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Smart search ranks by meaning as well as keywords (one row per story, last 45 days).
Weverse Data Leak Affects More Than 422,000 K-Pop Fan Accounts
HYBE-affiliated Weverse confirmed a leak affecting 422,584 K-pop fan accounts, exposing mainly internal identifiers unusable outside the platform.
Weverse, the fan platform operated by HYBE-affiliated Weverse Company, confirmed a security incident affecting 422,584 user accounts. The exposed information consisted mainly of internal identifiers that cannot be used outside the platform. The company was notified by the Korea Internet & Security Agency (KISA) on September 3.
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⚡ Weekly Recap: VMware Exploits, Windows 0-Day, MCP Attacks, Browser Hijacks and More
Weekly recap: suspected China-nexus APT exploited VMware vCenter CVE-2026-59310, Lazarus used Windows zero-day CVE-2026-68820, and macOS flaw dropped Monero miners.
The week's top stories included a suspected China-nexus APT exploiting VMware vCenter CVE-2026-59310 (CVSS 9.8), a directory-traversal flaw enabling arbitrary code execution, with backdoors, reverse SSH and Babuk-derived ransomware deployed, per QUIRSO. NCSC-NL reported active exploitation of macOS Screen Sharing flaw CVE-2026-65400 (CVSS 9.8) to install Monero miners on systems with port 5900 exposed. Lazarus Group exploited Windows zero-day CVE-2026-68820 (CVSS 7.0) in Operation Dream Job, delivering ForestTiger and Troy backdoors to defense and aerospace targets. GeoServer patched a critical SQL injection flaw exploited within hours of disclosure, and Jamf analyzed the new Amnesia Stealer for macOS with live browser control.
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Omniscience for the Masses: New Threats in the Metaverse's Democratized World Creation
First systematic assessment of 25 metaverse platforms reveals five novel world-creation attacks enabling covert user surveillance on Roblox, Horizon Worlds, and VRChat.
Researchers present the first systematic security and privacy assessment of metaverse world creators, surveying 25 platforms that support user-created worlds. They designed and implemented five novel attacks that abuse standard creator tools to violate spatial, visual, and auditory constraints, enabling covert user surveillance and manipulation without software vulnerabilities or developer-level privileges. Five previously proposed attacks were replicated using only standard world-creation features. The authors conclude that existing platform vetting, runtime protections, and creator policies are insufficient to mitigate malicious world creators.
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ExfilSquad Targets New Victims, Shares Data via Torrents
Extortion group ExfilSquad lists 13 new US, UK and Swedish victims and now distributes stolen data via peer-to-peer torrents.
ExfilSquad, a data-theft extortion collective that emerged in mid-2026, announced 13 new victims in the US, UK and Sweden with an August 5, 2026 negotiation deadline. The group skips ransomware, instead stealing data and threatening publication on a dark web leak site. Resecurity says its TTPs center on exploiting misconfigured Microsoft Dataverse, Power Pages, case management and CRM portals. The group now distributes stolen data through per-victim torrent trackers and web seeds, making leaks hard to contain.
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Making Knowledge Distillation Cheap Enough to Run at Scale
Hugging Face blog by Multiverse Computing describes techniques making knowledge distillation cheap enough for large-scale training.
A Hugging Face blog post from Multiverse Computing (CAI) presents methods for reducing the cost of knowledge distillation so it can be run at scale. The post is aimed at practitioners compressing large models into smaller, cheaper ones for production use.
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Microsoft Offers Up to $30,000 for Critical AI Flaws in Dynamics 365 and Power Platform
Microsoft expands AI bug bounty to Dynamics 365 and Power Platform, paying up to $30,000 for critical inference manipulation flaws.
Microsoft's bug bounty program offers up to $30,000 for critical 'Inference Manipulation' or 'Inferential Information Disclosure' bugs in Dynamics 365 and Power Platform, including Copilot Studio, AI Builder, Power Apps, Power Automate, and Dataverse. Payouts scale by report quality ($30,000/$20,000/$12,000 for critical) with important-severity AI flaws earning $6,000-$20,000, plus 20% multipliers for Dataverse privilege escalation and Plugin Sandbox escapes. Prompt injection affecting only the attacker, hallucinated execution, and system-prompt disclosure are excluded from scope.
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Quantization-Aware Healing: a compressed, 4-bit model that outperforms its full-precision original
Multiverse Computing details quantization-aware healing, producing a 4-bit compressed model that reportedly outperforms its full-precision original.
A Hugging Face blog post by Multiverse Computing's CAI team introduces quantization-aware healing for compressed models. The post claims the resulting 4-bit model outperforms the original full-precision model. No additional details or benchmarks were available in the provided text.
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Mathspace discloses data breach affecting over 1 million people
Mathspace disclosed a Metabase breach exposing data of 1,079,819 students, parents, and staff in Australia and New Zealand.
Mathspace confirmed attackers exploited a vulnerability in its self-hosted Metabase reporting system, gaining administrator access without legitimate login and downloading data on over 1 million people (1,079,819 total) in Australia and New Zealand. Access began August 10, data was downloaded August 27, and the theft was confirmed September 3, 2026. No credentials, academic records, or school-account links were exposed, but affected individuals are warned of targeted phishing. The incident joins a broader campaign against Metabase instances, including Trezor's provider ShipMonk, Framework, and Tally, linked to ShinyHunters via extortion emails and leak-site listings.
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Microsoft Offers $60,000 Bounty for Critical Cross-Tenant Vulnerabilities
Microsoft expands Dynamics 365 and Power Platform bug bounty, paying up to $60,000 for critical cross-tenant vulnerabilities.
Microsoft expanded its bounty incentives for Dynamics 365 and Power Platform, with qualifying rewards from $1,250 to $60,000. Critical cross-tenant vulnerabilities receive a 100% award multiplier and important ones 50%, while critical AI inference manipulation or inferential disclosure can earn up to $30,000. Scope covers Dynamics 365 apps, Power Apps, Power Automate, Copilot Studio, Power Pages, Dataverse, and selected on-premises products. Reports must be rated Critical or Important and submitted via the MSRC Researcher Portal.
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Feyospace-v1: How the Cyber Mercury Seven Trained Frontier Cyber Models
A seven-person independent team trained open-weight agentic cyber models via a data-centric post-training framework, ranking 10th on CyberGym and first at comparable scale.
The paper presents Feyospace-v1, a data-centric post-training framework combining five systems: Choulea (hidden reasoning analysis), SkyReal (teacher-sampling cost reduction), Hongzwang (bypassing teacher API restrictions), PSBreakup (restoring capabilities weakened by model merging), and Kreator (converting expert interventions into trainable reasoning). The data engine builds resettable coding, vulnerability, CTF, kernel-history, full-exploit, firmware, and device-backed environments, retaining only execution-verified and evidence-audited trajectories, yielding 164,269 trajectories for long-context supervised fine-tuning. Three checkpoints improve over their starting models by an average of 23.76% on the full CyberGym suite and 10.49% across pooled CTF suites. As of September 1, 2026, Feyospace-s1 achieves a 63.24% verified success rate, ranks 10th on the official CyberGym leaderboard, and all three checkpoints rank 1st among models at comparable parameter scales.
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Datamimic – don't let your coding agent invent its own test world
Datamimic is an open-source test data generation tool aimed at keeping coding agents from inventing their own test fixtures.
A Hacker News discussion (40 points) highlights Datamimic, an open-source rapiddweller GitHub project for generating realistic synthetic test data. The tool targets AI coding agents, aiming to prevent them from fabricating their own inconsistent test worlds. Only the repository link was shared, so details are limited.
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18 ways to check whether data can be trusted for AI
ETSI published TR 104 180 defining 18 data quality metrics, including fairness and privacy, to assess whether datasets are fit for AI.
ETSI's technical report TR 104 180 defines 18 metrics across four groups - intrinsic quality, usability and lineage, fairness, and privacy - each with calculation formulas, plus an open-source tool that scores datasets. Testing on an aircraft engine sensor dataset and a US census dataset revealed a roughly threefold gender gap in high earners (about 31% of men versus 11% of women) and two privacy failures: re-identification via age, race, sex, and country, and sensitive fields stored in plaintext. The working group included Sejong University, EGM, TTA, Daejeon University, and CNIT.
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The complex corporate web behind a $3.2 billion AI data center
Ars Technica probes diffuse accountability behind TeraWulf's $3.2B Lake Mariner AI data center after a June fire exposed safety and job gaps.
A June fire at the Lake Mariner data center in Somerset, New York exposed missing alarms, a nonfunctioning suppression system, and dry hydrants, highlighting how responsibility is split across TeraWulf (owner-operator), Fluidstack (operator), Google (lease guarantees and equity warrants), and Anthropic (compute customer). The article details local concerns over the gap between promised 165 permanent jobs and a projected 35-40, socialized grid costs, and Governor Hochul's moratorium on hyperscaler development. Anthropic's February 2026 pledge to cover electricity price increases applies to the site but leaves other commitments unverified.
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CUA-Universe: A Scalable and Dynamic Environment for Hybrid GUI+CLI Agents
Researchers introduce CUA-Universe, a pipeline turning real desktop software into hybrid GUI+CLI agent environments, lifting a 9B model's OSWorld success rate.
CUA-Universe is an environment-to-data pipeline that converts real desktop applications into hybrid GUI+CLI environments, scaling to 16 applications via App-Forge, Task-Weave, and Path-Steer. Training on its harvested trajectories shifted a 9B model toward effective GUI+CLI orchestration, yielding +39.3 points on CUA-Verse, +16.8 points success rate on OSWorld, and +7.84 points on OSWorld-MCP while cutting steps and tokens by up to 57% and 60%. The work addresses the scarcity of scalable hybrid environments for computer-use agents.
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Data access: the hidden cost of security vendor lock-in
Elastic compares SIEM data egress cost, latency, and fidelity across CrowdStrike, Microsoft, Google, and Splunk, arguing vendors engineer lock-in.
Elastic Security Labs published an opinion piece comparing how major SIEM and security vendors handle data egress, based on each vendor's public documentation as of September 2026. It rates CrowdStrike Falcon Data Replicator and Palo Alto Networks XSIAM Event Forwarding as restricted (paid add-ons with batch delays), Microsoft as partially open, Splunk as open, and Elastic as open with no export license. The piece argues frictionless ingestion paired with licensed or delayed egress is an intentional lock-in business model, and cites CrowdStrike's 2026 Global Threat Report eCrime breakout time of 29 minutes to argue real-time telemetry access is now essential.
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Security Data Isn’t the Problem. Security Context Is.
Horizon3 blog argues security context, not data volume, is the SOC bottleneck, promoting its NodeZero integration with CrowdStrike Falcon Next-Gen SIEM.
Horizon3.ai published a vendor blog explaining how its NodeZero Proactive Security Platform integration with CrowdStrike Falcon Next-Gen SIEM brings validated exposure findings into existing security operations workflows. The post argues SOCs are now limited by confidence rather than visibility, needing context to decide which issues matter. It cites a global chemical manufacturer that validated exploitable exposures with NodeZero before completing a $2 billion merger.
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Fortunate Recall: Ontology-Driven Memory Lifecycle Management for Persistent Coherence in LLMs
Fortunate Recall introduces ontology-based lifecycle policies for LLM memory, cutting confabulation roughly in half (e.g., 45.1% to 22.4%) versus Mem0.
Fortunate Recall (FR) is a composable policy layer that classifies personal facts into a 10+1 behavioral ontology and applies category-specific lifecycle rules including differential temporal decay, slot-key supersession, event-time validity, and retrieval routing. FR-Bank scores 76.9% on the new 516-question LifecycleBench, ahead of Mem0, A-MEM, Memory-R1, and MemoryOS (61%-70.5%), and 75.2% on LongMemEval-S. End-to-end, confabulation drops from Mem0's 45.1% to 22.4% over answered queries, with the ranking replicating on open-weight Kimi K2.5 and transferring to the independent BEAM benchmark (46.8% vs 32.9%).
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Towards Scalable and Cost-Efficient Vulnerability Detection: A Study on Automatic Query Generation
A study finds LLM-synthesized CodeQL queries improve average F1-score by 82% over baseline queries, offering scalable vulnerability detection versus direct LLM scanning.
Researchers conducted an empirical study evaluating whether LLMs can synthesize executable CodeQL queries from National Vulnerability Database vulnerability data. LLM-generated queries significantly enhanced baseline CodeQL suites, yielding an 82% improvement in average F1-score across a diverse set of real-world vulnerabilities. A cost-benefit analysis shows direct LLM-based scanning of entire repositories is often computationally and financially prohibitive, while LLM query synthesis offers a scalable and cost-effective alternative for large-scale vulnerability detection.
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Grouped Value Attention: Efficient KV Caching via On-Demand Key Reconstruction
Grouped Value Attention stores grouped values and reconstructs content keys via a learned linear map, cutting KV-cache size about 45-47% versus GQA.
GVA stores only grouped values and reconstructs content keys with a learned linear map absorbed into the query at decode time, while a small shared decoupled RoPE channel preserves positional information via a separately cached positional key. At 350M parameters trained on 30B FineWeb-Edu tokens, the 16-dimensional positional variant scores 44.18 average accuracy across five tasks versus 44.36 for GQA and 43.88 for MLA. Custom decoding kernels are in development with an open-source release planned.
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DataFlex-RL: An Evaluation Platform for RLVR Data Policies
DataFlex-RL benchmark of 13 RLVR data policies on Qwen2.5-7B finds none reproducibly beats uniform sampling under matched GRPO training.
DataFlex-RL is an evaluation platform comparing rollout-selection, reweighting, and mixture data policies for RLVR under a common GRPO recipe. Across 13 configurations and 12 matched seeds with Qwen2.5-7B-Base on 12 math, logic, and science benchmarks, uniform GRPO improved domain-balanced accuracy by 7.76 points, but no alternative policy achieved a statistically significant improvement. A corrected 12-seed Llama-3.1-8B-Base extension found no consistent winner, and math-heavy evaluation summaries were negatively correlated (-0.33) with domain-balanced summaries.
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AI Infra Summit: NVIDIA Vera Rubin and DSX Platform Advancements Showcase Energy Efficiencies of Optimizing Tokens Per Watt for AI Factories
At AI Infra Summit, NVIDIA showcased Vera Rubin and DSX gains up to 1.4x tokens per megawatt, plus Annapurna, d-Matrix, and Pinterest partnerships.
Ian Buck's AI Infra Summit keynote before 8,000+ attendees emphasized validated agentic tokens per megawatt as the emerging AI infrastructure metric. Announcements include Amazon's Annapurna Labs collaborating on NVHBM custom high-bandwidth memory, d-Matrix integrating NVLink Fusion with Raptor XPUs, and Pinterest using Blackwell plus Dynamo inference software for conversational visual discovery. Lambda reported 23% better performance per watt with DSX MaxLPS on Blackwell servers, running 19 nodes on a 16-node power budget. NVIDIA says DSX MaxLPS combined with Groq 3 LPX on Vera Rubin NVL72 targets up to 35X token throughput per megawatt versus GB200 NVL72 for 2-trillion-plus-parameter models.
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LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligence
LimiX-2 scales Contextual Mechanism Networks pretrained via context-conditional masked modeling, beating tabular foundation models on TabArena, TALENT, and BCCO benchmarks.
LimiX-2 is a new tabular model in the LimiX family, developed through model and data scaling guided by previously established scaling laws under the Contextual Mechanism Networks (CMNs) paradigm. It is pretrained with Context-Conditional Masked Modeling (CCMM) on synthetic datasets generated by structural causal models spanning diverse graph structures, functional mechanisms, and observation processes. It outperforms dataset-specific models and tabular foundation models on TabArena, TALENT, and BCCO, and its feature attention encodes direct causal relationships, enabling accurate causal skeleton recovery.
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OracleZoom: On-Policy Self-Distillation Inspired Reference-Constrained Recursive Image Super Resolution
OracleZoom enables recursive extreme-scale image super-resolution via reference-constrained on-policy distillation, reducing hallucinations at deep zoom scales.
OracleZoom tackles recursive super-resolution, where repeated feeding of predictions back into the same model leaves deeper-scale outputs unsupervised as required source resolution grows geometrically. The framework trains on its own trajectory while carrying the last ground-truth evidence beyond the supervision boundary, combining direct and cross-scale supervision, a no-reference quality objective, a KL-constrained pretrained latent prior, and EMA consistency. Across seven datasets it achieves state-of-the-art SR quality across zoom scales, averaging 0.713 CLIPIQA with larger gains at deeper scales and significantly reduced hallucinations. Code, data, and models are publicly released.
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Microsoft’s Project Zenith puts large AI models directly on developer PCs
Microsoft's Project Zenith delivers a ready-to-code Windows 11 experience running 30B+ parameter AI models locally on 64GB+ unified-memory PCs, starting with AMD Ryzen AI Halo.
Project Zenith is a preconfigured Windows 11 developer experience for PCs with at least 64 GB of unified memory and 250 GB/s or higher memory bandwidth, capable of running AI models with more than 30 billion parameters locally without metered cloud tokens. First systems are powered by AMD Ryzen AI Halo, with additional OEM and silicon partner devices expected in coming months. The environment ships with WSL and Linux containers, pinned developer tools, and day-one AI agent security features including OS-enforced agent identity and containment through Microsoft Execution Containers (MXC).
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Mathspace breach exposes data on over a million students and parents
Mathspace confirmed attackers exploited an unpatched Metabase SQL injection flaw to steal personal data of 1,079,819 students, parents, and staff in Australia and New Zealand.
Attackers accessed Mathspace's self-hosted Metabase reporting system without legitimate login, with unauthorized access dating back to 10 August 2026 and data downloaded on 27 August. Exposed data includes names, usernames, email addresses, country, and account metadata; no passwords, academic records, SSO tokens, or API credentials were taken. Framework, Tally, and Kilo Code disclosed similar breaches via the same Metabase SQL injection flaw in August 2026.
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AI labs have a data trust problem that their policies haven't solved
Nvidia, Palantir, and Booz Allen restrict Anthropic's Fable over data-retention distrust, exposing gaps in AI labs' customer data policies.
Nvidia limits Anthropic's Fable to non-sensitive work and runs its own Nemotron models for internal tasks, while Palantir blocks Fable deployment until Anthropic grants irrevocable zero-data-retention guarantees, and Booz Allen bans it for proprietary cybersecurity work. John Schulman and researcher Sarah Hooker explain that labs can still extract customer IP from metadata, user traces, and synthetic data even under zero data retention. The trust crisis crystallized around Tristan Buckmaster's accusation that OpenAI's Codex absorbed his Navier-Stokes drafts, though OpenAI later stated his prompts could not have influenced its model.
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WarmBloodAban/Minimax-h3_Singularity — new model trending #22 on Hugging Face
Community fine-tune Minimax-h3_Singularity enhances MiniMax-H3 video generation with HDR quality, distant face restoration, and improved motion, trending #22 on Hugging Face.
Minimax-h3_Singularity is a community fusion fine-tune of the MiniMax-H3 multimodal video generation model, built from multiple checkpoints and refined with pruning and weight optimization. It supports Text-to-Video, Image-to-Video, Reference-to-Video, and Video-to-Video workflows in ComfyUI, and claims improvements in HDR clarity, distant face restoration, motion fluidity, and fantasy VFX. The authors recommend pairing it with the minimax_h3_ref2v_turbo_4step_v0.1 LoRA for four-step accelerated inference, and an online demo is available via RunningHub.
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DataGrout helps enterprises control AI usage, governance and LLM costs
SelectHub launched DataGrout, an LLM cost and governance platform combining dynamic context pruning, symbolic inference and MCP gating, claiming about 60% token reduction.
SelectHub launched DataGrout, an LLM inference optimization and AI governance platform combining dynamic context pruning, a symbolic inference layer, and an MCP gateway with per-call token and cost monitoring. Early tests claim roughly 60% token reduction on data-intensive ERP and CRM integration tasks without accuracy loss. The platform connects via its Conduit SDK, MCP or JSON-RPC, offers its own MCP servers for SaaS apps such as Salesforce, SAP and ServiceNow, and supports bring-your-own-key LLM access or gateways like Amazon Bedrock and Kong.
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Switzerland's Federal Government Is Replacing Microsoft on 3k Computers
Switzerland's Federal Chancellery launched a pilot replacing Microsoft 365 with open-source openDesk on 3,000 federal workstations by end of 2027.
Switzerland launched a CHF 9 million pilot to migrate 3,000 federal workstations (about 7% of the federal workforce) from Microsoft 365 to the German open-source openDesk suite, targeting completion by end of 2027. The move follows a successful 172-employee proof of concept (PoC BOSS) and the 2024 EMBAG open-source law, driven by digital sovereignty concerns about foreign access to Swiss government data. The Swiss Army's Cyber Command plans a faster migration, replacing Microsoft 365 with openDesk by October 2026, while Microsoft is investing over CHF 325 million in Swiss AI and cloud infrastructure.
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Molecular Déjà Vu: Digit-Level Retrieval of Published Values in Frontier Language Models
Audit of 22 frontier models finds widespread verbatim retrieval of published molecular property values, with higher reasoning increasing recall of memorized numbers.
An arXiv audit tests 22 frontier LLMs across 12 molecular regression benchmarks for verbatim retrieval of published values. More than 50% of the LLMs show verbatim retrieval on five datasets, and identical experiments are flagged 89% more often at a high reasoning level than at the lowest one. Suppressing retrieval moves model prediction errors closer together in relative terms, suggesting predictive capability is not determined solely by memorized values.
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Mathspace Data Breach Exposes Over 1 Million People
Mathspace breach exposed data of 1,079,819 Australian and New Zealand users via exploited Metabase zero-day CVE-2026-72898; ShinyHunters claimed responsibility.
Mathspace disclosed a breach affecting 1,079,819 students, teachers, staff, and parents in Australia and New Zealand. Attackers exploited the Metabase SQL injection zero-day CVE-2026-72898 (CVSS 10), patched August 6, and accessed Mathspace's self-hosted instance from August 10; ShinyHunters claimed the Metabase hacks. Exposed data includes names, usernames, emails, and login dates; no passwords, academic records, or credentials were taken.
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AlayaVista: Streaming World Modeling from Panoramic States to Perspective Video
AlayaVista is a camera-controllable streaming video world model that decouples panoramic scene evolution from perspective synthesis, trained on a 1,318-hour 4K dataset.
AlayaVista builds a 360-degree scene prior from a single perspective image, evolves it as a camera-conditioned panoramic latent state, and maps it to perspective video via a latent viewport renderer plus a perspective refiner. Chunk-autoregressive generation and few-step distillation enable efficient streaming. The authors introduce MUGEN, a real-world panoramic video dataset with 1,318 hours of at-least-4K video and rich semantic and geometric annotations.
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Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization
ModerationBench shows foundation models can nearly triple Bluesky's moderation F1 (0.60 vs 0.22), with instruction- and example-driven guidance performing comparably.
Researchers built ModerationBench, a new benchmark of 4,000 manually annotated in-the-wild posts from Bluesky, to test whether foundation models can reliably operationalize content moderation policies. They systematically compare instruction-driven guidance (reasoning from policy precepts) with example-driven guidance (generalizing from precedents) for Vision-Language Models. Both paradigms achieve comparable peak effectiveness, and foundation models nearly triple the F1 of Bluesky's deployed moderation system on Random Posts (0.60 vs 0.22).
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Miles v0.1: Production-Level Post-Training
Radix Ark open-sources Miles v0.1, a full-stack RL post-training framework demonstrated with asynchronous agentic RL on GLM-5.2 744B-A40B across 64 GB300 GPUs.
Miles v0.1 is a full-stack, open-source system for frontier-scale reinforcement-learning post-training, built on slime with rollout engines on SGLang and trainers supporting NVIDIA Megatron-LM and PyTorch FSDP backends plus three weight-synchronization transports. It supports full-parameter RL, LoRA RL, on-policy distillation, supervised fine-tuning, true-on-policy rollout-training alignment, and extends to diffusion models. The end-to-end case study ran fully asynchronous agentic RL on GLM-5.2 744B-A40B for terminal-use coding tasks on 64 NVIDIA GB300 GPUs with a median step time of 263 seconds over the first 30 measured steps. The code is open-sourced on GitHub.
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