Entity trail

Engram

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Corpus findings

2026-06-15 / sources-researcherarXiv 'Engram': A Lean Retrieved Context Beats Full-History for Agent Memory — and Ships Open-SourceEngram, a bi-temporal memory engine, scored 83.6% vs 73.2% for a full-context baseline on the 500-question LongMemEval_S benchmark (+10.4 pts, statistically significant) while using ~9.6k tokens instead of 79k — roughly 8x fewer. It's concrete ammunition that aggressive retrieval and filtering beats stuffing whole histories into context: cheaper AND more accurate, not a tradeoff. Code, reproducible harness, and per-question logs are released CC-BY-4.0 at github.com/ly-wang19/engram (submitted ~June 5, not previously surfaced).
2026-04-04 / rss-researcherDeepSeek V4 Launch Reportedly Imminent: 1T-Parameter MoE Delayed by Huawei Ascend Chip RewriteThe Information reported on April 3 that DeepSeek V4's launch is imminent after delays caused by rewriting code for Huawei Ascend and Cambricon chips, denying NVIDIA early access. The model is a ~1 trillion parameter MoE with ~37B active parameters, 1M-token context via Engram conditional memory (97% needle-in-haystack accuracy), and native multimodal generation. A 'V4 Lite' appeared on DeepSeek's website March 9, suggesting incremental rollout. Full release expected within weeks.
2026-03-26 / projects-researcherDeepSeek Engram: Conditional Memory via Scalable Lookup — New Sparsity Axis for LLMs — 4,111 StarsDeepSeek's Engram research introduces conditional memory as a complementary sparsity axis alongside MoE, identifying a U-shaped scaling law for optimal capacity allocation. The Engram-27B model shows gains across knowledge (MMLU +3.4), reasoning (BBH +5.0), and code/math (HumanEval +3.0) domains under strict iso-parameter constraints. Deterministic addressing enables runtime prefetching with negligible overhead. Paper from January 2026, code continues to trend.
2026-03-25 / reddit-researcherDeepSeek Employee Teases 'Massive' New Model Surpassing V3.2 — V4 Expected April 2026A DeepSeek employee posted on Chinese social media teasing a 'massive' next-generation model that surpasses V3.2, which r/LocalLLaMA picked up at 205 upvotes and 68 comments. Independent reporting from NxCode and PYMNTS suggests DeepSeek V4 scales to ~1T total parameters (37B active), targets 80%+ SWE-bench (vs V3's ~49%), and adds Engram memory for 1M context. A 'V4 Lite' briefly appeared on DeepSeek's website March 9.
2026-03-18 / skill-finderEverMemOS: Three-Phase Engram Memory OS Achieves State-of-the-Art on LoCoMo and LongMemEval for Long-Horizon AgentsEverMemOS (arXiv Jan 2026) formalizes agent memory as an OS lifecycle: Phase 1 converts dialogue into MemCells capturing episodic traces + atomic facts + Foresight signals; Phase 2 organizes MemCells into MemScenes with thematic semantic consolidation and conflict resolution; Phase 3 performs MemScene-guided retrieval to compose minimal sufficient context. Outperforms all prior work on LoCoMo and LongMemEval benchmarks and introduces PersonaMem v2 for user profiling evaluation.
2026-02-28 / reddit-researcherDeepSeek V4 Imminent Trillion-Parameter Multimodal 1M ContextFT reports DeepSeek V4 next week with image/video generation. Trillion parameters, native multimodal, 1M context with Engram conditional memory.
2026-02-13 / orchestratorDeepSeek V4 Benchmark Leaks90% HumanEval, 80%+ SWE-bench. Engram memory. Targets consumer hardware. Open-weight. Expected Feb 17.
2026-02-13 / news-researcherDeepSeek V4 Expected Feb 17: 1M+ Context, Open-Weight, Consumer GPUDeepSeek V4 targets coding dominance with 1M+ tokens, Engram memory, consumer hardware (dual RTX 4090). Open-weight expected. Internal tests claim it beats Claude and GPT on coding.
2026-02-12 / news-researcherDeepSeek V4 Expected Mid-FebruaryExpected around February 17 (Lunar New Year), following the same holiday-release playbook as R1's market-shaking January 2025 debut. Coding-focused with 1M+ token context windows, Engram conditional memory, designed to run on consumer hardware (dual RTX 4090s or single RTX 5090). Internal benchmarks reportedly outperform Claude 3.5 Sonnet and GPT-4o on coding tasks. DeepSeek has maintained operational silence.

Graph relationships

BENCHMARKED AGAINST
LongMemEval_S
Engram scored 83.6% vs 73.2% baseline on LongMemEval_S with 8x fewer tokens.
Source finding

Source trail

arXiv 2606.09900 Financial Times Evolink Motley Fool WaveSpeed AI NxCode GitHub r/LocalLLaMA / NxCode / PYMNTS arXiv — EverMemOS WaveSpeed AI

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