KDD 2026 - Mapping LLM Capability Frontiers via Formalized and Calibrated Probes @TheOfficialACM
KDD 2026 - Mapping LLM Capability Frontiers via Formalized and Calibrated Probes  @TheOfficialACM
Uploaded July 2026 | Updated September 2026, 2 weeks ago
Tianxi Gao, Yufan Cai, Yusi Yuan, Jin Song Dong
KDD 2026 - Mapping LLM Capability Frontiers via Formalized and Calibrated ProbesKDD 2026 - Strainer: Encrypted Video Traffic Identification for Mixed Segment Transmission PatternSIGIR 2024 T2.4 [fp] Disentangled Contrastive Hypergraph Learning for Next POI RecommendationSecuring the Agent: Vendor-Neutral, Multitenant Enterprise Retrieval and Tool UseKDD 2026-FedTail-DT:ADual-TeacherFramework forLong-Tailed Heterog.FLwithCLIP Protot. andAdaptiveAgg.KDD 2026 - Beyond Language Processing: LLMs Rules-Injected Instruction Tuning for Traffic PredictionKDD 2026: DynaTree: Dynamic Agentic Retrieval Tree for Time-Sensitive News RetrievalKDD2026-Alignment-Free Multi-Modality Large-Small Model Bidirectional Collab. with Missing ModalityKDD 2026-MGTA: Multi-scale Graph Tokens Alignment for CTR Prediction via Pre-trained Language ModelsKDD2026-BridgingFront-Door Adjustment and Information Bottleneck for Identifiable Causal Represent.Research to Reality Building Production Ready LLM Apps Users Can TrustByteCast Ep85: Eric Allman
Association for Computing Machinery (ACM) |

KDD 2026 - Mapping LLM Capability Frontiers via Formalized and Calibrated Probes

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