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OpenAI, Anthropic, and other top AI labs are offering startups token credits and special promotions, as the AI labs seek lasting streams of enterprise revenue (Wall Street Journal)

Wall Street Journal: OpenAI, Anthropic, and other top AI labs are offering startups token credits and special promotions, as the AI labs seek lasting streams of enterprise revenue — Pitched battle for

applicationstechmeme

OpenAI just asked the US government to take a 5% stake in the company. The pitch: give every citizen a share in the profits of AI. The probl…

OpenAI just asked the US government to take a 5% stake in the company. The pitch: give every citizen a share in the profits of AI. The problem: AI has no profits. So the real plan is to give every cit

safetygary-marcus--x

OpenAI says GPT-5.6 Sol, along with Terra and Luna, will launch publicly on Thursday; a source says the US Department of Commerce cleared a broad rollout (Axios)

Axios: OpenAI says GPT-5.6 Sol, along with Terra and Luna, will launch publicly on Thursday; a source says the US Department of Commerce cleared a broad rollout — The U.S. Department of Commerce has g

model-releasestechmeme

Local Ai

OpenGlass: A Sensing-Computing Split Architecture for Local MLLM-Driven Real-Time Visual Assistance

arXiv:2607.03213v1 Announce Type: cross Abstract: We present OpenGlass, an open-source, privacy-oriented, local-first system for low-latency multimodal visual assistance, with a primary focus on blind

local-aiarxiv-cs-ai

Industry

OpenRouter: Chinese AI models have drawn 30%+ of token use by US companies each week since February 8, peaking at 46%, up from 11% over the previous 12 months (Kai Nicol-Schwarz/CNBC)

Kai Nicol-Schwarz / CNBC: OpenRouter: Chinese AI models have drawn 30%+ of token use by US companies each week since February 8, peaking at 46%, up from 11% over the previous 12 months — Chinese-built

industrytechmeme

OpenSIR: Open-Ended Self-Improving Reasoner

arXiv:2511.00602v4 Announce Type: replace Abstract: Recent advances in large language model (LLM) reasoning through reinforcement learning rely on annotated datasets for verifiable rewards, which may

OpenTinker: Separating Concerns in Agentic Reinforcement Learning

arXiv:2601.07376v2 Announce Type: replace Abstract: We introduce extsc{OpenTinker}, an open infrastructure for training large language model (LLM) agents with many LoRA-backed policies over shared exe

safetyarxiv-cs-ai

Operator-on-F complements value-equivalence: a planning-time diagnostic for latent world models

arXiv:2607.04464v1 Announce Type: cross Abstract: World-model evaluation for model-based reinforcement learning typically asks whether the learned model predicts reward and value well, which can leave

OpFlow: Learning Opportunity-Conditioned Choice Potentials for Robust OD Flow Prediction

arXiv:2607.03200v1 Announce Type: new Abstract: Origin-destination (OD) flow prediction is central to urban analytics, yet deep models trained on raw counts remain vulnerable to distribution shift. Th

applicationsarxiv-cs-lg

Agents

OptiAgent: End-to-End Optimization Modeling via Multi-Agent Iterative Refinement

arXiv:2607.05346v1 Announce Type: new Abstract: We propose OptiAgent, a multi-agent framework that, given a natural language description of an Operations Research problem, is able to output a solver-r

agentsarxiv-cs-ai

Agents

Optimal-Agent-Selection: State-Aware Routing Framework for Efficient Multi-Agent Collaboration

arXiv:2511.02200v2 Announce Type: replace Abstract: The emergence of multi-agent systems powered by large language models (LLMs) has unlocked new frontiers in complex task-solving, enabling diverse ag

agentsarxiv-cs-ai

Optimal Mixture-of-Experts Model Averaging for Conditional Generative Models

arXiv:2607.04360v1 Announce Type: cross Abstract: Conditional generative models have emerged as powerful tools for sampling from target conditional distributions, driving substantial advances across a

Optimality-Informed Neural Networks for Lunar Landing Trajectory Optimization

arXiv:2607.02741v1 Announce Type: cross Abstract: This paper develops an Optimality-Informed Neural Network (OINN) approach for the energy-optimal, free-final-time powered descent of a lunar lander fr

safetyarxiv-cs-lg

Optimizing Large Language Models for Causality Assessment in Pharmacovigilance: Developing a Performance Metric as Objective for Bayesian Hyperparameter Optimization

model-releasesarxiv-cs-cl

arXiv:2607.03704v1 Announce Type: new Abstract: Background: Growing individual case safety report (ICSR) volumes have intensified demand for scalable automated causality assessment. Large Language Mod

Optimizing ML Workload Partitioning between CPUs and CIM Accelerators for Heterogeneous Computing

arXiv:2607.05240v1 Announce Type: cross Abstract: Computing-in-Memory (CIM) accelerators execute Matrix-Vector Multiplications (MVMs) in memory, making them a compelling solution for Machine Learning

Order-based Causal Discovery for Multistage Processes

arXiv:2607.03971v1 Announce Type: cross Abstract: Causality has become an increasingly important tool for gaining a deeper understanding of complex systems. Among various causal analysis methods, caus

Agents

Organizational Memory for Agentic Business Process Execution

arXiv:2607.03228v1 Announce Type: new Abstract: LLM-based agents offer new opportunities for automating business process execution beyond the limits of rule-based systems. However, general-purpose LLM

agentsarxiv-cs-ai

OrthoReg: Orthogonal Regularization for Hybrid Symbolic-Neural Dynamical Systems

arXiv:2606.19145v2 Announce Type: replace-cross Abstract: Dynamical systems are fundamental to modeling the natural world, yet modeling them involves a persistent trade-off: manually prescribed mechan

OSF: On Pre-training and Scaling of Sleep Foundation Models

arXiv:2603.00190v2 Announce Type: replace-cross Abstract: Polysomnography (PSG) provides the gold standard for sleep assessment but suffers from substantial heterogeneity across recording devices and

Ossetic-COT: Designing a morphologically annotated corpus and morphological analyzer for Ossetic

arXiv:2607.04895v1 Announce Type: new Abstract: In this work we present the first morphologically annotated corpus for Iron Ossetic that conforms to the Universal Dependencies schema. The corpus inclu

Out-of-Distribution Detection in Molecular Complexes via Diffusion Models for Irregular Graphs

arXiv:2512.18454v3 Announce Type: replace Abstract: Predictive machine learning models generally excel on in-distribution data, but their performance degrades on out-of-distribution (OOD) inputs. Reli

Out-of-Distribution Generalization of Risk Aversion in Language Models

arXiv:2607.02755v1 Announce Type: cross Abstract: Training AIs to be risk-averse in resources could offer a failsafe in the event that AIs turn out misaligned. Misaligned but risk-averse AIs would ten

Out-of-distribution Neural Inference in Dynamical Ising Models

arXiv:2607.03039v1 Announce Type: new Abstract: Neural networks are increasingly used to infer hidden physical structure from dynamical observations, yet it remains unclear whether their out-of-distri

Overloading Large Vision-Language Models for Jailbreaking

arXiv:2607.02961v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) exhibit remarkable vision-language capabilities and are increasingly deployed in real-world applications such as pe

safetyarxiv-cs-cv

Oyster-II: Reinforcement Learning for Constructive Safety Alignment in Large Language Models

arXiv:2607.02914v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across diverse applications, yet ensuring their simultaneous safety, helpfulness,

safetyarxiv-cs-ai

P^3: Toward Versatile Embodied Agents

arXiv:2508.07033v2 Announce Type: replace Abstract: Embodied agents have shown promising generalization capabilities across diverse physical environments, making them essential for a wide range of rea

applicationsarxiv-cs-ro

PAGE: Towards Practical Human-level Gaze Target Estimation

arXiv:2607.04860v1 Announce Type: new Abstract: Gaze target estimation, the task of predicting where a person is looking in a scene, is crucial to understanding human attention and intent. It is a cha

researcharxiv-cs-cv

Paired Uterine Whole-Slide Images and Pathology Reports for Multimodal Computational Pathology

arXiv:2607.04020v1 Announce Type: new Abstract: Uterine diseases represent an important category of gynecologic pathology and require accurate histopathological assessment for diagnosis and treatment

researcharxiv-cs-cv

Panorama: Fast-Track Nearest Neighbors

arXiv:2510.00566v4 Announce Type: replace-cross Abstract: Approximate Nearest-Neighbor Search (ANNS) pipelines for high-dimensional neural embeddings spend the bulk of their query time in candidate ve

Parallelized Autoregressive Decoding for Omni-Modal Dense Video Captioning

arXiv:2607.02963v1 Announce Type: cross Abstract: Dense video captioning aims to generate temporally grounded descriptions of video events, benefiting both event-level video understanding and generati

Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models

arXiv:2512.14926v2 Announce Type: replace-cross Abstract: Focusing on low-resource languages is an essential step toward democratizing generative AI. In this work, we contribute to reducing the multim

Tutorials

Parametric Memory Decoding for Zero-Shot Routing in LoRA-Based External Parametric Memory

arXiv:2607.04118v1 Announce Type: cross Abstract: With the rise of parametric memory, LoRA-based External Parametric Memory (EPM) has emerged as a modular solution, but existing routing methods often

tutorialsarxiv-cs-ai

ParEVO: Synthesizing Code for Irregular Data: High-Performance Parallelism through Agentic Evolution

model-releasesarxiv-cs-lg

arXiv:2603.02510v2 Announce Type: replace Abstract: The transition from sequential to parallel computing is essential for modern high-performance applications but is hindered by the steep learning cur

Industry

Paris-based UMA, founded by ex-Tesla Optimus scientist Rémi Cadene and ex-Google DeepMind researcher Pierre Sermanet, demos its Northstar AI humanoid robot (Benoit Berthelot/Bloomberg)

Benoit Berthelot / Bloomberg: Paris-based UMA, founded by ex-Tesla Optimus scientist Rémi Cadene and ex-Google DeepMind researcher Pierre Sermanet, demos its Northstar AI humanoid robot — A former Tes

industrytechmeme

Parity-Aware Byte-Pair Encoding: Improving Cross-lingual Fairness in Tokenization

arXiv:2508.04796v3 Announce Type: replace-cross Abstract: Tokenization is the first -- and often least scrutinized -- step of most NLP pipelines. Standard algorithms for learning tokenizers rely on fr

safetyarxiv-cs-ai

PAST-TIDE: Prototype-Anchored Statement Tuning with Topic-Invariant Normalization for Stance Detection

arXiv:2607.04690v1 Announce Type: new Abstract: We introduce PAST-TIDE, our stance detection system addressing both subtasks of the StanceNakba Shared Task at NakbaNLP@LREC-COLING 2026. The main idea

Pathways of Visual Information Flow in Vision-Language Models

arXiv:2607.03358v1 Announce Type: cross Abstract: We study how visual information is routed in vision-language models (VLMs). Using causal patching on controlled synthetic and natural datasets, we fin

PCA of probability measures: Sparse and Dense sampling regimes

arXiv:2602.02190v2 Announce Type: replace-cross Abstract: A common approach to perform PCA on probability measures is to embed them into a Hilbert space where standard functional PCA techniques apply.

PDEFlow: Autonomous Agentic PDE Pipelines for Neural Operator Learning and Solver-Free Inference

arXiv:2607.05134v1 Announce Type: cross Abstract: We present PDEFlow, an autonomous agentic framework that turns user-level ODE and PDE descriptions into solver-backed neural-operator pipelines. The w

PDFBench: A Benchmark for De novo Protein Design from Function

arXiv:2505.20346v3 Announce Type: replace-cross Abstract: Function-guided protein design is a crucial task with significant applications in drug discovery and enzyme engineering. However, the field la

PedestrianDiffusion: Multimodal Generative Denoising and Dense State Estimation for Inertial Navigation

arXiv:2607.03349v1 Announce Type: cross Abstract: The accuracy of consumer-grade inertial navigation is bottlenecked by the stochastic noise of Micro-Electro-Mechanical Systems (MEMS). Traditional det

applicationsarxiv-cs-ai

Hardware

PEEK: Predictive Queue-Informed KV Cache Management for LLM Serving

arXiv:2607.02525v1 Announce Type: cross Abstract: We present PEEK, a lightweight scheduling and eviction framework for both online (streaming) and offline (batch) LLM serving; this paper focuses on th

hardwarearxiv-cs-ai

Perceiving Better Moments: Cover Frame Reselection and Enhancement for Live Photos with the Live2K Dataset

model-releasesarxiv-cs-cv

arXiv:2607.04151v1 Announce Type: new Abstract: Modern smartphones capture Live Photos, short video bursts surrounding a still image, offering a dynamic and engaging photographic experience. However,

Perceptual Flow Matching for Few-Step Generative Modeling

arXiv:2607.03524v1 Announce Type: new Abstract: We propose Perceptual Flow Matching (PFM), a simple yet effective framework for few-step generation in flow-matching models. Rather than performing velo

researcharxiv-cs-cv

Personalized Causal Recourse: A Human-In-The-Loop Approach

arXiv:2607.03425v1 Announce Type: new Abstract: Algorithmic recourse addresses the challenge of providing tailored recommendations to users affected by unfavorable machine learning decisions, in poten

pFedNavi: Structure-Aware Personalized Federated Vision-Language Navigation for Embodied AI

arXiv:2602.14401v2 Announce Type: replace-cross Abstract: Vision-Language Navigation VLN requires large-scale trajectory instruction data from private indoor environments, raising significant privacy

Local Ai

Phase-Preserving Trimodal Transformer for Tropical Forest Biomass Estimation Using Optical and PolInSAR Data

arXiv:2607.03663v1 Announce Type: cross Abstract: The accurate estimation of Above-Ground Biomass (AGB) in mature tropical forests remains a critical challenge in remote sensing, primarily due to the

local-aiarxiv-cs-ai

PhenoNEST: A Neuro-Symbolic Framework for Ontology-Aware Multimodal Plant Phenotyping and Trait Discovery

arXiv:2607.03245v1 Announce Type: new Abstract: High-throughput plant phenotyping generates valuable data that often remains trapped in unstructured text and isolated RGB images. To bridge this semant