# Ely-Eye Training Artifacts ## What this file is, and what it is not It is a record of what was produced on **2026-05-28** on the Windows + RTX 4090 machine that trained the adapters, from a `PRD.md` that no longer exists in this repository. The adapters themselves, the cartridge, and every `.ely_eye/` path listed below are runtime state and are **not tracked by git**, so a clone of this repository cannot reproduce them. What is **measured in this repository today** lives in `bench/reports/` and is reproducible with the commands in `README.md`. Nothing in this file belongs to that set. Scores that were never run against ground truth have been deleted rather than kept with a hedge; where a number is kept, the sentence next to it says what the number actually measures. ## Artifacts Generated on 2026-05-28 from the local `PRD.md`, ingested Evidence Atom `PRD_0b88b12114634a56:text`, visual Evidence Atom `ely_eye_dashboard_0180927ffdd3f8b0:image`, local UI proof images, and Qwen model snapshots. Every adapter has `training_trace.jsonl`, `training_summary.json`, and `training_proof.json` with RTX 4090 CUDA BF16 execution metadata, proof version 2, BF16 LoRA method, 8192-token max length, paged 8-bit AdamW, Unsloth-patched checkpointing mode, per-step loss, trainable parameter hashes before and after training, optimizer update norms, dataset hashes, environment versions, adapter weight SHA-256, LoRA tensor statistics, runtime adapter binding proof, runtime adapter generation proof, and proof-suite verified nonzero LoRA safetensors. | Adapter | Base model | Samples | Steps | Updates | Max length | Trainable params | Final loss | Loss delta | Max update L2 | Seconds | SHA-256 | |---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---| | HME-Core-LoRA | Qwen/Qwen3.5-9B | 37 | 37 | 10 | 8,192 | 29,097,984 | 0.0001596613001311198 | 0.22773139642958995 | 0.8183563251107077 | 104.4 | 8f97890e802f9adfa3bcb2c02b33d7b39dd8954654d7057d4058e1331e7b5df4 | | HME-Vision-LoRA | Qwen/Qwen3.5-9B | 10 | 20 | 5 | 8,192 | 1,996,672 | 2.0484652519226074 | -0.11261439323425293 | 0.6595521616294707 | 49.3 | 593f7295f466c5fba07bdae26d1d8798c5affbad8f3863cda9f1881d06453dec | | HME-TTT-VL | Qwen/Qwen3.5-9B | 10 | 20 | 5 | 8,192 | 8,288,128 | 1.5277884006500244 | 0.4080624580383301 | 1.2924649382409095 | 51.2 | 030b37480155a8eaa6ef4c2dfa819d5f2564838935654f0d826235eb66cc9de5 | | HME-Visual-MTP | Qwen/Qwen3.5-9B | 10 | 20 | 5 | 8,192 | 2,019,328 | 2.066615343093872 | -0.13076448440551758 | 0.1419292917069751 | 75.8 | 489da9460f51693fa8775ad4975a18f7400dd3fc41eac401d00305617c627717 | | HME-Router | Qwen/Qwen3.5-0.8B | 8 | 16 | 4 | 8,192 | 3,194,880 | 1.2781919240951538 | 1.0438536405563354 | 0.2871291982199057 | 28.8 | 9ef884a1078df71ba73376a29737a27e22e2ff25854352bfc2c7d960102d886e | | HME-Retrieval | Qwen/Qwen3-VL-Embedding-8B | 32 | 32 | 32 | 8,192 | 21,823,488 | 0.5665192604064941 | 0.91921067237854 | 1.3181151117874825 | 55.3 | 6490f03e445eb70da7020be992c6eccd3ca2b0e4c48e73a80a819ddac5faa8f3 | Adapter weight proof: | Adapter | LoRA tensors | Nonzero elements | Weight SHA-256 | |---|---:|---:|---| | HME-Core-LoRA | 256 | 29,097,984 | 4673ac90abdd8ef952dad9c2292aecadba161669c3b1fc3d1af5c075cf0ae62d | | HME-Vision-LoRA | 220 | 1,992,340 | dde2eb9051866634f9173366a6ffa3fbf9219e5b6d640eafb7f99d0258c52422 | | HME-TTT-VL | 412 | 8,283,800 | 9a572ac51c6df08e38898c6c30acd8ed6530c7e852611552baaec6a67862e6c5 | | HME-Visual-MTP | 4 | 1,028,316 | 58d058b862a542c042d27234b5b22344ba02ee6e016be6c8838b329ffea1c6f9 | | HME-Router | 192 | 3,194,880 | 90079b8f2c244efb00c2285816b98f51ff03cf06a9ba2335ee5bbdf24a1aff8f | | HME-Retrieval | 504 | 21,823,488 | bcb1381888ae84e82ab18256e152191e56a0892c46cdac81874a9c1fb1af14ee | Local adapter roots: ```text .ely_eye/adapters/hme_core_lora_qwen35_9b .ely_eye/adapters/hme_vision_lora_qwen35_9b .ely_eye/adapters/hme_ttt_vl_qwen35_9b .ely_eye/adapters/hme_visual_mtp_qwen35_9b .ely_eye/adapters/hme_router_qwen35_08b .ely_eye/adapters/hme_retrieval_qwen3_vl_embedding_8b ``` Cartridge binding: ```text .ely_eye/cartridges/ely_eye_prd_7e5cd27b8284/adapters/hme_core_lora .ely_eye/cartridges/ely_eye_prd_7e5cd27b8284/adapters/hme_vision_lora .ely_eye/cartridges/ely_eye_prd_7e5cd27b8284/adapters/hme_ttt_vl .ely_eye/cartridges/ely_eye_prd_7e5cd27b8284/adapters/hme_visual_mtp .ely_eye/cartridges/ely_eye_prd_7e5cd27b8284/adapters/hme_router .ely_eye/cartridges/ely_eye_prd_7e5cd27b8284/adapters/hme_retrieval ``` PRD proof suite: ```text .ely_eye/data/eval_proofs/prd_proof_suite_b03b88d7cfe5ba96/proof_suite.json .ely_eye/cartridges/ely_eye_prd_7e5cd27b8284/eval_proofs/prd_proof_suite_b03b88d7cfe5ba96/proof_suite.json ``` Runtime generation proof: ```text .ely_eye/data/eval_proofs/prd_proof_suite_b03b88d7cfe5ba96/runtime_generation.json transformers generated 218 tokens with hme_core_lora citation accuracy 1.00 ``` "Citation accuracy 1.00" there means every atom id the answer cited existed in the context that was handed to the model. It is not entailment: the check cannot tell a correct citation from a citation of the wrong passage, and the question used by that check contained its own answer. 100M memory capsule proof: ```text .ely_eye/cartridges/ely_eye_prd_7e5cd27b8284/memory_capsule.json.zst .ely_eye/cartridges/ely_eye_prd_7e5cd27b8284/memory_capsule_index.json .ely_eye/data/eval_proofs/prd_proof_suite_b03b88d7cfe5ba96/memory_capsule.json The Context Cartridge materialized a 100,000,000 token-equivalent memory capsule with 4,096 committed segments and a verified segment Merkle root. The 100M is a property of a capsule the check generated for itself, not 100 million tokens of ingested content: the segments were synthesized, and the check verified that the capsule hashed to its own recorded digest. What the store actually holds today is measured in `bench/reports/DENSE.md` (165,973 atoms, 153,857 vectors), and `Gaps.md` appendix B prices one million tokens of live KV cache at 148.9 GB in fp16. ``` Model-solved HashHop proofs: ```text .ely_eye/data/eval_proofs/prd_proof_suite_b03b88d7cfe5ba96/hashhop/0a10e6394ada0da7/model_text_hashhop.json .ely_eye/data/eval_proofs/prd_proof_suite_b03b88d7cfe5ba96/hashhop/60c9d5f5f1648ea0/model_visual_hashhop.json .ely_eye/data/eval_proofs/prd_proof_suite_b03b88d7cfe5ba96/hashhop/a29a310b31f9cef5/model_visual_hashhop.json .ely_eye/data/eval_proofs/prd_proof_suite_b03b88d7cfe5ba96/hashhop/7f7b8b1f54ec3d75/model_visual_hashhop.json .ely_eye/data/eval_proofs/prd_proof_suite_b03b88d7cfe5ba96/hashhop/4f24efd13342694c/model_visual_hashhop.json .ely_eye/data/eval_proofs/prd_proof_suite_b03b88d7cfe5ba96/hashhop/a3f49402ef2710a3/model_visual_hashhop.json .ely_eye/data/eval_proofs/prd_proof_suite_b03b88d7cfe5ba96/hashhop/0bd17526ab51392b/model_visual_hashhop.json Qwen/Qwen3.5-9B with HME-Core-LoRA solved a 1.01M token-equivalent 2-hop text HashHop and 6 Visual HashHop arenas at 262K token-equivalent. The check that produced this collected proofs until its pass-rate target was reached, so the file records that the model could solve these arenas, not a measured solve rate. The retry loop was removed in the audit pass; a re-run now reports the rate it got. ``` Benchmark source and sample proof: ```text .ely_eye/data/eval_proofs/prd_proof_suite_b03b88d7cfe5ba96/standard_benchmark_sources.json .ely_eye/data/eval_proofs/prd_proof_suite_b03b88d7cfe5ba96/standard_benchmark_samples.json .ely_eye/data/eval_proofs/prd_proof_suite_b03b88d7cfe5ba96/standard_benchmark_predictions.json MMMU-Pro, OCRBench, MMLongBench-Doc, VideoMME, LongVideoBench, RULER, and LongBench v2 loaded official streaming samples or official public metadata samples with sample hashes and source dataset SHA values. LongVideoBench uses `longvideobench/LongVideoBench-Meta` validation metadata with canonical `longvideobench/LongVideoBench` registry tracking. No benchmark score is quoted here. The prediction step read **one** sample per benchmark (`next(iter(stream))` with `streaming=True`), so MMMU-Pro and OCRBench produced a single local prediction each; a score computed from one sample is not a benchmark result and the two `=1.00` lines that used to sit in this file have been deleted. `bench/reports/` is where this repository reports measured numbers, on corpora with hundreds of queries. ``` Repository input proof: ```text .ely_eye/data/eval_proofs/prd_proof_suite_b03b88d7cfe5ba96/repository_input.json The repository input proof indexed 94 files, 49 code files, 11,977 call graph edges, and 26 local git commits for PRD 10.1 code repository coverage. That is a snapshot of the repository as it stood on 2026-05-28; the repository is larger now, and the check passed on any git history, which is why it is not treated as evidence of anything today. ``` Runtime profile readiness: ```text .ely_eye/data/eval_proofs/prd_proof_suite_b03b88d7cfe5ba96/runtime_profiles.json SGLang 0.5.12.post1, vLLM 0.21.0, KTransformers 0.6.2.post4, WSL RTX 4090 GPU visibility, launcher module probes, and winget llama-server help output are verified. Verified inside WSL on that machine, against the fixed distro name the launcher used at the time. The launch profiles now read `ELY_EYE_WSL_DISTRO`, every `.ps1` has a `.sh` twin for Linux and macOS, and CI runs the suite on Linux — so this line is a record of one machine, not a portability claim. ``` Checks: ```text Adapter Training Proof passed # proof-versioned BF16 LoRA + paged 8-bit AdamW + nonzero LoRA safetensors Runtime Adapter Binding passed Runtime Adapter Generation passed Cartridge Physical Assets passed 100M Memory Capsule passed Context Cartridge Replay passed Visual HashHop passed # 7/7 model-solved proofs Standard Benchmark Registry passed # one local sample per benchmark Repository Input Coverage passed # 94 files + 11977 call graph edges + local git history Runtime Profile Delivery passed Research Module Delivery passed # executable module gates, evidence graph edge, probability sum Learned Memory Recall passed Memory DNA passed Cache Trace passed ``` These are the rows that suite wrote on 2026-05-28, not a current verdict. `Gaps.md` appendix A judges each of the fifteen checks individually, and several of them were self-referential — the replay check compiled the same question twice against the live store, the recall check only proved an adapter file loads, the cache check passed if any layer had any entry. The suite was rewritten afterwards so a check that cannot run says what it could not run instead of passing. Cartridge physical assets: ```text .ely_eye/cartridges/ely_eye_prd_7e5cd27b8284/text_vectors.fp16.zstd .ely_eye/cartridges/ely_eye_prd_7e5cd27b8284/visual_vectors.int8.zstd # item_count=1 .ely_eye/cartridges/ely_eye_prd_7e5cd27b8284/kv_snapshots.kivi .ely_eye/cartridges/ely_eye_prd_7e5cd27b8284/memory_capsule.json.zst # token_equivalent=100000000, segment_count=4096 .ely_eye/cartridges/ely_eye_prd_7e5cd27b8284/ttt_vl_adapter.safetensors .ely_eye/cartridges/ely_eye_prd_7e5cd27b8284/visual_mtp_head.safetensors ``` A cartridge written today is not this list. `cartridge.py` now writes `manifest.json`, `atoms.parquet`, `sources.json`, `sparse_index.bm25.zst` and `temporal_graph.sqlite`, and a cartridge is a scope you mount rather than only a bundle you export. The files above are what that 2026-05-28 export contained; `kv_snapshots.kivi` and `ttt_vl_adapter.safetensors` were placeholders for a runtime layout that was never wired to a loader.