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AI for everyone in every language
Google says its AI now spans 300+ languages reaching 7 billion people, unveiling Gemini 3.5 Transcribe, Live Translate, and TranslateGemma models.
Google announced its technologies now support more than 300 languages spoken by 7 billion people, 86% of the global population. Gemini 3.5 Live Translate powers real-time spoken translation across 70 languages and 2,000+ language pairs, while Gemini 3.5 Transcribe is its most precise speech-to-text model. Its Universal Speech Model was trained on 12 million hours of audio using cross-lingual transfer learning, and TranslateGemma is a family of lightweight open translation models covering 55 languages that run on-device. Open-data partnerships include WAXAL covering 27 Sub-Saharan African languages and Project Vaani with 30,000+ hours of speech across 109 languages.
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HypoEvolve: Genetic Algorithms Enable Multi-Agent LLMs to Discover Scientific Hypotheses
HypoEvolve couples a generational genetic algorithm with specialized LLM agents to generate drug-repurposing hypotheses, beating six baselines on DepMap selectivity (0.171 vs 0.115).
HypoEvolve coordinates specialized LLM agents through a generational genetic algorithm in which scientific judgments and new proposals reshape a hypothesis population. Evaluation centers on drug repurposing, linking mechanistic explanations to target-level biological claims assessed via external measures adapted from DepMap and Open Targets. Across 34 cancer types, HypoEvolve scores highest against six baselines on both measures, with DepMap selectivity of 0.171 versus 0.115 for the strongest baseline, and gains generalize to held-out cancer types.
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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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CMA-OT: Hierarchical Expert Supervision for Dance-to-Music Generation
CMA-OT aligns a music generator's latent features with hierarchical expert representations via curriculum learning and scale-aware optimal transport, improving dance-to-music quality.
CMA-OT introduces curriculum-guided multi-scale representation alignment with scale-aware optimal transport for dance-to-music generation. An external music expert provides hierarchical supervision over the generator's latent features, progressively transferring musical knowledge for stable representation learning. The optimal transport mechanism handles temporal mismatch and semantic variation across expert scales. Experiments on two datasets show state-of-the-art rhythmic synchronization, perceptual quality, and overall music generation.
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IntentFuzz: A Protocol-Aware Fuzzer for Automated Invariant Violation Detection in Intent-Based Cross-Chain Bridges
IntentFuzz protocol-aware fuzzer recovers bridge structure from unannotated Solidity and confirmed 22 invariant violations across 24 real-world deployments.
IntentFuzz formalizes a taxonomy separating invariant violations from settlement exposures in intent-based cross-chain bridges, then recovers a bridge's intent structure and deposit/fill function roles from unannotated Solidity source. It classified deposit and fill functions with 100% recall and 82% combined precision, and achieved 100% recall and precision on 23 planted-bug mutants. Across 24 real-world deployments it confirmed 17 genuine invariant violations with heuristic-only input generation, rising to 22 with its LLM-assisted tier, spanning eight vulnerable GitHub repositories with findings reproducible against public deployed bytecode.
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Why the current tech backlash feels different
The Verge's Decoder mailbag discusses the current tech backlash, arguing AI hype overstates verifiability outside software engineering.
Nilay Patel's Decoder mailbag episode addresses listener feedback on the widely discussed 'software brain' essay. He argues AI hype is concentrated on software because code is verifiable through compilation, while domains like drug discovery, math and science lack equivalent verifiability. The episode also touches on AI backlash, surveillance, data centers and upcoming midterm coverage.
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On Identifying Sound Conditions for Frontrunning Resistance
Researchers formally define smart-contract frontrunning resistance, showing 55% of 393 audited vulnerabilities escape state-of-the-art detection, and find two undisclosed Ethereum flaws.
The paper gives the first formal definition of frontrunning vulnerability for smart contracts, grounded in how honest users interact with contracts rather than contract code alone. In a large-scale study of 287 smart contract audits, 55% of the 393 vulnerabilities reported by leading auditors fall outside the scope of state-of-the-art dynamic detection criteria. The authors present a sound algorithm for synthesizing secure interaction conditions and apply it to real-world contracts, uncovering previously undiscovered vulnerabilities in two Ethereum contracts.
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Introducing ChatGPT for Financial Services
OpenAI launches ChatGPT for Financial Services, pairing built-in market data with GPT-6 Astra for banking research workflows.
OpenAI introduced ChatGPT for Financial Services, a tailored ChatGPT Work experience shaped by design partners Morgan Stanley and Evercore, targeting investment banking and equity research. It bundles premium data from Daloopa, PitchBook, LSEG News, and Crunchbase hosted on OpenAI infrastructure with granular citations, optimized MCP connectors for S&P Global and FactSet, and 50+ connectors, plus planned entitlement integrations with S&P Capital IQ, LSEG, MSCI, Dow Jones Factiva, and Moody's. It runs GPT-6 Astra, which OpenAI claims is state of the art in information retrieval, financial reasoning, and artifact generation, and includes enterprise controls such as SAML SSO, SCIM, role-based access, and no default training on firm data.
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The Machines Are Calling: Measuring Automated and Synthetic Voices in Unwanted Inbound Calls
Voice honeypot measurement finds at least 26.9% of unwanted US inbound calls open with machine voices, 13.1% with fresh synthetic speech.
An interactive voice honeypot using language-model personas on real US numbers recorded 10,987 calls over 66 days, following the FCC's February 2024 ruling that AI-generated voices fall under the TCPA. Of 7,233 greeted calls, 13.8% opened with recordings replayed from other calls and 13.1% with fresh audio labeled synthetic, with replays making up 45% of the detector's flagged rate. Synthetic openings concentrated in lead-generation spam (33.8%) rather than fraud (21.1%), and only 0.44% of calls disclosed automation. Prevalence tracked how long a bait number had circulated, and campaigns outlasted their numbers, with one synthetic voice serving nine campaigns.
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T1: Terminal Agent Reinforcement Learning for Long-Horizon Tasks
T1, a 122B MoE terminal agent trained with reinforcement learning, reaches 64.0% on Terminal-Bench 2.1, surpassing GPT-5.4 and GLM-5.1 on long-horizon tasks.
T1 is a 122B mixture-of-experts model trained with reinforcement learning to operate a real shell in a cloud sandbox for up to 300+ tool-call turns per task, rewarded by executing each task's own verifier. The recipe combines aggressive warm starts, dense process rewards, TITO construction, and rollout routing replay, cutting the training-to-inference log-probability difference from 0.021 to 0.013 with zero token drift. Training used an out-of-distribution corpus disjoint from Terminal-Bench 2.1. Post-training raised the base model from 43.8% to 64.0% resolved on Terminal-Bench 2.1 and 27.9% on Long-Horizon Terminal Bench.
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HelmGuard Raises $7.3 Million for Agentic GRC and Security
Agentic GRC and security startup HelmGuard raised a $7.3 million seed round co-led by Infinity Ventures and Frontline.
HelmGuard, a UK-based startup founded in 2024 by former Palantir executive John Daley (CEO) and Jack Miller (CTO), announced a $7.3 million seed round co-led by Infinity Ventures and Frontline, with participation from FinTech Collective, Stage 2 Capital, and Entrepreneurs First. The company builds agentic governance, risk and compliance (GRC) software that collects risk signals directly from integrated source systems and uses AI agents to automate risk management, assurance, and control gap assessments. It claims the platform cuts assessment cycles to hours by unifying fragmented compliance and security data.
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Proofpoint SOC Analyst Agent Uses OpenAI Cyber Models
Proofpoint launched its SOC Analyst Agent in private preview, using OpenAI Daybreak models to automate security investigations with human-controlled remediation, GA expected end of Q3 2026.
The SOC Analyst Agent uses OpenAI Daybreak cyber models to enable natural-language investigations across Proofpoint alerts, logs, DLP events and user risk signals, and to automate recurring threat hunts, data security investigations and escalation reporting. It is currently in private preview with general availability expected by the end of Q3 2026, and it does not independently make account changes or take remediation actions. Proofpoint joined the OpenAI Daybreak Defense Network in June 2026 and is exploring additional uses for the models in threat research, data security and AI security workflows.
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TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model
Researchers present TANGO, a whole-body vision-language-action model enabling humanoid robots to traverse cluttered spaces from language instructions.
TANGO predicts 29-DoF joint-space actions from egocentric RGB observations and natural-language instructions for whole-body humanoid navigation, going beyond 2D path planning. It is trained entirely in simulation using global path planning, kinematic whole-body motion generation, obstacle-aware motion editing, and RL-based tracking. The model reports state-of-the-art simulation performance and was deployed zero-shot on a Unitree G1 humanoid without any real-world navigation training data.
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ReCite: Agentic Reasoning for Faithful Citation
ReCite is an agentic citation framework using claim-level reasoning and verification, outperforming large generative models in strict citation accuracy.
ReCite is a decoupled agentic framework that orchestrates location perception, intent-aware query planning, and reflective verification for citation recommendation. Trained on synthesized reasoning trajectories, the agent verifies claim-evidence consistency and triggers self-correction loops when retrieved candidates lack logical support. Experiments show the lightweight framework outperforms state-of-the-art massive generative models in strict citation accuracy, addressing misattribution where cited papers are real but logically unsupportive.
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Measuring the Security of the Evolving Software Supply Chain: a Research Agenda
Researchers propose a unified cross-ecosystem measurement agenda for software supply chain security, targeting dependency modeling and AI-generated dependency patterns.
The paper argues that existing quantitative measurement and vulnerability management approaches for software supply chain security are fragmented and ecosystem-specific, limiting comparable risk assessments. It lays out a research agenda starting with a Systematization of Knowledge to expose gaps in dependency modeling, transitive dependency treatment, and real-world exploitability of vulnerabilities. It further warns that AI-assisted development with coding LLMs will create dependency patterns not captured by traditional Software Composition Analysis tools, motivating a rethink of dependency modeling.
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GTIG AI Threat Tracker: From Prompting to Autonomy – The Evolution of Adversarial AI
GTIG's Q2 2026 tracker shows adversaries adopting agentic AI workflows, including credential harvesting in under six hours and supply chain attacks by UNC6780.
Google Threat Intelligence Group's Q2 2026 report documents adversaries moving from basic prompting to agentic AI workflows and automation, including a cloud compromise followed by agent-enabled mass credential harvesting executed in under six hours. It tracks financially motivated actor UNC6780 (TeamPCP) conducting large-scale open source supply chain compromises across PyPI, npm, and Docker Hub since March 2026, deploying credential stealers. The report also highlights growing targeting of proprietary AI models, source code, prompts, and API credentials, plus LLMJacking practices where adversaries steal developer credentials or hijack cloud infrastructure to run unauthorized AI workloads.
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Seeing is Not Believing: Breaking the Physical-to-Digital Trust Boundary in Robotics
Researchers show a single ROS 2 environment variable lets attackers inject fake telemetry and hijack robots while spoofing downstream remote attestation.
A pre-built hook loaded via one modified environment variable covertly intercepts and injects both telemetry and control signals before publication in ROS 2, breaking the physical-to-digital trust boundary in multi-robot task handovers. Attackers can also distribute compromised third-party Docker containers and auxiliary tools embedding the hooks. On a physical Franka Emika arm running Secure ROS 2, the attack injects fabricated telemetry in real time with roughly 3 ms jitter and achieved an 87% success rate even against an AI-based detector. Findings were responsibly disclosed to the ROS 2 development team.
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SynthGait-19K: A Physically Grounded Synthetic Video Dataset for Gait Parameter Estimation
Researchers release SynthGait-19K, a synthetic video dataset with 19,272 walking videos for training gait parameter estimation models.
SynthGait-19K is a physically grounded synthetic video dataset built from 6,427 MoCap sequences of 437 subjects, yielding 19,272 walking videos with SMPL motion and annotations for six gait parameters. The authors introduce Gait2Vid, a pipeline that unifies heterogeneous MoCap recordings and synthesizes RGB videos under controllable viewpoints, validating gait events against force-platform measurements. Using the dataset they benchmark direct RGB, pose-based, biomechanical, and human-mesh-recovery approaches, and introduce GaitXFormer as a direct RGB reference model. Findings show synthetic supervision transfers to real video, while spatial gait parameters are more sensitive to visual domain shift.
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TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model
Researchers introduce TANGO, a whole-body vision-language-action model enabling zero-shot language-guided humanoid navigation on the Unitree G1 robot.
TANGO addresses humanoid navigation in cluttered indoor environments by predicting 29-DoF joint-space actions directly from natural-language instructions and egocentric RGB, rather than 2D path planning. It is trained entirely in simulation via a pipeline combining global path planning, kinematic whole-body motion generation, obstacle-aware motion editing, and RL-based tracking. In simulation it achieves state-of-the-art vision-language navigation performance and transfers zero-shot to a Unitree G1 humanoid without any real-world navigation data.
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IFM Releases K2 Horizon: Six Apache 2.0 Models From 0.9B to 375B
MBZUAI's IFM released K2 Horizon, six Apache 2.0 models (0.9B-375B) with open training data, code, and checkpoints, claiming the largest fully open-source launch.
The Institute of Foundation Models (IFM), launched by MBZUAI, released K2 Horizon: six Apache 2.0 models (0.9B, 3.7B, 7B, 32B, 36B-A4B, 375B-A23B) shipping with the ~20-trillion-token pretraining corpus, intermediate checkpoints, training code, and logs, which IFM calls the largest fully open-source launch in AI history. The 375B-A23B scores 70.2 on Terminal-Bench 2.1 and 87.3 on GPQA Diamond; the 7B model posts 70.6 on SWE-bench Verified. New techniques include MoVA, which extends MoE routing into attention (36B total, ~4B active), and Uno, a LoRA adapter giving roughly 3x lossless decoding speedup. IFM's own reward-hacking audit re-scored 375B-A23B from 70.2% to 66.9% after flagging 24 of 712 Terminal-Bench trials.
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Agentic Visual Generation: From Generative Models to Agentic Control
Researchers propose an L0-L4 control taxonomy for agentic visual generation, classifying controllers from fixed conditioning to experience-adaptive decision-making.
This paper proposes a taxonomy for agentic visual generation organized by what the controller can directly control in the generation process, rather than by planning depth, tool count, or model size. Levels range from L1 Conditioning Control through L2 Execution Control, L3 Outcome-Adaptive Control, and L4 Experience-Adaptive Control, with L0 Fixed Support denoting systems without a deployed decision-making controller. The framework is applied across image, video, editing, 3D, world, slide, and user-interface generation to map how controller capabilities and mechanisms have evolved across the field.
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UniMate: One Unified Model to Animate Diverse Skeletons
Researchers introduce UniMate, a topology-aware diffusion transformer generating text-driven motion for arbitrary 3D skeletons without per-skeleton retraining.
UniMate is a unified foundation model that synthesizes articulated motion for arbitrary skeletons from a rigged 3D asset and a text prompt, with no test-time optimization or per-skeleton fine-tuning. It uses a topology-aware diffusion transformer combining graph-aware attention bias, a spectral rotary position embedding generalizing RoPE via the graph Laplacian, and a rest-pose topological conditioner. Trained on UniML3D, a curated set of 13,006 motion sequences spanning bipedal to serpentine skeletons, it outperforms state-of-the-art baselines and supports zero-shot cross-topology transfer, in-betweening, and text-guided editing.
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Multi-Step Tool-Calling over Korean Open Public APIs: A Benchmark and a Data-Synthesis Recipe
Researchers introduce KOPA-Bench, a 145-task Korean public API tool-calling benchmark, and EDGE, an execution-grounded data synthesis method.
An arXiv paper presents KOPA-Bench, a benchmark of 145 real-world tasks chaining multiple tool-calls across live Korean government APIs, motivated by data-sovereignty requirements for on-premise open-source LLM agents. It also introduces EDGE, an execution-grounded dynamic graph that keeps only tool-output-to-input links verified by live API calls before synthesizing executable multi-step trajectories. A 9B model fine-tuned with GRPO on the resulting dataset nearly matches its untuned 27B family sibling on KOPA-Bench and improves on the BFCL benchmark.
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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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When LLM Decompilers Recompile More and Preserve Less
Researchers show LLM decompiler outputs can recompile yet diverge behaviorally, proposing the Decompile-Diverge fuzzing oracle to catch hidden changes.
The paper demonstrates that LLM-based decompilers can produce code that recompiles and passes all shipped tests yet diverges on other legitimate inputs—4.9% overall and up to 13% for one system—and can make disclosed vulnerabilities vanish without a visible crash. Across 300 real GitHub functions and 287 CVE-grounded functions, a refinement LLM lifted Ghidra's build rate from 75% to 90% while Matched rate fell from 74% to 62%, with up to one tenth of vulnerabilities showing Crash Absence. Decompile-Diverge detects these gaps by synthesizing drivers, growing fuzzing corpora from the reference, and rerunning decompiled code on identical inputs.
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LLM-Driven Algorithm Design for Quantum Circuit Synthesis based on Binary Decision Diagrams
QuantumEvo uses an LLM to evolve BDD variable-ordering heuristics, achieving a 70.9% tie-or-win rate on quantum circuit cost versus baseline methods.
The QuantumEvo framework uses an LLM as a heuristic generator for quantum-cost-aware BDD variable ordering in reversible circuit synthesis, searching over heuristics initialized from multiple families and selecting them by downstream quantum circuit cost. The discovered heuristic HGA-QE modifies the sifting step inside a genetic algorithm and achieves a 70.9% tie-or-win rate against the per-function best baseline, with strict wins on 13.5% of functions. Advantages are clearer on benchmark suites not used for heuristic discovery.
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UniMate: One Unified Model to Animate Diverse Skeletons
UniMate is a topology-aware diffusion transformer generating articulated motion for arbitrary rigged skeletons from text, trained on 13,006 motion sequences.
UniMate is a unified foundation model that animates arbitrary rigged 3D skeletons from an asset and text prompt with no test-time optimization or per-skeleton retraining. It uses a topology-aware diffusion transformer combining graph-aware attention bias from joint relations and geodesic distances, a spectral rotary position embedding generalizing RoPE to kinematic trees via the graph Laplacian, and a global topological conditioner. The accompanying UniML3D dataset spans 13,006 motion sequences across bipedal, quadrupedal, avian, marine, insectoid, serpentine, and articulated rigid-object skeletons; the model outperforms baselines and supports zero-shot cross-topology transfer, in-betweening, expansion, and text-guided editing.
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Proofpoint Brings OpenAI GPT Cyber Models into Security Operations to Help Defenders Investigate Threats Faster
Proofpoint launched its SOC Analyst Agent, an agentic investigation tool powered by OpenAI Daybreak models, now in private preview with Q3 GA planned.
Proofpoint introduced the SOC Analyst Agent, the first capability to emerge from its membership in the OpenAI Daybreak Defense Network, which it joined in June 2026. The agent converts natural-language questions into structured, traceable investigation findings across Proofpoint alerts, logs, DLP events and user risk signals, while leaving remediation decisions to human analysts. It is in private preview with select beta customers, and general availability is expected by the end of Q3 2026. Proofpoint cites its 2025 report finding that 54% of organizations already use AI-enhanced capabilities to triage and investigate alerts.
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Puppeteer: Object-Grounded Posture-Aware Co-Speech Gesture Generation
Researchers present Puppeteer, a posture-aware, object-grounded diffusion model generating physically consistent co-speech gestures with temporal control.
Puppeteer decomposes long gestures into structured primitives encoded by a causal variational autoencoder into temporally ordered latent tokens. Conditional diffusion in the causal latent space conditions on speech signals, motion history, an initial posture reference, and object geometry to synthesize physically consistent gestures. The authors also introduce new evaluation metrics and release SceneGes, the first curated synthetic 3D dataset of embodied co-speech gestures with corresponding 3D objects.
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To See a World in a Living Context: Unified Indoor-Outdoor Urban World Generation
Researchers introduce HoloWorld, a unified text-driven framework generating coherent indoor-outdoor 3D urban worlds, improving average AQS over SOTA by 7.68%.
HoloWorld is a text-driven 3D generation framework that unifies indoor and outdoor urban world generation using a continuously updated cross-scale world context. It autoregressively generates urban exteriors with consistent spatial organization, grounded in 3D building instances and footprints, then produces building-specific interiors with geometry-constrained layouts that inherit exterior appearance. The authors claim it is the first framework to unify indoor and outdoor generation within one coherent 3D urban world, reporting a 7.68% average AQS improvement over prior SOTA and the highest average RDR score.
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[AINews] OpenAI to reach AGI bar by end-2026
OpenAI chief scientist Jakub Pachocki says unreleased Astra model meets the 'Automated AI Research Intern' goal; Altman expects internal AGI declaration by December 2026.
OpenAI chief scientist Jakub Pachocki says the unreleased Astra model fulfills the September 2026 'Automated AI Research Intern' target. Sam Altman told TIME he expects OpenAI to declare AGI achieved internally by December 2026. The roundup also covers Zhipu's GLM-5.3-Flash (320B total parameters, 18B active, 1M context), Google's Gemini Omni 1.1 Flash video model topping the Text-to-Video Arena, and the $399 open-source Microduck biped robot from Pollen Robotics and Hugging Face.
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Israel Is Running a Synthetic Think Tank to Influence AI Search Results
Israel funds the Hanover Institute, an AI-generated think tank by ad firm Piro, publishing 100+ articles to influence LLM chatbot answers about Israel.
The Hanover Institute for Public Policy, run by American ad firm Piro Inc and paid for by Israel through HAVAS Media and the state ad agency LaPam, has published more than 100 articles in under a month. The AI detection tool Pangram found tested articles entirely AI-written except their bibliographies, and the site includes an llms.txt file to make it easier for LLMs to scrape. FARA filings show a $900,000 project invoice plus a $100,000 payment, and Piro advertises an 'AI Story Optimization' service for shaping chatbot answers.
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[AINews] Andrew Ng gets into AI Engineering
Andrew Ng relaunches DeepLearning.AI around AI Engineering, defining four core skills from an analysis of 10,000+ job postings and expert interviews.
Andrew Ng, cofounder of Google Brain and Coursera, relaunched DeepLearning.AI with a focus on AI Engineering, basing the curriculum direction on an analysis of over 10,000 job postings plus interviews and surveys. He identifies four key skills: building and deploying AI applications, software engineering fundamentals, effective use of coding agents, and shaping the build with product sense. The Latent Space AI News issue also recaps agent ecosystem developments, including NVIDIA's 'Skill Lift' evaluation proposal showing skill scan scores correlate only weakly (Spearman rho = 0.14) with judged quality, and Konwinski's open-source persistent-agent 'microharness' Headlong, which achieved an unattended self-debugging repair in 48 minutes.
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Up to 30x More Work Per Watt: NVIDIA Vera Rubin NVL72 Sets a New Efficiency Standard for AI Agents
NVIDIA claims Vera Rubin NVL72 delivers up to 30x more work per watt, citing OpenRouter data that agentic workloads use 15x more tokens than chat.
NVIDIA positions the Vera Rubin NVL72 as a new efficiency standard for AI agents, claiming up to 30x more work per watt. The company cites OpenRouter data showing agentic AI workloads consume 15x more tokens than a simple chat request, using a financial-research agent example that spawns sub-agents and multiple tool calls. The piece is largely a product efficiency narrative rather than independent benchmarking.
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AI Agents Are Here. So Are the Threats.
Unit 42 demonstrates nine framework-agnostic attack scenarios against AI agents built with CrewAI and AutoGen, causing data leakage, credential theft and remote code execution.
Palo Alto Networks Unit 42 investigated how attackers can target agentic applications, implementing two functionally identical apps with the open-source CrewAI and AutoGen frameworks and executing the same attacks on both. Nine attack scenarios produce outcomes including information leakage, credential theft, tool exploitation and remote code execution. Findings show most vulnerabilities are framework-agnostic, arising from insecure design patterns, misconfigurations and unsafe tool integrations rather than flaws in the frameworks themselves. The team published defense strategies per scenario and open-sourced the source code and datasets on GitHub.
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OpenAI’s GPT-5.6 Sol runs up to 14× faster with Ultrafast mode
OpenAI launched GPT-5.6 Sol Ultrafast mode in limited preview, running up to 14x faster at 750 tokens per second via Cerebras inference.
OpenAI's GPT-5.6 Sol Ultrafast mode is available in limited preview through the OpenAI API, delivering up to 14x faster processing and up to 750 output tokens per second, powered by Cerebras under the companies' ultra-low-latency inference partnership. Preview customers are testing it in production for coding, commerce, financial research, and support applications. OpenAI is also using Ultrafast internally for incident response tasks such as log analysis and trace review, and for research workflows with multiple same-day experiment iterations.
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A Three-Layer Caching Architecture for Low-Latency LLM Web Search on Commodity CPU Hardware
OreoLook's three-layer Redis caching architecture cuts redundant LLM calls and embedding work for CPU-hosted web-search answer generation.
The paper describes a three-layer caching architecture for OreoLook (formerly lixSearch), an open-source LLM answer engine: a Redis session context window with Huffman-compressed disk overflow, a semantic query cache matching rephrasings via embedding cosine similarity, and a URL embedding cache deduplicating embedding computations. Deployed on a single 8-vCPU Intel Cascade Lake server with 30 Hypercorn workers across three containerized replicas, it achieved an 89.3% aggregate Redis keyspace hit rate, 0.1 ms read latency, and 1.38 MB memory overhead. An LRU eviction daemon migrates idle sessions to disk and rehydrates them for resumption hours or days later.
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Company Offering ‘100% Human-Written, Never AI’ Medical Research Is Entirely AI
404 Media found Research Gold, advertised as '100% human-written' medical research, is run entirely by AI with fake PhD staff.
Research Gold advertises PRISMA-compliant systematic reviews and meta-analyses drafted by PhD methodologists, but its listed experts are AI-generated personas that do not exist. Real methodologists, including evidence synthesis scientist Jenny Berrio, were listed without permission using photos copied from LinkedIn. Customer calls and quotes were handled by AI agents that insisted they were human, quoting $1,900 for a systematic review.
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