AI Core

Researchers from MIT, Carnegie Mellon University, New York University, and Stanford University developed an AI system called Ataraxos that combines a self-play reinforcement learning "blueprint strategy" with decision-time planning using a generative model, defeating the strongest human Stratego player by a record 15-1-4 margin and achieving a 39-2 record against top players at the Stratego world championship, while using less than one hundredth of DeepMind's DeepNash training examples and less than one thirtieth of its self-play games, and generalizing to other imperfect-information games such as Barrage Stratego, Hanabi, and Dou dizhu.

Cohere introduces RCP-nDCG@10, a retrieval evaluation methodology in which a calibrated AI judge grades every retrieved document against the same explicit relevance rubric and combines rubric answers with group-wise comparisons and calibration, so relevant documents absent from the answer key can still earn credit; in a blind study with 46 annotators, 273 queries and 289 head-to-head contests, reviewers sided with RCP-nDCG 70% of the time where the two metrics named different winners, and across all contests RCP-nDCG matched reviewer preference 77% of the time versus 52% for conventional nDCG, with the gain driven mainly by crediting relevant documents the answer key missed (19 percentage points).

Cohere released the Embed 5 family of embedding models in two tiers, Pro and Fast, which share a single embedding space and support 128K context, text/image/fused inputs, 100+ languages, and Matryoshka outputs from 2048 down to 256 dimensions; Pro averages 85.8 on ViDoRe V3 (an 8.8 gain over Embed 4), scores 80.1 on FinanceBench and 84.8 across the parsed-document suite, while Fast averages 84.5 on ViDoRe V3 with about 2.4x the document throughput of Pro, priced at $0.12 and $0.08 per million tokens respectively.

The work proposes FedTrust-GNN, a decentralized user-modeling framework that combines differentially private federated learning with secure multi-party computation, a permissioned blockchain using PBFT consensus, and a heterogeneous graph attention network (HGAT) that infers dynamic trust scores, with trust-weighted robust aggregation (TWRA, combining norm clipping and coordinate-wise median aggregation) providing Byzantine fault tolerance; on Federated EMNIST, Stack Overflow, and synthetic datasets with 10,000-100,000 participants it reports 94.2% accuracy (within 1.3% of centralized models), a reduction of label-flipping attack success from 34% to 6.1% (an 82% reduction), a 41% improvement in convergence stability, and blockchain performance of 1,200 TPS with 2.3-second finality.

The work presents a fast two-dimensional direction-of-arrival (DOA) estimation approach for low-elevation targets of very-high-frequency array radar: it uses the azimuth and pitch angle uncoupling properties of a uniform planar array to turn the 2D angle estimation problem into two 1D DOA estimation problems, retrieves target information in the azimuth and elevation dimensions with digital beamforming, and then estimates azimuth and pitch angles using the alternating direction method of multipliers, thereby reducing complexity and eliminating the need for eigenvalue decomposition during operation.

The study had 1,003 participants with depression read vignettes describing psychotherapy options with different levels of AI involvement (a human therapist without AI, assistive AI, collaborative AI, and fully autonomous AI) and rate them; participants consistently evaluated human therapists more favorably, reporting greater likelihood of seeking treatment, less hesitancy, and greater treatment acceptability, and compared with a human therapist they were willing to pay 31.6% less for therapists using assistive or collaborative AI and 57.

The work proposes Stability-Constrained Information-Theoretic Feature Selection (SCITFS), which integrates conditional entropy, normalized mutual information maximization, and a stability term penalizing feature-ranking variance across bootstrap samples into a single formally defined objective, implemented via a greedy forward-selection strategy with proven monotonicity guarantees at O(B n^2 m d^4 + k n^2); across eight benchmark datasets and five classifiers (SVM, Random Forest, k-NN, XGBoost, Logistic Regression), SCITFS outperforms Information Gain, Mutual Information, mRMR, ReliefF, Fisher Score, and JMI, achieving a 3.7% average accuracy improvement on SVM and 4.2% on Random Forest with 92.3% feature reduction while maintaining performance; Friedman test (χ² = 127.4, p < 0.

The paper presents Ridezy, a decentralized trust architecture that uses edge Artificial Intelligence and Internet of Things (AIIoT) to continuously monitor behavioural indicators such as lane discipline, speed compliance, braking behaviour, and traffic sign adherence, processes behavioural summaries off-chain while anchoring only cryptographic hashes, credibility updates, and payment records on Polygon smart contracts, and compares it against three representative trust models—a traditional rating-based system, an AI-only architecture, and a blockchain-only architecture—across behavioural detection performance, end-to-end latency, cost efficiency, throughput, reputation stability, tamper resistance, and component-wise ablation studies, with results showing higher behavioural fidelity and st