Systems Engineering | SEAMS Newsletter | BLOCK VECTOR Technologies

Receipts

Run stamp	20260313_121511
Tool version	v348
SEAM model	seamv1
Generated	2026-03-13 12:15:49
Total rows scanned	12557
Unique records	9514
Duplicate rate	24.2%
Seam candidate rate (deduped)	0.6%
Median views	11
Median downloads	4
Median conversion	0.154
Median citations	1

What these numbers mean

Click for definitions

Total rows scanned: total sampled rows across slices (rows × sorts × pages × terms). Not deduped.
Unique records: deduped count of unique record_id values in the consolidated dataset.
Duplicate rate: 1 − (unique / total). High duplication usually means the same papers repeat across sorts/buckets/pages.
SEAM candidate rate: candidates / unique (deduped) based on the SEAM model threshold.
Median views/downloads/conversion: robust central tendency across the current run’s unique records.
Top seam tells: the most common seam signals seen in the candidate set.
New keyword candidates: terms disproportionately associated with seam candidates (heuristic, min sample size).
Exclude/downweight terms: high-volume terms with low median conversion (heuristic, min sample size).

Engagement Comparison

	Rows	Unique	Median Views	Median Downloads	Median Conversion
Candidates	89	75	0	0	0.000
Non-candidates	12468	9439	12	4	0.156

Run Funnel

A quick read on where rows are being filtered out between collection, text availability, tell detection, near-miss status, and final candidate status.

Bucket Yield

Bronze shows total rows per bucket. Steel overlay shows candidate + near-miss yield so weak buckets stand out quickly.

SEAM Candidate Articles

Article	Bucket/Sources	Score	Views	DL	Cites	Evidence/snippet
Safety Assurance Framework for Pre-Designed Controllers: Control Barrier Function-based Safety Filters	`A_autonomous_ai_safety:bestmatch\|A_autonomous_ai_safety:mostrecent`	126	5	4	42	this work proposes a safety assurance framework that augments such controllers with a control barrier function (cbf)-based safety filter.
The effect of control barrier functions on energy transfers in controlled physical systems	`A_autonomous_ai_safety:bestmatch\|A_autonomous_ai_safety:mostrecent`	104	41	75	20	cbf; control-barrier functions; energy-based cbf; energy limitations; safety filter
Safe Execution of Learned Orientation Skills with Conic Control Barrier Functions	`A_autonomous_ai_safety:bestmatch`	86	27	61	26
Decentralised Runtime Monitoring for Access Control Systems in Cloud Federations	`I_authorization_gating:mostviewed`	78	161	139	26	therefore, in order to promote accountability and transparency of access control decisions in federated clouds, we present a decentralised runtime monitoring...
ENTRUST D4.2 Runtime Assurance & Certification Framework – First Release	`A_autonomous_ai_safety:bestmatch\|A_autonomous_ai_safety:mostdownloaded`	48	98	325	12
Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring	`B_systems_resilience:bestmatch\|B_systems_resilience:mostdownloaded\|B_systems_resilience:mostviewed`	46	197	179	12	in this paper, we propose an approach to use dnn uncertainty estimators to implement such supervisor. we first discuss advantages and disadvantages of e...
Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring	`B_systems_resilience:bestmatch\|B_systems_resilience:mostviewed`	46	109	42	12	lastly, we discuss a large-scale study conducted on four different subjects to empirically validate the approach, reporting the lessons-learned as guidance f...
The Authorization Boundary: Why MCP and AI Gateways Are Necessary—But Not Sufficient—for Regulated Agentic AI	`D_custom:bestmatch\|D_custom:mostdownloaded\|D_custom:mostrecent\|D_custom:mostviewed`	46	91	61	10	as ai agents transition from generating text to producing side effects—writing to databases, submitting regulatory filings, executing transactions&mdas...
Experiments for 'Runtime Monitoring for Markov Decision Processes'	`A_autonomous_ai_safety:bestmatch\|A_autonomous_ai_safety:mostdownloaded\|A_autonomous_ai_safety:mostviewed`	44	709	340	20
Control Barrier Functions in Multirotors: a Safety Filter for Obstacle Avoidance	`A_autonomous_ai_safety:bestmatch`	42	59	48	42
V-OCBF: Learning Safety Filters from Offline Data via Value-Guided Offline Control Barrier Functions	`A_autonomous_ai_safety:newest`	42	0	0	42
Explicit Control Barrier Function-based Safety Filters and their Resource-Aware Computation	`A_autonomous_ai_safety:newest`	42	0	0	42
D4.3 Runtime Assurance & Certification Framework – Final Release	`A_autonomous_ai_safety:bestmatch\|A_autonomous_ai_safety:mostrecent`	40	53	54	12
Towards transient frequency safety: A novel load frequency control with wind-storage system via control barrier function	`A_autonomous_ai_safety:newest`	24	0	0	24
Resilient safety-critical control for autonomous electric vehicles via disturbance-observer-based adaptive control barrier functions	`A_autonomous_ai_safety:newest`	24	0	0	24
Distributed Safety Critical Control among Uncontrollable Agents using Reconstructed Control Barrier Functions	`A_autonomous_ai_safety:newest`	24	0	0	24
Distributed Safety Critical Control among Uncontrollable Agents using Reconstructed Control Barrier Functions	`A_autonomous_ai_safety:newest`	24	0	0	24
Distributed Safety Critical Control among Uncontrollable Agents using Reconstructed Control Barrier Functions	`A_autonomous_ai_safety:newest`	24	0	0	24
Combinatorial Safety-Critical Coordination of Multi-Agent Systems via Mixed-Integer Responsibility Allocation and Control Barrier Functions	`A_autonomous_ai_safety:newest`	24	0	0	24
A Safety-Aware Shared Autonomy Framework with BarrierIK Using Control Barrier Functions	`A_autonomous_ai_safety:newest`	24	0	0	24
Learning Safety-Guaranteed, Non-Greedy Control Barrier Functions Using Reinforcement Learning	`A_autonomous_ai_safety:newest`	24	0	0	24
TTCBF: A Truncated Taylor Control Barrier Function for High-Order Safety Constraints	`A_autonomous_ai_safety:newest`	24	0	0	24
High Order Control Lyapunov Function - Control Barrier Function - Quadratic Programming Based Autonomous Driving Controller for Bicyclist Safety	`A_autonomous_ai_safety:newest`	24	0	0	24
Safety-Critical Control on Lie Groups Using Energy-Augmented Zeroing Control Barrier Functions	`A_autonomous_ai_safety:newest`	24	0	0	24
A Speed-Constrained Sliding Mode Position Controller for PMLSM Based on Control Barrier Function	`A_autonomous_ai_safety:newest`	22	0	0	22
Control Barrier Functions with Audio Risk Awareness for Robot Safe Navigation on Construction Sites	`A_autonomous_ai_safety:newest`	22	0	0	22
SQ-CBF: Signed Distance Functions for Numerically Stable Superquadric-Based Safety Filtering	`A_autonomous_ai_safety:newest`	22	0	0	22
Collision-Aware Density-Driven Control of Multi-Agent Systems via Control Barrier Functions	`A_autonomous_ai_safety:newest`	22	0	0	22
Provable Correct and Adaptive Simplex Architecture for Bounded-Liveness Properties	`A_autonomous_ai_safety:bestmatch\|A_autonomous_ai_safety:mostviewed`	20	126	74	20
Efficient COLREGs-compliant collision avoidance using turning circle-based control barrier function	`A_autonomous_ai_safety:newest`	20	0	0	20

Top Seam Tells

Tell	Count
1	89
domain	64
barrier function	46
safety	45
control barrier function	44
gate+action+domain	23
safety filter	17
runtime assurance	7
18	6
simplex	6

Note: Some sources do not provide abstracts/metrics; “tells” may be title-derived for those rows.

Near-Miss Diagnostics

Article	Bucket	Source	Tells	Views	DL	Why not candidate
Observable-Only AI Safety from Public Data: Robust Bottleneck Diagnosis with Auditable No-Meta Dynamic Programming, Anytime Confidence Sequences, and Dynamic IQC	`D_custom`	zenodo	3	54	22	Near threshold
Observable-Only AI Safety from Public Data: Robust Bottleneck Diagnosis with Auditable No-Meta Dynamic Programming, Anytime Confidence Sequences, and Dynamic IQC	`D_custom`	zenodo	3	54	22	Near threshold
Observable-Only AI Safety from Public Data: Robust Bottleneck Diagnosis with Auditable No-Meta Dynamic Programming, Anytime Confidence Sequences, and Dynamic IQC	`D_custom`	zenodo	3	54	22	Near threshold
Observable-Only AI Safety from Public Data: Robust Bottleneck Diagnosis with Auditable No-Meta Dynamic Programming, Anytime Confidence Sequences, and Dynamic IQC	`D_custom`	zenodo	3	54	22	Near threshold
PIC Standard: Provenance & Intent Contracts for AI Agent Action Safety	`D_custom`	zenodo	3	8	1	Near threshold
PIC Standard: Provenance & Intent Contracts for AI Agent Action Safety	`D_custom`	zenodo	3	8	1	Near threshold
From farm to fork: Enhancing meat traceability and safety with intelligent packaging	`A_autonomous_ai_safety`	crossref	3	0	0	No conversion; No metrics
Green-engineered rare-earth–doped ZnO-Y2O3 nanocomposites for efficient textile dye degradation: Operational parameter, environmental toxicity evaluation, and agricultural safety assessment	`B_systems_resilience`	crossref	3	0	0	No conversion; No metrics
Audit-Ready Deterministic Replay for Observable-Only Agents:\\ FOQL, InputSet Binding, Delayed Key Activation, Safety Gate, VRF/PRG, MathKernel, ZK	`J_refusal_hold`	openalex	3	0	0	No conversion; No metrics
Leveraging Traceability to Integrate Safety Analysis Artifacts into the Software Development Process	`C_standards_conformance`	arxiv	3	0	0	No conversion; No metrics
A Reference Architecture for Integrating Safety and Security Applications on Railway Command and Control Systems	`E_interface_icd`	zenodo	2	1407	1029	Near threshold
VV Methods Safety Assurance Position Paper	`C_standards_conformance`	zenodo	2	934	922	Near threshold
VV Methods Safety Assurance Position Paper	`C_standards_conformance`	zenodo	2	934	922	Near threshold
VV Methods Safety Assurance Position Paper	`C_standards_conformance`	zenodo	2	934	922	Near threshold
VV Methods Safety Assurance Position Paper	`C_standards_conformance`	zenodo	2	934	922	Near threshold
Systems architecting: a practical example of design space modeling and safety-based filtering within the AGILE4.0 project	`F_dsm_integration_structure`	zenodo	2	489	279	Near threshold
Systems architecting: a practical example of design space modeling and safety-based filtering within the AGILE4.0 project	`F_dsm_integration_structure`	zenodo	2	489	279	Near threshold
Systems architecting: a practical example of design space modeling and safety-based filtering within the AGILE4.0 project	`F_dsm_integration_structure`	zenodo	2	489	279	Near threshold
The influence of the facility nuclear safety case on the design of naval refit support equipment	`C_standards_conformance`	zenodo	2	219	439	Near threshold
The influence of the facility nuclear safety case on the design of naval refit support equipment	`C_standards_conformance`	zenodo	2	219	439	Near threshold

Rows shown here are not final SEAM candidates, but they were close enough to help tune thresholds, buckets, and tell logic.

Why rows did not become SEAM candidates

reason	rows	share
Low SEAM score	12468	100.0%
No seam tells detected	10240	82.1%
No measurable conversion	5788	46.4%
No engagement metrics	5569	44.7%
Missing abstract / summary text	5	0.0%
Source likely sparse on abstract/metrics	5	0.0%

These are diagnostic reasons counted across non-candidate rows. A single row can contribute to more than one reason, so the shares are directional rather than additive.

Bucket Performance

bucket	rows	unique	text	tells	near	cand	cand_rate	med_views	med_dl
C_standards_conformance	1610	963	1607	513	510	3	0.2%	8	1
D_custom	1574	902	1574	442	437	5	0.3%	16	4
A_autonomous_ai_safety	1750	1436	1750	402	334	68	3.9%	19	18
J_refusal_hold	623	548	621	227	224	3	0.5%	0	0
I_authorization_gating	1750	1526	1750	205	203	2	0.1%	13	6
B_systems_resilience	1750	1367	1750	198	192	6	0.3%	16	10
F_dsm_integration_structure	1750	1441	1750	169	167	2	0.1%	17.5	8.500
E_interface_icd	1750	1618	1750	161	161	0	0.0%	13	4

Use this to see which buckets are producing rows, text, tells, near-misses, and candidates.

Source Performance

source	rows	unique	text	tells	near	cand	cand_rate	med_views	med_dl
zenodo	6960	4133	6960	1020	995	25	0.4%	71	71
crossref	2000	1937	1997	470	457	13	0.7%	0	0
arxiv	1597	1566	1597	457	415	42	2.6%	0	0
openalex	2000	1878	1998	370	361	9	0.4%	0	0

Use this to compare which sources are providing richer data versus sparse metadata.

Top “New Keyword Candidates”

term	n	cand_rate	med_conv
A_autonomous_ai_safety	1750	3.9%	0.267
J_refusal_hold	623	0.5%	0.000
B_systems_resilience	1750	0.3%	0.300
D_custom	1574	0.3%	0.263
C_standards_conformance	1610	0.2%	0.069
I_authorization_gating	1750	0.1%	0.250
F_dsm_integration_structure	1750	0.1%	0.250
E_interface_icd	1750	0.0%	0.125

Heuristic: terms with high seam-candidate association (min 5 samples). Low-sample rows are highlighted and tooltipped.

Top “Exclude / Downweight Terms”

term	n	med_conv
D_custom	1000	0.547
J_refusal_hold	100	0.556
C_standards_conformance	860	0.698
E_interface_icd	1000	0.708
I_authorization_gating	1000	0.833
B_systems_resilience	1000	0.859
F_dsm_integration_structure	1000	0.881
A_autonomous_ai_safety	1000	1.022

Heuristic: Zenodo terms with low median conversion (min 10 samples, cutoff 0.02). Low-sample rows are highlighted and tooltipped.

Receipts

What these numbers mean

Engagement Comparison

Run Funnel

Bucket Yield

SEAM Candidate Articles

Top Seam Tells

Near-Miss Diagnostics

Why rows did not become SEAM candidates

Bucket Performance

Source Performance

Top “New Keyword Candidates”

Top “Exclude / Downweight Terms”

History and trends

Files