TIME trains LLMs to trigger compact, context-triggered reasoning via time tags and tick events, improving TIMEBench scores while cutting explicit reasoning tokens by an order of magnitude.
RAG meets temporal graphs: Time-sensitive modeling and retrieval for evolving knowledge
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IA-RAG is a hierarchical RAG framework that represents facts as Interval Event Units organized in a Thematic Forest under Allen's Interval Algebra, with Sub-graph Time Tightening for fuzzy intervals, and reports improved results on temporal QA benchmarks.
Empirical comparison on small industrial KG finds vector retrieval fails on structural queries while LLM planner with typed graph operators achieves higher F1 and generalizes to unseen queries.
CHRONOS is a three-layer system for evolving data marketplaces that applies neural-ODE temporal decay, changepoint-aware Shapley valuation, and EXP3-IX private coordination to achieve 0.937 recall, 2.74 qps, 161 ms latency, and epsilon 4.25 at delta 10^-6.
NAVIS improves concurrent search and update throughput in on-SSD graph vector search by up to 2.74x for insertions and 1.37x for searches through reduced position-seeking overhead.
A minimalist retrieval-and-generation framework using turn isolation and query-driven pruning outperforms complex memory systems by directly addressing signal sparsity and dual-level redundancy in dialogues.
A structured multi-agent RAG pipeline with temporal reranking and bounded contradiction reconciliation won Best Dynamic Evaluation at NeurIPS 2025 by aligning with user preferences over automatic metrics.
citing papers explorer
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TIME: Temporally Intelligent Meta-reasoning Engine for Context-Triggered Explicit Reasoning
TIME trains LLMs to trigger compact, context-triggered reasoning via time tags and tick events, improving TIMEBench scores while cutting explicit reasoning tokens by an order of magnitude.
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IA-RAG: Interval-Algebra-Driven Temporal Reasoning for Dynamic Knowledge Retrieval
IA-RAG is a hierarchical RAG framework that represents facts as Interval Event Units organized in a Thematic Forest under Allen's Interval Algebra, with Sub-graph Time Tightening for fuzzy intervals, and reports improved results on temporal QA benchmarks.
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Beyond Vector Similarity: A Structural Analysis of Graph-Augmented Retrieval for Industrial Knowledge Graphs
Empirical comparison on small industrial KG finds vector retrieval fails on structural queries while LLM planner with typed graph operators achieves higher F1 and generalizes to unseen queries.
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CHRONOS: Temporally-Aware Multi-Agent Coordination for Evolving Data Marketplaces
CHRONOS is a three-layer system for evolving data marketplaces that applies neural-ODE temporal decay, changepoint-aware Shapley valuation, and EXP3-IX private coordination to achieve 0.937 recall, 2.74 qps, 161 ms latency, and epsilon 4.25 at delta 10^-6.
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NAVIS: Concurrent Search and Update with Low Position-Seeking Overhead in On-SSD Graph-Based Vector Search
NAVIS improves concurrent search and update throughput in on-SSD graph vector search by up to 2.74x for insertions and 1.37x for searches through reduced position-seeking overhead.
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Back to Basics: Let Conversational Agents Remember with Just Retrieval and Generation
A minimalist retrieval-and-generation framework using turn isolation and query-driven pruning outperforms complex memory systems by directly addressing signal sparsity and dual-level redundancy in dialogues.
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NightFeats @ MMU-RAGent NeurIPS 2025: A Context-Optimized Multi-Agent RAG System for the Text-to-Text Track
A structured multi-agent RAG pipeline with temporal reranking and bounded contradiction reconciliation won Best Dynamic Evaluation at NeurIPS 2025 by aligning with user preferences over automatic metrics.