This paper introduces the Maritime Anomaly Detection Quality Index (MADQI), a novel composite metric for evaluating unsupervised anomaly detection models on AIS data without labeled data. It integrates four metrics: ARC, PPS, SDS, and ECE, normalized via multi-chunk evaluation and adaptive scaling. Experiments achieved an MADQI score of 80.37%, with ECE and ARC scores of 0.907 and 1.000, demonstrating effectiveness in detecting extreme anomalies and maintaining consistency.

A new fMRI generative framework called Dual-Spectral Flow Matching (DSFM) is proposed, cascading discrete wavelet transform and discrete cosine transform to capture multi-scale transients and low-frequency energy compaction of BOLD signals. Class-conditioned cosine-frequency representations are generated via spectral flow matching, then reconstructed into physiologically plausible time series via inverse transforms, improving downstream brain network classification. Accepted at ICLR 2026; code open-sourced.

This paper introduces a novel LLM architecture based on RBF networks that eliminates deep neural networks, finds the global optimum of the loss function in closed form in one iteration, and offers improved explainability and accuracy.

This study introduces a multi-model paradigm to study synthetic deception via LoRA fine-tuning of five transformer models. Linear probes detect deception with near-perfect AUC in early layers, and logistic regression probes outperform MLP probes, supporting the Linear Representation Hypothesis. Probes generalize across domains with minimal loss. Different models exhibit distinct representational regimes: collapse in Pythia/Llama/Qwen versus high-dimensional preservation in Gemma-2. The results show that robust, domain-invariant deception representations can be rapidly entrenched through modest supervised fine-tuning, with implications for activation-based monitoring.

The Unicorn framework uses a latent prototype codebook to decouple correlation modeling from channel identities, enabling scalable pretraining across heterogeneous datasets and outperforming existing methods in few-shot transfer scenarios.

Researchers developed a deep learning framework to predict hip dynamics from gait kinematics, comparing LSTM, Transformer, and Mamba. Transformer performed best on 60 healthy subjects and retained moderate predictive ability on osteonecrosis patients in zero-shot testing. The study demonstrates feasibility but calls for broader validation.

Quantum computing in the NISQ era requires hardware-level capabilities such as mid-circuit measurement, classical feedback, precise timing, and pulse-level access, all exposed by OpenQASM-3. However, no dataset existed to train LLMs on these hardware-oriented features. QASM-Eval fills this gap with an expert-verified test set of 100 tasks and a training set of 4,000 tasks covering classical logic, timing scheduling, pulse control, and complex workflows. An extended verifier automatically checks syntax, quantum states, and timeline. Evaluation shows that state-of-the-art LLMs struggle significantly on OpenQASM-3 tasks, but fine-tuning on QASM-Eval yields substantial improvements, providing a crucial benchmark for developing reliable LLM assistants in quantum programming.

MAVEN (Modular Agentic Verification and Execution Network) is a lightweight symbolic reasoning scaffold designed to enhance generalization in tool-calling environments through structured decomposition, adaptive tool orchestration, and intermediate verification. On the MAVEN-Bench stress test, MAVEN improves the GPT-OSS-120b base model from 48% to 71% accuracy without additional training, using an open-weight backbone at roughly 1/10 the cost of proprietary baselines.

Existing healthcare AI benchmarks fix provider responses, failing to evaluate mechanisms by their resulting equilibrium. This research recasts hospital mechanism design as program synthesis for language models, using a multi-agent simulator (Medi-Sim) and LLM-guided evolutionary code search to synthesize an inspectable mixed-objective program that eliminates up-coding, halves rejection, and retains most baseline funds.

A new paper proposes three rerooter designs to enhance subgoal-based policy tree search by using implicit subtask decomposition via the sqrt(LTS) algorithm, achieving state-of-the-art online training efficiency without explicit subgoal generation.

EHRBench is an automated and reliable benchmark that leverages electronic health records (EHR) through an EHR-LLM-KB pipeline to generate nearly 1 million QA items covering diagnosis, treatment, and prognosis tasks. It evaluated over 30 LLMs, revealing consistent performance trends and highlighting gaps toward clinically reliable systems. The work has been accepted at KDD 2026.

LLM agents adapt by updating external harnesses (prompts, skills, memories, tools), but it's unclear if base task-solving ability predicts harness self-evolution capability. The study shows harness-updating is flat across models, while harness-benefit is non-monotonic: mid-tier models benefit most, with weak and strong tiers benefiting less. Recommendations include investing in the task-solving agent over the evolver and focusing on harness invocation and long-horizon instruction following.

A paper on arXiv explores using MAP-Elites, a quality diversity algorithm, to procedurally generate First-Person Shooter (FPS) maps. The authors introduce two novel map representations (Point-Line and Spatial-Layout) and define metrics for topological and emergent properties. Using MAP-Elites with Sliding Boundaries (MESB) to evolve maps, results show new representations yield higher diversity and quality than previous methods.

This paper investigates encoding factored tasks (FTS) in SAT, proposing several encoding strategies and analyzing the impact of task transformations and parallelism on SAT-based planner performance.

PhyDrawGen is a neuro-symbolic pipeline that generates physically accurate diagrams from text. It uses an LLM to extract a scene graph, a deterministic solver to encode physics constraints, and a fine-tuned Qwen-VL model to iteratively correct violations. Evaluated on 1,449 problems, it outperforms GPT-5-image and Gemini models.

PayPal and Palantir cofounder Peter Thiel argues that AI poses a greater threat to math and STEM fields than to creative and communication roles. LinkedIn data shows rising demand for soft skills like storytelling, with some companies paying over $1 million. Meanwhile, some STEM majors face high unemployment, but not all. Thiel suggests AI may eliminate the need for math as a barrier in fields like medicine.

An AI agent for long-running, multi-step work and coding. Discussion on Product Hunt.

A recently patched exploit on Instagram allowed attackers to reset passwords for accounts without MFA using Meta AI, raising security concerns about AI integration.

Smart Rename is an open‑source tool that uses a local AI model (llava) to monitor folders and automatically rename image files with descriptive names in real time. It runs completely offline, protecting privacy, and is controlled via a system tray icon.

In a recent video, PewDiePie unveiled an AI workspace built on OpenCode, designed to streamline creative workflows with integrated AI tools.

MiniMax M3 is a multimodal foundation model supporting text, image, and video inputs with a 1M-token context window, optimized for long-horizon agentic work, coding, and tool use. It is now available on OpenRouter with competitive pricing.

A comprehensive guide to beta testing apps using Apple's TestFlight, covering installation, system requirements, managing updates, and testing different builds across iOS, iPadOS, macOS, tvOS, visionOS, and watchOS.

The UK Home Office's trial of AI facial age estimation for Channel migrants faces strong criticism over accuracy, demographic bias, and legality. The technology's mean absolute error of 1.88 years hides wide error tails, especially for non-white, female, or children in poor conditions. Legal experts argue current AI tools may violate applicants' rights to informed decision-making.

Moxie Docs creates living documentation for GitHub repos, provides MCP context for AI agents, a searchable workspace, and PR checks to keep docs honest.

Anthropic confidentially filed an S-1 for an IPO and shipped Claude Opus 4.8 with 4x code-reliability gain. NVIDIA unveiled Cosmos 3, Vera Rubin, and RTX Spark at GTC Taipei. Google retires Gemini 2.0 Flash. California passes AI chatbot toy ban; Illinois data center bill stalls.

Anthropic raised $65B at a $965B post-money valuation and shipped Claude Opus 4.8. NVIDIA used GTC Taipei to open Cosmos 3, ramp Vera Rubin into production, and put a 1-petaflop AI box on developer desktops. Google shuts Gemini 2.0 Flash down today. California's SB 867, which would ban AI companion chatbots in children's toys, cleared the Senate; Illinois's data-center regulation stalled in committee. The labs sprint. The states crawl.

Botcircuits is an open-source AI agent that combines LLM step reasoning with a deterministic state machine, enabling token-efficient and predictable multi-step automation. It features a CLI, workflow system with natural language authoring, skills, and MCP support.

The AI agent that handles checkout.

Mapa.ua is a real-time air raid alert map for Ukraine, tracking hostile drones, cruise missiles, ballistic missiles, and guided bombs. It offers historical replay, push notifications, and crowdsourced intelligence, all free and ad-free.

A video expresses strong frustration with AI, but contains no substantive content beyond the title.