प्रतिदिन अद्यतन किया जाता है1800 सत्यापित कहानियाँ
उत्पाद लॉन्च, नीति परिवर्तन, सुरक्षा अनुसंधान और उद्योग चाल की स्रोत-जांच की गई एआई कवरेज, एक गैर-लाभकारी शिक्षा टीम द्वारा सरल अंग्रेजी में समझाई गई है।
प्रत्येक कहानी सबसे मजबूत उपलब्ध साक्ष्य से जुड़ी होती है: उपलब्ध होने पर मूल स्रोत, अन्यथा स्पष्ट रूप से जिम्मेदार रिपोर्टिंग।
क्या हुआ, यह क्यों मायने रखता है, और क्या देखना है - शब्दजाल के बिना।
जब सिग्नल पतला होता है, तो हम फ़ीड को पैड करने के बजाय कुछ भी प्रकाशित नहीं करते हैं।
स्रोत-जांच की गई एआई कहानियां, नवीनतम सबसे पहले, उन लोगों के लिए जिन्हें प्रचार का पीछा किए बिना एआई को समझने की आवश्यकता है।
A new arXiv paper describes a self-supervised EEG method that reports 92.4% accuracy distinguishing Alzheimer’s disease from cognitively normal controls on the ADFTD cohort.
An arXiv paper introduces Agentic ESOpt, a proposed evolution-strategy framework for fine-tuning long-horizon language-model agents with inference-level GPU memory. The authors report gains for Qwen-3.5-27B on WebArena-Lite and improvements in 28 of 36 prompt-optimization settings.
An arXiv paper argues that mean squared error can misjudge irregular time-series forecasts and proposes a continuous-time metric tested across synthetic, semi-synthetic and eight real-world datasets.
An arXiv paper introduces Relative Transfer, a measure of how training examples at different difficulty levels affect one another, and proposes Transfer-aware Dynamic Curriculum Sampling for adapting training schedules during post-training.
An arXiv preprint describes Co-RL, a multi-agent reinforcement-learning framework in which separate models reward one another. The authors report gains across text-only and multimodal benchmarks without ground-truth labels, while acknowledging the risks of self-reinforcing errors and training collapse.
A new arXiv paper proposes Task Specialization Fine-Tuning to allocate limited fine-tuning across related task regions. It reports improved task coverage in optimization, control, and LLM experiments, but gives no numerical results or independent validation.
Accepted to the 2026 Asilomar Conference, a paper proposes SURE-Ridge, a closed-form method for causal-graph estimation in equal-variance linear Gaussian models. Authors report lower small-sample structural error and fastest tested-baseline runtime; the source does not establish performance beyond these assumptions.
Researchers describe J-Miner, a method that converts internal signals from fine-tuned language-model classifiers into inspectable rules, reporting up to 98.3% reproduction of source decisions and nearly the same average task accuracy in much smaller student models.
A single-author arXiv paper reports that giving an agent a tool to switch between specialized LoRA adapters helped it solve two synthetic coding tasks and reduced the reported capability tax by up to 18 times.
An ECML PKDD 2026 paper presents MultiSigBERT, combining narrative medical reports, structured records and temporal patient data to estimate individualized oncology risk scores. It reports a 0.743 concordance index on an independent test set; the source has no comparator results or clinical-deployment evidence.
An arXiv paper proposes optimizing multi-layer model edits through the complete forward pass, reporting the strongest combined results among tested methods in five of six model-dataset settings.
An arXiv paper reports that adversarial examples generated through publicly released EEG encoders can affect inaccessible downstream models without queries to their parameters, outputs, or gradients.
प्रत्येक सप्ताह एक उपयोगी ब्रीफिंग
सप्ताह की सत्यापित एआई समाचार, मूल डेटा, उपयोगी उपकरण, सीखने के विकल्प और ताज़ा एआई नौकरियां प्राप्त करें।
एक एआई पेशेवर को नियुक्त करना या एक उपयोगी एआई उत्पाद लॉन्च करना? इसे उन लोगों के सामने रखें जो यहां सीखने और अभिनय करने आए हैं।
एआई जॉब पोस्ट करें एक AI टूल सबमिट करें