Diagnose & recruit
Run a diagnosis
Every submission and every reviewer is embedded with SPECTER2. Fit is a percentile against the whole expert database: fit 99 means someone's recent work is closer to the paper than 99% of the 1.2 million active experts. Strong means fit 99 or more, a specialist 99.9 or more. A paper is ill when the pool cannot give it 3 strong reviewers at the same time within everyone's load, has no specialist, or, after solving, is under-assigned or has an assigned person outside the top 5%. A topic is a cluster of similar submissions; it is under-covered when at least 15% of its strong slots cannot be filled. Candidates must meet the saved reviewer bar and have no authorship or recent co-authorship with the paper's authors.
Diagnosed 2026-09-27 17:05 · 3 per paper, up to 5 papers each · bar: SIGMOD, VLDB, ICDE, EDBT, PODS, CIKM +1 · junior 2+ first-author in 5y or senior 4+ papers in 5y
Close the gaps — 5 experts would fill 5 of the 5 missing strong slots on the ill papers
Chosen one at a time: each takes up to 5 ill papers where they fit best, favouring the papers covered least so far. Invite more than you need; not everyone accepts.
Ill papers — most severe first
| Severity | Submission | Strong in pool | At once | Specialists | Best fit |
|---|---|---|---|---|---|
| 7.0 | FutureLight: An Efficient Future Traffic Data-Driven Reinforcement Learning Framework for Traffic Signal Controls
vldb26-2883
|
0 | 0 / 3 | 0 | 99.95 |
Recruit for this paper · 10 candidates | |||||
| 3.0 | Data-efficient Online Training for Direct Alignment in LLMs
vldb26-2955
|
2 | 2 / 3 | 0 | 99.70 |
Recruit for this paper · 10 candidates | |||||
| 2.0 | FairDAG: Consensus Fairness over Multi-Proposer Causal Design
vldb26-903
|
2 | 2 / 3 | 1 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 1.0 | RetroInfer: A Vector Storage Engine for Scalable Long-Context LLM Inference
vldb26-1426
|
9 | 3 / 3 | 0 | 99.99 |
Recruit for this paper · 10 candidates | |||||
| 1.0 | Bifrost: A Much Simpler Secure Two-Party Data Join Protocol for Secure Data Analytics
vldb26-1524
|
10 | 3 / 3 | 0 | 99.82 |
Recruit for this paper · 10 candidates | |||||
| 1.0 | BRIEF: Bi-level Coreset Selection for Efficient Instruction Tuning in LLMs
vldb26-1606
|
5 | 3 / 3 | 0 | 99.70 |
Recruit for this paper · 10 candidates | |||||
| 1.0 | Pisco: An Isolation Bug Case Reduction and Deduplication Framework
vldb26-1704
|
46 | 3 / 3 | 0 | 99.98 |
Recruit for this paper · 10 candidates | |||||
| 1.0 | Balancing the Blend: An Experimental Analysis of Trade-offs in Hybrid Search
vldb26-1727
|
106 | 3 / 3 | 0 | 99.91 |
Recruit for this paper · 10 candidates | |||||
| 1.0 | Scarf: Self-Adaptive Tuning via Multi-Objective Reinforcement Learning for Apache Flink
vldb26-1764
|
12 | 3 / 3 | 0 | 99.85 |
Recruit for this paper · 10 candidates | |||||
| 1.0 | Mil: Cost-guided Minimum Makespan Scheduling for Applications of Multiple LLMs
vldb26-1914
|
8 | 3 / 3 | 0 | 99.84 |
Recruit for this paper · 10 candidates | |||||
| 1.0 | Counting HyperGraphlets via Color Coding: a Quadratic Barrier and How to Break It
vldb26-2480
|
65 | 3 / 3 | 0 | 99.87 |
Recruit for this paper · 10 candidates | |||||
| 1.0 | Stress-Testing ML Pipelines with Adversarial Data Corruption
vldb26-2545
|
10 | 3 / 3 | 0 | 99.98 |
Recruit for this paper · 10 candidates | |||||
| 1.0 | In-depth Analysis of Graph-based RAG in a Unified Framework
vldb26-2555b (orig 1751)
|
16 | 3 / 3 | 0 | 99.85 |
Recruit for this paper · 10 candidates | |||||
| 1.0 | CloudGlide: Deconstructing the Landscape of Cloud-Based Analytics
vldb26-2556
|
40 | 3 / 3 | 0 | 99.88 |
Recruit for this paper · 10 candidates | |||||
| 1.0 | MGI: A Communication Framework for Data Processing in Massive GPU Infrastructures
vldb26-2659
|
17 | 3 / 3 | 0 | 99.89 |
Recruit for this paper · 10 candidates | |||||
| 1.0 | Error-bounded Point Cloud Compression Using Truncated Octahedron Quantization
vldb26-2988
|
23 | 3 / 3 | 0 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 1.0 | Meerkat: Scalable, Network-Aware Failure Recovery for the Internet of Things
vldb26-954
|
8 | 3 / 3 | 0 | 99.69 |
Recruit for this paper · 10 candidates | |||||
| 1.0 | Resilience-Aware Elastic Scaling for Cloud-Native Online DL Training on Multi-Tenant GPU Clusters
vldb26-988
|
12 | 3 / 3 | 0 | 99.82 |
Recruit for this paper · 10 candidates | |||||
| 1.0 | Efficient Task Assignment for Multi-Workerset Crowdsourcing with Time and Expense Considerations
vldb26-VLDB-D-25-00007R2
|
23 | 3 / 3 | 0 | 99.98 |
Recruit for this paper · 10 candidates | |||||
Topics — submission clusters; coverage = strong slots the pool can fill at once
| Topic | Papers | Coverage | Missing slots | Ill papers | Strong people |
|---|---|---|---|---|---|
| traffic · temporal · neural networks | 11 | 91% | 3 | 1 | 328 |
| self-explanations · entity · preference | 4 | 92% | 1 | 1 | 124 |
| consensus · rollback · quorums | 6 | 94% | 1 | 2 | 47 |
| optimizer · rewriting · cardinality | 46 | 100% | 0 | 0 | 377 |
| write · lsm-trees · buffer | 33 | 100% | 0 | 0 | 296 |
| text-to-sql · natural · nl2sql | 32 | 100% | 0 | 1 | 314 |
| approximate nearest · neighbor search · anns | 26 | 100% | 0 | 0 | 388 |
| clique · bipartite · seed | 24 | 100% | 0 | 1 | 345 |
| dpus · cores · compressed | 22 | 100% | 0 | 2 | 295 |
| rag · corpora · qpr | 18 | 100% | 0 | 2 | 332 |
| temporal · path queries · subgraph matching | 16 | 100% | 0 | 0 | 370 |
| valuation · binning · diversification | 14 | 100% | 0 | 1 | 344 |
| secure · multi-party · privacy-preserving | 12 | 100% | 0 | 1 | 160 |
| nosql · spark sql · configuration | 11 | 100% | 0 | 4 | 239 |
| time series · compression · video | 11 | 100% | 0 | 0 | 338 |
| instruction · automl · caps | 10 | 100% | 0 | 3 | 167 |
| neural · conrad · negative sampling | 8 | 100% | 0 | 0 | 265 |