The author tested an open-source Amazon AI Skill package containing 5 functions: keyword research, hot products, product research, niche market finder, and seller analysis. It supports skill-compatible AI agents such as Claude Code and Cursor; installation requires only the npx command, no API keys are needed, and it covers 12 sites. Using 'camping lantern' as an example, it demonstrated complete analysis outputs including search volume, competitive landscape, seasonality, long-tail keyword classification, and market entry recommendations.
Jaxnasium is an open-source JAX reinforcement learning library that uniformly represents observation and action spaces as PyTrees, thereby supporting discrete, continuous, image, composite spaces, and multi-agent environments without requiring modifications to the algorithm code. It provides a Gymnasium-style interface and wrappers for 11 environment suites, covering over 300 environments, and is compatible with Gymnasium and EnvPool environments with no performance loss. It also includes tools for multi-seed evaluation, hyperparameter sweeps, and project scaffolding; the code is open-sourced at https://github.com/ponseko/jaxnasium
The Trinity is a recursive self-improvement framework that couples world generation with Harness evolution through three roles—Creator, Explorer, and Evolver—restructuring the task tree into a hierarchical capability tree based on shared capability requirements. It proposes the Capability-Driven Real2Sim2Real method, learning capabilities in simplified verifiable simulation environments and evaluating transfer effects on original tasks. The Explorer evolves reusable modules along the capability tree, forming a fixed-topology Harness tree, which works with the Capability-Aware Harness Router to select module configurations. Average performance improved by 34 points on 28 training benchmarks and by 26 points on 18 unseen benchmarks (including two real-world embodied tasks).
This paper proposes a creative pre-ranking method using hierarchical empirical Bayes Thompson sampling to score recalled creatives before ad requests and filter the creative pool for each campaign. Creatives are hierarchically structured into four levels: audience group, product, campaign, and creative. Cold-start creatives automatically fall back to parent-level historical performance, with parameters solved in closed form requiring no tuning. The pre-ranking is based on audience groups rather than individual users, utilizing attribute counters to achieve low latency. In Roblox sponsored ad traffic replay, compared to equal-volume random filtering, the predicted play-through rate increased by 8.14%, forwarded creatives decreased by over 40%, and service time increased by only 0.2%. After online A/B testing, it has been deployed to the Roblox production environment.
Researchers from Xiaomi's Embodied Intelligence team, in collaboration with the University of Macau and Tsinghua University, released OmniBrain-0 on arXiv. It is a lightweight unified embodied agent model that coordinates manipulation and navigation through a shared high-level decision-making model. It employs a progressive training framework (including embodied pre-training, domain expert distillation, and multi-teacher online policy distillation) and a Rubric-Guided Recursive Self-Improvement (RSI) data flywheel. Its cross-domain performance and low inference/execution overhead were validated on LIBERO-Pro, LIBERO-Plus, RoboCasa365, RoboTwin 2.0, R2R-CE Val-Unseen, and multiple real-world manipulation tasks.
MedJev is an open System One model that runs locally on a laptop; it does not generate text and answers structured medical questions with a single forward inference pass. It approaches GPT-4's performance on MedQA (72.5%), USMLE (75.4%), and NEJM (80.1%), with Expected Calibration Error (ECE) ranging from 0.028 to 0.063. Its AUROC for in-hospital mortality prediction reaches 0.926, but patient-level tasks exhibit significant calibration bias (ECE 0.286). The authors propose a two-parameter recalibration method requiring only 50–200 labeled samples, which reduces ECE to 0.007. Code and evaluation frameworks are open-sourced.
The paper proposes h1, a lightweight agent loop framework that provides an efficient Lean 4 interface for large language models, without relying on manual decomposition or search structures. It achieves SOTA on PutnamBench, FATE-X, and FormalConjectures100-Solved, solves eight open problems in FormalConjectures, and matches or surpasses specialized workflows such as Goedel-Architect and AlphaProof Nexus under matched model conditions. Ablation studies indicate that an efficient Lean interface is more critical than merely simplifying the architecture.
This paper explores how AI assistance truly saves time, proposing a 'ledger perspective'—jointly modeling time savings with escape defect rates, defining time saved, excess escaped defects, and break-even defect cost; based on replication of METR developer studies, it shows AI helped in between 3% and 82% of 246 issues, with overall slowdowns of 19%–24% when uncorrected; validated in SEC file draft review experiments: when reviewers were incorrectly told that drafters had lower error rates, inspection intensity decreased, leading to incorrect numbers in 22.5% of briefings.
KORA is a deterministic-first execution control architecture that models requests as task paths through a pre-inference control layer, prioritizing deterministic or structured processing paths, validating results and recording telemetry, and escalating to model candidate paths only when deterministic processing is insufficient; in a reproducible 100-task deterministic-intensive benchmark, it avoided 80 out of 100 simulated model calls compared to a naive direct-call baseline, with zero mismatches in the deterministic subset; workloads include FAQ lookup, policy/rule checks, format validation, deterministic transformations, and a small number of ambiguous/generative tasks; additionally, a synthetic customer service local offline verification workload demonstrates path measurement and model call avoidance statistics.
The paper investigates the behavior of two mainstream vision-language models (VLMs) when transforming facial images into different racial (e.g., Black, White, Asian, Hispanic) and sexual orientation (homosexual/heterosexual) identities. It finds that their responses are non-deterministic and group-sensitive; generated images can be statistically distinguished and reinforce stereotypes. The bias also migrates to text reasoning and profiling tasks. The study further notes that this issue exists in the generation of other attributes such as criminality, potentially facilitating AI-assisted profiling.
Robinhood's Q3 2026 earnings report showed revenue of $1.424 billion, up 12% year-over-year, but crypto-related revenue dropped sharply by 55% to $120 million; during the same period, prediction market revenue reached $173 million, surging more than five times year-over-year and clearly exceeding crypto business revenue for the first time. Robinhood Chain generated $26 million in revenue in its first quarter online, with DEX trading volume exceeding $80 billion; prediction market event contract trading volume reached 16.4 billion contracts, an increase of more than seven times year-over-year.
As of mid-2026, Amazon, Meta, Oracle, and Alphabet all reported negative free cash flow in their latest financial reporting periods, primarily because massive capital expenditures on AI data centers far exceeded operating cash flows; Microsoft was the only hyperscale cloud provider that maintained positive free cash flow.
BitMiracle AI Lab released the official version of its research agent ClawsGO Science, supporting Claude Opus 5.5 decision-making, execution by 136 GPT-6 Astra agents, and automated verification via the Lean kernel; it has completed a machine-verifiable proof of the detailed balance conjecture for chemical reaction networks from 1974 on a cloud workstation; the platform comes pre-installed with 533 scientific software packages and 91 databases, covering 61 primary disciplines; the paper was published on arXiv, and the Lean source code is open-sourced on GitHub.
Shopify has redesigned its product variant management feature, supporting direct editing of variants on the product detail page without page redirects; updating variant pricing in bulk is reduced from five steps to two; a single product supports up to 2048 variants, each variant can have up to 20 barcodes added, and it supports saving packaging dimensions to automatically match order packing. This update will be rolled out to all merchants in December 2026.
Starting with Shopify POS version 11.17, automatic in-store customer identification is supported: when a customer pays with a bank card previously linked to Shopify Payments, the system can pre-fill de-identified contact information based on Shopify's network of hundreds of millions of buyers for optional digital receipts; upon customer authorization, a customer profile is created or associated, and purchase records are synchronized.
Shopify has introduced automatic content synchronization between Shop Store and online store homepages for some merchants, supporting the sync of images, videos, featured collections, product rows, and other content to Shop Store; content is adapted into native Shop modules, with non-homepage content such as product detail pages and About pages not supported; requires an Online Store 2.0 theme and is currently in early access.
Instagram Direct migrated its Android user interface from the View system to Jetpack Compose, building an AI-native codebase that reduced UI code volume by 50%, lowered AI agent session token costs by 33%, shortened execution time by 35%, and decreased human-machine interaction instances by 32%.
The Google Antigravity SDK has added support for local models, enabling developers to run builder agents locally to perform tasks such as code auditing, patch generation, and testing, while architect agents plan tasks in the cloud. In the demonstration, 97.2% of token consumption occurred locally, with source code not uploaded to the cloud. Initial support includes Gemma 4 26B A4B (via LiteRT), and it is also compatible with OpenAI-compatible local inference servers such as Ollama, LM Studio, and vLLM.
Anthropic has filed an S-1 registration statement with the U.S. SEC, planning to list on Nasdaq under the ticker code ANT; the document positions Claude Code as the core growth engine for its enterprise business, stating that it drives high-value customers' monthly average spending to 20 times that of regular users, and that over 90% of the company's codebase was generated by it in September 2026; meanwhile, it discloses that approximately 5%–7% ($1.7 billion–$2.3 billion) of revenue in the first three quarters came from customers banned for violating usage policies, with revenue recognized before the bans; Claude Code has evolved from a code suggestion tool into an autonomous programming agent and serves as the technical foundation for knowledge work products such as Cowork.