Grok Build 被众人唾骂，结果老马把它开源了
Grok Build 前两天属实是站在风口浪尖上了。 先给大家先复盘一下整个事情。 一份针对 grok 0.2.93 的流量分析显示，Grok Build 在测试中上传了整个 Git 仓库，其中包括未
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Introducing OpenAI Presence
Introducing OpenAI Presence, a proven enterprise AI agent platform that helps organizations deploy trusted voice and chat agents for customer and internal workflows.
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Cross-Modal UAV Object Tracking: State-Aware Representation Learning and A Unified Benchmark
arXiv:2607.18768v1 Announce Type: new Abstract: Unmanned Aerial Vehicle (UAV) object tracking has emerged as a popular research field with broad practical applications. Modern UAVs are increasingly equipped with both visible light and thermal infrared sensors. However, due to constraints in communi
Posterior Samplings are Missing Modalities Generators for Medical Image Translation
arXiv:2607.18763v1 Announce Type: new Abstract: Magnetic resonance imaging comes in various modality contrasts that provide complementary anatomical and pathological information. Complete multimodal acquisitions are often unavailable due to time and protocol constraints. This leads to real-world da
Weakly Supervised Pathology-Informed Representation Learning for PET-Based Content Retrieval of Intra-Tumour Heterogeneity
arXiv:2607.18762v1 Announce Type: new Abstract: We propose a weakly supervised 18FFDG PET representation-learning framework for content based medical image retrieval, using H&E derived information during training while preserving PET-only inference. The proposed method was designed to use H&E deriv
Continual Video-MLLM Adaptation over Evolving Domains
arXiv:2607.18716v1 Announce Type: new Abstract: Video multimodal large language models have shown strong capability in video understanding, yet their adaptation to sequentially evolving domains remains underexplored. In real-world deployments, video data often arrives continuously from heterogeneou
MissingBench-Verified: Probing Vision-Language Models' Inability to Detect Missing Object Parts
arXiv:2607.18673v1 Announce Type: new Abstract: Vision Language Models (VLMs) are well known for hallucinating non-existent objects in images. Objects with missing parts present a unique challenge for VLMs, stemming from both real-world knowledge bias and the scarcity of such images in training dat
DeforM: Reasoning-Guided Physics-Aware Video Generation via Spatial-Temporal Masking
arXiv:2607.18664v1 Announce Type: new Abstract: Video generation models achieve high visual quality but often struggle to generate physics-aware videos. Unlike rigid-body motion, which can be described by explicit trajectories or formulas, complex deformation dynamics remain challenging to synthesi
DuSPiT: Dual-Branch Sub-Patch Pixel Diffusion Transformer
arXiv:2607.18510v1 Announce Type: new Abstract: Diffusion Transformers achieve strong image generation performance, but most operate in compressed latent spaces. Pixel-space diffusion avoids this information loss, yet existing approaches map each raw image patch to a single token, forcing one repre
Style over Substance: A Shortcut Audit of Emotion-Description Preference Evaluation
arXiv:2607.18508v1 Announce Type: new Abstract: Preference over model-generated emotion descriptions is emerging as a standard evaluation metric for multimodal emotion understanding, exemplified by the MER2026 MER-Prefer track on EmoPrefer. Such benchmarks assume that predicting the preferred descr
Surprise Forcing: What to Remember, When to Skip in Long Video Generation
arXiv:2607.18436v1 Announce Type: new Abstract: Streaming autoregressive diffusion makes minute-scale video synthesis practical, but its bounded context and fixed denoising schedule allocate resources uniformly across a highly non-stationary sequence. A rolling key-value cache forgets distant visua
Hazard or Anomaly? Evaluating VLMs for Understanding Dangers and Discrepancies
arXiv:2607.18325v1 Announce Type: new Abstract: Modern safety-critical systems increasingly rely on human-robot interaction to reduce disaster risk and support decision-making during emergencies. Vision-Language Models (VLMs) are promising for these settings because they can interpret complex scene
Interactive Training 2: Auditable Control Plane for Live Model Training
arXiv:2607.18314v1 Announce Type: new Abstract: Experiment trackers show how training is progressing, but changing a live run still usually requires trainer-specific code. We present Interactive Training 2, an open-source control plane for steering training through a shared protocol. Training appli
Agentic Calibration of Grey-Box Simulation Models: An LLM-Driven Alternative
arXiv:2607.18308v1 Announce Type: new Abstract: Calibration of grey-box simulation models is a constrained optimization problem in which model evaluations are expensive, the parameter space can be high-dimensional, and the search must respect plausibility constraints. Although the simulation code i
Uncertainty Quantification for AI-Driven Crash Simulation Surrogates: A Comparative Study of Monte Carlo Dropout and Deep Ensemble on Open-Source Bumper Beam Benchmark
arXiv:2607.18294v1 Announce Type: new Abstract: Machine learning surrogate models are increasingly being explored in engineering product development to augment simulation-driven design, offering near-instantaneous predictions that complement computationally expensive high-fidelity analyses. However
Reliability Scales Inversely: Bigger Models Compound Mistakes Faster via a Hidden Auto-Regressive Risk Regime
arXiv:2607.18292v1 Announce Type: new Abstract: As language models scale, answers start truer but degrade faster: scaling buys capability but erodes reliability. The knowledge-gap account - more data, retrieval, or scale - misses an auto-regressive risk residual that scale sharpens: the model commi
Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios
arXiv:2607.18289v1 Announce Type: new Abstract: Continual anomaly detection (CAD) studies how models can adapt to evolving data distributions while retaining performance on previously observed regimes. CAD benchmarks, however, depend critically on how tasks are defined, filtered, ordered, and valid
Verifiable Self-Evolution for Open-Ended Dialogue Skills via Future-Feedback Prediction
arXiv:2607.18973v1 Announce Type: new Abstract: Textual skills provide a lightweight way to improve frozen language-model agents, but their self-evolution normally requires a stable validation signal. Such signals are natural in mathematics or code, where an answer can be checked after it changes,
Reasoning Error from Known Fact: Step-Level Self-Consistency Group Relative Policy Optimization for LLM
arXiv:2607.18915v1 Announce Type: new Abstract: With the rapid advancement of large language models (LLMs), modern systems not only possess strong foundational capabilities and extensive knowledge, but can also solve complex problems via long, multi-step reasoning. However, as reasoning traces beco
CASE: Causal Alignment and Structural Enforcement for Improving Chain-of-Thought Faithfulness
arXiv:2607.18820v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning is widely used to improve both the performance and interpretability of large language models (LLMs), yet the generated reasoning may not faithfully support the final answer. We study this problem from a causal perspect
RF-Agent: A Practical Framework for Building Language Agents for RFIC Design
arXiv:2607.18772v1 Announce Type: new Abstract: Large language models (LLMs) have driven rapid progress in electronic design automation (EDA), yet their application to radio-frequency (RF) circuit design remains limited by the scarcity of domain-specific datasets and standardized benchmarks. We pre
Dual Attention Residuals
arXiv:2607.18730v1 Announce Type: new Abstract: Recent work extends Transformer residual pathways along two complementary axes: historical retrieval selects information from earlier depths, whereas multi-stream methods maintain multiple residual trajectories. These capabilities have largely been st
Fusion Embedding: A Unified Embedding Space for Text, Image, Video, and Audio
arXiv:2607.18666v1 Announce Type: new Abstract: A single embedding space that covers text, images, video, and audio lets one index serve every query a user can pose. Embedding models built on vision-language backbones now lead text/image/video retrieval benchmarks but lack audio entirely, while aud
LatentMT: Machine Translation with Latent Reasoning
arXiv:2607.18618v1 Announce Type: new Abstract: Latent-reasoning looped language models (LoopLMs) offer a different scaling path for machine translation (MT): instead of increasing parameter count or emitting explicit chain-of-thought tokens, they spend additional recurrent computation inside hidde
Stochastic Meta-Unlearning: Bridging Language Backbone and Multimodal Unlearning
arXiv:2607.18615v1 Announce Type: new Abstract: Machine unlearning for vision-language models (VLMs) remains underexplored. Unlike language models, VLMs combine a language backbone with visual components, which makes unlearning more complex. There is a surprising phenomenon when moving from single-
The Story Shapes the Agent: Narrative Priors in LLM Behavior
arXiv:2607.18566v1 Announce Type: new Abstract: Persona prompting is widely used to steer LLM agent behavior, yet the narrative framing of a task can matter more than the assigned persona. We isolate this effect through structural isomorphism, constructing three text-based investigation games that
Reasoning Fine-Tuning Induces Persistent Latent Policy States
arXiv:2607.18532v1 Announce Type: new Abstract: Reasoning-specialized language models show large performance gains over base models, yet the internal changes responsible for improved multi-step reasoning remain poorly understood. It is unclear whether reasoning fine-tuning improves local token-leve
Search-on-Graph-R1: Training Large Language Models to Search Knowledge Graphs with Reinforcement Learning
arXiv:2607.18481v1 Announce Type: new Abstract: Knowledge graph question answering (KGQA) requires navigating from topic entities to an answer several relations away. Recent methods prompt a frontier LLM to explore the graph through a retrieval tool, but their reliance on frontier-scale inference m
PathReportEval: A Systematic Benchmark for Pathology Report Generation
arXiv:2607.18448v1 Announce Type: new Abstract: Pathology report generation from whole-slide images (WSIs) is a rapidly growing multimodal learning problem, yet progress is difficult to measure because existing studies use heterogeneous datasets, model settings, visual encoders, and evaluation prot