README.md

---
license: mit
language: en
base_model: microsoft/deberta-v3-base
tags:
  - evidential-deep-learning
  - uncertainty-estimation
  - belief-graph
  - agents
library_name: pytorch
---

# AEV — EDL Belief-Update Cascade Heads

Trained gating heads for the **EDL belief-update cascade** of the Agentic Evidential
Verifier (AEV) project. Each incoming observation `o_{t+1}` passes through a gated
cascade that maintains the agent's belief graph `S_t = (V_t, E_t, M_t)`:

```
observation o_{t+1}
  └─ EDL_A admission {admit, ignore}                    K=2
       └─ admit → Atomizer + Proposer
            └─ EDL_M merge {merge, append}              K=2
                 └─ append → EDL_R relation {supports, contradicts, none}  K=3
                              none + high U → quarantine
```

Every head outputs Dirichlet evidence `α = f_θ(·) + 1`; uncertainty `U = K / Σα`.
When `U ≥ τ` the argmax decision is overridden and the unit is quarantined.
All DeBERTa heads are full fine-tunes of `microsoft/deberta-v3-base` (cross-encoder,
max_len 256) trained with the Sensoy et al. 2018 EDL loss (Bayes-risk CE + annealed
KL to uniform).

## Checkpoints

| Path | Head | Labels | test macro-F1 | τ (dev) | Notes |
|---|---|---|---|---|---|
| `edl_head_admission/` | EDL_A | admit / ignore | **0.965** | 0.136 → 94% cov @ 1.6% sel. risk | HotpotQA-distractor + FEVER+ mismatch mining + tau-bench noise |
| `edl_head_admission_swe/` | EDL_A (SWE-aligned) | admit / ignore | 0.807 (silver) | budget15 fallback | 1-epoch continued FT on 3k SWE-trace weak labels + 50% replay |
| `edl_head_merge/` | EDL_M-text | merge / append | **0.888** | 0.230 → 82% cov @ 5.3% sel. risk | PAWS + QQP + VitaminC flagging revisions |
| `edl_head_merge_swe/` | EDL_M-text (SWE-aligned) | merge / append | 0.959 (silver) | budget15 fallback | same recipe as admission_swe |
| `edl_deberta/` | EDL_R v0 | supports / contradicts / none | **0.877** | 0.150 | wave-3 FEVER+ 763k checkpoint (FEVER + ANLI + VitaminC + Climate-FEVER), reused as relation head |
| `dep_estimator.pt` | DepNet (M-feat v0) | 8-way dependency type | 0.947 (type macro-F1) | — | 774-d input MLP; posterior coarsened to merge/append for the M-feat arm |

All DeBERTa heads: ECE ≤ 0.03, vacuity-AUROC 0.84–0.88 (in-domain).

**Deployment caveat (validated twice):** absolute τ does **not** transfer across
domains — zero-shot on SWE traces EDL_A quarantines everything and EDL_M merges
confidently-wrong. Use a conformal per-batch quantile instead:
`τ_eff = max(served τ, 85th percentile of batch U)`.

## Format & loading

Each directory contains `model.pt` (state dict) and `meta.json` (labels, backbone,
metrics, fitted τ). Load with the project trainer (repo `agentic-evidential-verifier`):

```python
# PYTHONPATH=src, conda env aev
from aev.train_heads import load_head           # DeBERTa heads
head = load_head("edl_head_admission")           # or any directory above
```

Requires `transformers>=5` gotchas: load with `dtype=torch.float32` and the
sentencepiece slow tokenizer (handled inside the project loader).

## Results context

Full experimental report: `EXPERIMENTS_REPORT_EDL_HEADS.md` in the project repo
(E0 dataset vetting → E1 head training → E2 domain alignment → E3 offline cascade
benchmark: 100% noise interception, −93% residual redundancy → E4 live A/B on
SWE-bench Lite: non-inferiority established).