Add v1.1 (9 scenes, tier4_multi_step, qwen2.5:3b comparison) alongside frozen v1.0

README.md

@@ -13,9 +13,56 @@ tags:
   - synthetic
 size_categories:
   - n<1K
+configs:
+  - config_name: v1.0
+    data_files: tasks.json
+    default: true
+  - config_name: v1.1
+    data_files: v1.1/tasks.json
 ---
 
-# MILO Benchmark v1.0
+# MILO Benchmark
+
+A small, versioned dataset of `(scene, instruction, ground-truth task
+spec)` triples for evaluating embodied task planning in AI2-THOR,
+built for the [MILO vision-language-robotics
+project](https://github.com/NaishaShetty/MILO). A companion Space —
+leaderboard + episode replay, static/pre-recorded since AI2-THOR needs
+a GPU/Unity this Space's free tier doesn't have — is live at
+[huggingface.co/spaces/naishashetty/milo_benchmark_companion](https://huggingface.co/spaces/naishashetty/milo_benchmark_companion).
+Every task pairs a natural-language instruction with a structured
+goal/object/target spec and a machine-checkable success predicate,
+against real AI2-THOR scenes.
+
+**This is a synthetic, AI2-THOR-derived dataset, not a human-collected
+one.** Every instruction was authored by a human against a live scan
+of each scene's real object inventory (`get_metadata()`), not
+generated by an LLM and not crowd-sourced — see each version's
+"Collection methodology" section below for exactly how, so nobody
+mistakes this for naturalistic human instruction data.
+
+## Versions in this repository
+
+This repository hosts two dataset versions side by side — **neither
+replaces the other**, and both are fully documented below:
+
+| Version | Tasks | Scenes | Tiers | Status | Data file |
+|---|---|---|---|---|---|
+| **`v1.0`** | 25 | 5 | 3 (`tier1_locate`/`tier2_pickup`/`tier3_store`) | Frozen — task IDs, scenes, and success predicates will never change | `tasks.json` (repo root) |
+| **`v1.1`** | 54 | 9 | 4 (adds `tier4_multi_step`) | Frozen (as of this version) — extends `v1.0` rather than mutating it | `v1.1/tasks.json` |
+
+`v1.1` is additive: every one of `v1.0`'s 25 tasks is carried into
+`v1.1` unchanged on every scoring-relevant field (`task_id`, `scene`,
+`goal`, `object`, `target`, `instruction`) — a score computed against
+either file's copy of a `v1.0` task_id is directly comparable. Use the
+config selector above (or
+`load_dataset("naishashetty/milo_benchmark", "v1.0")` /
+`load_dataset("naishashetty/milo_benchmark", "v1.1")`) to pick which
+version's `tasks.json` loads.
+
+---
+
+## MILO Benchmark v1.0
 
 A small, versioned dataset of `(scene, instruction, ground-truth task
 spec)` triples for evaluating embodied task planning in AI2-THOR,
@@ -36,7 +83,7 @@ generated by an LLM and not crowd-sourced — see "Collection
 methodology" below for exactly how, so nobody mistakes this for
 naturalistic human instruction data.
 
-## What's in it
+### What's in it
 
 25 tasks across 5 scenes:
 
@@ -80,7 +127,7 @@ non-scoring, human-readable context — for a handful of tasks it
 documents a **known, real limitation** the task deliberately keeps
 rather than hides (see below).
 
-## Success predicate
+### Success predicate
 
 A task is scored `goal_success = True` iff its goal condition holds
 against **live** AI2-THOR object state after execution (not just "did
@@ -99,7 +146,7 @@ Reference implementation: `backend/planning_evaluation/live_state.py`'s
 `check_goal_live()` in the [MILO
 repository](https://github.com/NaishaShetty/MILO) (this dataset's origin repo).
 
-## Baselines (v1.0, real runs, all three planners)
+### Baselines (v1.0, real runs, all three planners)
 
 | Planner | Goal success | tier1_locate | tier2_pickup | tier3_store | Notes |
 |---|---|---|---|---|---|
@@ -131,7 +178,7 @@ auth, then run
 `RUN_SIMULATOR_TESTS=true python -m planning_evaluation.run_benchmark`
 from the origin repository's `backend/` directory.
 
-## Difficulty tiers and why they were chosen this way
+### Difficulty tiers and why they were chosen this way
 
 Tier boundaries were chosen to exercise structurally different code
 paths in MILO's own rule-based planner (object resolution only, vs.
@@ -144,7 +191,7 @@ bug both lived specifically in the `tier3_store` code path, never in
 two specific, currently-still-open bugs this dataset intentionally
 keeps as ground truth.
 
-## Known limitations — kept deliberately, not hidden
+### Known limitations — kept deliberately, not hidden
 
 Two `tier3_store` tasks are **known, currently reproducible failures**
 against the reference planner, kept in v1.0 on purpose as honest
@@ -170,7 +217,7 @@ need a vision pipeline wired into the scoring harness; this dataset's
 reference runner does not do that yet (see the origin repo's Phase C
 vision-grounding work, which is not yet connected to this benchmark).
 
-### Addendum — perception-grounded `tier1_locate` check added (partially addresses the limitation above)
+#### Addendum — perception-grounded `tier1_locate` check added (partially addresses the limitation above)
 
 The limitation above is now **partially addressed, not resolved**: the
 reference runner (`backend/planning_evaluation/run_benchmark.py`) now
@@ -239,7 +286,7 @@ reliability on AI2-THOR's synthetic renders — a different, still-open
 question this dataset now measures separately instead of conflating
 with the first.
 
-## Collection methodology
+### Collection methodology
 
 1. For each of the 5 candidate scenes, a live AI2-THOR
    `Controller.step()`/`last_event.metadata` scan was taken to list
@@ -261,7 +308,7 @@ This is the same authoring discipline the origin repository already
 used for its `FloorPlan1`-only real-AI2-THOR task sets
 (`real_scenarios.py`), extended across scenes.
 
-## What this dataset does not cover
+### What this dataset does not cover
 
 - Only 5 of iTHOR's ~120 scenes (one per room type, plus a second
   kitchen) — not a claim of full scene coverage.
@@ -271,7 +318,7 @@ used for its `FloorPlan1`-only real-AI2-THOR task sets
   behavior is out of scope here).
 - English only.
 
-## Versioning
+### Versioning
 
 `v1.0` is frozen — task IDs, scenes, and success predicates in this
 version will not change. Future versions extend rather than mutate
@@ -279,16 +326,263 @@ version will not change. Future versions extend rather than mutate
 directory with its own `tasks.json`), so a score reported against
 `v1.0` stays reproducible indefinitely.
 
-## License
+### License
 
 MIT, matching the origin repository. AI2-THOR scene assets themselves
 are licensed separately by their own maintainers (Allen Institute for
 AI) — this dataset contains no scene assets, only task
 specifications/instructions referencing public AI2-THOR scene IDs.
 
-## Citation
+### Citation
 
 This is a research-adjacent project artifact, not a peer-reviewed
 publication. If referencing it, cite the origin repository
 ([github.com/NaishaShetty/MILO](https://github.com/NaishaShetty/MILO))
 and this dataset version (`milo_benchmark v1.0`).
+
+---
+
+## MILO Benchmark v1.1
+
+`v1.1` extends [`v1.0`](#milo-benchmark-v10) (above, same page) rather than replacing it --
+`v1.0` stays frozen and unchanged per its own versioning policy (see
+that card's "Versioning" section, and this project's
+`experiments/reports/phase_e_milo_benchmark_report.md` for the full
+methodology `v1.0` was built with, which this card assumes as
+background and does not repeat). Everything in `v1.0`'s card
+(collection methodology, success predicates, known limitations, the
+perception-grounded `tier1_locate` addendum) still applies unchanged
+to every task `v1.1` carries over from `v1.0`. This card documents only
+what is new.
+
+### What's new in v1.1
+
+- **4 more iTHOR scenes** (9 total, up from 5), chosen to extend room-type
+  coverage rather than duplicate it: `v1.0` already had 2 kitchens, 1
+  living room, 1 bedroom, 1 bathroom, so the 4 new scenes are 1 more
+  living room, 1 more bedroom, 1 more bathroom, and 1 more living room
+  again (living room ends up with 3 total; no third kitchen was added).
+  This is a meaningful extension, not an exhaustive sweep of iTHOR's
+  ~120 scenes -- see `v1.0`'s "What this dataset does not cover" for
+  why full scene-coverage was never this dataset's goal.
+- **A new `tier4_multi_step` difficulty tier** -- see below.
+- **29 new tasks**: 20 flat `tier1_locate`/`tier2_pickup`/`tier3_store`
+  tasks (5 per new scene, same 2/2/1 split `v1.0` uses) + 9
+  `tier4_multi_step` tasks (1 per scene, all 9 scenes -- the 5 original
+  `v1.0` scenes get a `tier4_multi_step` task added here too, since
+  `v1.1` is additive over `v1.0`'s task set, not just its scene list).
+  **Total: 54 tasks across 9 scenes** (`tasks.json`).
+- Every `v1.0` task_id, scene, goal/object/target, and instruction is
+  carried into `v1.1` with those **scoring-relevant fields identical**
+  (regression-tested, see `backend/tests/test_planning_evaluation.py`'s
+  `test_v1_1_frozen_v1_0_tasks_scoring_fields_match_v1_0`) -- a score
+  on `v1.0`'s 25 tasks stays directly comparable whether computed
+  against `dataset/v1.0/tasks.json` or `dataset/v1.1/tasks.json`'s
+  first 25 rows. **This is not a byte-identical-JSON claim**: the
+  free-text, non-scoring `notes` field was deliberately edited on 2 of
+  the 25 carried-over tasks when `v1.1` was authored --
+  `milo-v1-fp301-t3a`'s note gained a cosmetic "(and here, unchanged)"
+  clause, and `milo-v1-fp401-t3a`'s note was **substantively
+  rewritten**: `v1.0`'s text says the `_deposit()` non-openable-target
+  bug is still unfixed ("expected to fail this task until that bug is
+  fixed"), while `v1.1`'s text says that bug has since been fixed and
+  the task is now expected to succeed. Both files' `goal`/`object`/
+  `target`/`instruction`/`scene` for this task are unchanged either
+  way -- only the human-readable annotation was updated to stay
+  accurate.
+
+  This scene table also reflects an honest, not a data-driven,
+  balancing choice: `v1.0` had 2 kitchens and 1 each of living room/
+  bedroom/bathroom; `v1.1` adds 1 more scene to living room, bedroom,
+  *and* bathroom, landing on 3 living rooms rather than a 3rd kitchen.
+  A 3rd kitchen (`FloorPlan7`) was live-scanned during collection and
+  confirmed available/usable -- it was set aside in favor of living
+  room getting the 4th new scene with no principled reason beyond
+  needing to pick one room type to move toward parity with. iTHOR has
+  roughly 30 scenes per room type, so this was a real choice among
+  many available options, not a constraint.
+
+| Scene | Room type | Tasks | New in v1.1? |
+|---|---|---|---|
+| `FloorPlan1` | kitchen | 6 (5 + 1 tier4) | tier4 task only |
+| `FloorPlan5` | kitchen | 6 (5 + 1 tier4) | tier4 task only |
+| `FloorPlan201` | living room | 6 (5 + 1 tier4) | tier4 task only |
+| `FloorPlan301` | bedroom | 6 (5 + 1 tier4) | tier4 task only |
+| `FloorPlan401` | bathroom | 6 (5 + 1 tier4) | tier4 task only |
+| `FloorPlan202` | living room | 6 | scene + all 6 tasks |
+| `FloorPlan302` | bedroom | 6 | scene + all 6 tasks |
+| `FloorPlan402` | bathroom | 6 | scene + all 6 tasks |
+| `FloorPlan203` | living room | 6 | scene + all 6 tasks |
+
+Room-type totals: kitchen ×2, living room ×3, bedroom ×2, bathroom ×2.
+
+### `tier4_multi_step`: what it's designed to exercise
+
+`tier3_store`'s hardest task is still a **single-object** chain
+(locate -> navigate -> pickup -> locate target -> navigate ->
+(open) -> place -> (close)) -- every step serves one object reaching
+one destination. `tier4_multi_step` is a different, harder axis:
+**two independent single-object sub-goals in one instruction**, e.g.
+*"Put the mug in the cabinet and the spoon in the drawer."* Both
+sub-goals must be satisfied for the task to count as a success --
+completing only one is a partial result, not a pass. This is designed
+to probe **cross-object sequencing/planning depth**: does a planner
+(especially an LLM-driven one) correctly treat this as two separate
+goals to satisfy in sequence, or does it conflate them, drop one, or
+apply one sub-goal's object/target to the other?
+
+Concretely, each `tier4_multi_step` row's `goal`/`object`/`target`
+fields are `null`; instead it carries a `subtasks` list of two
+`{"goal", "object", "target"}` dicts, e.g.:
+
+```json
+{
+  "task_id": "milo-v1.1-fp1-t4a",
+  "scene": "FloorPlan1",
+  "room_type": "kitchen",
+  "difficulty_tier": "tier4_multi_step",
+  "instruction": "Put the knife away in the drawer and the cup away in the cabinet.",
+  "goal": null, "object": null, "target": null,
+  "subtasks": [
+    {"goal": "store", "object": "knife", "target": "drawer"},
+    {"goal": "store", "object": "cup", "target": "cabinet"}
+  ],
+  "notes": "..."
+}
+```
+
+**Why two independent `SingleTask`s, not a nested `MultiTask`**: this
+project's schema layer (`schemas.task.MultiTask`) already models an
+ordered decomposition into subtasks, but no planner in the origin
+repository (`RuleBasedPlanner`, `BehaviorTreePlanner`, `ReActPlanner`)
+implements a `MultiTask`-level `plan()` -- every one of them takes a
+`SingleTask`. Rather than build new multi-task planning machinery
+across all three planners (a materially larger, riskier change than
+this dataset extension calls for), the reference runner
+(`run_benchmark.py`) executes `tier4_multi_step`'s two `subtasks` as
+two sequential `TaskRunner.run()` calls against the *same* live
+simulator/episode (one Unity process, not restarted between
+sub-goals) -- each sub-goal's `WorldState` is freshly re-seeded from
+live metadata immediately before it plans, so the second sub-goal's
+planner sees the real post-first-sub-goal world. This is "sequencing
+across two independent sub-goals" implemented at the benchmark-harness
+level, not inside any planner. See `loader.BenchmarkTask.to_single_tasks()`
+and `run_benchmark._run_multi_subtask_episode()`.
+
+### Success predicate for `tier4_multi_step`
+
+`goal_success` is `True` iff **both** sub-goals' `check_goal_live()`
+result is `True` against **one** metadata snapshot taken after both
+sub-goals have been planned and executed, in order
+(`live_state.check_goal_live_multi()`, `MultiGoalResult.all_succeeded`).
+A planner that completes only one sub-goal, or that undoes the first
+sub-goal while pursuing the second, is scored a failure -- this is a
+genuinely stricter, conjunctive predicate, not an average or "best of
+two." `plan_success`/`execution_success` are likewise the AND across
+both sub-goals; both sub-goals are always attempted regardless of
+whether the first one's plan/execution succeeded (mirroring a real
+agent continuing to the next sub-goal rather than aborting the whole
+instruction over one failed part), and `failure_cause` records every
+sub-goal that failed, tagged by its own object/target.
+
+### Collection methodology (identical discipline to v1.0)
+
+Every new scene (`FloorPlan202`, `FloorPlan302`, `FloorPlan402`,
+`FloorPlan203`) and every `tier4_multi_step` task's two sub-goals
+(including the ones added to the 5 original `v1.0` scenes) were chosen
+the same way `v1.0`'s collection methodology section describes: a live
+AI2-THOR `Controller.step()`/`last_event.metadata` scan of each
+candidate scene's real object inventory (`objectType`, `pickupable`,
+`receptacle`, `openable`) was taken first; every task object/target
+was chosen only from that confirmed live list, never guessed. The 5
+original `v1.0` scenes were re-scanned for this pass (rather than
+reusing `v1.0`'s own recorded inventory) specifically to confirm the
+*new* `tier4_multi_step` objects/targets for those scenes actually
+exist live, since `v1.0`'s own scan only ever confirmed the objects
+`v1.0`'s own tasks use.
+
+`tier4_multi_step` targets were deliberately split between confirmed
+openable containers (Drawer, Cabinet, Fridge, Box, Safe) and confirmed
+non-openable receptacles (Shelf, SideTable, Sofa, CoffeeTable) across
+the 9 tasks -- exercising `_deposit()`'s `is_openable is False`
+carve-out (see `v1.0`'s card, "Known limitations" -- this bug is now
+fixed, see the origin repo's `phase_e_milo_benchmark_report.md`
+addendum) on both of a `tier4_multi_step` task's independent sub-goals
+in several cases (`FloorPlan202`, `FloorPlan402`'s second sub-goal,
+`FloorPlan401`), not only single-object `tier3_store` tasks.
+
+### Baselines (v1.1, real runs, all three planners)
+
+| Planner | Goal success | tier1_locate | tier2_pickup | tier3_store | tier4_multi_step | Notes |
+|---|---|---|---|---|---|---|
+| `rule_based` | 50/54 (92.6%) | 18/18 | 18/18 | 7/9 | 7/9 | Both `tier3_store` failures are the same real AI2-THOR placement-geometry limit `v1.0` already documents (`FloorPlan301`, now also `FloorPlan203` -- same task shape, independently reproducing). Both `tier4_multi_step` failures are a real, newly-surfaced harness gap (not a planner defect): a failed `place` in sub-goal 1 leaves the object physically held, and `WorldState` re-seeding between sub-goals has no signal for that, so sub-goal 2's plan assumes an empty hand and AI2-THOR rejects it. |
+| `behavior_tree` | 50/54 (92.6%) | 18/18 | 18/18 | 7/9 | 7/9 | Same task/plan-step outcomes as `rule_based` (shares its goal-handler templates); same failures for the same reasons. |
+| `react` (`qwen2.5:7b`, Q4_K_M, via Ollama, local) | 36/54 (66.7%) | 18/18 | 18/18 | 0/9 | 0/9 | `tier4_multi_step`'s 0/9 is the arithmetically expected composition of `tier3_store`'s already-0% rate (a tier requiring two consecutive successful `store` sequences cannot score above a planner's single-`store` success rate) -- confirmed by inspecting each failure, not assumed: every one shows the same precondition-mis-sequencing pattern `v1.0`'s Addendum 3 already documents. `goal_success`/`execution_success`/`plan_success` agree on every episode; 0/54 episodes needed a retry. |
+
+See the origin repository's `experiments/reports/
+phase_e_milo_benchmark_report.md`'s Addendum 7 for full methodology,
+per-failure root-cause detail (including the `tier4_multi_step`
+WorldState-reseeding gap above, tracked open in `docs/roadmap.md`, not
+fixed in this pass), cost/latency, and exact reproduction commands.
+
+### Second local model for `react`: `qwen2.5:3b` comparison
+
+A second small local model was run through the identical `react`
+harness/instrumentation against this same 54-task set, to see how
+model size trades off against accuracy/latency:
+
+| Model | Goal success | tier1_locate | tier2_pickup | tier3_store | tier4_multi_step | Avg latency/episode | Hardware |
+|---|---|---|---|---|---|---|---|
+| `qwen2.5:7b` (Q4_K_M) | 36/54 (66.7%) | 18/18 | 18/18 | 0/9 | 0/9 | 7156ms | RTX 4050 Laptop GPU, 6GB VRAM, 82%/18% GPU/CPU split |
+| `qwen2.5:3b` (Q4_K_M) | 36/54 (66.7%) | 18/18 | 18/18 | 0/9 | 0/9 | 3107ms | Same GPU, full GPU residency |
+
+`qwen2.5:3b` matches `qwen2.5:7b`'s goal-success rate **exactly,
+task-for-task** (verified via a full 54-row side-by-side comparison,
+0 differences) at roughly 2.3x lower average latency and modestly
+fewer tokens per episode -- a real cost/latency win with no accuracy
+cost observed on this task set.
+
+Investigated why the aggregate scores are identical (rather than
+taking the match at face value) by re-running 6 of these failing
+episodes (3 per model) with a diagnostic wrapper that captures the
+actual raw LLM completions -- reproducing the same outcomes as the
+full run. Real finding, verified directly on those 6 episodes (not
+re-checked against all 27 originally-classified failures from the
+full run): **both models' `tier3_store`/`tier4_multi_step` failures
+share a root cause -- neither model's proposals ever include a
+`locate` call for the destination/container object, only sometimes
+for the primary object being moved.** `qwen2.5:3b`'s proposals stall
+immediately at that gap in all 3 episodes checked. `qwen2.5:7b`, in
+the 1 of 3 checked episodes that got further, correctly completes
+`locate`/`navigate`/`pickup` on the primary object, then fails at
+placement by supplying the destination's name to `put_down`/`place`'s
+`target` field -- which the action schema defines as the *held
+object's* identity, not the destination -- a wrong-value mistake, not
+a missing one.
+
+This is treated as a real LLM reasoning/prompting limitation, not a
+bug in this dataset's reference planner code -- no precondition
+validation was weakened to work around it. See the origin repository's
+`experiments/reports/phase_e_milo_benchmark_report.md`'s Addendum 8
+for the full real transcripts and methodology, and `docs/roadmap.md`
+for the tracked, open finding (including a possible, not-yet-tried
+future direction: refining the `react` system prompt to explicitly
+require locating both the object and the destination before any
+`navigate`/`place` step).
+
+### Versioning
+
+`v1.1` is now itself frozen going forward, following the same policy
+`v1.0`'s card states: task_ids, scenes, and success predicates in this
+version will not change after this point. A future `v1.2` would extend
+again rather than mutate this file.
+
+### License
+
+MIT, matching the origin repository, identical to `v1.0`.
+
+### Citation
+
+Cite the origin repository
+([github.com/NaishaShetty/MILO](https://github.com/NaishaShetty/MILO))
+and this dataset version (`milo_benchmark v1.1`).