Search: “models”

> be me > 'the internet is polluted by ai slop, we need low-background tokens' > 'wouldnt it be cool if we could time travel and see what ou…

> be me > 'the internet is polluted by ai slop, we need low-background tokens' > 'wouldnt it be cool if we could time travel and see what our ancestors 100 years ago would say to us' > all the existin

model-releasesswyx--x

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Budget-Constrained Causal Bandits: Bridging Uplift Modeling and Sequential Decision-Making

DGX agent

arXiv:2604.26169v1 Announce Type: new Abstract: Treatment allocation under budget constraints is a central challenge in digital advertising: advertisers must decide which users to show ads to while sp

researcharxiv-cs-lg

30 Apr 2026

CoQuant: Joint Weight-Activation Subspace Projection for Mixed-Precision LLMs

arXiv:2604.26378v1 Announce Type: new Abstract: Post-training quantization (PTQ) has become an important technique for reducing the inference cost of Large Language Models (LLMs). While recent mixed-p

MoRFI: Monotonic Sparse Autoencoder Feature Identification

arXiv:2604.26866v1 Announce Type: new Abstract: Large language models (LLMs) acquire most of their factual knowledge during the pre-training stage, through next token prediction. Subsequent stages of

Thinking with Drafting: Optical Decompression via Logical Reconstruction

arXiv:2602.11731v2 Announce Type: replace Abstract: Existing multimodal large language models have achieved high-fidelity visual perception and exploratory visual generation. However, a precision para

VulStyle: A Multi-Modal Pre-Training for Code Stylometry-Augmented Vulnerability Detection

arXiv:2604.26313v1 Announce Type: cross Abstract: We present VulStyle, a multi-modal software vulnerability detection model that jointly encodes function-level source code, non-terminal Abstract Synta

When Annotators Disagree, Topology Explains: Mapper, a Topological Tool for Exploring Text Embedding Geometry and Ambiguity

arXiv:2510.17548v2 Announce Type: replace Abstract: Language models are often evaluated with scalar metrics like accuracy, but such measures fail to capture how models internally represent ambiguity,

safetyarxiv-cs-cl

Below-Chance Blindness: Prompted Underperformance in Small LLMs Produces Positional Bias Rather than Answer Avoidance

arXiv:2604.25249v1 Announce Type: new Abstract: Detecting sandbagging--the deliberate underperformance on capability evaluations--is an open problem in AI safety. We tested whether symptom validity te

Evaluating LLM Safety Under Repeated Inference via Accelerated Prompt Stress Testing

arXiv:2602.11786v2 Announce Type: replace Abstract: Traditional benchmarks for large language models (LLMs), such as HELM and AIR-BENCH, primarily assess safety through breadth-oriented evaluation acr

FARM: Enhancing Molecular Representations with Functional Group Awareness

arXiv:2410.02082v4 Announce Type: replace Abstract: We introduce Functional Group-Aware Representations for Small Molecules (FARM), a novel foundation model designed to bridge the gap between SMILES,

Local Ai

ollama run ministral-3:3b throwing error

The Ministral-3:3b model requires Ollama 0.13.1, which is in pre-release , and users encountering errors when running it face various issues including memory allocation problems and GPU/CPU offloading

local-air-ollama

Read the DeepSeek V4 Pro quickstart https://docs.together.ai/docs/deepseek-v4-quickstart

model-releasestogether-ai--x

DeepSeek V4 Pro is a language model available through Together AI's platform, with official quickstart documentation provided to help users get started with the model. The quickstart guide likely cove

The Thinking Pixel: Recursive Sparse Reasoning in Multimodal Diffusion Latents

arXiv:2604.25299v1 Announce Type: new Abstract: Diffusion models have achieved success in high-fidelity data synthesis, yet their capacity for more complex, structured reasoning like text following ta

Toward Multimodal Conversational AI for Age-Related Macular Degeneration

arXiv:2604.25720v1 Announce Type: cross Abstract: Despite strong performance of deep learning models in retinal disease detection, most systems produce static predictions without clinical reasoning or

researcharxiv-cs-cl

Voice, Bias, and Coreference: An Interpretability Study of Gender in Speech Translation

arXiv:2511.21517v2 Announce Type: replace Abstract: Unlike text, speech conveys information about the speaker, such as gender, through acoustic cues like pitch. This gives rise to modality-specific bi

safetyarxiv-cs-cl

50+ fully managed MCP servers now available for Google Cloud services

model-releasesgoogle-cloud-ai

At Google Cloud Next ‘26, we announced that more than 50 Google-managed Model Context Protocol (MCP) servers are generally available or in preview, with more on the way. Why it matters: To move beyond

A Comparative analysis of Layer-wise Representational Capacity in AR and Diffusion LLMs

arXiv:2603.07475v2 Announce Type: replace Abstract: Autoregressive (AR) language models build representations incrementally via left-to-right prediction, while diffusion language models (dLLMs) are tr

safetyarxiv-cs-cl

AeSlides: Incentivizing Aesthetic Layout in LLM-Based Slide Generation via Verifiable Rewards

arXiv:2604.22840v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong potential in agentic tasks, particularly in slide generation. However, slide generation poses a

Agri-CPJ: A Training-Free Explainable Framework for Agricultural Pest Diagnosis Using Caption-Prompt-Judge and LLM-as-a-Judge

arXiv:2604.23701v1 Announce Type: cross Abstract: Crop disease diagnosis from field photographs faces two recurring problems: models that score well on benchmarks frequently hallucinate species names,

AI Safety Training Can be Clinically Harmful

arXiv:2604.23445v1 Announce Type: cross Abstract: Large language models are being deployed as mental health support agents at scale, yet only 16% of LLM-based chatbot interventions have undergone rigo

safetyarxiv-cs-ai

An Empirical Evaluation of Locally Deployed LLMs for Bug Detection in Python Code

arXiv:2604.23361v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong performance on a wide range of software engineering tasks, including code generation and analysi

C-MORAL: Controllable Multi-Objective Molecular Optimization with Reinforcement Alignment for LLMs

arXiv:2604.23061v1 Announce Type: cross Abstract: Large language models (LLMs) show promise for molecular optimization, but aligning them with selective and competing drug-design constraints remains c

Clotho: Measuring Task-Specific Pre-Generation Test Adequacy for LLM Inputs

arXiv:2509.17314v3 Announce Type: replace-cross Abstract: Software increasingly relies on the emergent capabilities of Large Language Models (LLMs), from natural language understanding to program anal

Context-Aware Hospitalization Forecasting Evaluations for Decision Support using LLMs

arXiv:2604.23949v1 Announce Type: new Abstract: Medical and public health experts must make real-time resource decisions, such as expanding hospital bed capacity, based on projected hospitalization tr

safetyarxiv-cs-ai

Don't Make the LLM Read the Graph: Make the Graph Think

arXiv:2604.23057v1 Announce Type: new Abstract: We investigate whether explicit belief graphs improve LLM performance in cooperative multi-agent reasoning. Through 3,000+ controlled trials across four

DRIFT: Transferring Reasoning Priors for Efficient MLLM Fine-Tuning

arXiv:2510.15050v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have made rapid progress, yet their reasoning ability often lags behind strong text-only LLMs. Bridging thi

GAMMAF: A Common Framework for Graph-Based Anomaly Monitoring Benchmarking in LLM Multi-Agent Systems

arXiv:2604.24477v1 Announce Type: cross Abstract: The rapid integration of Large Language Models (LLMs) into Multi-Agent Systems (MAS) has significantly enhanced their collaborative problem-solving ca

Guided Speculative Inference for Efficient Test-Time Alignment of LLMs

arXiv:2506.04118v3 Announce Type: replace Abstract: We propose Guided Speculative Inference (GSI), a novel algorithm for efficient reward-guided decoding in large language models. GSI combines soft be

safetyarxiv-cs-lg

I'm so confused…

model-releasesjeremy-howard--x

I'm so confused… We're excited to partner with Google to offer Grounding With Exa inside of Gemini models! Using Exa's agent-first search, Gemini models can now access billions of websites, technical

Introducing NVIDIA Nemotron 3 Nano Omni: Long-Context Multimodal Intelligence for Documents, Audio and Video Agents

model-releaseshugging-face

NVIDIA's Nemotron 3 Nano Omni is a lightweight multimodal AI model capable of processing documents, audio, and video inputs for building intelligent agents. The model supports long-context understandi

LILogic Net: Compact Logic Gate Networks with Learnable Connectivity for Efficient Hardware Deployment

arXiv:2511.12340v2 Announce Type: replace Abstract: Efficient machine learning deployment requires models that account for hardware constraints. Because binary logic gates are the fundamental primitiv

researcharxiv-cs-lg

Long-Context Aware Upcycling: A New Frontier for Hybrid LLM Scaling

arXiv:2604.24715v1 Announce Type: new Abstract: Hybrid sequence models that combine efficient Transformer components with linear sequence modeling blocks are a promising alternative to pure Transforme

MermaidSeqBench: An Evaluation Benchmark for NL-to-Mermaid Sequence Diagram Generation

arXiv:2511.14967v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown great promise in generating structured diagrams from natural language descriptions, particularly Merma

Meta-CoT: Enhancing Granularity and Generalization in Image Editing

arXiv:2604.24625v1 Announce Type: cross Abstract: Unified multi-modal understanding/generative models have shown improved image editing performance by incorporating fine-grained understanding into the

ServImage: An Image Generation and Editing Benchmark from Real-world Commercial Imaging Services

arXiv:2604.24023v1 Announce Type: new Abstract: Recent image generation and editing models demonstrate robust adherence to instructions and high visual quality on academic benchmarks. However, their p

Sphere-Depth: A Benchmark for Depth Estimation Methods with Varying Spherical Camera Orientations

arXiv:2604.23432v1 Announce Type: cross Abstract: Reliable depth estimation from spherical images is crucial for 360{eg} vision in robotic navigation and immersive scene understanding. However, the on

The Pragmatic Persona: Discovering LLM Persona through Bridging Inference

arXiv:2604.24079v1 Announce Type: cross Abstract: Large Language Models (LLMs) reveal inherent and distinctive personas through dialogue. However, most existing persona discovery approaches rely on su

Toward Theoretical Insights into Diffusion Trajectory Distillation via Operator Merging

arXiv:2505.16024v2 Announce Type: replace-cross Abstract: Diffusion trajectory distillation accelerates sampling by training a student model to approximate the multi-step denoising trajectories of a p

researcharxiv-cs-ai

UpstreamQA: A Modular Framework for Explicit Reasoning on Video Question Answering Tasks

arXiv:2604.23145v1 Announce Type: cross Abstract: Video Question Answering (VideoQA) demands models that jointly reason over spatial, temporal, and linguistic cues. However, the task's inherent comple

YOLOv8 to YOLO11: A Comprehensive Architecture In-depth Comparative Review

arXiv:2501.13400v3 Announce Type: replace-cross Abstract: In the field of deep learning-based computer vision, YOLO is revolutionary. With respect to deep learning models, YOLO is also the one that is

researcharxiv-cs-ai

Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Data

arXiv:2604.24479v1 Announce Type: new Abstract: Computer-Aided Design (CAD) models are defined by their construction history: a parametric recipe that encodes design intent. However, existing large-sc

CharTide: Data-Centric Chart-to-Code Generation via Tri-Perspective Tuning and Inquiry-Driven Evolution

arXiv:2604.22192v1 Announce Type: new Abstract: Chart-to-code generation demands strict visual precision and syntactic correctness from Vision-Language Models (VLMs). However, existing approaches are

CNSL-bench: Benchmarking the Sign Language Understanding Capabilities of MLLMs on Chinese National Sign Language

arXiv:2604.22367v1 Announce Type: cross Abstract: Sign language research has achieved significant progress due to the advances in large language models (LLMs). However, the intrinsic ability of LLMs t

Conditional Diffusion Posterior Alignment for Sparse-View CT Reconstruction

arXiv:2604.21960v1 Announce Type: cross Abstract: Computed Tomography (CT) is a widely used imaging modality in medical and industrial applications. To limit radiation exposure and measurement time, t

safetyarxiv-cs-cv

Emergent Strategic Reasoning Risks in AI: A Taxonomy-Driven Evaluation Framework

arXiv:2604.22119v1 Announce Type: new Abstract: As reasoning capacity and deployment scope grow in tandem, large language models (LLMs) gain the capacity to engage in behaviors that serve their own ob

safetyarxiv-cs-ai

Applications

GazeVLA: Learning Human Intention for Robotic Manipulation

arXiv:2604.22615v1 Announce Type: new Abstract: Embodied foundation models have achieved significant breakthroughs in robotic manipulation, yet they still depend heavily on large-scale robot demonstra

applicationsarxiv-cs-ro

Applications

Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation

arXiv:2604.22098v1 Announce Type: new Abstract: Time introduces fundamental challenges in model development and deployment: models are usually trained on historical data while deployed on future data

applicationsarxiv-cs-cl

Knowledge Visualization: A Benchmark and Method for Knowledge-Intensive Text-to-Image Generation

arXiv:2604.22302v1 Announce Type: new Abstract: Recent text-to-image (T2I) models have demonstrated impressive capabilities in photorealistic synthesis and instruction following. However, their reliab