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markopoloaiinc/
Athena-mvrko-4B

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Athena (4B): a Large Event Model for shopping behavior

Athena predicts the exact next action a real shopper takes on a live retail page. Given the reduced page state and the interaction history, it emits the next action as structured JSON. On the full official OPeRA test set (992 actions) it scores 24.50% strict exact-match, first among every model tested, ahead of GPT-5.6, Claude Sonnet 5, and Claude Opus 4.8, as a 4-billion-parameter model.

Athena is the base model of the mvrko simulation track: the accurate, cheap, self-hostable foundation for agentic shopping, next-action planning, and behavioral simulation.

Developer: Markopolo AI
Model type: Decoder-only causal LM (dense), LoRA fine-tune of an open base model
Base model: Qwen/Qwen3-4B
Modality: reduced page state + interaction history → next action (action_type, semantic_id, input_text)
Benchmark: OPeRA next-action prediction, strict exact-match on the target element
Repository: ⟨FILL: final repo path — Decision 1.5b⟩
Release: ⟨FILL: version string⟩

Athena is a fine-tune of an open base model. The value is the training recipe, the observation-space engineering, the 32K long-context supervision, and a specialization a general-purpose model cannot reach by prompting. The base weights are the substrate; the moat is everything built on top.

TL;DR


Headline	24.50% exact-match on OPeRA (full 992), #1 vs current frontier
Base	Qwen3-4B · LoRA r=32, α=32 · 32K context
Output	Structured next-action JSON (schema below); 98.9% schema-valid on the 992
Reproducibility	Reproduced cold on `transformers 5.5.0` / `torch 2.8.0` / bf16 / greedy
What it is NOT	It does not emit calibrated probabilities. See Limitations

Results: full official OPeRA test set (n = 992)

Same test set (md5 1e02a30d…), same harness, same strict exact-match scorer for every model. Frontier models are evaluated zero-shot / prompted; Athena is fine-tuned.

Model	Params	Exact-match	Δ vs Athena	Margin
Athena (ours, fine-tuned)	4B	24.50%
GPT-5.6 (prompted)	frontier	22.58%	+1.92	parity (≈1σ, unpaired)
GPT-4.1 (published OPeRA baseline)	frontier	21.5%	+3.0	ahead of published SOTA
Claude Sonnet 5 (prompted)	frontier	18.35%	+6.15	separated (≈3.3σ)
Claude Opus 4.8 (prompted)	frontier	12.70%	+11.80	separated

Athena ranks first, ahead of every current-frontier model tested and the published baseline.

Honest statistical read

vs Claude Opus 4.8: separated. +11.8 points, far beyond sampling noise.
vs Claude Sonnet 5: separated. +6.15 points, ≈3.3σ.
vs published GPT-4.1 baseline: ahead. +3.0 points.
vs GPT-5.6: statistical parity, nominal edge. +1.92 points is ≈1σ (unpaired), a first-place finish with a nominal lead, not a statistically separated one. We report it as such. A paired McNemar test on the shared 992 is the correct way to sharpen this and is ⟨FILL: pending — Decision 0.2⟩.

We lead the board and state exactly how strong each margin is. Nothing is labeled "clear" unless the test supports it.

The task ceiling: what the frontier numbers reveal

The entire current frontier lands between 12% and 23% on OPeRA. The ceiling here is the difficulty of the task, not model size: predicting the exact element a human clicks, from dozens of candidates, is genuinely hard, and raw capability barely moves it. Athena's 24.50% is not "low". It is the best result on a benchmark where the strongest general models in the world sit below it. The lever that moves this number is behavioral specialization, not scale.

Why a specialist wins

The frontier models return clean, schema-valid JSON. They understand the task perfectly. They still lose, because next-action prediction requires knowing how real shoppers ground their intent in this interface, and that knowledge is behavioral, not linguistic. A prompt yields a fluent guess; it cannot supply behavior the model never learned.

The frontier models are excellent at language. Athena is excellent at shoppers.

Strongest supporting evidence, the OPeRA error analysis: the benchmark's own error taxonomy localizes frontier failure to grounding (naming the right element), not formatting. ⟨FILL: cite the specific error-type percentages with the paper section, from the benchmark source-fact sheet (artifact 1.7). Do not paraphrase from memory.⟩

Architecture and the core innovation: the observation space

The central innovation is not the weights. It is how the web page is represented to the model.

A learned observation space. Raw HTML is unlearnable at scale, since a single page blows past any practical context window. Athena consumes a structure-preserving reduction that keeps only the named, actionable elements (the ones an action can target) and discards the rest, turning a sprawling DOM into a compact, typed, model-legible page state. This is what makes the next-action target predictable instead of buried.
Long-context supervision at 32K. Real sessions carry long histories and large pages; Athena trains at a 32,768-token context so it conditions on the full journey, not a truncated snippet.
Behavioral fine-tuning. Trained directly on what shoppers do over this action space, with completion-only masking on the target action.

The observation-space parser (or a specification precise enough to rebuild it) is released so third parties can reproduce the input format. See Reproduction. ⟨FILL: link — artifact in Part 2⟩

Quickstart

Copied from a tested script. See Reproduction. Requires transformers==5.5.0.

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch, json

REPO = "⟨FILL: repo id⟩"
tok = AutoTokenizer.from_pretrained(REPO, trust_remote_code=True)
mdl = AutoModelForCausalLM.from_pretrained(REPO, dtype=torch.bfloat16,
        device_map="cuda", attn_implementation="sdpa", trust_remote_code=True).eval()

messages = [                       # see example_inputs/ for real OPeRA cases
    {"role": "system",  "content": "⟨exact system string — frozen prompt spec⟩"},
    {"role": "user",    "content": "⟨reduced page state + interaction history⟩\n\n## Next action:"},
]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
enc = tok(prompt, return_tensors="pt", truncation=True, max_length=32768).to("cuda")
out = mdl.generate(**enc, max_new_tokens=96, do_sample=False, pad_token_id=tok.pad_token_id)
print(tok.decode(out[0][enc["input_ids"].shape[1]:], skip_special_tokens=True))
# → {"action_type": "click", "click_type": "product_link", "semantic_id": "..."}

Input / output schema

Output object (validated on 98.9% of the 992 test outputs):

{"action_type": "click", "click_type": "product_link", "semantic_id": "active_item_list.<product>.product_detail"}

action_type: enum ⟨FILL: authoritative enum from schema.json (artifact 1.3)⟩
click_type: enum ⟨FILL: authoritative enum from schema.json⟩
semantic_id: the exact named target element.
input_text: present only for input actions. ⟨FILL: exact convention on non-input actions⟩

Scoring: strict exact-match. Predicted action_type and semantic_id (and input_text for inputs) must equal the ground truth. No partial credit.

Hardware

Precision: bf16 throughout (training and evaluation).

Weights: ~8 GB on disk (4B parameters, bf16, safetensors), loaded as a single merged model with no base-model dependency at inference.

Inference. Athena runs on a single GPU. The headline evaluation was produced on one NVIDIA B300 at 32,768-token context with batch size 2, greedy decoding, sdpa attention. A 24 GB card is the practical floor for full 32K-context inference; shorter contexts (4K to 8K, sufficient for most single-page states) fit comfortably in 16 GB. Memory is dominated by the bf16 weights plus the KV cache, which grows linearly with context length.

Efficiency

Athena is a self-hostable 4B model: 1 to 2 orders of magnitude cheaper per prediction than prompting a frontier reasoning API, with a direct answer and no billed reasoning tokens, batched local inference (no per-call round-trip, no rate limits), and data kept in-house. Exact multiples are computed from the dated cost model. ⟨FILL: efficiency multiple + pricing date — artifact 1.8⟩

Applications

Each tagged by which family component it requires and by maturity.

Application	Needs	Maturity
Next-action prediction / autocomplete of shopper intent	Athena (this model)	Benchmarked
Session replay: scoring a logged journey step by step	Athena (this model)	Benchmarked, this is how the 992-action result is measured
Behavioral simulation / free-running journey rollout	Athena plus an interactive page-state environment	Not demonstrated. Requires an environment that returns a new page state for a novel action; OPeRA provides logged trajectories only
Calibrated conversion / intent scoring	A different family component (calibrated intent head, AUC/ECE), not Athena	Separate model

Family note: Athena predicts next actions and is scored on exact match. It does not emit calibrated probabilities. The calibrated intent head (AUC/ECE) is a separate component of the mvrko family. See the model family map. Do not attribute calibration claims to this model.

Training details

Setting	Value
Base	`Qwen/Qwen3-4B`
Method	LoRA (r=32, α=32, dropout 0.1), bf16, gradient checkpointing
Target modules	q,k,v,o,gate,up,down proj
Context length	32,768 tokens
Objective	completion-only masking on the next-action target
Epochs / LR / schedule	1 / 1e-4 / cosine, warmup 0.03
Hardware	2× B300, DDP via torchrun
Decode (eval)	greedy (deterministic), max_new_tokens 96
⟨FILL from config.json / training config⟩	layers, heads, KV heads, head dim, vocab, tokens seen

Reproduction

The evaluation harness (runner, parser, scorer, frozen prompt file), the observation-space parser, per-example outputs for all models, and 3 to 5 example inputs are released at markopoloaiinc/Athena-mvrko-4B. Pinned versions and the run record: transformers 5.5.0, torch 2.8.0+cu129, bf16, greedy, batch 2. See eval_run_record.json.

Verification status (Part 3):

Cold-start reproduction of the 24.50% headline (indexed, no-dedup, md5 1e02a30d).
⟨FILL: cross-engine determinism (transformers↔vLLM agreement rate)⟩
⟨FILL: batch-invariance spot check (bs 1 vs 32)⟩
⟨FILL: fresh-environment quickstart test (< 15 min, by a non-author)⟩
⟨FILL: adversarial read against the OPeRA paper⟩
Schema validation: 98.9% of 992 outputs validate.

Limitations

Fine-tuned vs. prompted. Athena is fine-tuned on the task; frontier baselines are prompted zero-shot. This is a specialization comparison, the intended one, not a claim about raw model capability.
Observation format. All models are scored on Athena's reduced-HTML observation space; the frontier models see it cold. A different encoding could shift their numbers.
GPT-5.6 margin is within noise. +1.92 points at n=992 is a first-place tie with a nominal edge, pending a paired test.
This benchmark does not evidence calibration. Exact-match measures grounding accuracy, not probability quality. Athena emits no calibrated conversion signal.
Strict exact-match is unforgiving by design. Absolute scores are low because the task is hard, not because any model is failing.

Responsible use

⟨FILL: intended-use scope, out-of-scope uses, data-provenance and privacy note, and that predictions are behavioral estimates, not guarantees.⟩

Related work

OPeRA, the benchmark and its published baselines. ⟨FILL: full citation with the real author list copied from arXiv — artifact 1.7⟩
RL-based OPeRA methods. ⟨FILL: name the reinforcement-learning approaches on this benchmark explicitly, so the comparison table is not only "us vs. prompted frontier."⟩

License and citation

License: ⟨FILL: Decision 0.1 + the actual LICENSE file. Base Qwen/Qwen3-4B is Apache-2.0. State inherited obligations if releasing open.⟩
Citation:
bibtex
1@misc{athena_mvrko, 2 title = {Athena: a 4B Large Event Model for shopping-behavior next-action prediction}, 3 author = {Markopolo AI}, 4 year = {2026}, 5 note = {Markopolo AI} 6}
Contact: ⟨FILL⟩

Athena is a 4B Large Event Model that predicts real shopper behavior more accurately than the current frontier on a public benchmark: cheaply, self-hostably, and with every margin stated honestly. It is the foundation of the mvrko simulation track.