Hawky-ai/EGG-0.5.1
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EGG 0.5: public research preview and how to test it
#1 opened about 1 month ago
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Sri-Vigneshwar-DJ
Sri-Vigneshwar-DJĀ
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model about 1 month ago
Sri-Vigneshwar-DJĀ
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Sri-Vigneshwar-DJĀ
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Sri-Vigneshwar-DJĀ
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Sri-Vigneshwar-DJĀ
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Sri-Vigneshwar-DJĀ
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Sri-Vigneshwar-DJĀ
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We ran Feather DB v0.8.0 on LongMemEval (ICLR 2025) ā 500 questions across real multi-session conversations, up to 115K tokens each.
**Score: 0.693** Ā· GPT-4o full-context baseline: 0.640
Full 500-question run with Gemini-Flash: **$2.40**
Per-axis breakdown:
ā Info-extraction: **0.942**
ā Knowledge-update: **0.714**
ā Multi-session: **0.606**
ā Temporal: **0.477** ā the hard one, Phase 9 addresses this
Architecture: Hybrid BM25+dense Ā· adaptive temporal decay Ā· embedded (no server) Ā· p50 = 0.19ms Ā· MIT
Raw results + audit JSONs: Hawky-ai/longmemeval-results
We ran Feather DB v0.8.0 on LongMemEval (ICLR 2025) ā 500 questions across real multi-session conversations, up to 115K tokens each.
**Score: 0.693** Ā· GPT-4o full-context baseline: 0.640
Full 500-question run with Gemini-Flash: **$2.40**
Per-axis breakdown:
ā Info-extraction: **0.942**
ā Knowledge-update: **0.714**
ā Multi-session: **0.606**
ā Temporal: **0.477** ā the hard one, Phase 9 addresses this
Architecture: Hybrid BM25+dense Ā· adaptive temporal decay Ā· embedded (no server) Ā· p50 = 0.19ms Ā· MIT
pip install feather-dbRaw results + audit JSONs: Hawky-ai/longmemeval-results
Feather DB v0.8.0 ā LongMemEval benchmark results + open questions on temporal reasoning
#1 opened 7 months ago
by Sri-Vigneshwar-DJ
Sri-Vigneshwar-DJĀ
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Sri-Vigneshwar-DJĀ
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Sri-Vigneshwar-DJĀ
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Sri-Vigneshwar-DJĀ
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Sri-Vigneshwar-DJĀ
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