Restricting the precision of agentic retrieval models to shared 8-bit exponent formats (FP32↔BF16) preserves representational alignment and prevents decision-boundary drift in climate finance early warning systems.

This discovery has a Claude-generated validation package with a full experimental design.

Precise Hypothesis

Spine & Adversarial ReadReady for validation

“This hypothesis tests whether enforcing a shared 8-bit exponent format across floating-point precision modes in an agentic retrieval model's inference pipeline measurably reduces decision-boundary flip rates in climate finance risk classification compared to mixed-precision inference, without a statistically significant accuracy cost.”

• highThe entire premise conflates exponent-format consistency with numerical stability; IEEE-754 BF16 and FP32 already share exponent width by design (8 bits), so 'shared 8-bit exponent' is not a novel intervention — it's simply choosing BF16 over FP16, a well-known existing practice in ML systems engineering.
  The EVP's ablation (Step 7) is designed precisely to test whether this is merely rediscovering 'use BF16 not FP16,' which is already standard advice. The protocol does not yet resolve whether the claimed effect exceeds what is already documented in existing mixed-precision training literature (e.g., NVIDIA/Google AMP best practices) — this must be checked against that literature before claiming novelty; current EVP cannot confirm or deny this without working web search access, a genuine gap flagged in EXTERNAL_CONFLICTS.
• mediumWhy these specific datasets (World Bank CCKP, CBI bonds, IPCC synthetic) and not standard ML robustness benchmarks? The choice of climate-finance-specific data may not isolate the numerical-precision mechanism from domain-specific confounds (e.g., sparse/noisy labels in bond default data), and the methodology does not justify why climate finance is mechanistically special versus any other threshold-based agentic decision task.
  Partial resolution: the datasets are chosen for ecological validity to the stated application (climate finance early warning), but the core numerical-stability claim is domain-agnostic and could be tested more cheaply on synthetic/standard benchmarks (GLUE-style classification, MNIST-scale boundary tests) first. The EVP should add a cheaper domain-agnostic pilot (not currently included) before spending the full budget on climate-specific data, which have smaller N and noisier ground truth than standard ML benchmarks — this is a methodology gap the current design does not fully address.
• mediumFormal verification via interval/affine arithmetic on only the last 2 layers + classification head is a weak substitute for whole-model guarantees, yet the impact statement claims 'formally verified stability' for the full system — this is an overclaim relative to what the methodology can actually deliver.
  Acknowledged directly in KNOWN_FAILURE_MODES; the EVP should require that any public claim of 'formal verification' be scoped explicitly to the verified subnetwork, not the full agentic pipeline, and that whole-model verification be listed as future work rather than implied as achieved. This is not yet resolved and should be a condition of any publication arising from this work.

Experimental Protocol

ROI Projection

Implementation Sketch

# Pseudocode: precision-drift ablation harness

for arch in [RAG_LLM_8B, DomainRanker_small]:
    model = load_frozen_checkpoint(arch)
    for dataset in [WorldBank_CCKP, CBI_bonds, IPCC_synthetic]:
        inputs, labels, thresholds = load_dataset(dataset)
        boundary_set = select_boundary_proximal(inputs, baseline_scores_fp32, top_decile=True)

        results = {}
        for precision_cfg in [BF16, FP32, MIXED_FP16_FP32, SHARED_EXP8]:
            set_global_dtype_policy(precision_cfg)   # custom autocast hooks
            scores = model.infer(boundary_set, deterministic=True, seed=42)
            decisions = threshold(scores, thresholds)
            results[precision_cfg] = {
                'decisions': decisions,
                'scores': scores,
                'accuracy': eval_accuracy(decisions, labels),
            }

        flip_rate = compare_decisions(results[SHARED_EXP8], results[FP32]) \
                    vs compare_decisions(results[MIXED_FP16_FP32], results[FP32])
        bootstrap_test(flip_rate, n_resamples=10000, correction='bonferroni')

        # formal verification on final 2 layers + head
        bound_shared = interval_arithmetic_bound(model.tail_layers, precision=SHARED_EXP8)
        bound_mixed  = interval_arithmetic_bound(model.tail_layers, precision=MIXED_FP16_FP32)
        assert bound_shared.width < bound_mixed.width  # tighter bound = success signal

log_all_artifacts_to(repo, include=['checkpoints','numeric_logs','verification_certs'])