model releases

Meta rolls out Plus plans for Instagram, Facebook, and WhatsApp globally, will test 7.99/mo. and 19.99/mo. Meta AI plans, a $49.99/mo. creator plan, and more (Sarah Perez/TechCrunch)

Sarah Perez / TechCrunch: Meta rolls out Plus plans for Instagram, Facebook, and WhatsApp globally, will test 7.99/mo. and 19.99/mo. Meta AI plans, a $49.99/mo. creator plan, and more — Meta is doubli

model-releasestechmeme

METATR: A Multilingual, Evolving Benchmark for Automatic Text Recognition

arXiv:2605.26712v1 Announce Type: new Abstract: Benchmarks that reflect the diversity and complexity of real-world documents are essential for accurately evaluating Automatic Text Recognition (ATR) sy

MobileExplorer: Accelerating On-Device Inference for Mobile GUI Agents via Online Exploration

arXiv:2605.26546v1 Announce Type: new Abstract: Mobile graphical user interface (GUI) agents enable AI models to autonomously operate smartphones on behalf of users. However, most existing systems foc

MobileMoE: Scaling On-Device Mixture of Experts

arXiv:2605.27358v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) has become the de facto architecture for hundred-billion-parameter language models, yet its advantages at sub-billion scales

Model discovery for dynamical systems with complex-valued product units

arXiv:2605.27158v1 Announce Type: new Abstract: Discovering the governing equations of a dynamical system from observed trajectories provides deeper insight into its structure than mere prediction of

Model-Harness-Task fit! it’s clear that RL post-training produces a model-harness fit via tool shapes and prompting as models are trained wi…

model-releasesharrison-chase--x

Model-Harness-Task fit! it’s clear that RL post-training produces a model-harness fit via tool shapes and prompting as models are trained with the harness in the loop. Mentioned this in a previous Lan

Modeling Dynamic Mixtures of Time-Delay Systems from Streaming Time Series

arXiv:2605.26191v1 Announce Type: cross Abstract: This research addresses the problem of adaptive modeling in time-series data streams with clear input-output relationships. This problem is challengin

MolPIF: A Parameter Interpolation Flow Model for Molecule Generation

arXiv:2507.13762v4 Announce Type: replace Abstract: Motivation: Structure-based drug design (SBDD) has advanced with deep generative models, but bridging the gap between continuous atomic coordinates

More Expressive Feedforward Layers: Part I. Token-Adaptive Mixing of Activations

arXiv:2605.26647v1 Announce Type: cross Abstract: Feedforward network (FFN) layers account for a large fraction of parameters and nonlinear expressivity in Transformer-based large language models (LLM

MRT: Masked Region Transformer for Layered Image Generation and Editing at Scale

arXiv:2605.27235v1 Announce Type: new Abstract: Layered image generation and editing is a fundamental capability that enables layer-wise reuse, editing, and composition of generated visual content, an

MTL-FNO: A Lightweight Multi-Task Fourier Neural Operator for Sparse Field Reconstruction

arXiv:2605.26718v1 Announce Type: new Abstract: Efficient onboard multi-field sparse reconstruction is essential for the autonomous operation of aerospace vehicles. While existing deep learning models

Multi-Agent Causal Discovery Using Large Language Models

arXiv:2407.15073v4 Announce Type: replace Abstract: Causal discovery aims to identify causal relationships between variables and is a fundamental problem across the sciences. Traditional statistical c

MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding

arXiv:2605.26320v1 Announce Type: cross Abstract: The application of generalist multimodal models (GMMs) to specialized scientific domains remains limited due to the scarcity of comprehensive domain-s

Near-Optimal Regret in Adversarial Kernel Bandits

arXiv:2605.26585v1 Announce Type: new Abstract: We study the adversarial kernel bandit problem, in which the loss at each round is induced by an arbitrary bounded element of a reproducing kernel Hilbe

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models

arXiv:2605.26895v1 Announce Type: cross Abstract: Normalization layers in modern large language models (LLMs) consist of a deterministic normalization operation and a learnable scale vector. While the

NestedKV: Nested Memory Routing for Long-Context KV Cache Compression

arXiv:2605.26678v1 Announce Type: new Abstract: Long-context language models are limited by the memory footprint of the key-value (KV) cache. Existing training-free KV compression methods usually rank

Neural Autoregressive Control Variates for the Quantum Monte Carlo Sign Problem

arXiv:2605.26814v1 Announce Type: cross Abstract: We train a pair of autoregressive models to construct zero-mean control variates to mitigate the sign problem in quantum Monte Carlo simulations. The

Not All Disagreement Is Learnable: Token Teachability in On-Policy Distillation

arXiv:2605.26844v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on its own rollouts with token-level teacher supervision. Recent selective OPD methods exploit the non-uni

O-MARC: Omni Memory-Augmented Compression Distillation for Efficient Video Understanding

arXiv:2605.26584v1 Announce Type: new Abstract: Omnimodal large language models enable unified audio video understanding, but long joint token sequences make inference costly, and existing benchmarks

OCR-Reasoning Benchmark: Unveiling the True Capabilities of MLLMs in Complex Text-Rich Image Reasoning

arXiv:2505.17163v2 Announce Type: replace-cross Abstract: Recent advancements in multimodal slow-thinking systems have demonstrated remarkable performance across various visual reasoning tasks. Howeve

ODOV: Benchmark the Open-Domain Open-Vocabulary Object Detection

arXiv:2508.01253v2 Announce Type: replace Abstract: Existing studies typically investigate domain shift and category shift as independent problems, however, in real-world scenarios, the two types of s

Omanic: Towards Step-wise Evaluation of Multi-hop Reasoning in Large Language Models

arXiv:2603.16654v2 Announce Type: replace-cross Abstract: Evaluating the reasoning abilities of large language models (LLMs) solely from final answers can obscure failures in intermediate steps, espec

OMD-GraphRAG: Enhancing GraphRAG with Ontology-Guided Extraction, Multi-Dimensional Clustering and Dual-Channel Fusion

arXiv:2603.25152v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) systems face significant challenges in complex reasoning, multi-hop queries, and domain-specific QA. While exis

OmniInteract: Benchmarking Real-World Streaming Interaction for Real-Time Omnimodal Assistants

arXiv:2605.26485v1 Announce Type: cross Abstract: We introduce OmniInteract, a streaming benchmark for real-time omnimodal large language models evaluated through native online inference over audio-vi

OmniRetriever: Any-to-Any Audio-Video-Text Retrieval via Fusion-as-Teacher Distillation

arXiv:2605.26641v1 Announce Type: new Abstract: Unified multimodal embedding spaces have become the standard interface for cross-modal retrieval and multimodal RAG, and recent audio-video-text (AVT) e

OmniToM: Benchmarking Theory of Mind in LLMs via Explicit Belief Modeling

arXiv:2605.26322v1 Announce Type: new Abstract: Theory of Mind (ToM), the ability to infer others' knowledge, intentions, and emotions, is commonly evaluated in large language models (LLMs) using end-

On the Hidden Costs of Counterfactual Knowledge Training in LLM Unlearning

arXiv:2605.27083v1 Announce Type: new Abstract: Counterfactual tuning (CFT) has emerged as a promising paradigm for Large Language Model (LLM) unlearning by training models to generate alternative fic

On the Sensitivity of Instruction-tuned LLMs to Harmful Sentences in Long Inputs

arXiv:2510.05864v2 Announce Type: replace Abstract: Large language models (LLMs) increasingly operate on long inputs, yet their behavior when harmful sentences are sparsely embedded within such inputs

ORLoopBench: Solver-in-the-Loop Benchmarks for Self-Correction and Behavioral Rationality in Operations Research

arXiv:2601.21008v3 Announce Type: replace-cross Abstract: Operations Research practitioners debug infeasible models through an iterative process: inspecting Irreducible Infeasible Subsystems ( IIS), i

OSMa-Bench++: Toward Open-Ended Benchmarking of Semantic Mapping for Manipulation with Prompt-Generated Synthetic Scenes

arXiv:2605.26831v1 Announce Type: new Abstract: Semantic mapping methods are increasingly used as intermediate scene representations for downstream robotic reasoning and manipulation, yet their evalua

PashtoTTS-Bench: automated screening for low-resource non-Latin-script text-to-speech

arXiv:2605.26978v1 Announce Type: new Abstract: Text-to-speech (TTS) evaluation for low-resource non-Latin-script languages can fail when it relies on a single ASR round-trip word error rate (WER). A

PaTAS: A Framework for Trust Propagation in Neural Networks Using Subjective Logic

arXiv:2511.20586v4 Announce Type: replace Abstract: Trustworthiness has become a key requirement for the deployment of artificial intelligence systems in safety-critical applications. Conventional eva

Periodic Topological Deep Learning for Polymer Design and Discovery

arXiv:2605.26833v1 Announce Type: cross Abstract: Polymers underpin applications across energy, healthcare, and materials science, yet their vast chemical space makes systematic discovery challenging.

PersianMedQA: Evaluating Large Language Models on a Persian-English Bilingual Medical Question Answering Benchmark

arXiv:2506.00250v4 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved remarkable performance on a wide range of Natural Language Processing (NLP) benchmarks, often surpassing

PersLitEval: Fine-grained Benchmark and Evaluation of LLMs on Persian Literature Questions

arXiv:2605.27015v1 Announce Type: new Abstract: Despite impressive multilingual capabilities, large language models (LLMs) remain poorly evaluated on literary knowledge in non-English languages. We in

Persona2Web: Benchmarking Personalized Web Agents for Contextual Reasoning with User History

arXiv:2602.17003v2 Announce Type: replace-cross Abstract: Large language models have advanced web agents, yet current agents lack personalization capabilities. Since users rarely specify every detail

'PhyWorldBench': A Comprehensive Evaluation of Physical Realism in Text-to-Video Models

arXiv:2507.13428v3 Announce Type: replace-cross Abstract: Video generation models have achieved remarkable progress in creating high-quality, photorealistic content. However, their ability to accurate

PIDM-DP: Physics-Informed Diffusion with Dormand-Prince Integration for Chaotic System Identification and State Reconstruction across Multiple Dynamical Regimes

arXiv:2605.26619v1 Announce Type: new Abstract: Reconstructing continuous state trajectories of chaotic dynamical systems from sparse, noisy observations remains a fundamental open problem in nonlinea

PilotTTS: A Disciplined Modular Recipe for Competitive Speech Synthesis

arXiv:2605.27258v1 Announce Type: cross Abstract: Building state-of-the-art text-to-speech (TTS) systems typically demands millions of hours of proprietary data and complex multi-stage architectures,

PitchBench: Measuring Pitch Hearing in Audio-Language Models

arXiv:2605.26176v1 Announce Type: cross Abstract: Audio-language models (ALMs) are increasingly used in real-world applications that require understanding music, from music tutoring and transcription

Position: AI Safety Requires Effective Controllability

arXiv:2605.27117v1 Announce Type: new Abstract: AI safety is still largely framed as alignment: training models to follow human preferences, safety policies, and normative constraints. That framing ha

PRBench: A Standardized Probabilistic Robustness Benchmark

arXiv:2511.01724v3 Announce Type: replace Abstract: Deep learning models are notoriously vulnerable to imperceptible perturbations. Most existing research centers on adversarial robustness (AR), which

Pretrained Approximators for Low-Thrust Trajectory Cost and Reachability

arXiv:2605.26790v1 Announce Type: new Abstract: Low-thrust trajectory design relies heavily on repeated evaluations of fuel consumption and transfer feasibility, which require expensive optimal contro

PRISM: A Multi-Dimensional Benchmark for Evaluating LLM Peer Reviewers

arXiv:2605.26730v1 Announce Type: new Abstract: The rapid growth in submissions to machine learning venues has strained the scientific peer-review system and intensified interest in LLM-based automate

PRISM: Position-encoded Regressive Inverse Spectral Model for Multilayer Thin-Film Design

arXiv:2605.26502v1 Announce Type: new Abstract: The inverse problem of multilayer thin-film optical coatings design represents a complex combinatorial-continuous optimization challenge. We present PRI

Probing Cultural Awareness in LLMs: A Case Study of Cross-Culture Aesthetic Stylistics

arXiv:2605.27296v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in diverse cultural contexts, yet their ability to master aesthetic stylistics, i.e., the strateg

Prompt Injection Detection is Regime-Dependent: A Deployment-Aware Evaluation with Interpretable Structural Signals

arXiv:2605.26999v1 Announce Type: new Abstract: Prompt injection poses a critical threat to the safe deployment of large language models, yet existing detection approaches are typically evaluated unde

Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks

arXiv:2605.26243v1 Announce Type: new Abstract: Graph neural networks (GNNs) achieve strong performance on relational data, but real-world graphs are often distributed across organizations that cannot