Update model card: decision-model tags, Kev credit, related work

README.md

@@ -12,10 +12,13 @@ tags:
 - pointer-network
 - selective-prediction
 - calibration
-- decision
+- decision-making
+- fast-decision-model
+- system-1
+- model-routing
+- gui-grounding
 datasets:
 - osunlp/Multimodal-Mind2Web
-pipeline_tag: depth-estimation
 ---
 
 # Gemma 4 E2B + LoRA pointer head for web element selection (Multimodal-Mind2Web)
@@ -29,6 +32,10 @@ Given a task, the previous actions, a full-page screenshot and a list of candida
 - Speed: in the live form test below (short pages, one screenshot strip, about 33 candidates) a decision took about 0.2 s for the model plus 0.1 s for candidate extraction and screenshot on an A100. Inputs of Mind2Web size (several strips, up to 4600 tokens) are slower; we did not benchmark that separately.
 - Honest summary of what it is good and bad at is in [Summary of results](#summary-of-results) and [Limitations](#limitations-and-what-this-model-is-not).
 
+**Role: a fast decision module ("System 1").** The model is meant as the quick, cheap first decision in a larger agent: it picks the next element or abstains with a confidence score, and low-confidence steps are escalated to a stronger model ("System 2", for example a large VLM) or a human. It is a decision/routing component, not a standalone agent.
+
+**Approach: a calibrated decision head, "Kev-like".** Instead of generating the next action as text, the model makes a *decision over a closed set*: a query vector (from the task, history and screenshot) is scored against one key per candidate element plus a learned "none of these" key, and a softmax gives a probability for every option. This pointer-style, "Kev-like" decision head has three consequences we care about: (1) one forward pass picks the element, no token-by-token decoding; (2) the top-1 probability is a ready-made confidence score, and "none" is an explicit option, so the model can abstain; (3) because decisions and confidences are explicit numbers, they can be evaluated and calibrated with standard tools (ECE, Brier score, risk-coverage curves, temperature scaling) and used to route uncertain steps to a stronger model. In this release the decision quality and the confidence ranking are measured (see results); the confidences themselves are still raw and need temperature scaling on held-out data before being read as probabilities. The "Kev-like" design follows [Kev](https://github.com/jaredpalmer/kev) by Jared Palmer (open-source family of "System One" decision models: a small LoRA adapter plus a pointer head on a Qwen base, [weights on Hugging Face](https://huggingface.co/collections/jaredpalmer/kev), Apache-2.0), which in turn is described as Jev-like; see [Related work](#related-work-and-credit).
+
 > **Status:** single training run, single seed. Confidence scores are **raw (not temperature-scaled)** for this checkpoint. Numbers are not directly comparable with published Mind2Web numbers (see [Evaluation protocol](#evaluation-protocol)).
 
 ## Summary of results
@@ -177,6 +184,12 @@ The scripts in `code/` reproduce the pipeline: `m2w_prep.py` (candidates, TF-IDF
 - The model was trained on [Multimodal-Mind2Web](https://huggingface.co/datasets/osunlp/Multimodal-Mind2Web). **We did not verify the dataset's license for this card; check the dataset page and terms before any commercial use.** No dataset content is included in this repository.
 - Screenshots and DOM snapshots in the dataset come from third-party websites.
 
+## Related work and credit
+
+- **Kev** (Jared Palmer, 2026): open-source, Apache-2.0 family of small "System One" decision models that answer typed questions (choice, score, ...) with probabilities, built as a LoRA adapter plus a small pointer head on a Qwen base ([code](https://github.com/jaredpalmer/kev), [weights](https://huggingface.co/collections/jaredpalmer/kev)). The idea of a LoRA adapter with a pointer head over a closed set of options, and of reading calibrated probabilities from it, comes from this line of work. We are not affiliated with it, and this model is not a Kev checkpoint.
+- **What is different here:** a different backbone (Gemma 4 E2B, a vision-language model), screenshots as input, a task-specific candidate set built from the DOM (hundreds of web elements per step), a learned "none" key and an operation head, LoRA on attention and MLP projections of layers 0-29 (rank 32), and an evaluation on Multimodal-Mind2Web with calibration and risk-coverage metrics. We did not compare against Kev checkpoints.
+- **Benchmarks and data:** Mind2Web and Multimodal-Mind2Web (see Citation).
+
 ## Citation
 
 If you use this model, please also cite the benchmark and the multimodal extension:
@@ -194,4 +207,4 @@ If you use this model, please also cite the benchmark and the multimodal extensi
   booktitle = {ICML},
   year      = {2024}
 }
-```
+```