• Bilingual Router LoRA: Qwen2.5-0.5B-Instruct, base → adapter, 576 frozen synthetic tests. One seed and run, with the schema supplied to both models. Strict all-field accuracy improved while strict JSON validity fell; unseen-operation failures remain visible. These are experimental results, not production performance. Model & reproduction.
• Evidence Agent: the 9/20 result belongs to the documented local Qwen3-14B run on October 3, 2026. The live 4B demo has no equivalent verified score. Tool traces support inspection, not a correctness guarantee. Free GPU quota can interrupt inference; labeled tool walkthroughs remain separate. Demo & setup.
• JSON Wire Browser Lab: A browser-local workbench separates UTF-8 decoding, JSON parsing and re-encoding, with fixed byte fixtures and a frozen Python reference. A large integer can change even when the parsed value round-trips. These checks do not prove semantic correctness or sanitize untrusted input. Exported reports include input bytes and runtime details; review them before sharing. Public lab & source.
• Offline Evidence Finder: Browser-local English and Chinese lexical retrieval exports exact quotes, UTF-16 offsets and source text. After a disclosed sample-extraction correction, one of four unanswerable queries still matched an unrelated excerpt. These hand-picked development queries are not a blind retrieval benchmark; a location match does not establish truth, answerability or semantic support. No model inference or training. Public lab & source · English write-up · 中文说明.
• Tiny Hash Intent: One from-scratch fit on BANKING77 is evaluated as float/int8 storage with raw/calibrated probabilities: four representations, not four trained models. All 301 errors on the official 3,080-row test set are retained; the two quantization-changed predictions are wrong before and after. This is a previously seen benchmark, not a historical blind test. The English 77-class closed set always selects a known class, with no out-of-domain rejection. The browser dequantizes int8 storage for floating-point inference; no integer-kernel or speed-up claim. Public model demo & source · English write-up · 中文说明.
• Split Boundary Audit · Group Planner: A local browser planner keeps caller-declared groups and groups connected by exact text together across train, validation and test, then exports the full plan and assigned records for the included Python auditor. It preserves every record, conflicting labels and achieved-ratio deviations. The 36-record synthetic example has 12 declared groups and 10 connected units; an 80/10/10 target yields 30/3/3 records, with one normalization candidate and one conflicting-label set retained for review. Normalized matches are candidates only. A retained counterexample shows the single-pass greedy planner is not globally optimal. Group provenance is supplied by the caller; a CLEAR audit does not prove statistical independence or absence of semantic leakage. Original Apache-2.0 code and synthetic examples; no model or external dataset. Public planner, source & audit · English write-up · 中文说明.

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