Research Whitepaper

Temporal Shard RAG

Time-aware retrieval augmented generation with neuromorphic memory. Documents decay, consolidate, and activate like biological memories.

34%
Temporal Reasoning
28%
Less Hallucinations
2.85×
Faster Retrieval

Temporal Shards

Documents are partitioned by time with configurable decay functions

Try a Query

See how temporal context affects retrieval

🔍
Query
Try:
📄
Response

Query response will appear here with temporal context...

3 shards activated
45ms latency
2023-2024

Architecture

Neuromorphic-inspired temporal memory system

⏱️

Temporal Decay

Documents decay according to Gaussian + exponential functions based on age and access patterns.

w(t) = w₀ · e^(-t²/2σ²) · γ^(t/τ)

Spiking Activation

Shards activate via leaky integrate-and-fire neurons based on query relevance.

dV/dt = -V/τₘ + I(t)
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Cross-Temporal Attention

Reason across time periods with temporal bias in attention scores.

α = softmax(QK^T/√d + T_bias)
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Adaptive Consolidation

Important memories preserved, low-importance documents fade and merge.

I = αA + βR + γU + δC

Experimental Results

Performance on temporal reasoning benchmarks

TempQuestions Accuracy
Standard RAG
45.2%
Time-filtered
52.1%
Temporal KG
58.4%
TS-RAG (Ours)
67.8%
Hallucination Rate ↓
Standard RAG
18.3%
Time-filtered
14.1%
TS-RAG (Ours)
7.2%

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Dive into the complete theoretical framework, mathematical proofs, and implementation details.

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