GoMini v6

A small, local decision model for a bounded browser loop.

GoMini is a full fine-tune of THX-01 for selecting the next action from an explicit set of valid candidates. It is designed for AgentGo browser workflows: a larger model supplies a bounded subgoal; GoMini chooses among prepared actions; AgentGo executes permitted actions and checks their effects. Ordinary verified steps can stay local, with the larger model called when the subgoal finishes or needs help.

Created by Mhmadhassnz.

Development release, not a passed production gate. V6 continues v5 on 29,000 decisions: v5's set plus 12,000 new ones recorded in AgentGo 0.1.6 on new practice families (rows of identical buttons, scroll until found, retry after an error, covering dialogs, wait until enabled, "the 3rd option", focus a box, collapsed sections) and 577 steps of a large model on MiniWoB++ pages it solved. On held-out practice pages it picks the right step 96.6% of the time (v5 on the same pages: 76.6%). Through AgentGo 0.1.6's delegate loop, on BrowserGym MiniWoB++ pages it never saw, it finishes 53 of 65 subgoals (v5 on 0.1.6: 42; v5 on 0.1.5: 39). Calibrated fallback reliability and an independent final audit remain pending.

What GoMini does

GoMini scores supplied candidates in a non-autoregressive forward pass. Choices can represent a click, a supplied field/value pair, a prepared read method, or explicit wait, done, escalate and stop controls. It does not generate an arbitrary plan, new selector, tool argument, value or chat response.

Large model: bounded goal + completion condition + permissions
                              |
AgentGo: observe -> prepare valid choices -> GoMini selects
             ^                                 |
             |                    independent permission checks
             |                                 |
             +----- verify actual effects <- execute allowed choice
                              |
                   verified done / needs_help / stopped
                              |
                         return to planner

The release contains reference selection clients, not a complete production AgentGo integration or permission system. Every client response carries execution_authorized: false; the supervisor remains responsible for execution.

Results at a glance

v6

Per task (v5 -> v6): focus-text 0 -> 4/5, click-collapsible 0 -> 4/5, social-media 0 -> 3/5; click-tab-2 2/5 and form-sequence-2 0/5 for both (the latter's instruction never says to submit).

Inside AgentGo, driven by a large model

One model (gpt-6-luna) drives AgentGo on 38 tasks (24 BrowserGym MiniWoB++ tasks and 14 single-page-app tasks), a fresh session per task, both setups running at the same time, twice (76 runs each). AgentGo 0.1.6 with GoMini v6 solves 94.7% of the runs with no wrong action, against 86.8% and 6 wrong actions for AgentGo 0.1.5 with GoMini v5. The planner did not delegate to GoMini in these runs, so they measure the browser with GoMini installed; what GoMini does on its own is the sweep above. Details: AgentGo 0.1.6 benchmarks.

v5 (kept for reference)

Per-family and per-task numbers are in EVALUATION.md.

v4 (kept for reference)

These are native v4 development results. Local suites used actual model selections and AgentGo execution/readback on owned procedural pages. They are not public-web accuracy, independent test scores or Q8 browser scores.

Zero false-done predictions were observed in the listed native action, form, viewport and navigation suites. These include scenarios where a correct stop or escalation is success; they do not prove universal task completion or reliable failure detection. Navigation's oracle requires explicit scrolling even when a browser click could auto-reveal its target.

Q8_0 delivery smoke: 64 attempted synthetic/TRAIN-derived inputs; 56 supported inputs matched both exact token IDs and native top choices. Eight inputs were rejected before Q8 decision inference by the packing guard. Maximum absolute probability drift on compared inputs was 0.0008011311. This checks conversion and interfaces, not browser success.

Read EVALUATION.md for every native episode subgroup, all 19 read methods, offline source breakdowns, rejection counts, methodology and remaining evaluation. Machine-readable summaries and the original packing failure are in reports/.

Download

Use revision v6 for the tagged release (v5 and v4 stay available). Checksums are in SHA256SUMS.txt, with conversion identities and status in PROVENANCE.json. No Q4 or intermediate F16 GGUF is distributed.

Quickstart

Use a compatible GPU PyTorch environment with Transformers, Safetensors and NumPy. The recorded environment and Q8 server requirements are in ENVIRONMENT.md. CPU portability and other GPU stacks have not been validated by this release. It is a custom decision checkpoint; loading it as AutoModelForCausalLM or sending chat-completion messages is not supported.

Download and run the included single-request example with that environment:

import subprocess
import sys
from pathlib import Path
from huggingface_hub import snapshot_download

root = Path(snapshot_download("Mhmadhassnz/GoMini", revision="v6"))
subprocess.run([sys.executable, str(root / "examples" / "choose.py")], check=True)

The example reads example_request.json, serializes it to one line and asks the native chooser to select a candidate. It does not execute browser actions. For an integration, keep adapter/choose_native.py resident: write one compact JSON object per line to stdin and read one JSON result per line from stdout. Diagnostic messages use stderr.

For Q8_0, first start a compatible ModernBERT decision-capable llama.cpp server with /tokenize and /v1/systemone, following ENVIRONMENT.md. Its binary is not included. Then run:

subprocess.run([
    sys.executable, str(root / "examples" / "choose.py"),
    "--q8", "--url", "http://127.0.0.1:8765",
], check=True)

Use adapter/q8_client.py for a resident guarded Q8 client. Keep its packing and runtime token checks: some literal-special-token/trailing-whitespace inputs do not match native per-piece packing and must escalate. Native/Q8 agreement on the limited smoke suite does not establish equivalence for every possible input.

Integrating with AgentGo

The recommended loop and implementation details are in LIMITATIONS_AND_AGENTGO_INTEGRATION.md.

1. Have the large model specify the subgoal, completion predicate, permitted tab and origin, exact supplied values, effect permissions, deadline and step budget.
2. Build a fresh compact observation and bind valid candidate keys to prepared browser operations. Include explicit wait, stop, done and escalation controls.
3. Use the shared gomini.agentgo/1 packer. Keep the 512-token instruction/options head budget, 1,024-token total, maximum 24 choices and 48 tokens per rendered option. Reject oversize input rather than silently truncating it.
4. Independently check permissions, tab/origin/revision, candidate membership, exact field/value binding and attempt limits before executing any selection.
5. Verify effects from browser evidence. Stop on cancellation; return compact evidence to the larger model on verified completion, ambiguity, missing information, repeated failure or exhausted budget.

For a known fixed request such as an element rectangle, deterministic dispatch to the correct prepared method can be better than asking a model to rediscover the mapping. A successful command or HTTP 200 does not prove goal completion.

Limitations

• General browsing is unproven. Local fixtures share families and some markup; split-separated local labels are not independent real websites or accounts.
• Human transfer is weaker than the procedural average. The adapted dataset includes the annotated target in each shortlist. Its scores are not official Mind2Web benchmark results or full browser trajectories.
• 1,088 registry names are not 1,088 verified tools. Training includes synthetic description routing, including aliases; actual read-method checks cover 19 canonical methods, with prepared arguments and no write-operation coverage.
• Confidence is uncalibrated. V4 uses temperature 1.0; confident mistakes occurred. Probability, margin and entropy must not grant permission. The unused auxiliary action head was not supervised and must not gate safety.
• Fallback is an unmeasured property. Do not assume every failed action will request help. Validate independent checks and larger-model handoffs separately.
• Q8 browser evaluation and final audit remain pending. Packing guards reject some inputs; reference clients are not a hardened supervisor. No general >90% success, latency, multilingual retention or production-readiness claim is made.

Training and provenance

V6 is a full fine-tune of the v5 checkpoint on 28,317 decisions (v5's 14,902-record set plus 12,049 new teacher records on 0.1.6's practice pages and 577 agent-labelled steps; the best of 2 epochs by held-out pages, batch 8 x 4 accumulation, AdamW 3e-6, same objective). The agent-labelled steps are the actions of gpt-6-luna on MiniWoB++ pages outside every evaluation set, taken only from runs the page scored as solved; held-out records are whole pages (seeds 39001-39300). No evaluation page (the 24 agent-benchmark tasks, the 13 GoMini sweep task types, the 14 SPA tasks) was used.

V5 is a full fine-tune of the v4 checkpoint on 14,902 new decisions (2 epochs, AdamW 3e-6, same objective), recorded by a teacher on 6,000 procedurally generated practice pages served to AgentGo 0.1.5 and packed with AgentGo's own packer; held-out records are whole pages. No MiniWoB++ or other benchmark page was used. The recipe and per-family counts are in TRAINING.md.

V4 continues the prior native checkpoint with 53,146 accepted unique training records, combining procedural examples, synthetic registry routing, adapted Mind2Web snapshots and verified decisions on owned local AgentGo fixtures. It reached epoch 2 / step 5,660 using AdamW, BF16 autocast, FP32 optimization and a stable log-score plus spherical-score objective. Embeddings and all encoder blocks were trainable; this was full fine-tuning.

See TRAINING.md for the recipe and source counts, and native/training-settings.json for exact data file hashes and settings. No raw website corpus or complete collection harness is hosted here. Development influenced selection; frozen calibration/final-test records were not consumed for this handoff.

Credits and license

GoMini builds on THX-01 by HAL-X AI, with its mmBERT encoder attribution preserved in NOTICE. Upstream weights and bundled runtime retain their Apache 2.0 license. The pinned foundation revision is b323afcef149f9288a9d0daef4badd67b9b87c61.

Mind2Web is credited to Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Samuel Stevens, Boshi Wang, Huan Sun and Yu Su (2023). Its dataset is CC BY 4.0; the local adaptation changes representation, packing and split assignment. See THIRD_PARTY_DATA.md and the official Mind2Web project.

AgentGo is the browser integration target and execution environment for the reported local fixture checks. AgentGo browser software is not distributed in this model repository.