Search: “models”

Identifying and Mitigating Gender Cues in Academic Recommendation Letters: An Interpretability Case Study

arXiv:2604.12337v1 Announce Type: new Abstract: Letters of recommendation (LoRs) can carry patterns of implicitly gendered language that can inadvertently influence downstream decisions, e.g. in hirin

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Research

IMU: Influence-guided Machine Unlearning

DGX agent

arXiv:2508.01620v3 Announce Type: replace-cross Abstract: Machine Unlearning (MU) aims to selectively erase the influence of specific data points from pretrained models. However, most existing MU meth

researcharxiv-cs-cv

15 Apr 2026

Invariant Features for Global Crop Type Classification

arXiv:2509.03497v3 Announce Type: replace Abstract: Accurate global crop type mapping supports agricultural monitoring and food security, yet remains limited by the scarcity of labeled data in many re

Inverse Design of Inorganic Compounds with Generative AI

arXiv:2604.11827v1 Announce Type: cross Abstract: Machine learning is revolutionizing chemistry. Beyond the value of predictive models accelerating virtual screening, generative AI aims at enabling in

KnowRL: Boosting LLM Reasoning via Reinforcement Learning with Minimal-Sufficient Knowledge Guidance

arXiv:2604.12627v1 Announce Type: new Abstract: RLVR improves reasoning in large language models, but its effectiveness is often limited by severe reward sparsity on hard problems. Recent hint-based R

Leveraging Weighted Syntactic and Semantic Context Assessment Summary (wSSAS) Towards Text Categorization Using LLMs

arXiv:2604.12049v1 Announce Type: cross Abstract: The use of Large Language Models (LLMs) for reliable, enterprise-grade analytics such as text categorization is often hindered by the stochastic natur

LLM-Guided Prompt Evolution for Password Guessing

arXiv:2604.12601v1 Announce Type: cross Abstract: Passwords still remain a dominant authentication method, yet their security is routinely subverted by predictable user choices and large-scale credent

MedConcept: Unsupervised Concept Discovery for Interpretability in Medical VLMs

arXiv:2604.11868v1 Announce Type: new Abstract: While medical Vision-Language models (VLMs) achieve strong performance on tasks such as tumor or organ segmentation and diagnosis prediction, their opaq

Mema: Memory-Augmented Adapter for Enhanced Vision-Language Understanding

arXiv:2603.00655v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable performance by aligning pretrained visual representations with the linguistic know

Mid-training is essential for LLM reasoning, IBM study shows

IBM Research has demonstrated that mid-training — a dedicated training phase between initial pre-training and fine-tuning — is essential for improving reasoning capabilities in large language models (

researchibm-research

More details: https://blog.comfy.org/p/ernie-image-day-0-support

ComfyUI announced day-zero support for ERNIE Image, Baidu's image generation model, with full implementation details available on their official blog. The integration allows users to run ERNIE Image m

local-aicomfyui--x

NucleusAI/Nucleus-Image (Samples)

local-air-stablediffusion

The Reddit post about NucleusAI/Nucleus-Image appears to be a community showcase of sample outputs from a text-to-image generative AI model called Nucleus-Image, developed by NucleusAI. The post is sh

Orthogonal Subspace Projection for Continual Machine Unlearning via SVD-Based LoRA

arXiv:2604.12526v1 Announce Type: cross Abstract: Continual machine unlearning aims to remove the influence of data that should no longer be retained, while preserving the usefulness of the model on e

Parametric Interpolation of Dynamic Mode Decomposition for Predicting Nonlinear Systems

arXiv:2604.12103v1 Announce Type: cross Abstract: We present parameter-interpolated dynamic mode decomposition (piDMD), a parametric reduced-order modeling framework that embeds known parameter-affine

Hardware

PipeLive: Efficient Live In-place Pipeline Parallelism Reconfiguration for Dynamic LLM Serving

arXiv:2604.12171v1 Announce Type: cross Abstract: Pipeline parallelism (PP) is widely used to partition layers of large language models (LLMs) across GPUs, enabling scalable inference for large models

hardwarearxiv-cs-lg

ReflectCAP: Detailed Image Captioning with Reflective Memory

arXiv:2604.12357v1 Announce Type: new Abstract: Detailed image captioning demands both factual grounding and fine-grained coverage, yet existing methods have struggled to achieve them simultaneously.

Should There be a Teacher In-the-Loop? A Study of Generative AI Personalized Tasks Middle School

arXiv:2602.15876v1 Announce Type: cross Abstract: Adapting instruction to the fine-grained needs of individual students is a powerful application of recent advances in large language models. These gen

SIRI-Bench: Challenging VLMs' Spatial Intelligence through Complex Reasoning Tasks

arXiv:2506.14512v4 Announce Type: replace Abstract: Large Language Models (LLMs) have undergone rapid progress, largely attributed to reinforcement learning on complex reasoning tasks. In contrast, wh

Spatial-Spectral Adaptive Fidelity and Noise Prior Reduction Guided Hyperspectral Image Denoising

arXiv:2604.12600v1 Announce Type: new Abstract: The core challenge of hyperspectral image denoising is striking the right balance between data fidelity and noise prior modeling. Most existing methods

local-aiarxiv-cs-cv

The Verification Tax: Fundamental Limits of AI Auditing in the Rare-Error Regime

arXiv:2604.12951v1 Announce Type: new Abstract: The most cited calibration result in deep learning -- post-temperature-scaling ECE of 0.012 on CIFAR-100 (Guo et al., 2017) -- is below the statistical

Thought-Retriever: Don't Just Retrieve Raw Data, Retrieve Thoughts for Memory-Augmented Agentic Systems

model-releasesarxiv-cs-cl

arXiv:2604.12231v1 Announce Type: new Abstract: Large language models (LLMs) have transformed AI research thanks to their powerful internal capabilities and knowledge. However, existing LLMs still fai

Unlocking the Potential of Grounding DINO in Videos: Parameter-Efficient Adaptation for Limited-Data Spatial-Temporal Localization

arXiv:2604.12346v1 Announce Type: new Abstract: Spatio-temporal video grounding (STVG) aims to localize queried objects within dynamic video segments. Prevailing fully-trained approaches are notorious

VideoFlexTok: Flexible-Length Coarse-to-Fine Video Tokenization

arXiv:2604.12887v1 Announce Type: new Abstract: Visual tokenizers map high-dimensional raw pixels into a compressed representation for downstream modeling. Beyond compression, tokenizers dictate what

local-aiarxiv-cs-cv

ZipVoice-Dialog: Non-Autoregressive Spoken Dialogue Generation with Flow Matching

model-releasesarxiv-cs-cl

arXiv:2507.09318v2 Announce Type: replace-cross Abstract: Generating spoken dialogue is inherently more complex than monologue text-to-speech (TTS), as it demands both realistic turn-taking and the ma

Tutorials

A Data-driven Loss Weighting Scheme across Heterogeneous Tasks for Image Denoising

arXiv:2301.06081v4 Announce Type: replace-cross Abstract: In a variational denoising model, weight in the data fidelity term plays the role of enhancing the noise-removal capability. It is profoundly

tutorialsarxiv-cs-cv

A-IO: Adaptive Inference Orchestration for Memory-Bound NPUs

arXiv:2604.09752v1 Announce Type: cross Abstract: During the deployment of Large Language Models (LLMs), the autoregressive decoding phase on heterogeneous NPU platforms (e.g., Ascend 910B) faces seve

AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes

arXiv:2601.02149v3 Announce Type: replace-cross Abstract: We propose a neural network-based model capable of learning the broad landscape of working regimes in quantum dot simulators, and using this k

AIM-Bench: Benchmarking and Improving Affective Image Manipulation via Fine-Grained Hierarchical Control

arXiv:2604.10454v1 Announce Type: new Abstract: Affective Image Manipulation (AIM) aims to evoke specific emotions through targeted editing. Current image editing benchmarks primarily focus on object-

AIMER: Calibration-Free Task-Agnostic MoE Pruning

arXiv:2603.18492v2 Announce Type: replace Abstract: Mixture-of-Experts (MoE) language models increase parameter capacity without proportional per-token compute, but the deployment still requires stori

BareBones: Benchmarking Zero-Shot Geometric Comprehension in VLMs

arXiv:2604.10528v1 Announce Type: new Abstract: While Vision-Language Models (VLMs) demonstrate remarkable zero-shot recognition capabilities across a diverse spectrum of multimodal tasks, it yet rema

Safety

Calibration Collapse Under Sycophancy Fine-Tuning: How Reward Hacking Breaks Uncertainty Quantification in LLMs

arXiv:2604.10585v1 Announce Type: cross Abstract: Modern large language models (LLMs) are increasingly fine-tuned via reinforcement learning from human feedback (RLHF) or related reward optimisation s

safetyarxiv-cs-ai

CARE-ECG: Causal Agent-based Reasoning for Explainable and Counterfactual ECG Interpretation

arXiv:2604.10420v1 Announce Type: new Abstract: Large language models (LLMs) enable waveform-to-text ECG interpretation and interactive clinical questioning, yet most ECG-LLM systems still rely on wea

ClawGuard: A Runtime Security Framework for Tool-Augmented LLM Agents Against Indirect Prompt Injection

arXiv:2604.11790v1 Announce Type: cross Abstract: Tool-augmented Large Language Model (LLM) agents have demonstrated impressive capabilities in automating complex, multi-step real-world tasks, yet rem

local-aiarxiv-cs-ai

CodaRAG: Connecting the Dots with Associativity Inspired by Complementary Learning

arXiv:2604.10426v1 Announce Type: cross Abstract: Large Language Models (LLMs) struggle with knowledge-intensive tasks due to hallucinations and fragmented reasoning over dispersed information. While

Hardware

CodeQuant: Unified Clustering and Quantization for Enhanced Outlier Smoothing in Low-Precision Mixture-of-Experts

arXiv:2604.10496v1 Announce Type: new Abstract: Outliers have emerged as a fundamental bottleneck in preserving accuracy for low-precision large models, particularly within Mixture-of-Experts (MoE) ar

hardwarearxiv-cs-lg

Correct me if I’m wrong: Ollama can’t fine tune like Unsloth Studio

Ollama is a local inference engine designed for running pre-built LLMs on your own machine, and it does not include fine-tuning capabilities — this distinction is correct. Unsloth (and its Unsloth Stu

local-air-ollama

CPAM: Context-Preserving Adaptive Manipulation for Zero-Shot Real Image Editing

arXiv:2506.18438v2 Announce Type: replace Abstract: Editing natural images using textual descriptions in text-to-image diffusion models remains a significant challenge, particularly in achieving consi

CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics

arXiv:2512.21877v3 Announce Type: replace-cross Abstract: Cricket is the second most popular sport worldwide, with billions of fans seeking advanced statistical insights unavailable through standard w

DecepGPT: Schema-Driven Deception Detection with Multicultural Datasets and Robust Multimodal Learning

arXiv:2603.23916v2 Announce Type: replace-cross Abstract: Multimodal deception detection aims to identify deceptive behavior by analyzing audiovisual cues for forensics and security. In these high-sta

Defending against Backdoor Attacks via Module Switching

arXiv:2504.05902v2 Announce Type: replace-cross Abstract: Backdoor attacks pose a serious threat to deep neural networks (DNNs), allowing adversaries to implant triggers for hidden behaviors in infere

researcharxiv-cs-cl

Delta Rectified Flow Sampling for Text-to-Image Editing

arXiv:2509.05342v3 Announce Type: replace Abstract: We propose Delta Rectified Flow Sampling (DRFS), a novel inversion-free, path-aware editing framework within rectified flow models for text-to-image

DERM-3R: A Resource-Efficient Multimodal Agents Framework for Dermatologic Diagnosis and Treatment in Real-World Clinical Settings

arXiv:2604.09596v1 Announce Type: new Abstract: Dermatologic diseases impose a large and growing global burden, affecting billions and substantially reducing quality of life. While modern therapies ca

DiningBench: A Hierarchical Multi-view Benchmark for Perception and Reasoning in the Dietary Domain

arXiv:2604.10425v1 Announce Type: new Abstract: Recent advancements in Vision-Language Models (VLMs) have revolutionized general visual understanding. However, their application in the food domain rem

Enabling Global, Human-Centered Explanations for LLMs:From Tokens to Interpretable Code and Test Generation

arXiv:2503.16771v3 Announce Type: replace-cross Abstract: As Large Language Models for Code (LM4Code) become integral to software engineering, establishing trust in their output becomes critical. Howe

local-aiarxiv-cs-lg

Safety

Enhancing Fine-Grained Spatial Grounding in 3D CT Report Generation via Discriminative Guidance

arXiv:2604.10437v1 Announce Type: new Abstract: Vision--language models (VLMs) for radiology report generation (RRG) can produce long-form chest CT reports from volumetric scans and show strong potent

safetyarxiv-cs-cv

Ernie Image Turbo is Capable of ...

local-air-stablediffusion

ERNIE Image Turbo is an open text-to-image generation model developed by Baidu's ERNIE-Image team, serving as the distilled release of the full ERNIE-Image model and built on a single-stream Diffusion

Evaluating Visual Prompts with Eye-Tracking Data for MLLM-Based Human Activity Recognition

arXiv:2604.09585v1 Announce Type: cross Abstract: Large Language Models (LLMs) have emerged as foundation models for IoT applications such as human activity recognition (HAR). However, directly applyi

GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs

arXiv:2511.11653v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have emerged as powerful tools for passage reranking in information retrieval, leveraging their superior reasonin