README: highlight multi-agent collaboration + real failure/recovery trajectories (unique value vs synthetic benchmarks)

README.md

@@ -17,7 +17,9 @@ size_categories:
 
 # MimirAether Agent Traces
 
-**7,028 unique real agent sessions · 35,247+ tool calls** from a self-hosted agent runtime in daily production use (May 2026 – present).
+**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.
+
+**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.
 
 > 🔗 **This dataset is the runtime trace of [MimirAether](https://github.com/Wanxian-Liu/Mimir-Aether)** — an open-source, self-hosted agent runtime under **active development** (code, docs, roadmap & CI on GitHub). Issues and feedback on data quality are welcome on either repo.
 
@@ -27,6 +29,8 @@ size_categories:
 
 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.
+
 | Field | Description |
 |---|---|
 | `session_id` | unique session identifier |
@@ -38,7 +42,19 @@ Each row = one complete agent session:
 
 ## Why this matters
 
-Real-world agent behavior data is scarce. Most published agent benchmarks are synthetic. This dataset captures **what an agent actually does in production** — including failure modes (see `exit_reason` distribution), tool-usage patterns, and session-length distribution from a runtime that powers real work.
+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.
+
+### Dataset statistics (full corpus)
+
+| 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 |
+
+> **`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.
 
 Useful for: agent behavior modeling, tool-use pattern mining, failure analysis, run-boundary research, agent memory studies.
 
@@ -62,15 +78,26 @@ Generated by [MimirAether](https://github.com/Wanxian-Liu/Mimir-Aether) — an o
 | `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 | 执行溯源 trace 前 8 位（join 审计流用） |
-| `trigger_source` | enum | `feishu` / `api` / `buzz-watcher` / `watchdog` / `cron` |
-| `task_category` | enum | 9 类任务分类（见下） |
+| `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）
+### 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).
+
+### 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.
+- **Batch-level `model`**: see *Model field caliber* above.
+
 ### 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。