--- license: cc-by-4.0 task_categories: - text-generation language: - en tags: - social-simulation - multi-agent - llm-agents - oasis pretty_name: divdata — heterogeneous LLM social-simulation corpus size_categories: - 1M 0. Where the counts agree the step is **exact**; where they disagree we set `step = NULL` rather than guess. Where both methods give a value they agree on 99.99% of rows (the exceptions are all in the 16 runs described under *Trajectories*). Coverage: **90.2% of posts, 91.8% of comments, 91.7% of actions, 93.3% of impressions** carry an exact step. `runs.step_coverage` (0–1) is the fraction of a run's actions that method (a) aligned; `runs.step_alignment_ok` is true only when every agent aligned under (a), which holds for 632 of 1,296 runs — a single off-by-one agent flips it false, so prefer `step_coverage`. If you need strict step semantics, filter to `step.notna()`. For time-ordering within a run, `created_at` is always present and always monotone. > **Erratum (2026-09-09).** Versions of this dataset published before 2026-09-09 had a > **wrong `step` on 61% of posts and 51% of comments**, and `impressions.step` was off by > one activation. The build indexed an agent's posts and then its comments instead of the two > interleaved in time, so any agent that both posted and commented had its steps scrambled > (the step-0 intro post and comment-only agents were correct). `created_at` and every > non-step column were unaffected. If you used `step` from an earlier download, re-download. > The fix is in `build_divdata.py` in the code release, and `BUILD_LOG.md` here has the > before/after numbers. **2. Impressions are exposure, and exposure is extremely unequal.** `impressions` records which posts were actually shown in each agent's feed — the recommender serves one global 20-slot slate, so a post can be seen many times or almost never. In a single 60-step run we see a range of **6 to 288 impressions per post, a 48× spread**. Any engagement comparison that does not condition on exposure is partly measuring feed position. Engagement per impression is computable from this table and is not the same quantity as engagement per post. In the source databases each feed refresh embedded the full text of all 20 slate posts, which was ~48% of every file. Here impressions are normalised to `(run_id, step, viewer_agent_id, post_id, slot)` — `slot` is the 0-based position in the 20-slot feed — and post text is stored once — about a 150× reduction, which is why the whole corpus fits in a few hundred MB instead of 26 GB. ## Trajectories: every activation, including the passive ones `actions.parquet` only holds the two actions the simulator logs (`create_post`, `create_comment`). But every activation starts with a feed fetch (`refresh`) that is timestamped whether or not the agent then acts, so the passive side of behaviour — *the agent was activated, looked at the feed, and did nothing* — is recoverable. `trajectories.parquet` has **one row per agent activation**: | column | meaning | |---|---| | `run_id`, `agent_id`, `model` | as elsewhere | | `activation_idx` | k-th activation of this agent in this run; `-1` is the step-0 intro post (written without a feed fetch) | | `step` | simulation tick, exact where aligned (see below), else NULL | | `refreshed_at` | wall-clock time the agent fetched its feed | | `status` | `ok` / `exhausted` (all retries failed to parse) / `exception`; NULL where unaligned | | `n_attempts` | generation attempts the harness made for this activation | | `action` | `create_post` / `create_comment` / `none` | | `acted_at`, `latency_s` | when the action landed, and seconds after the feed fetch | | `post_id` | own post (`create_post`) or **target** post (`create_comment`) | | `comment_id` | the comment written, if any | | `tool_name`, `tool_args` | the tool call the model actually emitted (JSON). Kept even when the action never landed — e.g. a comment aimed at a `post_id` that did not exist — so *chosen* and *realised* action can be told apart | | `response_text` | the model's visible output before the tool call: its stated deliberation, for families that state one | | `reasoning` | the model's hidden thinking channel, for families that expose one | | `finish_reason`, `errors` | generation finish reason; parse errors of failed attempts, `|`-joined | | `extra_in_window` | True for a 2nd+ action inside one feed-fetch window (16 runs; see below) | **How agents choose feed items.** Join `trajectories.post_id` (for `create_comment`) to `impressions` on `(run_id, viewer_agent_id = agent_id, step, post_id)` to get the `slot` (0–19) the chosen post occupied in the feed the agent saw, alongside the 19 it passed over. Then `response_text` / `reasoning` say *why*, where the family says so. Coverage of the stated deliberation is a property of the model, not of the harness: | model | activations | `response_text` | `reasoning` | passive | |---|---:|---:|---:|---:| | gpt-oss-20b | 247,489 | 0.4% | 4.8% | 0.3% | | Magistral-Small-2509 | 149,666 | 86.2% | 91.7% | 1.1% | | Qwen3-32B | 102,546 | 44.6% | 44.7% | 0.3% | | GLM-4-32B-0414 | 54,430 | 0.0% | 0.0% | 0.6% | | gemma-4-31B-it | 39,744 | 93.0% | 93.4% | 1.2% | gpt-oss and GLM-4 emit no visible deliberation at all; for those the decision record is the tool call plus the feed it was chosen from. **What is not kept.** The prompt itself. Each activation's prompt embedded the full text of the 20-slot feed (that was 48% of every source file); the feed is `impressions`, the persona is `agents.persona`, and the system directive is in the released code. **Alignment.** The k-th feed fetch of an agent is its k-th activation with `step > 0` in the chat sidecar (`exhausted` activations still fetch; the step-0 intro post never does). Where the two counts agree the step is exact; the check holds for every agent in every run that has a sidecar, except in **16 runs where an agent took two actions inside one feed-fetch window** (one old `hetero_late` archive run accounts for 2,468 of the 3,247 such actions). Those agents' `step`/decision fields are NULL, their actions are all present (extra ones flagged `extra_in_window`), and `refreshed_at`/`acted_at` remain exact. Overall `step` coverage in `trajectories` is **89.5% of activations**. Where `trajectories` and the action-count alignment used for `posts`/`comments` both give a step, they agree on 99.99% of rows; the disagreements are all inside those 16 runs. ## Known data-quality issues - **`OLMo-3.1-32B-Think` is degenerate**: 25.5% of its authored texts are under 15 characters. Exclude it or caveat it in any analysis. - **Assistant-voice leakage varies ~26× by model.** Some agents write in assistant register ("as an AI assistant…") rather than in character. Strict rates: gpt-oss-20b 0.04%, gemma-4-31B 0.15%, Qwen3-32B 0.27%, OLMo 0.91%, Magistral-Small 1.06%, GLM-4-32B 1.35%, Hermes-4-14B 1.65%. Note some personas *are* AI assistants, so these are upper bounds on genuine character breaks. - Prompt/tool scaffolding leaks into content in ≤0.02% of texts across every model. - Runs missing a metadata sidecar (~1.6%) are excluded entirely; model attribution would be impossible. ## Build The tables are produced by `build_divdata.py` (six core tables) then `build_trajectories.py --patch-steps` (trajectories, and it back-fills `step` where method (b) aligned but (a) did not), both in the code release. `BUILD_LOG.md` is the run log of the current build. ## Provenance `run_id` encodes the source path, so any row is traceable to the original database. Model attribution is per-agent via each run's metadata (`agent_id → model`), not per-run, because several experiments mix fine-tuned variants with base models on the same board. Fine-tuned Magistral variants (`Magistral--mistral`) appear alongside base models. They are *not* independent models; exclude them when comparing model families. ## License CC BY 4.0. Generated text; no human-subject data. Personas derive from a public community archive and are pseudonymous.