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F[x] Labs

Technical depth, in the open.

The research that gives our implementation work leverage: retrieval, agent orchestration, and operational intelligence.

01  Current research

Active experimental systems.

DFRR retrieval kernel

active

A high-recall retrieval kernel for cases where standard HNSW configurations fall short.

ACTIVE

HNSW recall degradation

active

Characterizing where and why graph-index recall drops past 1M vectors.

ACTIVE

Agent orchestration

ongoing

Coordination patterns for multi-step agent workflows with human-in-the-loop gates.

ONGOING

Workflow memory

exploratory

Durable operational memory: what an agent system should remember between runs.

EXPLORATORY

Graph-aware retrieval

exploratory

Using structure between documents to improve multi-hop retrieval quality.

EXPLORATORY

Multi-vector search

benchmarking

Late-interaction and multi-vector representations under real latency budgets.

BENCHMARKING
bench/dfrr — recall@10
$ fx bench dfrr --dataset ops-corpus
# n=50k · 8 hops · cold cache
recall@10 0.94 (+0.23 vs baseline)
p95 latency 41ms
writing benchmark report…
recall@10
0.94DFRR
+0.23 vs baseline
p95 latency
41ms
within budget
corpus
2.4M chunks
512-dim
eval suites
6harnesses
reproducible
02  Writing

Essays from the lab.

Why HNSW recall degrades past a million vectors

Retrieval9 minqueued

Designing for multi-hop retrieval error

Agents7 minqueued

Operational memory: what agents should remember

Systems6 minqueued

Evaluation-first RAG, or: measure before you tune

Benchmarks11 minqueued

Talk research or partnerships.

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