Semantic Reach

From Database to Databrain

[Semantic Reach @SemanticReach]

We saturated every memory benchmark.

Then realized: benchmarks test retrieval, not cognition.

So we pivoted from RAG → Reasoning-Augmented Generation.

We built the Knowledge Layer.

Manifesto ↓

https://t.co/HbZh6HERUA

[Michael Myles @UseYourNews]

@matanSF at @SemanticReach we turn proprietary knowledge into composable, manipulable meaning structures that agents can reason over natively and persistently *right at the data layer* so the models get just the right minimum context they need without giving up data ownership or control.

[Michael Myles @UseYourNews]

@john_ssuh We already built the world’s first unified knowledge substrate at @SemanticReach everyone either is or will be chasing what we already have.

[Semantic Reach @SemanticReach]

We're building the semantic foundations and agentic infrastructure that will power AI systems as they become more autonomous and self-directed.

Follow our CEO @NathanAnecone for more insights!

[Nathan Anecone @NathanAnecone]

@AnthropicAI 's recent article on self-improving AI caused a bit of a stir, but I've seen surprisingly little work on the necessary and sufficient conditions for RSI.

I argue that pure RSI might be far more difficult than many in the field might believe, for borderline metaphysical reasons.

tl;dr: Certain liar paradox-like constraints on self-referentiality and the work of mathematician David Wolpert on the physical limits of inference engines conspire to make total closed-loop RSI not just difficult but potentially logically incoherent. Link in the replies.

[Michael Myles @UseYourNews]

A recent #IBM video lays out the choice between RAG and long context windows and asks: Is #RAG still needed? As frontier models context windows grow, it becomes tempting to "context dump" and hope the model is smart enough to sort through it. For certain tasks this is feasible, but it also just pushes the problem back.
We argue that the choices presented are a false dichotomy, and the real solution is a new type of context engine that does a lot more work behind the curtain.
👉https://t.co/HLCjJeyFuN

[Semantic Reach @SemanticReach]

@UseYourNews Hyperbinder tames your token burn by unifying data into smart semantic memory.

No more blowtorch budgets. Slash redundant queries and AI costs with efficient caching & reasoning.

[Michael Myles @UseYourNews]

This guy needs Hyperbinder😂

[Semantic Reach @SemanticReach]

https://t.co/f9mAQoADlQ

[Semantic Reach @SemanticReach]

We're opening limited access to HyperBinder — the agentic knowledge layer that replaces bloated context management with intelligent representation. Building serious agents & want to see what Reasoning Augmented Generation looks like in practice? Apply for the closed beta👇

[Semantic Reach @SemanticReach]

@nicoloboschi We’re not open source, but all benchmarks and eval code are public on GitHub. Anyone can request an API key and reproduce results via the HyperBinder API end-to-end.
Request API key: [email protected]

[Semantic Reach @SemanticReach]

Hindsight claims #1 on the Agent Memory Benchmark.
The data says otherwise.
(Hindsight → HyperBinder)

LongMemEvalS: 94.6% → 100%
PersonaMem 32k: 86.6% → 100%
LifeBench EN: 71.5% → 100%

HyperBinder outperforms across every benchmark.
GitHub: https://t.co/AbjcwxkRWL

[Semantic Reach @SemanticReach]

@Vectorizeio 64.1% isn't #1 when 84% exists.

Semantic Reach's HyperBinder on BEAM 10M.

📊 https://t.co/sRuJiuGQG4

Reach out to [email protected] for an API key.

[Semantic Reach @SemanticReach]

Questions or API access: [email protected]

[Semantic Reach @SemanticReach]

Hindsight claims #1 on the Agent Memory Benchmark.
The data says otherwise.
(Hindsight → HyperBinder)

LongMemEvalS: 94.6% → 100%
PersonaMem 32k: 86.6% → 100%
LifeBench EN: 71.5% → 100%

HyperBinder outperforms across every benchmark.
GitHub: https://t.co/AbjcwxkRWL

[Semantic Reach @SemanticReach]

Questions or API access: [email protected]

[Michael Myles @UseYourNews]

Just another saturated benchmark by Semantic Reach. First it was LongMemEval 100% (https://t.co/yT5PWTjRBz), now it’s LifeBench.

Semantic Reach just hit 100% on LifeBench.

2,003 / 2,003 questions
0 misses
0 abstentions

This isn’t prompt tuning.
This isn’t bigger models.

This is a different approach to memory.

HyperBinder uses high-dimensional symbolic binding + associative recall to unify structured + unstructured data into a single system — no fragmentation, no retrieval gaps.

While traditional RAG pipelines degrade with scale, we’re seeing perfect recall across complex, multi-session reasoning tasks.

Full results + code:
GitHub: https://t.co/yKFZTbnp2Q
HyperBinder: https://t.co/LErHVBxIwE

[Michael Myles @UseYourNews]

‼️We just built the Post-RAG database‼️
Traditional RAG is over.
With HyperBinder, Semantic Reach created a single unified vector-symbolic engine that natively delivers vector search, graph reasoning, and relational queries in one cohesive space. No more stitching together 3+ tools.
✅100% across all LongMemEval categories.
✅True compositional & multi-hop reasoning.
The RAG era is ending. The Post-RAG era has begun.
#PostRAG #HyperBinder @SemanticReach

[Semantic Reach @SemanticReach]

Paper👇
https://t.co/xJaqrUiLRt

[Semantic Reach @SemanticReach]

We built a legal AI system at Semantic Reach that beats fine-tuned transformers…

without training a single parameter.🥶

Powered by HyperBinder.🔥

* 94.8% accuracy on CUAD-SL
* 0 GPU compute
* deterministic results

The twist:🧵

[Semantic Reach @SemanticReach]

Paper👇
https://t.co/xJaqrUiLRt

[Semantic Reach @SemanticReach]

We built a legal AI system at Semantic Reach that beats fine-tuned transformers…

without training a single parameter.🥶

Powered by HyperBinder.🔥

* 94.8% accuracy on CUAD-SL
* 0 GPU compute
* deterministic results

The twist:🧵

[Semantic Reach @SemanticReach]

The takeaway:

For structured problems,
query architecture✅ > model size.