announce Type: replace Abstract: Large Reasoning Models (LRMs) have achieved remarkable success on reasoning-intensive tasks such as mathematics and programming. However, their enha

Tutorials

How to Approximate Inference with Subtractive Mixture Models

arXiv:2604.16714v1 Announce Type: new Abstract: Classical mixture models (MMs) are widely used tractable proposals for approximate inference settings such as variational inference (VI) and importance

tutorialsarxiv-cs-lg

Large Language Models Are Still Misled by Simple Bias Ensembles

arXiv:2505.16522v3 Announce Type: replace Abstract: With the evolution of large language models (LLMs), their robustness against individual simple biases has been enhanced. However, we observe that th

Local Ai

Learning to Seek Help: Dynamic Collaboration Between Small and Large Language Models

arXiv:2604.17827v1 Announce Type: new Abstract: Large language models (LLMs) offer strong capabilities but raise cost and privacy concerns, whereas small language models (SLMs) facilitate efficient an

local-aiarxiv-cs-cl

Multilingual Training and Evaluation Resources for Vision-Language Models

arXiv:2604.18347v1 Announce Type: new Abstract: Vision Language Models (VLMs) achieved rapid progress in the recent years. However, despite their growth, VLMs development is heavily grounded on Englis

researcharxiv-cs-cl

Non-Stationarity in the Embedding Space of Time Series Foundation Models

arXiv:2604.16428v1 Announce Type: new Abstract: Time series foundation models (TSFMs) are widely used as generic feature extractors, yet the notion of non-stationarity in their embedding spaces remain

researcharxiv-cs-lg

PDDL-Mind: Large Language Models are Capable on Belief Reasoning with Reliable State Tracking

arXiv:2604.17819v1 Announce Type: new Abstract: Large language models (LLMs) perform substantially below human level on existing theory-of-mind (ToM) benchmarks, even when augmented with chain-of-thou

Prior-Fitted Functional Flow: In-Context Generative Models for Pharmacokinetics

model-releasesarxiv-cs-lg

arXiv:2604.17670v1 Announce Type: new Abstract: We introduce Prior-Fitted Functional Flows, a generative foundation model for pharmacokinetics that enables zero-shot population synthesis and individua

Representation Before Training: A Fixed-Budget Benchmark for Generative Medical Event Models

model-releasesarxiv-cs-lg

arXiv:2604.16775v1 Announce Type: new Abstract: Every prediction from a generative medical event model is bounded by how clinical events are tokenized, yet input representation is rarely isolated from

Revisiting a Pain in the Neck: A Semantic Reasoning Benchmark for Language Models

arXiv:2604.16593v1 Announce Type: new Abstract: We present SemanticQA, an evaluation suite designed to assess language models (LMs) in semantic phrase processing tasks. The benchmark consolidates exis

SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation

arXiv:2511.10370v2 Announce Type: replace Abstract: Geospatial foundation models (GFMs) for Earth observation often fail to perform reliably in environments underrepresented during pretraining. We int

SpeechMedAssist: Efficiently and Effectively Adapting Speech Language Models for Medical Consultation

arXiv:2601.04638v2 Announce Type: replace Abstract: Medical consultations are intrinsically speech-centric. However, most prior works focus on long-text-based interactions, which are cumbersome and pa

SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models

arXiv:2604.16995v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a promising paradigm for training reasoning-oriented models by leveraging rule-based reward signals. However,

StableMTL: Repurposing Latent Diffusion Models for Multi-Task Learning from Partially Annotated Synthetic Datasets

arXiv:2506.08013v2 Announce Type: replace Abstract: Multi-task learning for dense prediction is limited by the need for extensive annotation for every task, though recent works have explored training

researcharxiv-cs-cv

SynopticBench: Evaluating Vision-Language Models on Generating Weather Forecast Discussions of the Future

arXiv:2604.16451v1 Announce Type: new Abstract: Recent advances in visual-language models (VLMs) have led to significant improvements in a plethora of complex multimodal tasks like image captioning, r