Search: “models”

ReproMIA: A Comprehensive Analysis of Model Reprogramming for Proactive Membership Inference Attacks

arXiv:2603.28942v3 Announce Type: replace Abstract: The pervasive deployment of deep learning models across critical domains has concurrently intensified privacy concerns due to their inherent propens

researcharxiv-cs-lg

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Model Releases

Reward Hacking in the Era of Large Models: Mechanisms, Emergent Misalignment, Challenges

DGX agent

arXiv:2604.13602v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) and related alignment paradigms have become central to steering large language models (LLMs) and multi

model-releasesarxiv-cs-lg

16 Apr 2026

Local Ai

Simplicity Prevails: The Emergence of Generalizable AIGI Detection in Visual Foundation Models

arXiv:2602.01738v2 Announce Type: replace Abstract: While specialized detectors for AI-Generated Images (AIGI) achieve near-perfect accuracy on curated benchmarks, they suffer from a dramatic performa

local-aiarxiv-cs-cv

Tools

Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers

This guide covers how to train and fine-tune multimodal embedding and reranker models using the Sentence Transformers library, enabling systems to work with both text and image data simultaneously. It

toolshugging-face

UNBOX: Unveiling Black-box visual models with Natural-language

arXiv:2603.08639v2 Announce Type: replace Abstract: Ensuring trustworthiness in open-world visual recognition requires models that are interpretable, fair, and robust to distribution shifts. Yet moder

safetyarxiv-cs-cv

A Foot Resistive Force Model for Legged Locomotion on Muddy Terrains

model-releasesarxiv-cs-ro

arXiv:2604.12006v1 Announce Type: new Abstract: Legged robots face significant challenges in moving and navigating on deformable and highly yielding terrain such as mud. We present a resistive force m

Beyond Output Correctness: Benchmarking and Evaluating Large Language Model Reasoning in Coding Tasks

arXiv:2604.12379v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly rely on explicit reasoning to solve coding tasks, yet evaluating the quality of this reasoning remains chall

Bipedal-Walking-Dynamics Model on Granular Terrains

model-releasesarxiv-cs-ro

arXiv:2604.11981v1 Announce Type: new Abstract: Bipeds have demonstrated high agility and mobility in unstructured environments such as sand. The yielding of such granular media brings significant sin

Fragile Preferences: A Deep Dive Into Order Effects in Large Language Models

arXiv:2506.14092v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in decision-support systems for high-stakes domains such as hiring and university admissions,

Fully Homomorphic Encryption on Llama 3 model for privacy preserving LLM inference

arXiv:2604.12168v1 Announce Type: cross Abstract: The applications of Generative Artificial Intelligence (GenAI) and their intersections with data-driven fields, such as healthcare, finance, transport

Hard Negative Sample-Augmented DPO Post-Training for Small Language Models

model-releasesarxiv-cs-lg

arXiv:2512.19728v2 Announce Type: replace Abstract: Large language models (LLMs) continue to struggle with mathematical reasoning, and common post-training pipelines often reduce each generated soluti

Local Ai

HintMR: Eliciting Stronger Mathematical Reasoning in Small Language Models

arXiv:2604.12229v1 Announce Type: new Abstract: Small language models (SLMs) often struggle with complex mathematical reasoning due to limited capacity to maintain long chains of intermediate steps an

local-aiarxiv-cs-ai

Mining Large Language Models for Low-Resource Language Data: Comparing Elicitation Strategies for Hausa and Fongbe

arXiv:2604.12477v1 Announce Type: cross Abstract: Large language models (LLMs) are trained on data contributed by low-resource language communities, yet the linguistic knowledge encoded in these model

PolicyLLM: Towards Excellent Comprehension of Public Policy for Large Language Models

arXiv:2604.12995v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly integrated into real-world decision-making, including in the domain of public policy. Yet, their ability t

Tutorials

A Tale of Two Temperatures: Simple, Efficient, and Diverse Sampling from Diffusion Language Models

arXiv:2604.09921v1 Announce Type: new Abstract: Much work has been done on designing fast and accurate sampling for diffusion language models (dLLMs). However, these efforts have largely focused on th

tutorialsarxiv-cs-lg

Assessing the Pedagogical Readiness of Large Language Models as AI Tutors in Low-Resource Contexts: A Case Study of Nepal's K-10 Curriculum

arXiv:2604.09619v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) into educational ecosystems promises to democratize access to personalized tutoring, yet the readiness

bacpipe: a Python package to make bioacoustic deep learning models accessible

arXiv:2604.11560v1 Announce Type: cross Abstract: 1. Natural sounds have been recorded for millions of hours over the previous decades using passive acoustic monitoring. Improvements in deep learning

researcharxiv-cs-ai

CheeseBench: Evaluating Large Language Models on Rodent Behavioral Neuroscience Paradigms

arXiv:2604.10825v1 Announce Type: new Abstract: We introduce CheeseBench, a benchmark that evaluates large language models (LLMs) on nine classical behavioral neuroscience paradigms (Morris water maze

Comparative Analysis of Large Language Models in Healthcare

arXiv:2604.10316v1 Announce Type: new Abstract: Background: Large Language Models (LLMs) are transforming artificial intelligence applications in healthcare due to their ability to understand, generat

DA-PTQ: Drift-Aware Post-Training Quantization for Efficient Vision-Language-Action Models

arXiv:2604.11572v1 Announce Type: new Abstract: Vision-Language-Action models (VLAs) have demonstrated strong potential for embodied AI, yet their deployment on resource-limited robots remains challen

researcharxiv-cs-ro

Enhancing Multimodal Large Language Models for Ancient Chinese Character Evolution Analysis via Glyph-Driven Fine-Tuning

arXiv:2604.11299v1 Announce Type: cross Abstract: In recent years, rapid advances in Multimodal Large Language Models (MLLMs) have increasingly stimulated research on ancient Chinese scripts. As the e

Environmental Footprint of GenAI Research: Insights from the Moshi Foundation Model

arXiv:2604.11154v1 Announce Type: new Abstract: New multi-modal large language models (MLLMs) are continuously being trained and deployed, following rapid development cycles. This generative AI frenzy

Evaluating Reliability Gaps in Large Language Model Safety via Repeated Prompt Sampling

arXiv:2604.09606v1 Announce Type: new Abstract: Traditional benchmarks for large language models (LLMs), such as HELM and AIR-BENCH, primarily assess safety risk through breadth-oriented evaluation ac

GeoArena: Evaluating Open-World Geographic Reasoning in Large Vision-Language Models

arXiv:2509.04334v4 Announce Type: replace Abstract: Geographic reasoning is a fundamental cognitive capability that requires models to infer plausible locations by synthesizing visual evidence with sp

GS4City: Hierarchical Semantic Gaussian Splatting via City-Model Priors

arXiv:2604.11401v1 Announce Type: new Abstract: Recent semantic 3D Gaussian Splatting (3DGS) methods primarily rely on 2D foundation models, often yielding ambiguous boundaries and limited support for

researcharxiv-cs-cv

How Robust Are Large Language Models for Clinical Numeracy? An Empirical Study on Numerical Reasoning Abilities in Clinical Contexts

arXiv:2604.11133v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly being explored for clinical question answering and decision support, yet safe deployment critically requir

Applications

Inferring Dynamic Physical Properties from Video Foundation Models

arXiv:2510.02311v2 Announce Type: replace Abstract: We study the task of predicting dynamic physical properties from videos. More specifically, we consider physical properties that require temporal in

applicationsarxiv-cs-cv

Influencing Humans to Conform to Preference Models for RLHF

arXiv:2501.06416v3 Announce Type: replace-cross Abstract: Designing a reinforcement learning from human feedback (RLHF) algorithm to approximate a human's unobservable reward function requires assumin

safetyarxiv-cs-ai

Is There Knowledge Left to Extract? Evidence of Fragility in Medically Fine-Tuned Vision-Language Models

arXiv:2604.09841v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly adapted through domain-specific fine-tuning, yet it remains unclear whether this improves reasoning bey

Jailbreaking the Matrix: Nullspace Steering for Controlled Model Subversion

arXiv:2604.10326v1 Announce Type: cross Abstract: Large language models remain vulnerable to jailbreak attacks -- inputs designed to bypass safety mechanisms and elicit harmful responses -- despite ad

safetyarxiv-cs-ai

LaMI: Augmenting Large Language Models via Late Multi-Image Fusion

arXiv:2406.13621v2 Announce Type: replace Abstract: Commonsense reasoning often requires both textual and visual knowledge, yet Large Language Models (LLMs) trained solely on text lack visual groundin

Latent Structure of Affective Representations in Large Language Models

arXiv:2604.07382v2 Announce Type: replace-cross Abstract: The geometric structure of latent representations in large language models (LLMs) is an active area of research, driven in part by its implica

safetyarxiv-cs-ai

MASS: Motion-Aware Spatial-Temporal Grounding for Physics Reasoning and Comprehension in Vision-Language Models

arXiv:2511.18373v2 Announce Type: replace Abstract: Vision Language Models (VLMs) perform well on standard video tasks but struggle with physics-related reasoning involving motion dynamics and spatial

Agents

MiniMax M2.7 is now available in LM Studio. This model excels at agentic tool calling 🛠️ Requires at least ~138GB to run locally https://lm…

MiniMax M2.7 is a large language model now available for local deployment through LM Studio, notable for its strong performance in agentic tool calling tasks. The model requires a substantial minimum

agentslm-studio--x

Muddit: Liberating Generation Beyond Text-to-Image with a Unified Discrete Diffusion Model

arXiv:2505.23606v4 Announce Type: replace-cross Abstract: Unified generation models aim to handle diverse tasks across modalities -- such as text generation, image generation, and vision-language reas

researcharxiv-cs-cv

NovBench: Evaluating Large Language Models on Academic Paper Novelty Assessment

arXiv:2604.11543v1 Announce Type: cross Abstract: Novelty is a core requirement in academic publishing and a central focus of peer review, yet the growing volume of submissions has placed increasing p

On Harnessing Idle Compute at the Edge for Foundation Model Training

model-releasesarxiv-cs-lg

arXiv:2512.22142v2 Announce Type: replace-cross Abstract: The foundation-model ecosystem remains highly centralized because training requires immense compute resources and is therefore largely limited

PSF-Med: Measuring and Explaining Paraphrase Sensitivity in Medical Vision Language Models

arXiv:2602.21428v2 Announce Type: replace Abstract: Medical Vision Language Models (VLMs) can change their answers when clinicians rephrase the same question, a failure mode that threatens deployment

Reasoning Resides in Layers: Restoring Temporal Reasoning in Video-Language Models with Layer-Selective Merging

arXiv:2604.11399v1 Announce Type: cross Abstract: Multimodal adaptation equips large language models (LLMs) with perceptual capabilities, but often weakens the reasoning ability inherited from languag

safetyarxiv-cs-cl

Retrieval-Augmented Large Language Models for Evidence-Informed Guidance on Cannabidiol Use in Older Adults

arXiv:2604.09548v1 Announce Type: cross Abstract: Older adults commonly experience chronic conditions such as pain and sleep disturbances and may consider cannabidiol for symptom management. Safe use

Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models

arXiv:2604.11711v1 Announce Type: new Abstract: Occlusion, where target structures are partially hidden by surgical instruments or overlapping tissues, remains a critical yet underexplored challenge f

ShapShift: Explaining Model Prediction Shifts with Subgroup Conditional Shapley Values

arXiv:2604.11200v1 Announce Type: cross Abstract: Changes in input distribution can induce shifts in the average predictions of machine learning models. Such prediction shifts may impact downstream bu

researcharxiv-cs-ai

SODA: Semi On-Policy Black-Box Distillation for Large Language Models

arXiv:2604.03873v2 Announce Type: replace-cross Abstract: Black-box knowledge distillation for large language models presents a strict trade-off. Simple off-policy methods (e.g., sequence-level knowle

Text-to-Image Models and Their Representation of People from Different Nationalities Engaging in Activities

arXiv:2504.06313v5 Announce Type: replace Abstract: This paper investigates how popular text-to-image (T2I) models, DALL-E 3 and Gemini 3 Pro Preview, depict people from 206 nationalities when prompte

Applications

Tuning Language Models for Robust Prediction of Diverse User Behaviors

arXiv:2505.17682v2 Announce Type: replace-cross Abstract: Predicting user behavior is essential for intelligent assistant services, yet deep learning models often struggle to capture long-tailed behav

applicationsarxiv-cs-ai

We've tested new OSS models the moment they're released for a while at Lindy. Inference is our #1 cost by a lot (more than payroll) — cuttin…

model-releasesharrison-chase--x

We've tested new OSS models the moment they're released for a while at Lindy. Inference is our #1 cost by a lot (more than payroll) — cutting it by 2-5x would be transformative. Last year, OSS models

Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models

arXiv:2604.10079v1 Announce Type: new Abstract: Supervised Fine-Tuning (SFT) is the standard approach for adapting large language models (LLMs) to downstream tasks. However, we observe a persistent fa

Breaking Block Boundaries: Anchor-based History-stable Decoding for Diffusion Large Language Models

arXiv:2604.08964v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have recently become a promising alternative to autoregressive large language models (ARMs). Semi-autoregressive