README.md

---
license: other
license_name: lfm-open-license-v1.0
license_link: https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M/blob/main/LICENSE
library_name: transformers
pipeline_tag: text-classification
tags:
- betterwright
- accessibility
- browser-agent
- reranking
- long-context
base_model: LiquidAI/LFM2.5-Encoder-350M
---

# BetterWright Encoder 350M

A task-conditioned accessibility-tree relevance encoder for reducing browser
agent context while preserving action targets and evidence. It is trained from
`LiquidAI/LFM2.5-Encoder-350M` in bfloat16.

All 355,011,331 parameters are trained; this checkpoint does not use LoRA,
adapters, or a frozen backbone. The model has three outputs: chunk relevance,
token/node relevance, and a confidence signal for deterministic fallback.
BetterWright keeps its original snapshot whenever the confidence gate is not
met.

The training corpus contains 500,000 deduplicated structural examples grounded
in 5,256 independently generated and audited tasks from 141 real-site domains.
Domains—not rows—define the split: 110 train, 8 validation, and 23 held-out
test domains.
The corpus is primarily BetterWright accessibility trees, with a smaller share
of alternate accessibility serializations for robustness.

## Input

```text
[BETTERWRIGHT_TASK]
<the current browser task or observation query>
[ACCESSIBILITY_SUBTREE]
<BetterWright aria tree chunk>
```

The released checkpoint is progressively trained at 8K, 16K, 32K, and 64K
sequence lengths. Normal inference should still use hierarchical chunks for
lower latency; 64K is intended for unusually large dumps and packed tabs.

## Quick start

```python
import torch
from transformers import AutoModel, AutoTokenizer

model_id = "ProCreations/betterwright-encoder-350m"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModel.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
).eval()
batch = tokenizer(
    "[BETTERWRIGHT_TASK]\nOpen pricing\n[ACCESSIBILITY_SUBTREE]\n"
    '- link "Pricing" [ref=e12]',
    return_tensors="pt",
)
with torch.inference_mode():
    output = model(**batch)
relevance = torch.sigmoid(output.logits)
confidence = torch.sigmoid(output.uncertainty_logits)
token_relevance = torch.sigmoid(output.token_logits)
```

Production pruning should use the validated thresholds in
`relevance_config.json`, deterministic must-retain rules, and exact full-tree
fallback. A raw relevance score alone is not a safe deletion decision.

## Training

- 500,000 unique pairs: 77.7% BetterWright trees, 13.1% alternate ARIA trees,
  and 9.1% compact accessibility outlines.
- 391,777 train, 25,891 validation, and 82,332 untouched-test pairs, split by
  domain before structural expansion.
- Full-parameter BF16 AdamW training with global batch 128, a cosine schedule,
  and a class weight derived from the exact training distribution.
- A balanced positive/negative packed-tree curriculum at 8K, 16K, 32K, and
  64K tokens, with exact required-node span supervision.

## Evaluation policy

Fallback thresholds are selected on validation domains. The production policy
uses eight ranked 1,800-character structural regions, preserves referenced
nodes, bounded child context, ancestry, and deterministic must-retain states,
and returns the exact original tree on low confidence, errors, insufficient
benefit, or an over-limit result.

| Domain split | Tasks | Target/evidence recall | Perfect-task recall | Pruning coverage | Mean token savings when active | Token-weighted savings |
| --- | ---: | ---: | ---: | ---: | ---: | ---: |
| Validation (8 domains) | 288 | 100.000% | 100.000% | 12.50% | 33.38% | 3.42% |
| Held-out test (23 domains) | 877 | 99.584% | 99.544% | 22.46% | 44.09% | 9.69% |
| Post-training reserve (17 additional domains) | 161 | 100.000% | 100.000% | 33.54% | 16.78% | 2.42% |

The exact reports and frozen policy are distributed as `eval.json`,
`reserve_eval.json`, and `relevance_config.json`. The 99.5% target is evaluated
on whole tasks and required accessibility refs, not inferred from training
loss. A nine-site live navigational replay retained and clicked 6/9 current
targets; two large pages exceeded the runtime ceiling after fallback and one
site did not expose the requested label. This limitation is intentionally not
hidden by substituting easier sites.

## Safety and limitations

This is a relevance model, not an autonomous browser agent. It must not be the
only path for security decisions. Snapshot text is untrusted data. Consumers
should keep deterministic must-retain rules and fall back to the full tree on
model errors, timeouts, low confidence, or insufficient retained context.
Savings are substantial when pruning activates, but coverage is deliberately
conservative and very large pages can still fall back above the caller's size
ceiling. This checkpoint is therefore an alpha integration, not a claim that
every page becomes smaller.