Multi-Vector Impact Score (MVIS) Overview

• Multi-Vector Impact Score (MVIS) is a composite metric that linearly fuses normalized impact and safety signals into a bounded score, offering clear interpretability and resistance to manipulation.
• It employs diverse input vectors—such as detection precision, collision relevance, weighted citations, and artifact adoption—to evaluate performance in both autonomous systems and academic domains.
• MVIS enables transparent decision-making through dynamic weighting, robust normalization, and decision bands that inform both safety assessments and research impact evaluations.

The Multi-Vector Impact Score (MVIS) is a composite metric class of increasing importance in both machine perception safety evaluation and research impact assessment, characterized by the linear fusion of multiple, loosely orthogonal "impact" or "safety" signals. MVIS achieves interpretability and robustness by mapping disparate outcomes or input vectors—typically standardized, weighted, and normalized—to a single scalar value, bounded (either by definition or by normalization procedures) to facilitate intuitive interpretation and thresholding. MVIS has recently emerged in two prominent domains: (1) autonomous system safety assessment, where it quantifies composite, scenario-aware perception risk, and (2) scholarly impact credentialing, where it drives transparent, manipulation-resistant market-based peer evaluation frameworks.

1. Formal Definitions and Mathematical Construction

Autonomous Systems Safety

In the context of autonomous vehicle perception, MVIS is defined per test sequence of T frames as

$$\mathrm{MVIS} = w_D \left[ \frac{1}{T} \sum_{t=1}^T f_{c,t} (f_{t,t} S_{D,t}) \right] + w_T \left[ \frac{1}{T} \sum_{t=1}^T f_{c,t} (f_{t,t} S_{T,t}) \right]$$

where $S_{D,t}$ and $S_{T,t}$ are frame-wise detection and tracking safety sub-scores (each normalized to [0,1]), $f_{c,t}$ is a collision-relevance factor, and $f_{t,t}$ is a soft real-time perception latency penalty. The weights $w_D, w_T$ (with $w_D + w_T = 1$) control emphasis on detection versus tracking (Volk et al., 16 Dec 2025).

Research Impact Credentialing

For academic publishing, MVIS for paper $p$ over a set of impact vectors $V = \{v_1, ..., v_k\}$ is given by

$$\mathrm{MVIS}_p = \sum_{v \in V} \alpha_v \, \phi_v(s_{v,p})$$

with $s_{v,p}$ the raw signal for vector $v$, $\phi_v$ the vector-specific normalization (e.g., z-score, percentilization, or bounded transformation), and $\alpha_v$ non-negative weights summing to one (Sankaralingam, 16 Dec 2025).

MVIS centralizes the aggregation of multiple heterogeneous metrics, ensuring a unified, easily interpretable score.

2. Input Vectors and Their Operationalization

In Perception Safety

The MVIS framework for autonomous vehicles fuses the following five vectors (Volk et al., 16 Dec 2025):

1. Detection and Tracking Precision: Quantified via mean CLEAR metrics (MODA, MODP for detection; MOTA, normalized MOTP for tracking), with detection overlap scaled by object size and distance.
2. Distance and Size Adaptation: IoU-based scoring is adjusted by a cover ratio and a piecewise smooth function, further modulated by normalized distance for each ground-truth object.
3. Collision Relevance: Collision-relevance factor $f_{c,t}$ is zero if any undetected ground-truth object is predicted (over a short horizon) to violate RSS-defined safety distance.
4. Potential Collision Damage: Uses classified impact velocity and accident class to assign a per-object severity value, with per-frame severity determined by the worst (minimum) undetected, RSS-relevant object.
5. Real-Time Performance: Detection latency is compared to physical braking time; long-tail latencies penalize perception-time factor $f_{t,t}$.

In Research Impact

Typical vectorization includes (Sankaralingam, 16 Dec 2025):

• Weighted Citations: Normalized via z-score or rank percentile.
• Artifact Adoption: Aggregates GitHub forks and similar markers; normalization uses min-max scaling with capping.
• Cross-Disciplinary Citations & Patents: Patent and ex-field citation counts, z-scored.
• Replication Outcomes: Number of independent replications, compressed (e.g., $x \mapsto x/(1+x)$).
• Community Endorsements: Badge counts normalized to percentiles.

Community governance sets the relative weights ($\alpha_v$), which can be dynamically reweighted to address gameability concerns.

3. Aggregation, Weighting, and Normalization Schemes

MVIS relies on normalization of input vectors to ensure comparability and stability. For safety, intermediate quantities (e.g., IOU-scaled MODP) are rescaled to lie within [0,1]; for impact assessment, normalization functions (z-score, rank, min-max) systematically limit extreme values and harmonize distributions. Weights may be set arbitrarily (subject to convex combination), with empirical defaults of equal weighting in safety contexts, or community-specified allocations in scholarly impact.

This aggregation approach delivers a bounded, interpretable scalar. For perception, weights allow focus on detection or tracking as desired. In research impact, dynamic rebalancing mitigates metric gaming.

4. Interpretation, Decision Bands, and Use Cases

Safety Evaluation

MVIS ∈ [0,1] is mapped to qualitative safety bands:

• [0.0–0.2]: insufficient (high fatality risk)
• (0.2–0.4]: bad (serious injuries likely)
• (0.4–0.6]: good (minor injuries low probability)
• (0.6–0.8]: very good (material damage unlikely)
• (0.8–1.0]: excellent (safe) (Volk et al., 16 Dec 2025)

A perception pipeline scoring, for example, MVIS = 0.19 will be flagged “bad”, indicating severe safety concerns regardless of recall or mAP figures.

Research Credentialing

A composite MVIS of about 0.44 (see explicit worked example) would, after 3-year lookback, inform both individual portfolio assessment and aggregate community rankings of scholarly work (Sankaralingam, 16 Dec 2025). Investor Ratings (IR) are updated based on portfolio-MVIS alignment, directly modulating future influence.

5. Robustness, Manipulation Resistance, and Limitations

MVIS introduces manipulation resistance by:

• Multi-Vector Hardening: Gaming requires simultaneous control of citations, forks, patents, replications, and endorsements (Sankaralingam, 16 Dec 2025).
• Normalization & Clipping: Extreme outliers are capped in normalization steps, preventing small coalitions from skewing the score.
• Robust Aggregation and Governance: Alpha weights and normalization functions can be community-modified when manipulation is detected.
• Cross-graph Analysis: Pattern mining in investment and citation/patent graphs penalizes collusive or anomalous investor behaviors.

In the safety context, MVIS is sensitive to adverse operating conditions: physically motivated parameters (e.g., braking deceleration) can be adjusted for weather effects. However, MVIS does not address semantic misclassification or downstream planning errors. Numerous thresholds and tuning parameters require platform/domain-specific calibration (Volk et al., 16 Dec 2025).

6. Case Studies and Experimental Evidence

Autonomous Perception Scenarios

In head-to-head comparisons, MVIS demonstrates heightened sensitivity relative to mAP or recall. For example, a standard detection stack missing RSS-relevant, high-velocity pedestrians (mAP ≈ 0.51, recall ≈ 0.60) may yield an MVIS below 0.2, appropriately signaling unsafe operation. Conversely, higher mAP in benign scenarios directly correlates with high MVIS, but MVIS steeply penalizes failures on collision-critical cases (Volk et al., 16 Dec 2025).

Impact Market Protocol

In agent-based simulations, baseline gem-paper recall using classic peer review protocols is ≈34.2%; introduction of MVIS-driven IR calibration in an open Impact Market context elevates recall to ≈87% under moderate-skill conditions and >99% in communities with modest forecasting skill. These results confirm that MVIS, when used in conjunction with transparent investment markets and robust calibration, outperforms binary or single-vector benchmarks for identifying high-impact work (Sankaralingam, 16 Dec 2025).

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Summary Table: MVIS Instantiations

Domain	Input Vectors (examples)	Aggregation
Perception Safety	MODA, MODP, MOTA, MOTP, latency, RSS	Weighted sum (0–1)
Research Impact	Citations, forks, patents, badges	Weighted sum (normed)

The MVIS construct offers a framework adaptable to domains requiring multidimensional, robust, and interpretable composite metrics. It addresses blind spots in legacy single-vector protocols and enforces higher resistance to targeted manipulation while maintaining operational utility in real-world scenarios.