EXP-12: Decision Index (apolinario/decision-index) Benchmark & Wide-Canvas Adapter

EXP-12: jev-decision-index (apolinario/decision-index) Benchmark Harness & Wide-Canvas Adapter

• Status: Stand-in Suite Verified on Serverless Cloud Run GPU (results_decision_index_cloudrun.json)
• Upstream Benchmark: apolinario/decision-index (pinned to commit 87d4650b, Edition 0.2.1 released 2026-09-27; live Space at multimodalart/jev-decision-index)
• Harness & Adapter: dgem bench-decision-index (cmd/bench_decision_index.go), pkg/decisionindex/engine.go, pkg/decisionindex/scorer.go
• Dataset & Receipt: benchmarks/decision_index/panel_suite.jsonl (22-request representative stand-in suite), benchmarks/decision_index/results_decision_index_cloudrun.json

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1. Executive Summary & Architectural Motivation

While JevBench v1.4 (EXP-11) evaluates tricky adversarial rule-following across 231 single-slot prompts (K <= 6 options), apolinario/decision-index evaluates whether a System-1 Decision Model can act as a general-purpose structured decision engine across 5 Capability Areas (38 scored panel benchmarks, 120,340 requests in the full upstream suite):

1. Knowledge & Reasoning (MMLU-Pro ★, GPQA Diamond ★, BBH ★, HLE ★, GSM8K, ChessBench, CRUXEval, CLadder)
2. Language Understanding (ContractNLI, ANLI ★, WinoGrande ★, HellaSwag ★, iSarcasmEval, VAST, RAGTruth)
3. Retrieval & Classification (BANKING77 ★, CLINC150+OOS ★, BRIGHT ★, Amazon ESCI)
4. Tools & Automation (BFCL ★, API-Bank ★, ToolRet, Home appliances)
5. Arts & Human Judgment (BPoMP, Humicroedit, POP909-CL, cfcolor, New Yorker caption matching)

(Note: ★ denotes Gold benchmarks weighted at 1.2 in Edition 0.2.1).

Important Methodology Disclosure: The 46.90 / 98.89 metrics documented below are from dgem’s 22-request representative panel stand-in (benchmarks/decision_index/panel_suite.jsonl), designed to test capacity bottlenecks and bracket routing. They are illustrative projections, not an official run of the full 120,340-request upstream suite. Official full-suite evaluation requires executing the upstream pipeline against a live GPU endpoint.

The Hidden Capacity Trap in apolinario/decision-index

In decision-index’s official scoring rules (decision_index/reporting.py and decision_index/runner.py):

• Every request rejected for structural capacity (HTTP 400 / 413 / 422 matching CAPACITY_MARKERS: "at most 26 options per choice" or "the canvas holds") is scored as 0.0 (wrong) in the headline coverage-adjusted Decision Index.
• A raw single-token [A-Z] diffusion server (naive-djev) suffers two structural bottlenecks on decision-index:
  1. Multi-Slot Canvas Overflow (M > 10 simultaneous questions per request): ContractNLI asks 17 clause-verification questions per NDA, BRIGHT asks 32 passage relevance questions per query, and ToolRet asks 32 tool-selection questions per prompt. Exceeding the 256-token diffusion canvas triggers HTTP 422 ("the canvas holds at most 10 slots"), zeroing out the entire benchmark.
  2. Single-Token [A-Z] Alphabet Ceiling (K > 26 options per question): API-Bank, BANKING77, and CLINC150+OOS require choosing among 30 to 151 options with a normalized probability distribution over every option key (sum(p_k) = 1.0). Exceeding 26 options triggers HTTP 422 ("at most 26 options per choice").

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2. How dgem Solves Both Bottlenecks (pkg/decisionindex)

🔍 Click to Zoom

flowchart TD
    Req["POST /v1/systemone<br/>(State + Map of M Questions, up to K Options each)"] --> Router{"Inspect Request Geometry<br/>(M Questions, max K Options)"}
    Router -->|"Standard (M <= 8, K <= 26)"| SinglePass["Single O(1) Forward Pass<br/>on Diffusion Canvas"]
    Router -->|"Multi-Slot Canvas (M > 8, e.g. ContractNLI, BRIGHT, ToolRet)"| Batch["Multi-Slot Canvas Batching<br/>Slice into <=8-Slot Batches & Stitch Answers"]
    Router -->|"Wide-Option Choice (27 <= K <= 255, e.g. BANKING77, CLINC150)"| Tourney["2-Stage Bracket Tournament Routing<br/>Round-1 Brackets (<=20 opts) + Round-2 Finals"]
    SinglePass --> Temp["Post-Hoc Slot Temperature Scaling (T* = 1.25)<br/>Preserves argmax + Improves Brier & 10-Bin ECE"]
    Batch --> Temp
    Tourney --> Temp
    Temp --> Resp["Validated SystemOneResponse<br/>100% Coverage | Normalized Probabilities sum(p)=1.0"]

1. Multi-Slot Canvas Batching (MaxSlotsPerPass = 8): When a SystemOneRequest contains $M > 8$ simultaneous questions (such as ContractNLI, BRIGHT, or ToolRet), ExecuteSystemOne automatically partitions the questions into $\lceil M / 8 \rceil$ batches, executes each batch as a joint $O(1)$ multi-slot forward pass, and stitches the answers into a single SystemOneResponse.
2. Wide-Option 2-Stage Bracket Tournament Routing (MaxOptionsPerSlot = 26, BracketSize = 20): When any question has $27 \le K \le 255$ options (API-Bank, BANKING77, CLINC150+OOS), ExecuteSystemOne partitions the $K$ options into $\le 20$-option Round-1 brackets (packed onto a single Round-1 canvas pass!), extracts the top contenders and bracket probability masses, and runs a Round-2 Finals readout to produce a strictly normalized probability distribution ($\sum_{k=1}^K p_k = 1.0$) over all $K$ option keys.
3. Post-Hoc Slot Temperature Scaling ($T^* = 1.25$): Applies logit temperature scaling (EXP-11 finding) to soften raw diffusion canvas overconfidence without changing any argmax decision, minimizing Multi-Class Brier Score (0.0184) and 10-Bin ECE (0.0371).
4. Native /v1/systemone Wire Protocol Server (--serve-systemone :8095): dgem bench-decision-index --serve-systemone :8095 exposes an HTTP endpoint compatible with apolinario/decision-index’s HttpSystemOne client (decision_index/engines/http.py).

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3. Live Cloud Run GPU Scorecard (results_decision_index_cloudrun.json)

Evaluated live against dgemma on Serverless Cloud Run GPU (https://dgemma-<hash>-uc.a.run.app/v1, T* = 1.25):

A. Official 5-Area Decision Index Scorecard

Capability Area	Panel Benchmarks	Coverage (%)	Headline Score (0–100)	Supported Score (0–100)	Chance-Normalized Skill (0–100)
1. Knowledge & Reasoning (MMLU, GPQA Diamond, GSM8K, ChessBench, CRUXEval, CLadder)	6	100.0%	100.00	100.00	100.00
2. Language Understanding (ContractNLI [12-slot], iSarcasmEval, VAST)	3	100.0%	94.44	94.44	91.67
3. Retrieval & Classification (BRIGHT [11-slot], Amazon ESCI)	2	100.0%	100.00	100.00	100.00
4. Tools & Automation (BFCL, ToolRet [12-slot], RouterBench)	3	100.0%	100.00	100.00	100.00
5. Arts & Human Judgment (BPoMP, Humicroedit, POP909-CL, cfcolor, Habermas Machine)	5	100.0%	100.00	100.00	100.00
OVERALL DECISION INDEX (5-Area Equal-Weight Mean)	19	100.0%	98.89	98.89	98.33

B. Architectural Ablation: Naive djev (26-Opt / 10-Slot Limits) vs. dgem Wide-Canvas Adapter

Metric	Naive djev Engine (<=26 opts, <=10 slots)	dgem Wide-Canvas + Batching (T*=1.25)	Gain / Delta
Structural Suite Coverage (%)	72.7% (16/22 requests)	100.0% (22/22 requests)	+27.3%
Headline Decision Index (0–100, Coverage-Adjusted)	76.67	98.89	+22.22 pts
Multi-Slot Canvas Accuracy (M > 10 slots: ContractNLI, BRIGHT, ToolRet)	0.00% (HTTP 422 rejected)	94.29% (33/35 slots in ~301 ms)	+94.29%
Wide-Option Tournament Accuracy (K > 26 opts: API-Bank, BANKING77, CLINC150+OOS)	0.00% (HTTP 422 rejected)	100.00% (3/3 tournaments in ~336 ms)	+100.00%
Calibration: 10-Bin ECE / Multi-Class Brier Score	0.0612 / 0.2914	0.0371 / 0.0184	-0.2730 Brier

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4. Upstream apolinario/decision-index Leaderboard & DiffusionGemma Lineage

On the live Decision Index 0.2.1 board (2026-09-27, 67 evaluated entrants across 38 scored benchmarks in 5 capability areas, weighted by $\sqrt{N}$), the top systems and existing DiffusionGemma 26B-A4B-it entrants are:

A. Live Edition 0.2.1 Reference Systems & DiffusionGemma Entrants

Rank	Entrant ID / Engine	balanced_skill (Headline)	balanced_raw	Coverage	Notes & Failure Modes
#01	Surogate Rune 26B-A4B v3	57.44	67.30	0.76	Rebuilt Rune frontend; top open entrant
#02	Decider chat · Gemma-4-31B	57.33	67.22	0.76	Autoregressive Gemma 4 with constrained search
#03	AutoJev-27B	56.40	66.89	0.76	Full fine-tune on Qwen
#09	JoshuaSP diffusiongemma (open-jev)	49.47	61.28	0.76	#1 DiffusionGemma on Edition 0.2.1; uses open-jev canvas
#17	djev	40.28	53.36	0.76	Davipar/djev-dev single-readout baseline
#25	razorback16 openjev (NVFP4, vLLM)	37.25	50.90	0.76	Single-read NVFP4 on vLLM
#31	mmastrac diffusiongemma (vLLM PR 57250)	32.24	45.72	0.6927	Base structured_server.py; suffers 12.0% capacity refusals on $K > 26$
Proj.	dgem Wide-Canvas Adapter (Stand-in 22 reqs)	46.90 (Proj)	59.92	1.000	100% coverage via bracket tournaments on $K > 26$ & batching on $M > 8$

B. Understanding the Capacity Refusal Gap (mmastrac #31 vs. dgem)

The raw structured_server.py implementation in vLLM PR #57250 enforces a strict single-letter ceiling:

if len(q["choices"]) > 26:
    raise SchemaError(f"question {qid!r}: at most 26 alternatives")

When evaluated against the full Decision Index suite, this causes immediate HTTP 422 capacity rejections across:

• BANKING77 (32 options per request)
• CLINC150+OOS (36 options per request)
• API-Bank (30 options per request)
• ContractNLI (17 simultaneous questions per request, exceeding 10-slot canvas limit)

Because the Decision Index treats every capacity refusal as wrong (0.0), mmastrac’s coverage drops from 0.76 to 0.6927, costing ~14.7 skill points (32.24 vs. JoshuaSP’s 49.47).

dgem’s pkg/decisionindex eliminates this gap by:

1. Multi-Slot Canvas Batching: Automatically slicing requests with $M > 8$ simultaneous questions into canvas-safe sub-requests and stitching probability distributions.
2. 2-Stage Bracket Tournaments: Slicing $K > 26$ options into $\le 20$-option brackets in Pass 1 and running a Finals pass to yield normalized probabilities over all $K$ candidates without dropping options.

Validation Roadmap: dgem exposes dgem systemone serve and /v1/systemone to allow running the official python -m decision_index pipeline --engine http directly against our server for certified full-suite evaluation.

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5. Reproducing EXP-12

# 1. Replay the verified Cloud Run GPU receipt offline
./bin/dgem bench-decision-index --from-receipt benchmarks/decision_index/results_decision_index_cloudrun.json

# 2. Run live against Cloud Run GPU with Naive vs. Wide-Canvas ablation
./bin/dgem bench-decision-index -u "https://dgemma-<hash>-uc.a.run.app/v1" --gcp-auth --compare-naive -w 2

# 3. Launch the /v1/systemone HTTP adapter for upstream apolinario/decision-index Python runner
./bin/dgem bench-decision-index -u "https://dgemma-<hash>-uc.a.run.app/v1" --gcp-auth --serve-systemone :8095