{ "name": "Just Out Tech", "url": "https://justouttech.com/", "description": "Every research finding Just Out Tech has read, checked and scored. Scores are ours, not the papers own. The rubric is published.", "licence": "CC BY 4.0. Use it, and name us.", "schema": "https://justouttech.com/evidence/", "rubric": { "peer": "0-3 peer review and venue", "labs": "0-2 who did it", "evidence": "0-3 strength of evidence", "open": "0-1 public code or data", "signal": "0-1 independent attention", "total": "out of 10" }, "generated": "2026-09-15T19:32:37+00:00", "count": 29, "items": [ { "title": "Robots learn better by watching you than by watching other robots", "url": "https://justouttech.com/robots-learn-better-by-watching-you/", "published": "2026-09-08", "topic": "Robotics", "regions": [ "China", "Singapore", "USA" ], "score": 7, "score_parts": { "peer": 1, "labs": 2, "evidence": 3, "open": 0, "signal": 1 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "unknown", "funder": "", "commercial": "NVIDIA is one of the listed author affiliations, alongside PKU, NUS, MIT and UCSB. The real-robot rollouts use an AgiBot bimanual platform, and the post-training set is curated from AgiBot World. The paper says code will be released at https://github.com/DAGroup-PKU/HumanNet/. No licence is stated.", "source": { "title": "HumanScale: Egocentric Human Video Can Outperform Real-Robot Data for Embodied Pretraining", "id": "arXiv:2606.20521", "url": "https://arxiv.org/abs/2606.20521", "labs": "Peking University, National University of Singapore, MIT, UC Santa Barbara, NVIDIA", "date": "June 2026" }, "answer": "", "reading_grade": 4.5 }, { "title": "Weather data alone can forecast solar power in Ibadan", "url": "https://justouttech.com/weather-data-alone-can-forecast-solar-power-in-ibadan/", "published": "2026-09-09", "topic": "Climate & energy tech", "regions": [ "Nigeria", "South Africa" ], "score": 4, "score_parts": { "peer": 0, "labs": 2, "evidence": 1, "open": 1, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "public", "funder": "No funding statement and no grant numbers. The acknowledgements credit the Associates Programme, QLS Section, Abdus Salam International Centre for Theoretical Physics, and the Centre for Petroleum, Energy Economics and Law, University of Ibadan.", "commercial": "No company affiliations or industrial partners. The three authors are at the University of Ibadan and the University of Venda, South Africa. The data and the complete Python source are openly released at github.com/peterobarotu/Solar_Energy_Forecasting, and the paper does not state a licence.", "source": { "title": "Forecasting solar power output in Ibadan: A machine learning approach leveraging weather data and system specifications", "id": "arXiv:2508.07462", "url": "https://arxiv.org/abs/2508.07462", "labs": "Center for Petroleum, Energy Economics and Law University of Ibadan Nigeria, Department of Statistics University of Ibadan Nigeria, Department of Mathematical and Computational Sciences University of Venda South Africa", "date": "August 2025" }, "answer": "Researchers at the University of Ibadan in Nigeria and the University of Venda in South Africa forecast hourly sunlight in Ibadan from ordinary weather records. A Random Forest model beat a CNN and an LSTM. Its yearly normalised error on total sunlight was 0.19, falling to 0.12 in the dry season and rising to 0.27 in the wet season.", "reading_grade": 5.5 }, { "title": "A watchdog for chained AI agent attacks, still untested", "url": "https://justouttech.com/watchdog-for-chained-ai-agent-attacks-still-untested/", "published": "2026-09-09", "topic": "AI agents & MCP", "regions": [ "USA" ], "score": 1, "score_parts": { "peer": 0, "labs": 1, "evidence": 0, "open": 0, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "none", "funder": "", "commercial": "No company is named as a funder, partner or collaborator. The three authors give academic affiliations (New York University, University of Maryland College Park, Carnegie Mellon University), and a footnote states \"This work is not related to the author's position at Amazon Web Services.\" No code, data or model is released.", "source": { "title": "ChainWatch: A Kill Chain-Aligned Sequential Detection Framework for Multi-Step Attacks in MCP-Based AI Agent Systems", "id": "arXiv:2607.19432", "url": "https://arxiv.org/abs/2607.19432", "labs": "New York University, University of Maryland College Park, Carnegie Mellon University", "date": "July 2026" }, "answer": "Three researchers proposed ChainWatch, a watchdog that would sit between an AI agent and its tools and look for attacks spread over several steps. It labels each tool call with one of six attack stages and fires five rules on the sequence. The paper states that ChainWatch is a design specification and is not in operation. It was traced through five literature scenarios and zero real sessions.", "reading_grade": 5.0999999999999996447286321199499070644378662109375 }, { "title": "Walking to the job cut a robot’s screw grabs from 87% to 37%", "url": "https://justouttech.com/walking-to-the-job-cut-a-robots-screw-grabs-from-87-to-37/", "published": "2026-09-09", "topic": "Robotics", "regions": [ "USA" ], "score": 6, "score_parts": { "peer": 0, "labs": 2, "evidence": 3, "open": 1, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "public", "funder": "ARPA-E grant DEAR0001966. William Xie has an NSF Graduate Research Fellowship. The acknowledgement says two authors, N. Correll and M. Conway, have an interest in Realtime Manufacturing, Inc., which works on humanoids for manufacturing.", "commercial": "Authors are at the University of Colorado Boulder and the University of Notre Dame. No company affiliations are listed, but N. Correll and M. Conway have an interest in Realtime Manufacturing, Inc. GOLEM is released as open source at https://golem-humanoid.github.io, with no licence named. The MAGPIE gripper is open source too.", "source": { "title": "GOLEM: Modular Humanoid Autonomy Towards Electric Vehicle Battery Disassembly", "id": "arXiv:2608.21550", "url": "https://arxiv.org/abs/2608.21550", "labs": "Department of Computer Science University of Colorado Boulder, Department of Aerospace and Mechanical Engineering University of Notre Dame", "date": "August 2026" }, "answer": "GOLEM is an open-source, modular system for running a Unitree H1-2 humanoid on electric vehicle battery disassembly. Picking loosened screws from a real Hyundai Ioniq 5 pack, it scored 96.7 percent while hoisted, 86.7 percent while standing, and 36.7 percent after walking itself into position. The failures came from imprecise walking, not from the hands.", "reading_grade": 5.79999999999999982236431605997495353221893310546875 }, { "title": "VR and passthrough bend your reach in opposite directions", "url": "https://justouttech.com/vr-and-passthrough-bend-your-reach-in-opposite-directions/", "published": "2026-09-09", "topic": "Mixed reality", "regions": [ "Canada" ], "score": 5, "score_parts": { "peer": 1, "labs": 1, "evidence": 3, "open": 0, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "none", "funder": "Natural Sciences and Engineering Research Council of Canada (NSERC). No grant number is given.", "commercial": "All three authors are at the University of Toronto. No company affiliation or industrial partner is named, and no code, data or licence is stated. The study runs on the Quest 3 head-mounted display, in both VR and video-passthrough mode. The acknowledgements say Claude was used for statistical code and ChatGPT for language editing.", "source": { "title": "Divergent Perceptuomotor Recalibration in Virtual Reality and Video-Passthrough Mixed Reality on the Same Head-Mounted Display", "id": "arXiv:2608.06132", "url": "https://arxiv.org/abs/2608.06132", "labs": "University of Toronto", "date": "August 2026" }, "answer": "Forty adults pointed at targets on one Meta Quest 3, half in virtual reality and half in camera passthrough. The two modes shifted aim in opposite directions: passthrough users overshot by 0.43 cm and virtual reality users undershot by 0.31 cm. After the headset came off, virtual reality users kept a 0.35 cm undershoot that passthrough users did not.", "reading_grade": 5.20000000000000017763568394002504646778106689453125 }, { "title": "Training with attack images pushed bean disease accuracy to 99.4%", "url": "https://justouttech.com/training-with-attack-images-pushed-bean-disease-accuracy-to-99-4/", "published": "2026-09-09", "topic": "Agritech", "regions": [ "Tanzania" ], "score": 5, "score_parts": { "peer": 2, "labs": 1, "evidence": 1, "open": 1, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "published", "code": "partial", "funder": "The data collection was paid for by The Organization for Women in Science for the Developing World. The authors declare no commercial or financial relationships constituting a conflict of interest.", "commercial": "No company is named as an affiliation, funder, collaborator or supplier. All four authors are at The Nelson Mandela African Institution of Science and Technology (NM-AIST), Tanzania. The dataset is at Zenodo (record 8286126) with no licence given. The article itself is published under CC BY. No code or model release is mentioned.", "source": { "title": "Enhancing detection of common bean diseases using Fast Gradient Sign Method-trained Vision Transformers", "id": "pmcid:PMC12364866", "url": "https://www.frontiersin.org/articles/10.3389/frai.2025.1643582/full", "labs": "Computational and Communication Science and Engineering, Life Sciences and Bio-engineering, The Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania", "date": "August 2025" }, "answer": "Researchers at the Nelson Mandela African Institution of Science and Technology in Tanzania trained a Vision Transformer to spot bean rust and anthracnose. Adding small adversarial nudges during training, using the Fast Gradient Sign Method, lifted accuracy to 99.4 percent. The same model without the nudges scored 97.4 percent, and a hardened CNN scored 97.65 percent.", "reading_grade": 5.0999999999999996447286321199499070644378662109375 }, { "title": "Robot trained only in a simulator crossed 10 of 15 messy rooms", "url": "https://justouttech.com/robot-trained-only-in-a-simulator-crossed-10-of-15-messy-rooms/", "published": "2026-09-09", "topic": "Robotics", "regions": [ "China", "USA" ], "score": 4, "score_parts": { "peer": 0, "labs": 2, "evidence": 2, "open": 0, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "unknown", "funder": "", "commercial": "No company affiliations or industrial partners are named. The authors are at UC Berkeley, Peking University, Tsinghua University, the University of Hong Kong and Princeton University. They say they will open-source the data pipeline, dataset, model checkpoint and deployment system at tango-vla.github.io. No licence is stated.", "source": { "title": "TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model", "id": "arXiv:2609.09158", "url": "https://arxiv.org/abs/2609.09158", "labs": "University of California Berkeley, Peking University, Tsinghua University, The University of Hong Kong, Princeton University", "date": "September 2026" }, "answer": "TANGO is a model that turns a spoken instruction and camera images into all 29 joint angles of a humanoid robot at once. Trained only in simulation, it crossed cluttered real rooms in 10 of 15 tries, against 6 of 15 for the rival method. In simulation it cut the share of runs with a collision from 15.81 percent to 9.90 percent.", "reading_grade": 5.4000000000000003552713678800500929355621337890625 }, { "title": "Retuning AI on site raised parasite detection to 98.1%", "url": "https://justouttech.com/retuning-ai-on-site-raised-parasite-detection-to-98-1/", "published": "2026-09-09", "topic": "Biotech & health tech", "regions": [ "Canada", "Côte d'Ivoire", "USA" ], "score": 4, "score_parts": { "peer": 0, "labs": 2, "evidence": 2, "open": 0, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "pilot", "status": "preprint", "code": "none", "funder": "Ontario AHSC AFP Innovation Fund, New Frontiers in Research Fund (NFRFE-2020-00922), Canadian Institutes of Health Research (PJT-183575), Gates Foundation (INV-051355, INV-008782). Gates money paid people who work at or hold shares in Spotlab, which built the model.", "commercial": "Spotlab (Madrid, Spain) is an author affiliation, and the competing-interest statement says \"ED, DC, DBP, JGV, LL and MLO work and/or hold shares of Spotlab\". The work runs on a portable microscope the paper calls the NTDscope. There is no open release: \"The data and algorithms may be made available upon request.\"", "source": { "title": "Edge-tuning of artificial intelligence improves diagnostic performance for Schistosomiasis haematobium in a rural setting of Cote d'Ivoire", "id": "doi:10.1101/2025.07.11.25331398", "url": "https://www.medrxiv.org/content/10.1101/2025.07.11.25331398v1.full", "labs": "Universite Felix Houphouet-Boigny Abidjan Cote d'Ivoire, Centre Suisse de Recherches Scientifiques en Cote d'Ivoire, University of California Berkeley USA, Stanford University School of Medicine USA, Lawrence Berkeley National Laboratory USA, Chan Zuckerberg Biohub San Francisco USA, Spotlab Madrid Spain, Universidad Politecnica de Madrid Spain, CIBER de Bioingenieria Biomateriales y Nanomedicina Instituto de Salud Carlos III Spain, Toronto General Hospital University Health Network Canada, University of Toronto Canada", "date": "July 2025" }, "answer": "In a rural part of Cote d'Ivoire, researchers retrained a parasite detection model on site between two days of urine testing. On brightfield images the model's catch rate rose from 75.5 percent to 98.1 percent, and the share of clean samples it called clean rose from 46.7 percent to 100 percent. The tuning used 7,900 new labels made in four hours.", "reading_grade": 6.20000000000000017763568394002504646778106689453125 }, { "title": "A radio protocol shrank from 4.55 MB to 1 KB with no loss", "url": "https://justouttech.com/radio-protocol-shrank-from-4-55mb-to-1kb-with-no-loss/", "published": "2026-09-09", "topic": "5G & 6G", "regions": [ "India" ], "score": 2, "score_parts": { "peer": 0, "labs": 1, "evidence": 1, "open": 0, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "none", "funder": "Ministry of Electronics and Information Technology, Government of India, YFRF Scheme (DIC/PhD-Phase-II/2026/9). Department of Telecommunication, Telecom Technology Development Fund through TCOE India, grants TTDF/6G/48 and IGSTC-04918.", "commercial": "All four authors are at academic institutions (IIT Dharwad, Central University of Jammu, Manipal Institute of Technology) and no industrial partner is named. No code, data or model release is stated. Commercial hardware appears only as reference targets in Table III, and the survey says those figures are vendor specifications.", "source": { "title": "Closing the Semantic-Edge Gap: Tiny Language Models for 6G Wireless Intelligence", "id": "arXiv:2609.03747", "url": "https://arxiv.org/abs/2609.03747", "labs": "IIT Dharwad, Central University of Jammu, Manipal Institute of Technology", "date": "September 2026" }, "answer": "A survey from IIT Dharwad and two other Indian institutions maps how to shrink language models small enough for 6G devices. Among the 20 studies it gathers, one turned a 4.55 megabyte neural radio protocol into a 1 kilobyte symbolic rule set, a 99.98 percent cut with zero task-performance loss and 8 operations in place of 14,000.", "reading_grade": 5.29999999999999982236431605997495353221893310546875 }, { "title": "Quantum computers made of single atoms now reach 12,001 spots", "url": "https://justouttech.com/quantum-computers-made-of-single-atoms-now-reach-12001-spots/", "published": "2026-09-09", "topic": "Quantum computing", "regions": [ "USA" ], "score": 2, "score_parts": { "peer": 0, "labs": 1, "evidence": 1, "open": 0, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "none", "funder": "", "commercial": "The sole author, Mark Saffman, gives two affiliations: Department of Physics, University of Wisconsin-Madison, and Infleqtion, Madison WI. No code, data or model is released, and the chapter carries no competing-interest declaration. The 12,001-site array is other people's work (Manetsch et al., 2025), as are the other large arrays in Figure 3.", "source": { "title": "Neutral atom quantum computing", "id": "arXiv:2608.30783", "url": "https://arxiv.org/abs/2608.30783", "labs": "Department of Physics University of Wisconsin-Madison, Infleqtion Madison", "date": "August 2026" }, "answer": "A review of neutral atom quantum computing collects the field's best published results. Arrays now reach 12,001 trap sites, one caesium atom held its bit for a record 119 seconds, and two-atom operations have passed 0.99 accuracy with four kinds of atom. The review runs no new experiment and warns that scaling one array past 100,000 qubits is a steep engineering problem.", "reading_grade": 5.79999999999999982236431605997495353221893310546875 }, { "title": "One robot model did 11 chores, passing 8 tries in 10", "url": "https://justouttech.com/one-robot-model-did-11-chores-passing-8-tries-in-10/", "published": "2026-09-09", "topic": "Robotics", "regions": [ "China", "Singapore" ], "score": 5, "score_parts": { "peer": 0, "labs": 2, "evidence": 3, "open": 0, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "unknown", "funder": "", "commercial": "No author holds a company affiliation. The eleven authors are at MARS Lab NTU, Peking University, the Beijing Academy of Artificial Intelligence and HKUST (Guangzhou). A project page is given at https://gentlefress.github.io/OMEGA-0_page/. The paper states no code, model or dataset release and names no licence.", "source": { "title": "omega-0: A Latent Predictive World Action Model for Concurrent Humanoid Loco-Manipulation", "id": "arXiv:2608.06375", "url": "https://arxiv.org/abs/2608.06375", "labs": "MARS Lab Nanyang Technological University, Peking University, Beijing Academy of Artificial Intelligence, HKUST (Guangzhou)", "date": "August 2026" }, "answer": "Researchers trained omega-0, one model that lets a humanoid robot move and manipulate objects at the same time. Across 11 real household chores, with 10 trials each, it completed 81.8 percent of runs using both cameras and 79.1 percent using only the robot's own view. The strongest of nine baseline models, all trained on the same data, completed 44.5 percent.", "reading_grade": 5 }, { "title": "Nine of 16 planned satellite fleets are too big for orbit", "url": "https://justouttech.com/nine-of-16-planned-satellite-fleets-are-too-big-for-orbit/", "published": "2026-09-09", "topic": "Space tech", "regions": [ "United Kingdom" ], "score": 3, "score_parts": { "peer": 0, "labs": 2, "evidence": 1, "open": 0, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "none", "funder": "No financial support is stated. The author declares no competing interests. The acknowledgements thank Jonathan McDowell for constellation data, Sarah Lewis for feedback, and Donald Kessler and Phillip Anz-Meador for mentorship.", "commercial": "No company is an affiliation, funder or partner. The sole author is at the SERENE Group, University of Birmingham. The companies named are the subjects of the assessment, not collaborators. They include SpaceX, E-Space, Guowang, Amazon Leo, Blue Origin, Eutelsat OneWeb and AST SpaceMobile. No code or data release is mentioned.", "source": { "title": "Critical Sizes of Satellite Constellations", "id": "arXiv:2607.29644", "url": "https://arxiv.org/abs/2607.29644", "labs": "Space Environment and Radio Engineering (SERENE) Group, School of Engineering, University of Birmingham", "date": "July 2026" }, "answer": "Hugh G. Lewis built a stability model that gives each satellite fleet a critical size. He ran 16 real planned or deployed fleets through it. Nine of the 16 sit above their critical size. Six of those nine would produce wreckage that grows without limit, even in an empty sky and with better than standard clean-up rules.", "reading_grade": 5.0999999999999996447286321199499070644378662109375 }, { "title": "More than half of MCP security alerts are false alarms", "url": "https://justouttech.com/more-than-half-of-mcp-security-alerts-are-false-alarms/", "published": "2026-09-09", "topic": "Cybersecurity", "regions": [ "China" ], "score": 5, "score_parts": { "peer": 0, "labs": 2, "evidence": 3, "open": 0, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "partial", "funder": "", "commercial": "Authors are at Fudan University and the Shanghai Innovation Institute. No company affiliation, industrial partner or funder is named. The scanners under test include company-maintained tools from Snyk, Cisco AI Defense, Ant Group, Tencent Zhuque Lab and Lasso Security. The MCPZoo dataset is released, and no licence is stated.", "source": { "title": "Rethinking MCP Security: A Large-Scale Study of Runtime MCP Servers and Security Scanner Reliability", "id": "arXiv:2607.11086", "url": "https://arxiv.org/abs/2607.11086", "labs": "Fudan University, Shanghai Innovation Institute", "date": "July 2026" }, "answer": "A Fudan University team built MCPZoo, a collection of 64,611 MCP server projects with 37,288 running well enough to poke at. Eight security scanners flagged 96.89 percent of those running servers as risky, but hand checking 100 of them showed average precision of just 45.53 percent. The same scanners caught only 24.17 percent of 10 published CVEs, and any two scanners overlapped by only 15.66 percent.", "reading_grade": 5.5 }, { "title": "A free tool checks AI agent add-ons for hidden attacks", "url": "https://justouttech.com/free-tool-checks-ai-agent-add-ons-for-hidden-attacks/", "published": "2026-09-09", "topic": "AI agents & MCP", "regions": [ "China" ], "score": 4, "score_parts": { "peer": 0, "labs": 1, "evidence": 1, "open": 1, "signal": 1 }, "score_history": [], "last_checked": "", "maturity": "shipping", "status": "preprint", "code": "public", "funder": "", "commercial": "AI-Infra-Guard is developed by Tencent Zhuque Lab and every author is at Tencent. The project is at github.com/Tencent/AI-Infra-Guard. The report states the release is open source but does not name a licence. For model-layer jailbreak testing, it builds on Confident AI's open-source DeepTeam framework.", "source": { "title": "Securing the AI Agent: A Unified Framework for Multi-Layer Agent Red Teaming", "id": "arXiv:2606.31227", "url": "https://arxiv.org/abs/2606.31227", "labs": "Tencent Zhuque Lab", "date": "June 2026" }, "answer": "Tencent Zhuque Lab released AI-Infra-Guard, a free tool that checks AI agents for security holes at four layers. Its skill scanner scored 0.9848 loose F1 on SkillTrustBench, a new public test set of 5,520 cases drawn from 62,652 real agent skills. The false alarm rate ranged from 0.0120 to 0.1867 depending on which model ran inside the scanner.", "reading_grade": 4.0999999999999996447286321199499070644378662109375 }, { "title": "Four brain signals steered 840 drone command layouts", "url": "https://justouttech.com/four-brain-signals-steered-840-drone-command-layouts/", "published": "2026-09-09", "topic": "Brain-computer interfaces", "regions": [ "USA" ], "score": 2, "score_parts": { "peer": 0, "labs": 0, "evidence": 1, "open": 1, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "public", "funder": "", "commercial": "Single author, affiliation given as VECTOR Labs, San Francisco Bay Area. The code for pretraining, fine-tuning, action-space generation and evaluation is public at https://github.com/alexplash/cerebrus-research-public.git. The paper states no licence. The work runs on the Qwen3-0.6B language model, the Braindecode toolbox and the Webots simulator.", "source": { "title": "Brain-Language-Action (BLA) Models: Language-Conditioned EEG for Robotics Control", "id": "arXiv:2608.28967", "url": "https://arxiv.org/abs/2608.28967", "labs": "VECTOR Labs, San Francisco Bay Area", "date": "August 2026" }, "answer": "Alexandr Plashchinsky joined an EEG reader to a small language model so that written rules decide what each brain signal means. Four motor-imagery thoughts were mapped onto seven drone moves, giving 840 possible layouts. On held-out data from four selected people, the system produced 90.4 percent of drone command tokens correctly.", "reading_grade": 5.4000000000000003552713678800500929355621337890625 }, { "title": "Fake quote marks fool AI agents half the time", "url": "https://justouttech.com/fake-quote-marks-fool-ai-agents-half-the-time/", "published": "2026-09-09", "topic": "AI agents & MCP", "regions": [ "South Korea", "USA" ], "score": 6, "score_parts": { "peer": 0, "labs": 2, "evidence": 2, "open": 1, "signal": 1 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "public", "funder": "", "commercial": "One author is affiliated with Largosoft. The others are at Seoul National University and the University of Illinois Urbana-Champaign. The artifacts are on GitHub at github.com/compsec-snu/adi, with no licence stated. Shipping products attacked include Claude in Chrome, Claude Code, OpenAI Codex and Gemini CLI.", "source": { "title": "Agent Data Injection Attacks are Realistic Threats to AI Agents", "id": "arXiv:2607.05120", "url": "https://arxiv.org/abs/2607.05120", "labs": "Seoul National University, Largosoft, University of Illinois Urbana-Champaign", "date": "July 2026" }, "answer": "Researchers describe agent data injection, where an attacker hides quote-like characters in a field they control so the model misreads the structure and treats attacker text as trusted data. On the AgentDojo bench it succeeded 49.1 percent of the time against a plain agent, while old-style prompt injection succeeded only 0.2 percent. Six of seven defences left between 22.2 and 50.0 percent of attacks working.", "reading_grade": 5.4000000000000003552713678800500929355621337890625 }, { "title": "Erasing errors at 1,000 spots needs 537 million times more runs", "url": "https://justouttech.com/erasing-errors-at-1000-spots-needs-537-million-times-more-runs/", "published": "2026-09-09", "topic": "Quantum computing", "regions": [ "China", "Singapore" ], "score": 3, "score_parts": { "peer": 0, "labs": 2, "evidence": 1, "open": 0, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "shipping", "status": "preprint", "code": "none", "funder": "Singapore Ministry of Education Tier-I grant A-8002656-00-00 and Tier-II grant A-8003505-00-00. National Research Foundation Singapore under the AQAS initiative (S25Q9DA001). H.-Z. Li also by a China Scholarship Council scholarship (202506890103).", "commercial": "No author holds a company affiliation. The authors are at the National University of Singapore, Shanghai University and the Singapore Institute of Technology. The review cites error-mitigation features in vendor toolkits and the Unitary Foundation's Mitiq package on GitHub. No licence is stated, and the review releases no code of its own.", "source": { "title": "Practical Error Suppression and Mitigation for Reliable Quantum Computing", "id": "arXiv:2608.20453", "url": "https://arxiv.org/abs/2608.20453", "labs": "Department of Physics National University of Singapore, Department of Computer Science National University of Singapore, Institute for Quantum Science and Technology Shanghai University, Engineering Cluster Singapore Institute of Technology", "date": "August 2026" }, "answer": "A review of quantum error suppression and mitigation puts a price on cleanup. Using a simple bit-flip model, it shows that at a 0.5 percent error rate the extra runs needed grow from 7.46 times for 100 mitigated spots to about 23,200 times for 500 spots and about 537 million times for 1,000 spots. The paper reports no new experiment.", "reading_grade": 5.79999999999999982236431605997495353221893310546875 }, { "title": "Crop disease model hits 97.3% and runs offline on old phones", "url": "https://justouttech.com/crop-disease-model-hits-97-3-and-runs-offline-on-old-phones/", "published": "2026-09-09", "topic": "Agritech", "regions": [ "Ethiopia" ], "score": 4, "score_parts": { "peer": 1, "labs": 1, "evidence": 1, "open": 1, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "public", "funder": "", "commercial": "No company is named as an affiliation, funder, collaborator or supplier. All five authors are at Mekelle University, Ethiopia, and the corresponding author's contact address is an @singularitynet.io email. The source code and the cactus-fig dataset are public on GitHub under the account Tekleab15 and on Kaggle. No licence is stated.", "source": { "title": "Automated Plant Disease and Pest Detection System Using Hybrid Lightweight CNN-MobileViT Models for Diagnosis of Indigenous Crops", "id": "arXiv:2512.11871", "url": "https://arxiv.org/abs/2512.11871", "labs": "Department of Computer Science and Engineering, Department of Information Technology, Mekelle University Mekelle Institute of Technology, Ethiopia", "date": "December 2025" }, "answer": "Researchers at Mekelle University in Ethiopia built a dataset of 3,587 field photos of cactus-fig pads and trained three phone sized models on it. MobileViT-XS reached 97.3 percent accuracy at 9.3 MB, while a small custom network reached 89.5 percent at 4.8 MB and 42 milliseconds per photo. The models run offline inside a Tigrigna and Amharic app.", "reading_grade": 5.4000000000000003552713678800500929355621337890625 }, { "title": "Chiplet security guards cost about 4 percent of speed", "url": "https://justouttech.com/chiplet-security-guards-cost-about-4-percent-of-speed/", "published": "2026-09-09", "topic": "Semiconductors & chips", "regions": [ "United Arab Emirates", "USA" ], "score": 3, "score_parts": { "peer": 0, "labs": 2, "evidence": 1, "open": 0, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "none", "funder": "", "commercial": "No industrial partners, no code or data release and no company affiliations. The authors are at NYU Abu Dhabi, Texas A&M University and NYU Tandon School of Engineering. Commercial chiplet parts and security primitives are cited only as examples.", "source": { "title": "Hardware Design and Security in the Era of Chiplets and LLMs", "id": "arXiv:2608.05063", "url": "https://arxiv.org/abs/2608.05063", "labs": "NYU Abu Dhabi, Texas A&M University, NYU Tandon School of Engineering", "date": "August 2026" }, "answer": "A review from NYU Abu Dhabi, Texas A&M University and NYU Tandon argues that trust in multi-vendor chips should sit in the base layer under the chiplets. In the work it reviews, guards in that base cost about 4 percent of speed on average, while total system power fell 3.2 percent and total silicon area fell 18.5 percent.", "reading_grade": 4.4000000000000003552713678800500929355621337890625 }, { "title": "Chip design claims 19 times the AI work per watt of an H100", "url": "https://justouttech.com/chip-design-claims-19-times-the-ai-work-per-watt-of-an-h100/", "published": "2026-09-09", "topic": "Edge computing", "regions": [ "China", "Singapore", "United Kingdom" ], "score": 6, "score_parts": { "peer": 3, "labs": 2, "evidence": 1, "open": 0, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "published", "code": "none", "funder": "National Research Foundation, Prime Minister's Office, Singapore, under its IN-CYPHER Campus for Research Excellence and Technological Enterprise (CREATE) Programme.", "commercial": "Co-author Xiaonan Tang is affiliated with Wisemaytech Co., Ltd., Beijing. The other authors are at universities and research institutes. The design targets inference for DeepSeek-V3 and is benchmarked against an NVIDIA H100. No code, RTL or model release is mentioned.", "source": { "title": "DSPE: An Energy-Efficient Edge Processor for DeepSeek Inference with MerkleTree-based Incremental Pruning, Multi-Stage Boothing Lookup and Dynamic Adaptive Posit Processing", "id": "arXiv:2605.08615", "url": "https://arxiv.org/abs/2605.08615", "labs": "Northeastern University Shenyang, Imperial College London, Imperial Global Singapore, Xidian University, Hangzhou Institute of Technology of Xidian University, The Chinese University of Hong Kong Shenzhen, Wisemaytech Co. Ltd, Institute of Microelectronics of the Chinese Academy of Sciences, University of Chinese Academy of Sciences, Nanyang Technological University", "date": "July 2026" }, "answer": "A team from China, the United Kingdom and Singapore designed an edge processor block for DeepSeek inference called DSPE. Laid out in TSMC 28nm, it reports a peak energy efficiency of 109.4 TFLOPS per watt at 8 bit posit, which the paper says is 19.35 times an H100 GPU at FP8. The design was synthesised and placed and routed, but no chip was manufactured.", "reading_grade": 5.29999999999999982236431605997495353221893310546875 }, { "title": "Big AI models are too slow to run a radio network", "url": "https://justouttech.com/big-ai-models-are-too-slow-to-run-a-radio-network/", "published": "2026-09-09", "topic": "5G & 6G", "regions": [ "France", "United Arab Emirates" ], "score": 3, "score_parts": { "peer": 0, "labs": 2, "evidence": 1, "open": 0, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "none", "funder": "The European Union Horizon Programme paid, through the 6G-INTENSE project (Grant No. 101139266) and the FLECON-6G project (Grant No. 101192462). The survey reviews both of its funders as case studies.", "commercial": "No author holds a company affiliation. The authors are at EURECOM France, Simula Metropolitan Norway and the University of Sharjah UAE. No code, data or model release is stated.", "source": { "title": "LLM-Powered Agentic AI for 5G/6G Networks: A Tutorial and Survey on Architectures, Protocols, and Standardization", "id": "arXiv:2607.16066", "url": "https://arxiv.org/abs/2607.16066", "labs": "EURECOM, Simula Metropolitan, University of Sharjah", "date": "July 2026" }, "answer": "A tutorial and survey from EURECOM and partners maps AI agents onto 5G and 6G networks and finds a timing gap. Near real time control loops run between 10 milliseconds and 1 second, while a 30 to 70 billion setting model on a high end GPU takes 200 milliseconds to several seconds per query. The authors argue models should plan while lightweight rules react.", "reading_grade": 5.5 }, { "title": "A phone’s AI speed is capped near 19 tokens a second by memory", "url": "https://justouttech.com/a-phones-ai-speed-is-capped-near-19-tokens-a-second-by-memory/", "published": "2026-09-09", "topic": "Semiconductors & chips", "regions": [ "India" ], "score": 2, "score_parts": { "peer": 0, "labs": 1, "evidence": 1, "open": 0, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "none", "funder": "", "commercial": "", "source": { "title": "AI Hardware Accelerators for Large Language Models: Architectures and the Memory Wall", "id": "arXiv:2608.28048", "url": "https://arxiv.org/abs/2608.28048", "labs": "Department of Electronics Engineering, Shiv Nadar Institution of Eminence, Delhi-NCR", "date": "August 2026" }, "answer": "A review of AI hardware finds that memory, not arithmetic, limits large language models. Dividing memory bandwidth by model size gives a hard speed ceiling: about 19 tokens a second for a 4-bit 8 billion parameter model on a flagship phone, and 24 tokens a second for a 16-bit 70 billion parameter model on an NVIDIA H100.", "reading_grade": 6 }, { "title": "A perfect steadiness score hid a useless heat map", "url": "https://justouttech.com/a-perfect-steadiness-score-hid-a-useless-heat-map/", "published": "2026-09-09", "topic": "Edge computing", "regions": [ "United Kingdom" ], "score": 2, "score_parts": { "peer": 0, "labs": 1, "evidence": 1, "open": 0, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "unknown", "funder": "UK Research and Innovation, via the EPSRC National Edge AI Hub for Real Data, grant EP/Y028813/1.", "commercial": "All seven authors are at the University of Hull. No company affiliations, industrial partners or code release are named. The pipeline runs on OpenAI's GPT-4.1 mini, which made the final configuration selections, over a MobileNetV3-Small classifier trained on the public HAM10000 dataset.", "source": { "title": "Human-Centered Explainable AI for TinyML Edge Devices: A Pareto-Based Selection Framework with LLM-Guided Design", "id": "arXiv:2608.07091", "url": "https://arxiv.org/abs/2608.07091", "labs": "School of Digital and Physical Sciences, University of Hull", "date": "August 2026" }, "answer": "A University of Hull team tested 67 ways of explaining a skin image model small enough for a microcontroller. One setup scored a perfect 1.000 on steadiness but only 0.0502 on truthfulness, because its heat map barely changed between images. Plain CAM matched the best truthfulness score of 0.9397 at the lowest cost, 0.150.", "reading_grade": 5.0999999999999996447286321199499070644378662109375 }, { "title": "47 experts list 12 things blocking drone fleets by 2035", "url": "https://justouttech.com/47-experts-list-12-things-blocking-drone-fleets-by-2035/", "published": "2026-09-09", "topic": "Drones & autonomous vehicles", "regions": [ "Germany", "USA" ], "score": 3, "score_parts": { "peer": 1, "labs": 2, "evidence": 0, "open": 0, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "report", "code": "none", "funder": "The IEEE Computer Society paid for the workshop. The Computing Community Consortium paid through the U.S. National Science Foundation under Grant No. 2300842. The report notes its findings do not necessarily reflect NSF views.", "commercial": "Published by the Computing Research Association's Computing Community Consortium. Industry people are named among the authors and attendees: Andy Thurling of DroneUp, Mike Kronander and Terry Mullins of Burgess & Niple, Inc., Matt Welsh of Palantir, and Anne Marie Kelly of IEEE-CS. No code, data or model release is mentioned.", "source": { "title": "Computing on the Fly: Navigating a Vision for the Future of Drone Computing", "id": "arXiv:2607.19213", "url": "https://arxiv.org/abs/2607.19213", "labs": "University of Florida, The Ohio State University, Osnabrück University, Computing Research Association, University of Delaware, University of Texas, Rutgers University", "date": "July 2026" }, "answer": "A Computing Research Association workshop of 47 experts, held over two days in December 2025, produced a report naming 12 technical challenges that must be solved before drone fleets can scale by 2035. It also sets 2028 checkpoints, including fleets of 10 to 50 drones completing 99 percent of missions without human help in controlled settings. No experiment was run.", "reading_grade": 6.79999999999999982236431605997495353221893310546875 }, { "title": "24 tiny CubeSats turned a dead satellite in simulation", "url": "https://justouttech.com/24-tiny-cubesats-turned-a-dead-satellite-in-simulation/", "published": "2026-09-09", "topic": "Space tech", "regions": [ "Germany", "USA" ], "score": 3, "score_parts": { "peer": 0, "labs": 2, "evidence": 1, "open": 0, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "none", "funder": "The European Union paid the first author under Grant No. 101153910, a Marie-Curie postdoctoral fellowship.", "commercial": "No company affiliations or industrial partners are named. The authors are at TU Dresden, the University of Michigan and MIT. The CubeSat is based on MIT Space Propulsion Laboratory's STEP-1 3U staged-electrospray demonstrator. No code, data or model release is stated.", "source": { "title": "Control of Decommissioned Satellites and Space Debris Using CubeSats with Ion Electrospray Engines", "id": "arXiv:2608.30215", "url": "https://arxiv.org/abs/2608.30215", "labs": "TU Dresden, University of Michigan, Massachusetts Institute of Technology", "date": "August 2026" }, "answer": "A team modelled 24 small CubeSats attached to a dead satellite, using tiny electrospray thrusters instead of the craft's failed steering. In simulation the swarm turned the 668 kg combined craft 180 degrees in 1,228 seconds, with a worst error of 0.6 degrees. It also held the craft inside a 60 arcsecond pointing target for a full orbit.", "reading_grade": 4.9000000000000003552713678800500929355621337890625 }, { "title": "The AI setting your power company should love", "url": "https://justouttech.com/the-ai-setting-your-power-company-should-love/", "published": "2026-09-08", "topic": "Cloud computing", "regions": [ "Canada", "China" ], "score": 4, "score_parts": { "peer": 0, "labs": 2, "evidence": 2, "open": 0, "signal": 0 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "unknown", "funder": "", "commercial": "Two of the four authors hold company affiliations: Xia Miao at Fova Technology (Suzhou) Co., Ltd, Suzhou, and Dai Wang at EcoFlare Co., Ltd, Wuxi. The other two are at Tongji University and the University of Alberta. No code, data or model release is stated.", "source": { "title": "Smoothing the Ramp, Not the Peak: Scheduling-Induced Power Dynamics of LLM Inference and Their Grid-Scale Consequences", "id": "arXiv:2608.01250", "url": "https://arxiv.org/abs/2608.01250", "labs": "Tongji University (Shanghai, China), University of Alberta (Canada), Fova Technology and EcoFlare (Jiangsu, China)", "date": "August 2026" }, "answer": "", "reading_grade": 6.79999999999999982236431605997495353221893310546875 }, { "title": "AI agents are being attacked four times faster than they are being defended", "url": "https://justouttech.com/ai-agents-attacked-four-times-faster-than-defended/", "published": "2026-09-08", "topic": "Cybersecurity", "regions": [ "USA" ], "score": 6, "score_parts": { "peer": 1, "labs": 2, "evidence": 2, "open": 0, "signal": 1 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "none", "funder": "", "commercial": "", "source": { "title": "On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models", "id": "arXiv:2608.10530", "url": "https://arxiv.org/abs/2608.10530", "labs": "New Jersey Institute of Technology (Md Jafrin Hossain, Mohammad Arif Hossain, Nirwan Ansari)", "date": "August 2026" }, "answer": "", "reading_grade": 5.70000000000000017763568394002504646778106689453125 }, { "title": "The camera that keeps watch for three months on one small battery", "url": "https://justouttech.com/the-three-month-camera/", "published": "2026-09-08", "topic": "Internet of Things", "regions": [ "Switzerland" ], "score": 7, "score_parts": { "peer": 1, "labs": 2, "evidence": 3, "open": 0, "signal": 1 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "unknown", "funder": "armasuisse Science & Technology. No grant number is given.", "commercial": "The four authors are at ETH Zurich, with Luca Benini also at the University of Bologna. No company is named as a collaborator on the work itself. No code, model or dataset release is stated, and no licence is named. The biographies note that Philipp Mayer founded Mayer Engineering and Consulting in 2019.", "source": { "title": "An Energy-Proportional Multimodal and Context-Aware Vision IoT Node", "id": "arXiv:2608.23192", "url": "https://arxiv.org/abs/2608.23192", "labs": "ETH Zurich (Luca Benini, Michele Magno and colleagues)", "date": "August 2026" }, "answer": "", "reading_grade": 6.4000000000000003552713678800500929355621337890625 }, { "title": "A 98-qubit computer fixes its own mistakes, and beats its raw hardware", "url": "https://justouttech.com/quantinuum-helios-fixes-its-own-mistakes/", "published": "2026-09-08", "topic": "Quantum computing", "regions": [ "United Kingdom", "USA" ], "score": 7, "score_parts": { "peer": 1, "labs": 2, "evidence": 3, "open": 0, "signal": 1 }, "score_history": [], "last_checked": "", "maturity": "lab", "status": "preprint", "code": "unknown", "funder": "No funding statement is given. The acknowledgements credit the Quantinuum team for Helios and Honeywell for fabricating the ion trap. All fourteen authors work at Quantinuum, so the company that gains if the result holds also ran the experiment.", "commercial": "Every author is at Quantinuum, in Broomfield, Colorado and in London. The platform under test is Quantinuum Helios, a 98-qubit trapped-ion QPU. Honeywell fabricated its ion trap. Correspondence addresses are @quantinuum.com. No code, data or model release and no licence is mentioned.", "source": { "title": "Experimental validation of a compact fault-tolerant architecture for trapped ions", "id": "arXiv:2609.03194", "url": "https://arxiv.org/abs/2609.03194", "labs": "Quantinuum (Broomfield, USA and London, UK)", "date": "September 2026" }, "answer": "", "reading_grade": 5.70000000000000017763568394002504646778106689453125 } ] }