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 2026-10-12 04:19 · 3 per paper, up to 3 papers each · bar: SIGIR, WWW, SIGKDD, WSDM, TOIS, ACL +2 · junior 2+ first-author in 5y or senior 5+ papers in 5y
Close the gaps — 44 experts would fill 110 of the 110 missing strong slots on the ill papers
Chosen one at a time: each takes up to 3 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; showing 40 of 138
Strong in pool counts the pool members who are a strong match for the paper (fit 99 or more) and have no conflict with it, however busy they are. Can get is how many of them the paper can actually have, of the 3 it needs, when every paper draws on the same people and nobody takes more than their load: a paper with strong reviewers in the pool can still get none, because other papers need them just as much. Specialists have fit 99.9 or more.
| Severity | Submission | Strong in pool | Can get | Specialists | Best fit |
|---|---|---|---|---|---|
| 8.0 | How Far Are We from Automatically Identifying Violations of the Data Minimization Principle in Privacy Policies?
sigir26-048 · 2 assigned
|
1 | 0 / 3 | 0 | 99.17 |
Recruit for this paper · 10 candidates | |||||
| 7.5 | Seeing the Whole Through the Parts: Discovering Objects through Semantic Part Mining in Weak Supervision
sigir26-228 · 3 assigned
|
9 | 0 / 3 | 1 | 99.90 |
Recruit for this paper · 10 candidates | |||||
| 7.0 | Tokens to Types: Context Editing with Selective Entity Abstraction for Grounded Generation
sigir26-187 · 2 assigned
|
51 | 0 / 3 | 19 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 7.0 | Uncertainty Quantification for Retrieval-Augmented Reasoning
sigir26-193 · 2 assigned
|
74 | 0 / 3 | 35 | 99.99 |
Recruit for this paper · 10 candidates | |||||
| 7.0 | Why Advanced Encoders Lag on Sparse Retrieval? The Answer and an Approach to Bridging Vocabulary Gaps
sigir26-199 · 2 assigned
|
24 | 0 / 3 | 4 | 99.99 |
Recruit for this paper · 10 candidates | |||||
| 7.0 | Balanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systems
sigir26-204 · 2 assigned
|
96 | 0 / 3 | 50 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 7.0 | Balanced Frequency Decoupling: Energy-Aware Multi-Scale Preference Modeling for Sequential Recommendation
sigir26-205 · 2 assigned
|
83 | 0 / 3 | 52 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 7.0 | Benchmarking Filtered Approximate Nearest Neighbor Search Algorithms on Transformer-based Embedding Vectors
sigir26-206 · 2 assigned
|
131 | 0 / 3 | 48 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 7.0 | Beyond the Single Path: Divergent Reasoning for LLM-based Recommendation
sigir26-207 · 2 assigned
|
136 | 0 / 3 | 63 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 7.0 | Constructing Hard-Positive Query-Document Pairs for Dense Retrieval via Phrase Representativeness
sigir26-209 · 2 assigned
|
125 | 0 / 3 | 47 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 7.0 | Exploring Test-time Scaling via Prediction Merging on Large-Scale Recommendation
sigir26-215 · 2 assigned
|
114 | 0 / 3 | 53 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 7.0 | Generation-Augmented Video Corpus Moment Retrieval
sigir26-217 · 2 assigned
|
37 | 0 / 3 | 9 | 99.99 |
Recruit for this paper · 10 candidates | |||||
| 7.0 | GIGP+: A CPU-GPU Co-Processing Engine for Multi-Vector Retrieval
sigir26-218 · 2 assigned
|
72 | 0 / 3 | 12 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 7.0 | HyFormer: Revisiting the Roles of Sequence Modeling and Feature Interaction in CTR Prediction
sigir26-220 · 2 assigned
|
108 | 0 / 3 | 59 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 7.0 | Inductive Dual-Polarity Modeling via Static-Dynamic Disentanglement for Dynamic Signed Networks
sigir26-221 · 2 assigned
|
25 | 0 / 3 | 2 | 99.99 |
Recruit for this paper · 10 candidates | |||||
| 7.0 | Learning from Natural Language Feedback for Personalized Question Answering
sigir26-222 · 2 assigned
|
105 | 0 / 3 | 40 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 7.0 | MLLMRec: A Preference Reasoning Paradigm with Graph Refinement for Multimodal Recommendation
sigir26-224 · 2 assigned
|
98 | 0 / 3 | 48 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 7.0 | SilverTorch: A Unified Model-based System to Democratize Large-Scale Recommendation on GPUs
sigir26-230 · 2 assigned
|
98 | 0 / 3 | 38 | 99.99 |
Recruit for this paper · 10 candidates | |||||
| 7.0 | Think with Grounding: Curriculum Reinforced Reasoning with Video Grounding for Long Video Understanding
sigir26-232 · 2 assigned
|
57 | 0 / 3 | 7 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 6.0 | A Voronoi Cell Formulation for Principled Token Pruning in Late-Interaction Retrieval Models
sigir26-201 · 3 assigned
|
93 | 0 / 3 | 23 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 6.0 | An Action-Aware Generative Sequence Modeling for Short Video Recommendation
sigir26-203 · 3 assigned
|
83 | 0 / 3 | 43 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 6.0 | Contrastive Flow Matching for Collaborative Filtering
sigir26-210 · 3 assigned
|
107 | 0 / 3 | 57 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 6.0 | Countering Interest Over-Smoothing: Distilling Latent Factors via Diffusion for Multi-Interest Retrieval
sigir26-211 · 3 assigned
|
116 | 0 / 3 | 68 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 6.0 | Diagnosing and Mitigating Mid-Sequence Degradation in Recommender Systems
sigir26-213 · 3 assigned
|
113 | 0 / 3 | 55 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 6.0 | Efficient Sparse Retrieval with Lightweight Superblock Pruning
sigir26-214 · 3 assigned
|
107 | 0 / 3 | 29 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 6.0 | Fusion and Alignment Enhancement with Large Language Models for Tail-item Sequential Recommendation
sigir26-216 · 3 assigned
|
105 | 0 / 3 | 62 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 6.0 | LLM-EDT: Large Language Models Enhanced Cross-domain Sequential Recommendation with Dual-phase Training
sigir26-223 · 3 assigned
|
103 | 0 / 3 | 44 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 6.0 | Search for Coverage: Learning Coverage-Aware Retrieval with Augmented Sub-Question Answerability
sigir26-227 · 3 assigned
|
76 | 0 / 3 | 21 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 6.0 | Set-Based Cross-Domain Recommendation
sigir26-229 · 3 assigned
|
113 | 0 / 3 | 62 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 6.0 | SPRINT: Scalable and Predictive Intent Refinement for LLM-Enhanced Session-based Recommendation
sigir26-231 · 3 assigned
|
101 | 0 / 3 | 53 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 6.0 | Think, But Don't Tell: Implicit Reasoning for LLM-based Sequential Recommendation via Multi-Teacher Distillation
sigir26-233 · 3 assigned
|
132 | 0 / 3 | 64 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 5.0 | When Vision Meets Texts in Listwise Reranking
sigir26-198 · 2 assigned
|
92 | 1 / 3 | 29 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 5.0 | A Parametric Memory Head for Continual Generative Retrieval
sigir26-200 · 2 assigned
|
100 | 1 / 3 | 37 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 5.0 | Hierarchical Cluster-based Open-World Graph Active Learning
sigir26-219 · 2 assigned
|
26 | 1 / 3 | 3 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 5.0 | PurifAI: Detecting and Fixing Search-Induced Distortions in Web-Augmented LLMs
sigir26-226 · 2 assigned
|
39 | 1 / 3 | 4 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 4.0 | Towards Conflict-aware Selective Knowledge Unlearning for Continual Few-shot Knowledge Graph Completion
sigir26-190 · 3 assigned
|
63 | 1 / 3 | 12 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 4.0 | Unifying Granularity and Reliability: A Robust and Efficient Framework for Text-based Person Retrieval
sigir26-194 · 3 assigned
|
27 | 1 / 3 | 7 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 4.0 | Bridging Behavior and Semantics for Time-aware Cross-Domain Sequential Recommendation
sigir26-208 · 3 assigned
|
111 | 1 / 3 | 64 | 99.99+ |
Recruit for this paper · 10 candidates | |||||
| 4.0 | Optimizing Marketing Subsidies via Counterfactual Learning with Asymmetric Reward Function
sigir26-225 · 3 assigned
|
19 | 1 / 3 | 8 | 99.99 |
Recruit for this paper · 10 candidates | |||||
| 3.5 | HCPRA: A Hierarchical Cognition-Perception-Reasoning Agent Framework for Emotion-Cause Pair Extraction in Conversations
sigir26-141 · 3 assigned
|
5 | 2 / 3 | 1 | 99.94 |
Recruit for this paper · 10 candidates | |||||
Topics — submission clusters; coverage = the strong places the pool can fill when all papers are served together, within everyone's load
A topic is a group of similar submissions (grouped by their embeddings), named from the keywords an LLM extracted from each paper, in the way the expertise database's were: the research area most of the group's papers belong to, and the concepts that set the group apart.
| Topic | Papers | Coverage | Missing slots | Ill papers | Strong people |
|---|---|---|---|---|---|
| Information retrievalimage retrieval · cross-modal retrieval · weakly supervised object detection under-covered | 10 | 73% | 8 | 9 | 66 |
Recruit for this topic · 12 candidates | |||||
| Information retrievaldense retrieval · multi-vector retrieval · late-interaction retrieval under-covered | 22 | 74% | 17 | 13 | 186 |
Recruit for this topic · 12 candidates | |||||
| Recommender systemssequential recommendation · cross-domain recommendation · click-through rate prediction under-covered | 34 | 78% | 23 | 17 | 169 |
Recruit for this topic · 12 candidates | |||||
| graph neural networks · subgraph matching · structural overfitting under-covered | 11 | 79% | 7 | 6 | 151 |
Recruit for this topic · 12 candidates | |||||
| Recommender systemsgenerative recommendation · large language model-based recommendation · preference optimization under-covered | 30 | 82% | 16 | 16 | 178 |
Recruit for this topic · 12 candidates | |||||
| knowledge graph reasoning · knowledge graph question answering · knowledge base question answering under-covered | 19 | 82% | 10 | 12 | 136 |
Recruit for this topic · 12 candidates | |||||
| Recommender systemscollaborative filtering · multimodal recommendation · graph-based recommendation under-covered | 23 | 83% | 12 | 11 | 162 |
Recruit for this topic · 12 candidates | |||||
| fairness · personalized question answering · bias mitigation | 9 | 85% | 4 | 7 | 146 |
| visual document retrieval · multimodal retrieval-augmented generation · multimodal large language model | 21 | 86% | 9 | 15 | 146 |
| Information retrievalranking · learning to rank · two-tower model | 7 | 90% | 2 | 5 | 145 |
| conversion rate prediction · violation detection · tabular data synthesis | 11 | 91% | 3 | 6 | 156 |
| multimodal fake news detection · retrieval-augmented generation · search-induced distortion | 10 | 93% | 2 | 4 | 135 |
| Information retrievalrelevance judgment · evaluation · unjudged document problem | 6 | 94% | 1 | 3 | 132 |
| Information retrievaluser search behavior · agentic search · evaluation | 11 | 100% | 0 | 10 | 164 |
| table retrieval · entity matching · entity linking | 9 | 100% | 0 | 4 | 133 |