# NAVER D2SF Invests in On-Device AI Startup TraceWeave

- Link: https://www.thailand-business-news.com/pr-news/naver-d2sf-invests-in-on-device-ai-startup-traceweave
- Published: 2026-10-21T07:00:00+07:00
- Author: PR Newswire

◦ TraceWeave develops its own post-Transformer AI architecture that enables AI 
   agents to run on memory-constrained devices
 ◦ Completes paid pilot in mobility; pursues commercialization across robots, drones,
   smart glasses, and more
 ◦ NAVER D2SF continues to invest in on-device AI and next-generation device technologies

SEOUL, South Korea, Oct. 21, 2026 /PRNewswire/ — NAVER D2SF has made a new investment
in TraceWeave, an on-device AI startup led by co-founder and CEO Jaeyoung Park. 
TraceWeave is the team behind Phase-TITAN, a post-Transformer architecture that 
understands long contexts and carries out multi-step tasks while keeping memory 
usage constant. NAVER D2SF recognized the company’s core technology for enabling
AI agents on resource-constrained devices.

As AI expands to robots, vehicles, smart glasses, and drones, its applications are
advancing beyond simple chatbots toward complex reasoning and decision-making. In
Transformer-based models, the key-value cache (KV-Cache) grows with context length,
requiring more memory. On-device constraints in memory, battery life, and heat have
made it difficult to implement AI agents that understand long contexts and perform
complex tasks.

TraceWeave’s Phase-TITAN is a new AI architecture designed to resolve this memory
bottleneck by replacing the conventional Transformer architecture altogether. TraceWeave
develops its own AI models rather than relying on existing open-source models. Memory
usage remains constant as context grows. According to TraceWeave, at a 1M-token 
context without quantization, a comparable Transformer’s KV-Cache holds tens of 
gigabytes or more, while its 1B Phase-TITAN model holds 0.04 GB. This enables agents
to call tools, interpret results, and decide on their next actions entirely on the
device, without cloud calls. TraceWeave’s internal benchmarks on RULER and BFCL 
indicate that Phase-TITAN maintains performance on par with Transformer models of
the same size and larger.

TraceWeave has demonstrated a multi-step agent running on a smartphone using only
its CPU, without cloud calls. In mobility, it completed a paid pilot with the autonomous
driving affiliate of a global automotive group, identifying driving-data frames 
that improve the partner’s autonomous driving model without human annotation. It
is also validating in-vehicle agent performance with a global automotive electronics
software company and exploring opportunities in robots, drones, smart glasses, and
wearables. TraceWeave was a Top 20 finalist in NVIDIA’s 2026 Inception Grand Challenge.

TraceWeave was co-founded in October 2025 by researchers with backgrounds at ETH
Zurich, Google, and KAIST. The team combines engineering expertise grounded in computational
neuroscience and physics with business development capabilities, designing its own
architecture and validating it in device environments that reflect real-world customer
needs. In this seed round led by Mirae Asset Capital, TraceWeave raised approximately
KRW 3 billion at a pre-money valuation of approximately KRW 30 billion. Based at
NAVER D2SF @Gangnam, the company plans to use the funding to expand its research
and engineering team for next-generation architecture development and grow its business.

"Taking the KV cache out was never the hard part. Staying capable without it was,"
said Jaeyoung Park, co-founder and CEO of TraceWeave. "The best part is no part.
Phase-TITAN already delivers Transformer-class capability without the Transformer’s
memory tax, and the next one goes well beyond its class, with the memory still fixed."

"On-device AI is a core technology for implementing complex, advanced AI across 
diverse device environments beyond PCs, and it also offers advantages in reducing
cloud call costs and protecting privacy," said Sanghwan Yang, Head of NAVER D2SF."
TraceWeave is a team that is innovating AI architecture to overcome the limitations
of existing AI models and bring AI to on-device environments."

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