How TuringJian X Uses AI to Quantify and Verify Trust in Product Authentication

Background and Motivation

At WAIC 2026, Richard Sutton warned that simply scaling pre‑training data no longer yields model improvements, while a panel of academicians emphasized a shift from "who can compute more" to "who can understand deeper"—high‑quality vertical data becomes the decisive factor. This insight underpins the belief that generic data is reaching its limit and that industry‑specific, high‑knowledge‑density data will drive differentiated AI capabilities.

Vertical Solution: TuringJian X

Turing DeepVision, a Tsinghua‑incubated company, adopts a human‑in‑the‑loop "dirty work" strategy to accumulate expert‑labeled data with a 100 % "wrong‑order‑compensate" service, then builds a vertical large model. The core architecture, called LLM‑EDC (LLM‑Enhanced Discriminative Core), decouples task understanding (handled by a large language model) from fine‑grained visual discrimination (handled by expert models routed via a Mixture‑of‑Experts mechanism).

Unlike generic multimodal models that excel at intent parsing but struggle with minute physical details, the expert discriminators excel at detecting subtle features such as embossing edge smoothness. The system first uses the LLM to identify relevant regions, then routes those regions to specialized models, aggregates their outputs, and generates a structured assessment.

Evidence‑Driven Training (RLVR)

Traditional models learn a simple "image → label" mapping. TuringJian X learns a triplet "image – conclusion – evidence". Experts annotate not only the truth value but also the key visual cues and reasoning. During training, a verifiable‑reward reinforcement learning (RLVR) framework rewards the model for correctly locating evidence, identifying anomalies, and producing a coherent reasoning chain, upgrading the objective from mere label accuracy to evidence‑based correctness.

Category‑Specific Capabilities

Bag assessment: Demonstrated on a high‑fake Coach bag; the system highlighted ink spacing and edge thickness as evidence for a fake verdict.

Coin valuation: Identified a rare Chongning coin as genuine, described "in‑bone rust" patterns, and provided a market estimate of ¥500‑¥700.

Ceramics: Detected artificial aging on a high‑fake piece by spotting mismatched lion and floral line work.

Clothing: Detected four defect points on a garment, using CLLAD and DAMN to handle long‑tail defect samples.

Shoes (AR glasses): Real‑time point‑and‑shoot verification with AR glasses, uniquely targeting the authentication scenario.

Cosmetics: Performed font‑forensic analysis on packaging, comparing stroke skeletons, ink diffusion, and embossing to a "production fingerprint".

Food (Meal analysis): Retrieved nutritional data via RAG and performed structured reasoning to estimate calories and macronutrients.

Benchmark Evaluation

A random sample of 100 real orders (1:1 genuine‑fake) was collected from multiple physical authentication centers. Three metrics were defined: identification accuracy, latency, and reasonability score (0‑5). TuringJian X was compared against three leading human‑image authentication platforms (D, W, Y) and top generic multimodal models.

Results:

Accuracy: 100 % in bags, shoes, apparel, cosmetics; 95.5 % in coins.

Latency: Consistently faster than the fastest human platform across all five categories.

Reasonability: Average score 3.48, outperforming generic multimodal models (≈2.00) by 1.48 points.

The evaluation shows that the LLM‑EDC design—LLM for task planning and MoE routing to expert discriminators—delivers stable, cross‑category performance, confirming the advantage of combining general reasoning with specialized visual expertise.

Implications for Trust Infrastructure

TuringJian X transforms trust in product transactions from subjective expert opinion to a transparent, verifiable process. Each assessment provides an evidence chain that can be audited, and the service offers a "wrong‑order‑compensate" guarantee, effectively turning trust into a measurable, enforceable asset. The system is available as a free mini‑program and web app, positioning it as a foundational layer for trustworthy e‑commerce.