model releases

Shape: A Self-Supervised 3D Geometry Foundation Model for Industrial CAD Analysis

arXiv:2604.22826v1 Announce Type: new Abstract: Industrial CAD workflows require robust, generalizable 3D geometric representations supporting accuracy and explainability. We introduce Shape, a self-s

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Model Releases

ShredBench: Evaluating the Semantic Reasoning Capabilities of Multimodal LLMs in Document Reconstruction

DGX agent

arXiv:2604.23813v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable performance in Visually Rich Document Understanding (VRDU) tasks, but their capabili

model-releasesarxiv-cs-cl

28 Apr 2026

SIV-Bench: A Video Benchmark for Social Interaction Understanding and Reasoning

arXiv:2506.05425v2 Announce Type: replace-cross Abstract: Understanding social interaction, which encompasses perceiving numerous and subtle multimodal cues, inferring unobservable mental states and r

SketchVLM: Vision language models can annotate images to explain thoughts and guide users

arXiv:2604.22875v1 Announce Type: cross Abstract: When answering questions about images, humans naturally point, label, and draw to explain their reasoning. In contrast, modern vision-language models

Skill Retrieval Augmentation for Agentic AI

arXiv:2604.24594v1 Announce Type: cross Abstract: As large language models (LLMs) evolve into agentic problem solvers, they increasingly rely on external, reusable skills to handle tasks beyond their

SMSI: System Model Security Inference: Automated Threat Modeling for Cyber-Physical Systems

arXiv:2604.23905v1 Announce Type: cross Abstract: Threat modeling for cyber-physical systems (CPS) remains a largely manual exercise. This project presents SMSI (System Model Security Inference), a hy

Snap launches AI Sponsored Snaps, a conversational ad format in Snapchat's Chat tab that lets users talk to brand-specific AI agents for product recommendations (Aisha Malik/TechCrunch)

Aisha Malik / TechCrunch: Snap launches AI Sponsored Snaps, a conversational ad format in Snapchat's Chat tab that lets users talk to brand-specific AI agents for product recommendations — Snapchat an

model-releasestechmeme

SoccerRef-Agents: Multi-Agent System for Automated Soccer Refereeing

arXiv:2604.23392v1 Announce Type: new Abstract: Refereeing is vital in sports, where fair, accurate, and explainable decisions are fundamental. While intelligent assistant technologies are being widel

SolarFCD: A Large-Scale Dataset and Benchmark for Solar Fault Classification in Photovoltaic Systems

arXiv:2604.23662v1 Announce Type: new Abstract: The increasing global deployment of solar photovoltaic (PV) systems needs robust, scalable, and automated inspection technologies capable of detecting a

SPAGS: Sparse-View Articulated Object Reconstruction from Single State via Planar Gaussian Splatting

arXiv:2511.17092v4 Announce Type: replace Abstract: Articulated objects are ubiquitous in daily environments, and their 3D reconstruction holds great significance across various fields. However, exist

Spatiotemporal Degradation-Aware 3D Gaussian Splatting for Realistic Underwater Scene Reconstruction

arXiv:2604.23551v1 Announce Type: new Abstract: Reconstructing realistic underwater scenes from underwater video remains a meaningful yet challenging task in the multimedia domain. The inherent spatio

SpecRLBench: A Benchmark for Generalization in Specification-Guided Reinforcement Learning

model-releasesarxiv-cs-lg

arXiv:2604.24729v1 Announce Type: new Abstract: Specification-guided reinforcement learning (RL) provides a principled framework for encoding complex, temporally extended tasks using formal specificat

Speech Enhancement Based on Drifting Models

arXiv:2604.24199v1 Announce Type: cross Abstract: We propose Speech Enhancement based on Drifting Models (DriftSE), a novel generative framework that formulates denoising as an equilibrium problem. Ra

Speech-FT: Merging Pre-trained And Fine-Tuned Speech Representation Models For Cross-Task Generalization

arXiv:2502.12672v4 Announce Type: replace-cross Abstract: Fine-tuning speech representation models can enhance performance on specific tasks but often compromises their cross-task generalization abili

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

SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine Translation

arXiv:2505.16637v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have recently demonstrated remarkable capabilities in machine translation (MT). However, most advanced MT-specifi

Stabilizing Efficient Reasoning with Step-Level Advantage Selection

arXiv:2604.24003v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong reasoning performance by allocating substantial computation at inference time, often generating long and ver

StoryTR: Narrative-Centric Video Temporal Retrieval with Theory of Mind Reasoning

arXiv:2604.23198v1 Announce Type: new Abstract: Current video moment retrieval excels at action-centric tasks but struggles with narrative content. Models can see extit{what is happening} but fail to

Strategic Bidding in 6G Spectrum Auctions with Large Language Models

arXiv:2604.24156v1 Announce Type: cross Abstract: Efficient and fair spectrum allocation is a central challenge in 6G networks, where massive connectivity and heterogeneous services continuously compe

StratRAG: A Multi-Hop Retrieval Evaluation Dataset for Retrieval-Augmented Generation Systems

arXiv:2604.22757v1 Announce Type: cross Abstract: We introduce StratRAG, an open-source retrieval evaluation dataset for benchmarking Retrieval-Augmented Generation (RAG) systems on multi-hop reasonin

Stress-Testing Emotional Support Models: Moving from Homogeneous to Diverse Help Seekers

arXiv:2601.07698v2 Announce Type: replace Abstract: As emotional support chatbots have recently gained significant traction across both research and industry, a common evaluation strategy has emerged:

StructAlign: Structured Cross-Modal Alignment for Continual Text-to-Video Retrieval

arXiv:2601.20597v2 Announce Type: replace Abstract: Continual Text-to-Video Retrieval (CTVR) is a challenging multimodal continual learning setting, where models must incrementally learn new semantic

Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency

arXiv:2604.24380v1 Announce Type: new Abstract: While Large Vision Language Models (LVLMs) demonstrate impressive capabilities, their substantial computational and memory requirements pose deployment

Supernodes and Halos: Loss-Critical Hubs in LLM Feed-Forward Layers

arXiv:2604.23475v1 Announce Type: cross Abstract: We study the organization of channel-level importance in transformer feed-forward networks (FFNs). Using a Fisher-style loss proxy (LP) based on activ

SWE-QA: Can Language Models Answer Repository-level Code Questions?

arXiv:2509.14635v2 Announce Type: replace Abstract: Understanding and reasoning about entire software repositories is an essential capability for intelligent software engineering tools. While existing

SwissGov-RSD: A Human-annotated, Cross-lingual Benchmark for Token-level Recognition of Semantic Differences Between Related Documents

arXiv:2512.07538v3 Announce Type: replace Abstract: Recognizing semantic differences across documents is crucial for text generation evaluation and content alignment, especially in cross-lingual setti

Switch Attention: Towards Dynamic and Fine-grained Hybrid Transformers

arXiv:2603.26380v2 Announce Type: replace Abstract: The attention mechanism has been the core component in modern transformer architectures. However, the computation of standard full attention scales

SycoPhantasy: Quantifying Sycophancy and Hallucination in Small Open Weight VLMs for Vision-Language Scoring of Fantasy Characters

arXiv:2604.24346v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly deployed as evaluators in tasks requiring nuanced image understanding, yet their reliability in scoring

Symmetric Equilibrium Propagation for Thermodynamic Diffusion Training

arXiv:2604.23806v1 Announce Type: cross Abstract: The reverse process in score-based diffusion models is formally equivalent to overdamped Langevin dynamics in a time-dependent energy landscape. In ou

SynthPert: Enhancing LLM Biological Reasoning via Synthetic Reasoning Traces for Cellular Perturbation Prediction

arXiv:2509.25346v2 Announce Type: replace Abstract: Predicting cellular responses to genetic perturbations represents a fundamental challenge in systems biology, critical for advancing therapeutic dis

TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training

arXiv:2604.24088v1 Announce Type: cross Abstract: Handling communication overhead in large-scale tensor-parallel training remains a critical challenge due to the dense, near-zero distributions of inte

Test of Time: Rethinking Temporal Signal of Benchmark Contamination

arXiv:2509.00072v3 Announce Type: replace Abstract: Post-cutoff performance decay has been widely interpreted as a temporal signal for benchmark contamination. We critically examine this belief and de

TexOCR: Advancing Document OCR Models for Compilable Page-to-LaTeX Reconstruction

arXiv:2604.22880v1 Announce Type: new Abstract: Existing document OCR largely targets plain text or Markdown, discarding the structural and executable properties that make LaTeX essential for scientif

TextGround4M: A Prompt-Aligned Dataset for Layout-Aware Text Rendering

arXiv:2604.24459v1 Announce Type: new Abstract: Despite recent advances in text-to-image generation, models still struggle to accurately render prompt-specified text with correct spatial layout -- esp

The Alignment Target Problem: Divergent Moral Judgments of Humans, AI Systems, and Their Designers

arXiv:2604.24155v1 Announce Type: cross Abstract: The quest to align machine behavior with human values raises fundamental questions about the moral frameworks that should govern AI decision-making. M

The California Coastal Commission has issued a formal apology to @elonmusk and SpaceX, adding that it will not consider political views or s…

model-releaseselon-musk--x

The California Coastal Commission has issued a formal apology to @elonmusk and SpaceX, adding that it will not consider political views or speech in future regulatory decisions. • The Commission admit

The cracks inside OpenAI are deepening, and the numbers don’t lie. When your own CFO is sounding the alarm, something is seriously wrong. Ch…

model-releasesgary-marcus--x

The cracks inside OpenAI are deepening, and the numbers don’t lie. When your own CFO is sounding the alarm, something is seriously wrong. Check this out: 1: OpenAI missed its target of 1 billion weekl

The FIDO Alliance launches two working groups to establish industry standards for securing AI agent transactions; Google contributes the Agent Payments Protocol (Lily Hay Newman/Wired)

Lily Hay Newman / Wired: The FIDO Alliance launches two working groups to establish industry standards for securing AI agent transactions; Google contributes the Agent Payments Protocol — AI agents ma

model-releasestechmeme

The Kerimov-Alekberli Model: An Information-Geometric Framework for Real-Time System Stability

arXiv:2604.24083v1 Announce Type: new Abstract: This study introduces the Kerimov-Alekberli model, a novel information-geometric framework that redefines AI safety by formally linking non-equilibrium

The Optimal Sample Complexity of Multiclass and List Learning

model-releasesarxiv-cs-lg

arXiv:2604.24749v1 Announce Type: new Abstract: While the optimal sample complexity of binary classification in terms of the VC dimension is well-established, determining the optimal sample complexity

The Override Gap: A Magnitude Account of Knowledge Conflict Failure in Hypernetwork-Based Instant LLM Adaptation

arXiv:2604.23750v1 Announce Type: cross Abstract: Hypernetwork-based methods such as Doc-to-LoRA internalize a document into an LLM's weights in a single forward pass, but they fail systematically 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

The Price of Agreement: Measuring LLM Sycophancy in Agentic Financial Applications

arXiv:2604.24668v1 Announce Type: new Abstract: Given the increased use of LLMs in financial systems today, it becomes important to evaluate the safety and robustness of such systems. One failure mode

The Randomness Floor: Measuring Intrinsic Non-Randomness in Language Model Token Distributions

arXiv:2604.22771v1 Announce Type: cross Abstract: Language models cannot be random. This paper introduces Entropic Deviation (ED), the normalised KL divergence between a model's token distribution and

The Rise of Large Language Models and the Direction and Impact of US Federal Research Funding

arXiv:2601.15485v2 Announce Type: replace-cross Abstract: Federal research funding shapes the direction, diversity, and impact of the US scientific enterprise. Large language models (LLMs) are rapidly

The Security Cost of Intelligence: AI Capability, Cyber Risk, and Deployment Paradox

arXiv:2604.23058v1 Announce Type: cross Abstract: Firms are deploying more capable AI systems, but organizational controls often have not kept pace. These systems can generate greater productivity gai

The Surprising Effectiveness of Membership Inference with Simple N-Gram Coverage

arXiv:2508.09603v2 Announce Type: replace Abstract: Membership inference attacks serves as useful tool for fair use of language models, such as detecting potential copyright infringement and auditing

Think-at-Hard: Selective Latent Iterations to Improve Reasoning Language Models

arXiv:2511.08577v2 Announce Type: replace-cross Abstract: Improving reasoning abilities of Large Language Models (LLMs), especially under parameter constraints, is crucial for real-world applications.