view: refresh parquet view layer (union schema · 14 cols · zstd)

README.md

@@ -18,11 +18,15 @@ configs:
     data_files:
       - split: train
         path: data/train.parquet
+  - config_name: legacy
+    data_files:
+      - split: train
+        path: data/legacy.parquet
 ---
 
 # MimirAether Agent Traces
 
-**7,124 unique real agent sessions · 35,000+ tool calls** from a **multi-agent system in daily production** — four cooperating agents (orchestrator, executor, red-team reviewer, verifier) running real workloads since May 2026.
+**9,299 unique real agent sessions · 90,608 tool calls in the curated view** from a **multi-agent system in daily production** — four cooperating agents (orchestrator, executor, red-team reviewer, verifier) running real workloads since May 2026.
 
 **What makes this corpus different**: it is not single-agent coding transcripts. It captures **inter-agent collaboration in production** — task dispatch, cross-audits, failure & recovery (real incidents: gateway freezes, API outages, deadlocks — with their debug sessions included), and self-correction loops. If you train or evaluate agents on *cooperation*, failure recovery, or long-horizon operations, this is the rare real-world source.
 
@@ -35,100 +39,97 @@ configs:
 ```python
 from datasets import load_dataset
 
+# curated corpus: 4,404 sessions, schema v1+v2 mixed, one row = one agent session
 ds = load_dataset("kelikelibababian/mimir-agent-traces", split="train")
-print(ds[0])          # one row = one complete agent session
+
+# pre-filter archive: 8,670 sessions, v1-era, kept as-is for provenance
+legacy = load_dataset("kelikelibababian/mimir-agent-traces", "legacy", split="train")
 ```
 
-The viewer table is served as **Parquet** (`data/train.parquet`) — a unified-schema view over the append-only JSONL source (`data/mimir-agent-traces.jsonl`, which is kept as-is and never rewritten). It covers the **curated main file (4,168 sessions)**; the larger pre-filter archive is kept separately as `data/mimir-agent-traces-legacy-full.jsonl` and is **not** part of this table. Rows uploaded before schema v2 carry `null` in the seven v2-only columns, so the curated corpus loads as a single table.
+Both configs are Parquet **views**: `data/train.parquet` over the append-only JSONL `data/mimir-agent-traces.jsonl`, and `data/legacy.parquet` over `data/mimir-agent-traces-legacy-full.jsonl`. **Raw JSONL is never rewritten** — Parquet is the view, JSONL is the source of truth. Rows uploaded before schema v2 carry `null` in the seven v2-only columns, so the curated corpus loads as a single table.
+
+## Statistics
+
+Snapshot: **2026-10-19**.
+
+| Metric | Curated (`default`) | Pre-filter archive (`legacy`) |
+|---|---:|---:|
+| Rows (sessions) | 4,404 | 8,670 |
+| **Unique sessions (union of both configs)** | **9,299** | — |
+| Total tool calls | 90,608 | 73,740 |
+| Avg / median tool calls per session | 20.6 / 13 | — |
+| Sessions with zero tool calls | 23 (0.5%) | — |
+| Duration p50 / p90 | 113 s / 593 s | — |
+| Schema v2 rows | 2,236 (50.8%) | — |
+| Schema v1 rows (no `schema_version`) | 2,168 (49.2%) | — |
+
+`exit_reason` (curated): `null` 3,305 · `natural` 983 · `empty_response` 43 · `max_turns` 29 · `empty_content` 26 · `interrupt` 12 · `verify_exhausted` 4 · `billing_exhausted` 2.
+
+> **`exit_reason` caveat**: most v1 rows are `null` — the recorder only started writing `session_end` telemetry partway through the corpus, and pre-2026-08-29 `session_end` events lack the `exit_reason` key. Even in schema v2 batches the value can be `null` when the underlying telemetry omits it. The v2 gate guarantees the session *ended*; it cannot backfill a missing reason.
 
 ## What's inside
 
-Each row = one complete agent session:
+Each row = one complete agent session.
 
-> **Schema note**: v1 rows have `task_desc_sanitized` capped at 200 chars; from schema v2 (2026-09-13) the cap is 300. v1 rows (uploaded before 2026-09-13) **lack** the `schema_version` field — absence of the field = v1. The taxonomy is "9 categories designed, 7 reachable" via the tool-sequence heuristic — see the v2 section below.
+> **Schema note**: v1 rows cap `task_desc_sanitized` at 200 chars; from schema v2 (2026-09-13) the cap is 300. v1 rows lack the `schema_version` field — **absence of the field = v1**.
 
 | Field | Type | Description |
 |---|---|---|
-| `session_id` | string | unique session identifier (salt-free SHA-1 prefix) |
+| `session_id` | string | session identifier (two conventions — see *Known limitations*) |
 | `n_tool_calls` | int64 | number of tool calls in session |
 | `duration_seconds` | float64? | wall-clock duration |
-| `exit_reason` | string? | how the run ended (`natural` / `max_turns` / `interrupt` / `circuit_breaker` / `tool_storm` / `api_failure`); `null`/`unknown` for pre-2026-08-29 sessions |
+| `exit_reason` | string? | how the run ended (`natural` / `max_turns` / `interrupt` / `circuit_breaker` / `tool_storm` / `api_failure` / `empty_response` / `empty_content`); `null` for many v1 sessions |
 | `tool_sequence` | list<string> | ordered sequence of tool names (capped at 80) |
 | `has_exit_reason` | bool | whether telemetry recorded an exit reason |
-| `task_desc_sanitized` | string | task description, truncated (200 chars in v1, 300 in v2) |
-| `schema_version` | string? | `"2.0"` for v2 rows; **`null` for v1 rows** (absence = v1) |
+| `task_desc_sanitized` | string | task description, truncated (200 chars v1 / 300 chars v2) |
+| `schema_version` | string? | `"2.0"` for v2 rows; **`null` for v1 rows** |
 | `agent_id` | string? | which agent ran it — `mimir` / `hermes` / `openclaw` / `loki` |
 | `model` | string? | batch-level model name (see *Model field caliber*) |
 | `trace_id_prefix` | string? | first 8 hex of the trace id, for joining the audit stream |
-| `trigger_source` | string? | `feishu` / `api` / `buzz-watcher` / `watchdog` / `cron` |
+| `trigger_source` | string? | `feishu` / `api` / `hermes-dispatch` / `buzz-watcher` / `cli` / `cron` |
 | `task_category` | string? | heuristic task class (weak signal — see below) |
 | `task_subcategory` | string? | finer task class |
 
-> **v1 rows carry `null` in the seven v2-only columns** — the Parquet view pads them so the whole corpus loads as one table.
+### Categories (heuristic, curated v2 rows only)
 
-## Why this matters
+Assigned by a deterministic tool-name voting table (no LLM), so treat them as a **weak signal for stratification**, not ground truth.
 
-Real-world agent behavior data is scarce; **real-world multi-agent failure data is nearly nonexistent**. Most published agent benchmarks are synthetic. This dataset captures what agents actually do in production — including **genuine incident-response sessions** (e.g., a cron job freezing the event loop, diagnosed via py-spy and fixed live; API-provider outages with retry/backoff traces), tool-usage patterns, and self-audit loops where an agent finds its own bug and files a correction. Failure trajectories are included deliberately: they are the hardest data to synthesize and the most valuable for robustness training.
+| Category | Rows |
+|---|---:|
+| Terminal & Coding | 1,455 |
+| Retrieval | 584 |
+| Other (zero-tool or tied votes) | 111 |
+| Browser | 48 |
+| Agent Tools | 22 |
+| Memory | 16 |
+| Communication / Creative / Analysis | 0 in this snapshot |
 
-### Dataset statistics (full corpus)
+### Model field caliber
 
-| Metric | Value |
-|---|---|
-| Sessions | 7,028 unique |
-| Total tool calls | 36,995+ |
-| Duration | p50 = 0s (many short automated wakeups) · p90 = 132s |
-| `exit_reason` | `natural` 247 · `max_turns` 18 · `empty_response` 14 · `api_failure` 8 · `interrupt` 2 · `unknown` 6,739 |
-| `tool_sequence` non-empty | 6,936 |
+`model` is a **batch-level** value (trajectories carry no per-session model). Resolution order: runtime telemetry (`last_context_usage.json`, what the gateway actually used) → `config.yaml` `model.default` → legacy `.env` `MIMIR_MODEL`. A model switch mid-window makes the batch value approximate for sessions before the switch; exact per-session models land once the runtime writes model into `session_start` (planned).
 
-> **`exit_reason` caveat**: 96% of v1 rows are `unknown` — the recorder only started writing `session_end` telemetry partway through the corpus, and pre-2026-08-29 `session_end` events lack the `exit_reason` key. Even in schema v2 batches, `exit_reason` can be `null` when the underlying telemetry omits it (observed: ~22% in the first v2 preview window, which spans legacy sessions). The v2 gate guarantees the session *ended*; it cannot backfill a missing reason.
+## Why this matters
+
+Real-world agent behavior data is scarce; **real-world multi-agent failure data is nearly nonexistent**. Most published agent benchmarks are synthetic. This dataset captures what agents actually do in production — including **genuine incident-response sessions** (e.g., a cron job freezing the event loop, diagnosed via py-spy and fixed live; API-provider outages with retry/backoff traces), tool-usage patterns, and self-audit loops where an agent finds its own bug and files a correction. Failure trajectories are included deliberately: they are the hardest data to synthesize and the most valuable for robustness training.
 
 Useful for: agent behavior modeling, tool-use pattern mining, failure analysis, run-boundary research, agent memory studies.
 
 ## Sanitization
 
-All identifiers are hashed; user names, paths, emails and internal infrastructure names are redacted. Task descriptions truncated to 200 chars. Tool arguments/outputs NOT included (privacy) — only the tool-name sequence and metadata. Each batch is scanned for personal identifiers before upload; any row that fails the scan is dropped.
-
-## Source
-
-Generated by [MimirAether](https://github.com/Wanxian-Liu/Mimir-Aether) — an open-source, auditable agent runtime.
-
-
-## Schema v2.0（2026-09-13 起）
-
-自 2026-09-13 起新增批次采用 **schema v2**，v1 行保留（混合读取安全）：
-
-### v2 新增字段
-
-| 字段 | 类型 | 说明 |
-|---|---|---|
-| `schema_version` | `"2.0"` | 行级版本标识（前向兼容） |
-| `agent_id` | enum | `mimir` / `hermes` / `openclaw` / `loki` |
-| `model` | string | 运行模型名 |
-| `trace_id_prefix` | string | trace id 的 hex 段前 8 位（类型前缀 `run_`/`tr_` 已剥除，join 审计流用；32-bit 熵）。**仅 2026-09-13 X2-a 注入体系上线后的会话有值，历史会话为 null** |
-| `trigger_source` | enum | `feishu` / `api` / `buzz-watcher` / `watchdog` / `cron`（同上，仅新会话有值） |
-| `task_category` | enum | 9 类任务分类设计，实际 **7 类可达**（Communication/Creative 极少；见下）；**由工具序列启发式投票推断，非原生标注**；零工具会话归 Other |
-| `task_subcategory` | string? | 细分（可空） |
-
-### 任务分类法（9 类设计 · 7 类实际可达）
-
-`Terminal&Coding` · `Browser` · `Agent Tools` · `Retrieval` · `Memory` · `Communication` · `Analysis` · `Creative` · `Other`
-
-> **Heuristic disclosure**: categories are assigned by a deterministic tool-name voting table (no LLM). In the full corpus, `Communication` and `Creative` are structurally rare; ~31% of a recent window lands in `Other` (mix of zero-tool sessions and tied votes). Treat `task_category` as a *weak signal* for stratification — not ground truth.
-
-### Model field caliber
-
-`model` is a **batch-level** value (trajectories carry no per-session model). Resolution order: runtime telemetry (`last_context_usage.json`, what the gateway actually used) → `config.yaml` `model.default` → legacy `.env` `MIMIR_MODEL`. A model switch mid-window makes the batch value approximate for sessions before the switch; exact per-session models land once the runtime writes model into `session_start` (planned).
+All identifiers are hashed; user names, paths, emails and internal infrastructure names are redacted. Task descriptions truncated (200 chars v1 / 300 chars v2). Tool arguments/outputs are **not** included (privacy) — only the tool-name sequence and metadata. Every batch is scanned for personal identifiers before upload; any row that fails the scan is dropped.
 
-### Known limitations
+## Known limitations
 
-- **Session identity**: `session_id` is a salt-free SHA-1 prefix of Mimir's raw session id. A session continued across midnight (≈4% of sessions) keeps the same id; the dataset keeps the **first uploaded version** of such a session and later continuation files are skipped (no upsert). Rows are always duplicate-free and never wrong — at worst the tail of a continued session is missing. This will disappear naturally once the runtime emits unique ids per session.
+- **Session identity has two conventions.** Curated v2 rows use a **salt-free SHA-1 prefix** of the runtime session id; legacy rows use the **first 8 characters of the raw session id**. Both are deterministic and stable, but **do not mix them when joining** across configs.
+- **Session continuation**: a session continued across midnight (≈4% of sessions) keeps the same id; the dataset keeps the **first uploaded version** and later continuation files are skipped (no upsert). Rows are always duplicate-free — at worst the tail of a continued session is missing.
 - **Batch-level `model`**: see *Model field caliber* above.
+- **`task_category` is heuristic** — see the caveat above.
 
-### Provenance
+## Provenance
 
-每批上传记录于 `manifest.jsonl`（批次/日期/会话数/备注）。原始会话文件**永不上传**——只有聚合统计行。
+Every upload is recorded in `manifest.jsonl` (batch / date / sessions / notes); `schema_v2.json` documents the v2 row contract. Raw runtime session files are **never** uploaded — only aggregated statistic rows.
 
-### 相关项目
+### Related
 
-- 💻 运行时源码：[MimirAether](https://github.com/Wanxian-Liu/Mimir-Aether)（活跃开发中）
-- 📖 归因体系：commit trailer `Agent: <id>` + 双流审计（工具级+钩子级）+ `who_did.py` 自查入口
+- 💻 Runtime source: [MimirAether](https://github.com/Wanxian-Liu/Mimir-Aether) (actively developed)
+- 📖 Attribution system: commit trailer `Agent: <id>` + dual-stream audit (tool-level + hook-level) + `who_did.py` self-check entry point

data/legacy.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:014456f566d8702f58c8158d50d9b51e0782c08e0f821ed031a1c6fb03ffb2e0
+size 408898

data/train.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b6c8ba5e03f46325ee60b735f290d52da8a5e19c1d8375d65725be35b414aed5
-size 427037
+oid sha256:ca146d1a0f13e9b02470b9c572feda4294e48d7246a1264a343a95335ca71c4b
+size 448063