Breaking the 100M Token Limit: EverMind's MSA Architecture Achieves Efficient End-to-End Long-Term Memory for LLMs

The research introduces a novel memory architecture called MSA (Memory Sparse Attention). Through a combination of the Memory Sparse Attention mechanism, Document-wise RoPE for extreme context extrapolation, KV Cache Compression with Memory Parallelism, and a Memory Interleave mechanism supporting complex reasoning, MSA achieves a 100-million-token long-term memory framework for LLMs. It delivers industry-leading results on mainstream long-context QA and Needle-In-A-Haystack (NIAH) benchmarks. Remarkably, when scaling the context length from 16K to 100M tokens, the model's performance degrades by less than 9%, demonstrating extraordinary scalability.

SAN MATEO, Calif., March 19, 2026 /PRNewswire/ -- On March 18, EverMind, a pioneer in AI memory infrastructure, released a landmark research paper, Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokens, introducing a novel architecture that enables large language models to achieve efficient, end-to-end long-term memory at the unprecedented scale of 100 million tokens. The paper is published on Zenodo (https://zenodo.org/records/19103670) and open-sourced on GitHub (https://github.com/EverMind-AI/MSA).

This approach can be viewed as a memory plug-in for large models, providing a fresh perspective and direction for solving the long-term memory problem. In today's era of exploding Agent ecosystems, this work stands as a potential milestone in ushering in the new epoch of "Memory-as-a-Service".

1.  The "Impossible Triangle" of LLM Long-Term Memory

In recent years, the capability boundaries of LLMs have continuously expanded. However, when it comes to lifelong, fine-grained memory retention, they still face an insurmountable chasm. Scenarios such as literary analysis requiring the comprehension of extensive novel series, Digital Twins demanding persona consistency across multi-turn dialogues, or Agent systems needing to trace long historical records, all place stringent demands on the model's effective context length. Yet, mainstream LLMs, constrained by the quadratic complexity of Full Attention mechanisms, have long had their effective context windows limited to around 1 million (1M) tokens — a far cry from the estimated hundreds of millions of tokens comprising a human's lifelong memory capacity.

To break through this bottleneck, academia and industry have explored three main technical paradigms. However, while attempting to solve the problem, each paradigm has fallen into new dilemmas, forming an irreconcilable "Impossible Triangle":

• Parameter-Based Memory: This method "burns" knowledge directly into model parameters via continuous training or fine-tuning (e.g., LoRA). While it offers high precision, it suffers from poor scalability, high updating costs, and a high susceptibility to catastrophic forgetting.
• External Storage-Based Memory: Represented by Retrieval-Augmented Generation (RAG), this approach externalizes memory into vector databases. It boasts excellent scalability, but its non-end-to-end, decoupled "retrieve-then-generate" nature makes retrieval precision a performance bottleneck, struggling to achieve deep semantic alignment.
• Latent State-Based Memory: This paradigm utilizes the model's internal hidden states (such as KV cache) as working memory. It provides high semantic fidelity but faces a direct conflict between efficiency and capacity. Methods retaining local KV caches offer high precision but limited scalability; whereas linear attention-based methods (like RWKV or DeltaNet) achieve linear complexity but suffer severe precision degradation in ultra-long contexts due to lossy compression.

The following table summarizes the key trade-offs across all three paradigms:

Against this backdrop, the MSA paper sets an ambitious goal: to design an end-to-end trainable latent state memory framework that scales to 100M tokens with linear complexity while maintaining high precision. The emergence of MSA aims to directly challenge and shatter the aforementioned "Impossible Triangle," endowing LLMs with true "lifelong memory."

2.  Deep Dive into the MSA Architecture: Four Pillars of Innovation

The revolutionary nature of MSA does not stem from a single technological breakthrough, but rather from a cohesive, systemic stack of architectural innovations. These components work in synergy to form the bedrock of its high performance.

2.1  The Core Foundation: Memory Sparse Attention

The core idea of MSA is to introduce a differentiable, content-based sparsification mechanism into the Transformer's attention layer. Instead of forcing the model to attend to all historical memories during inference, it designs an efficient "Routing" module to dynamically select the most relevant memory subsets for computation.

The MSA layer is wrapped within a standard Pre-Norm Transformer block, replacing the traditional Self-Attention layer. This plug-and-play design ensures seamless integration into existing LLM architectures without disruptive overhauls. The heart of the innovation is an elegant "Dual-Routing" mechanism that selects the best evidence from massive documents without brute-force full computation:

• Input & Projection: The model receives the external knowledge base and the current query, projecting them into four independent representations: Q (Query), K (Key), V (Value), and a specialized Routing Key (K^R).
• First-Level Routing (Topic-level Screening): The model performs coarse-grained topic screening across the entire knowledge base using an attention mechanism and token-wise mean pooling. An auxiliary contrastive loss (L_aux) is computed here to enforce routing sparsity and prevent query collapse onto a few popular documents.
• Second-Level Routing (Token-level Fine-screening): Within the relevant documents identified in the first step, the model conducts finer "content screening" via Pooling and Top-k operations, selecting the most critical sentences or paragraphs at the token level.
• Final Attention Computation: Only the selected, most essential K and V vectors are loaded into the attention computation. These sparsified memory K/Vs are concatenated with the query's K/Vs and fed into the final Multi-head Attention layer to generate the output.

The ingenuity of this mechanism lies in internalizing the "retrieval" step of RAG systems into an end-to-end trainable neural network module. Unlike RAG, which relies on external, fixed similarity metrics (like cosine distance), MSA's router is co-optimized with the generation task during training via a supervised contrastive loss. This fundamentally solves the core pain point of misaligned objectives between RAG's "retrieval" and "generation," serving as the key to achieving high precision.

2.2  The Key to Scalability: Document-wise RoPE

To successfully extrapolate from shorter training texts (e.g., 64k tokens) to 100M-level inference texts, handling positional information is a critical challenge. If traditional global positional encoding is used, positional indices will shift drastically when the number of documents during inference far exceeds that during training, leading to severe performance degradation.

MSA proposes a concise and efficient solution: assigning an independent set of Rotary Positional Embeddings (RoPE) to each individual document. This means that regardless of how many documents are in the memory bank, the model's internal "coordinate system" when reading each document always starts from 0 and remains stable. This design decouples the internal relative position of a document from its absolute position in the global memory, allowing the position-awareness learned during training to generalize losslessly to inference scenarios with massive document banks. Complementing this, Global RoPE is applied to the active query context, with position IDs offset by the number of retrieved documents, ensuring the model perceives the query as a logical continuation of the retrieved background information.

2.3  Engineering Implementation: KV Cache Compression & Memory Parallel

Theoretical feasibility must be translated into reality through engineering. At the 100M-token scale, even after compression, the KV cache storage requirement reaches approximately 169 GB — far exceeding the aggregate 160 GB VRAM capacity of a standard 2×A800 node. MSA resolves this physical bottleneck through an ingenious "Memory Parallel" strategy built on two key observations:

• Tiered Storage: During the routing phase, the model only needs the relatively small Routing Keys (K^R) for quick matching; the massive Content KVs (K and V) are only needed after Top-k selection. Thus, K^R is distributed across GPU VRAM for low-latency retrieval, while K and V are offloaded to CPU DRAM.
• Asynchronous Fetching: Once the GPU completes routing and determines the Top-k documents, the system asynchronously fetches only the required Content KVs from CPU memory to the GPU for the final generation computation. This "fast search (GPU), slow fetch (CPU)" strategy elegantly shifts the storage bottleneck from limited VRAM to massive CPU memory, enabling 100M-token inference on just two A800 GPUs.

2.4  Complex Reasoning Capability: Memory Interleave

For complex questions that require integrating multiple pieces of evidence scattered across different documents (i.e., multi-hop reasoning), a single "retrieve-generate" cycle often falls short. To address this, MSA introduces the Memory Interleave mechanism, which allows the model to perform multiple rounds of "generative retrieval → context expansion" loops:

In the first round, the model generates the IDs of documents it deems most relevant based on the original query. The system fetches the original text of these documents and appends them to the query, forming a richer "intermediate query." In the next round, the model generates new document IDs based on this enriched context. This cycle continues until the model determines that the accumulated evidence is sufficient, at which point it switches to generating the final answer.

This iterative reasoning chain simulates the thought process of a human detective: "Discover clue A → Follow the vine to find clue B → Integrate A and B to form a complete evidence chain." It endows MSA with the ability to dynamically plan its information-gathering path, which is a crucial reason for its outstanding performance on Multi-hop QA tasks. The model autonomously determines how many retrieval rounds are needed per query, rather than relying on a pre-defined fixed number of retrieved documents.

3.  Re-interpreting Experimental Data: Validating MSA's Value

The paper validates the effectiveness of the MSA architecture from multiple dimensions through exhaustive experiments. We highlight the three most revealing findings.

3.1  Astonishing Scalability and Robustness

On the RULER Needle-In-A-Haystack (NIAH) benchmark, as the context scales from 32K to 1M tokens, MSA's accuracy only drops from 98.77% to 94.84% — a mere 3.93 percentage-point decline across a 32-fold expansion. In stark contrast, the unmodified Qwen3-4B backbone plummets to 24.69% at 1M tokens, and even the 80B Qwen3-Next model degrades to 80.78% at 1M tokens. In the even more extreme MS MARCO QA test, when expanding the memory scale from 16K to 100M tokens (spanning 4 orders of magnitude), MSA's performance score only drops from 4.023 to 3.669, a degradation rate of less than 9%. This intuitively proves the architecture's exceptional robustness against massive irrelevant information (noise) interference.

3.2  The Power of End-to-End Optimization

On the average scores of 9 QA benchmarks, the 4B-parameter MSA model (average score 3.760) significantly outperforms complex RAG systems built on the identical Qwen3-4B foundation, including those with a Reranker. Specifically, MSA achieves average improvements of 16.0%, 11.5%, and 14.8% over standard RAG, RAG with reranking, and HippoRAG2, respectively. More strikingly, on several datasets, its performance even surpasses top-tier RAG systems composed of the SOTA KaLMv2 retriever paired with the massive 235B-parameter Qwen3 model — a system with 58× more parameters. This fully demonstrates the high-precision advantage brought by MSA's end-to-end optimization.

3.3  The Indispensability of Each Component

Ablation studies clearly quantify the contribution of each innovation. Compared to the baseline MSA-S1 model, removing the Memory Interleave mechanism leads to a 5.3% average performance drop, with HotpotQA suffering a 19.2% decline — confirming its essentiality for multi-hop reasoning. Removing the auxiliary routing supervision in Continual Pre-training causes a severe 31.3% average performance degradation (43.1% on HotpotQA), as errors in initial document retrieval compound during subsequent interleaving steps. Removing the Original Text Injection causes the most severe slide of 37.1%, with DuReader experiencing a 46.2% drop, indicating that precise final answers still rely critically on the semantic details of the original text. This proves that MSA is an intricately designed, organic whole where every component is indispensable.

4.  Conclusion: MSA's Originality and Core Value

In summary, the true value of MSA lies not merely in releasing a powerful long-context model, but in providing the AI memory field with a brand new, fully validated technical infrastructure that simultaneously achieves scalability, precision, and efficiency. It proves that we do not have to make painful compromises between the "low precision" of RAG and the "high cost" of full attention. By cleverly combining the idea of sparsification with the end-to-end learning capabilities of neural networks, building an independent, scalable "Memory Layer" compatible with LLMs is entirely feasible.

This paints an exciting blueprint for the future development of the AI ecosystem: Memory can act as an independent, pluggable service, freely combined with various reasoning cores (LLMs). User data and "memory assets" will no longer be locked into any single model or vendor. From this perspective, MSA is not just an excellent academic paper; it is likely a milestone that inaugurates the new era of Memory-as-a-Service.

5.  Background: EverMind and Shanda Group's "Discoverative AI" Vision

To fully understand the driving force behind the MSA research, it is necessary to examine it within the macro-strategic context of its creator, EverMind, and its parent company, Shanda Group. EverMind is one of the core teams deeply incubated by Shanda Group's founder, Tianqiao Chen, in the AI field. Its mission is to conquer the long-term memory challenge of AI, moving towards AI's Self-Evolving capability.

According to recent interviews with Tianqiao Chen by Bloomberg and TMTPost, Shanda Group's AI strategy does not focus on the current mainstream "Generative AI," but aims to build a more pioneering "Discoverative AI" ecosystem. Its ultimate goal is to have AI assist humans in discovering new knowledge and solving fundamental problems like disease and energy, rather than merely imitating and recombining existing information. In this grand vision, two foundational technologies are placed at the core:

• MiroMind — Reasoning: This team is dedicated to enabling models to proactively seek evidence from the external world and revise hypotheses like scientists, achieving true reasoning reliability and insightful discovery through paths like verifiable reasoning.
• EverMind — Memory: This team's mission is to build an infinitely scalable, high-fidelity long-term memory system for AI that is independent of any specific model. Only when AI possesses a stable and reliable memory foundation can it conduct effective, cross-temporal complex reasoning and knowledge creation, ultimately achieving Self-Evolving AI.

Therefore, EverMind and MiroMind jointly constitute the core driving force of Shanda Group's "Discoverative AI" blueprint, corresponding to the two core pillars of cognitive science: Memory and Reasoning. The MSA architecture introduced in this paper is exactly the core technological achievement of the EverMind team in practicing the "Memory-as-a-Service" concept. Its underlying design and technical route not only represent a breakthrough in the existing long-context bottleneck but also profoundly reflect Shanda Group's long-term investment and firm determination in building independent, autonomous, and controllable AI infrastructure.

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Breaking the 100M Token Limit: EverMind's MSA Architecture Achieves Efficient End-to-End Long-Term Memory for LLMs

Breaking the 100M Token Limit: EverMind's MSA Architecture Achieves Efficient End-to-End Long-Term Memory for LLMs

Breaking the 100M Token Limit: EverMind's MSA Architecture Achieves Efficient End-to-End Long-Term Memory for LLMs

Breaking the 100M Token Limit: EverMind's MSA Architecture Achieves Efficient End-to-End Long-Term Memory for LLMs

Breaking the 100M Token Limit: EverMind's MSA Architecture Achieves Efficient End-to-End Long-Term Memory for LLMs

Breaking the 100M Token Limit: EverMind's MSA Architecture Achieves Efficient End-to-End Long-Term Memory for LLMs

Breaking the 100M Token Limit: EverMind's MSA Architecture Achieves Efficient End-to-End Long-Term Memory for LLMs

SINGAPORE, March 19, 2026 /PRNewswire/ --

Overview

Beyond Borders: "The Global Language of Sake" is a landmark industry initiative celebrating 60 years of diplomatic relations between Singapore and Japan. Bringing together leading Japanese sake breweries, Singapore-based chefs, distributors and industry stakeholders, the event spotlights sake as a versatile global beverage, transcending traditional Japanese cuisine and integrating seamlessly into Singapore's diverse culinary landscape.

The initiative also aims to expand the appreciation and application of Japanese sake beyond Japanese dining contexts, supporting the continued growth of sake exports from Japan to Singapore. Japan remains one of Singapore's key trading partners, and Japanese sake has gained increasing popularity among a broad spectrum of Singaporean consumers. By encouraging chefs, restaurateurs and beverage professionals to explore new culinary pairings and applications, the event seeks to strengthen sake's presence within Singapore's dynamic food and beverage scene.

Jointly organised by JETRO Singapore, JFOODO, and the Embassy of Japan in Singapore, and supported by the Ministry of Agriculture, Forestry and Fisheries of Japan (MAFF), the event will feature curated sake tastings, chef-led food pairings and an industry panel discussion exploring strategies to broaden sake's role in Singapore's evolving dining culture.

Purpose

• To break the stereotype that sake pairs only with Japanese cuisine
• To encourage adoption across Singapore restaurants and the wider F&B ecosystem
• To strengthen Singapore–Japan culinary collaboration
• To support industry knowledge exchange and trade opportunities

Highlights:

1. Tasting Showcase of 30 Japanese Sake Labels

The tasting will feature 30 to 40 different sake selections, presented by local distributors and industry partners. The sakes span a wide range of styles, from Kijoshu (dessert-style sake) to distinctive barrel-aged expressions, and this tasting offers guests the opportunity to engage directly with producers and gain deeper insight into the craftsmanship behind each sake.

Many showcased labels are recipients of international accolades including IWC (International Wine Challenge), Kura Master and global sustainability awards. Following the tasting, participating sakes will be available for immediate trade purchase, with local wholesalers and distributors in attendance to facilitate business discussions and support the expansion of sake offerings within Singapore's food and beverage sector.

2. Five chef-curated sake pairing dishes

Five distinguished chefs from Singapore's dynamic dining scene each present a sake-pairing dish. This cross-cultural pairing segment demonstrates how sake harmonises beyond Japanese cuisine and its compatibility with global flavours.

• Chef Tamara Chavez – Canchita Peruvian Cuisine & Latinada Festival (Latin American)
• Chef Edward Chong – Peach Blossoms (contemporary, innovative Cantonese cooking)
• Chef Wayne Liew – Keng Eng Kee Seafood (Singaporean zi char)
• Chef Denis Lucchi – Buona Terra (contemporary Italian fine-dining cuisine)
• Chef Janice Wong – Janice Wong (Contemporary desserts and chocolate creations)

PAIRING MENU

Tamara Chavez (Canchita Peruvian Cuisine & Latinada Festival)	Ceviche Nikkei The Ceviche Nikkei brings together cured yellowfin tuna and Hokkaido scallops dressed in a vibrant Japanese-Peruvian leche de tigre, balanced with creamy avocado purée, crunchy canchita corn and crisp plantain chips. The dish delivers layers of bright citrus acidity, delicate sweetness from the seafood, and textural contrast.	This is paired with Nichi Nichi Yamadanishiki Sake, a refined junmai sake from Kyoto brewed using premium Yamadanishiki rice. With its elegant umami character and gentle 11% alcohol profile, the sake complements the natural sweetness of the tuna and scallops while softening the sharp citrus notes of the leche de tigre. Its clean, polished finish refreshes the palate between bites, allowing the dish's acidity, creaminess and crunch to remain balanced and expressive.
Edward Chong (Peach Blossoms)	Crispy Scales Fillet of Amadai with Spicy Pineapple Sauce and Assam Curry The dish is layered with a vibrant Southeast Asian influence with the spicy pineapple sauce adding bright acidity and tropical sweetness, while the assam curry introduces depth, tang and gentle spice.	This dish is paired with Daishichi Junmai Ginjo Kimoto Masakura Sake from Fukushima. Brewed using the traditional kimoto method and polished to 58%, the sake has at 15% alcohol content, offering a polished, precise and elegant profile with refined umami and balanced acidity. Its structured yet clean character enhances the natural sweetness of the amadai while harmonising with the dish's tangy and spicy elements.
Wayne Liew (Keng Eng Kee Seafood)	Coffee Pork Featuring Premium Japanese Cuts An iconic dish that represents the evolving cuisine of Singapore as a multi-racial nation. The sauce is made up of coffee powder, coffee essence, honey and apple jam, allowing the balance of coffee aroma and sweetness. A dish that has travelled around the nations such as Denmark, Spain, America and Korea.	This dish is paired with 16th Kurouemon Champion Sake from Nagano. Brewed with a polishing ratio of 55% and an alcohol content of 15%, this sake is known for its rich umami character and structured depth. Its savoury intensity complements the robust flavours of the coffee-infused sauce, while its balanced profile helps round out the sweetness in the dish.
Denis Lucchi (Buona Terra)	Wagyu Beef Tripe with Squid, Tomato, Lardo Wagyu beef tripe and squid are gently stewed in a savoury tomato sauce, creating depth and warmth, before being finished with semi-dried Amela tomatoes, marjoram, sautéed squid and crisp lardo. The combination delivers a balance of umami richness, natural sweetness and aromatic herbs, while the crispy lardo adds a touch of indulgent texture.	This dish is paired with Katafune Honjozo Sake from Niigata. With an alcohol content of 16% and a polishing ratio of 60%, this sake presents a light, fresh and balanced dry profile. Its clean and versatile character cuts through the richness of the tripe and lardo while complementing the natural sweetness of the squid and tomatoes.
Janice Wong (2am: dessertbar)	Emerald Layers Delicate matcha crispy tuile layered with a creamy sake-infused pastry cream, balanced with earthy matcha and complemented with sake kasu. Often used in desserts, sake kasu (sake lees) brings fruity, savoury and umami-rich notes, with a gentle fermented aroma that ties the dish together in a balance of bitterness, creaminess and subtle sweetness.	This dish is paired with Noguchi Yamahai Aiyama Muroka Nama Genshu 2019 Junmai Daiginjo, an unfiltered raw sake from Ishikawa. Brewed with Aiyama rice and polished to 50%, this 2019 vintage sake presents a bold, mature profile with firm umami depth and a full-bodied structure at 17% alcohol. Its richness complements the earthy intensity of matcha while balancing the dessert's bittersweet notes.

Tamara Chavez

(Canchita Peruvian Cuisine & Latinada Festival)

Ceviche Nikkei

The Ceviche Nikkei brings together cured yellowfin tuna and Hokkaido scallops dressed in a vibrant Japanese-Peruvian leche de tigre, balanced with creamy avocado purée, crunchy canchita corn and crisp plantain chips. The dish delivers layers of bright citrus acidity, delicate sweetness from the seafood, and textural contrast.

This is paired with Nichi Nichi Yamadanishiki Sake, a refined junmai sake from Kyoto brewed using premium Yamadanishiki rice. With its elegant umami character and gentle 11% alcohol profile, the sake complements the natural sweetness of the tuna and scallops while softening the sharp citrus notes of the leche de tigre. Its clean, polished finish refreshes the palate between bites, allowing the dish's acidity, creaminess and crunch to remain balanced and expressive.

Edward Chong

(Peach Blossoms)

Crispy Scales Fillet of Amadai with Spicy Pineapple Sauce and Assam Curry

The dish is layered with a vibrant Southeast Asian influence with the spicy pineapple sauce adding bright acidity and tropical sweetness, while the assam curry introduces depth, tang and gentle spice.

This dish is paired with Daishichi Junmai Ginjo Kimoto Masakura Sake from Fukushima. Brewed using the traditional kimoto method and polished to 58%, the sake has at 15% alcohol content, offering a polished, precise and elegant profile with refined umami and balanced acidity. Its structured yet clean character enhances the natural sweetness of the amadai while harmonising with the dish's tangy and spicy elements.

Wayne Liew

(Keng Eng Kee Seafood)

Coffee Pork Featuring Premium Japanese Cuts

An iconic dish that represents the evolving cuisine of Singapore as a multi-racial nation. The sauce is made up of coffee powder, coffee essence, honey and apple jam, allowing the balance of coffee aroma and sweetness. A dish that has travelled around the nations such as Denmark, Spain, America and Korea.

This dish is paired with 16th Kurouemon Champion Sake from Nagano. Brewed with a polishing ratio of 55% and an alcohol content of 15%, this sake is known for its rich umami character and structured depth. Its savoury intensity complements the robust flavours of the coffee-infused sauce, while its balanced profile helps round out the sweetness in the dish.

Denis Lucchi

(Buona Terra)

Wagyu Beef Tripe with Squid, Tomato, Lardo

Wagyu beef tripe and squid are gently stewed in a savoury tomato sauce, creating depth and warmth, before being finished with semi-dried Amela tomatoes, marjoram, sautéed squid and crisp lardo. The combination delivers a balance of umami richness, natural sweetness and aromatic herbs, while the crispy lardo adds a touch of indulgent texture.

This dish is paired with Katafune Honjozo Sake from Niigata. With an alcohol content of 16% and a polishing ratio of 60%, this sake presents a light, fresh and balanced dry profile. Its clean and versatile character cuts through the richness of the tripe and lardo while complementing the natural sweetness of the squid and tomatoes.

Janice Wong

(2am: dessertbar)

Emerald Layers

Delicate matcha crispy tuile layered with a creamy sake-infused pastry cream, balanced with earthy matcha and complemented with sake kasu. Often used in desserts, sake kasu (sake lees) brings fruity, savoury and umami-rich notes, with a gentle fermented aroma that ties the dish together in a balance of bitterness, creaminess and subtle sweetness.

This dish is paired with Noguchi Yamahai Aiyama Muroka Nama Genshu 2019 Junmai Daiginjo, an unfiltered raw sake from Ishikawa. Brewed with Aiyama rice and polished to 50%, this 2019 vintage sake presents a bold, mature profile with firm umami depth and a full-bodied structure at 17% alcohol. Its richness complements the earthy intensity of matcha while balancing the dessert's bittersweet notes.

3. Industry Panel Discussion

Title:Exploring New Opportunities for Japanese Sake in Singapore's Vibrant Culinary Scene

Moderated by Debbie Yong, this succinct panel discussion features the opinions of industry professionals:

• Veronica Phua – Marketing & Industry Expert
• Paul Liew – Third Generation Owner, Keng Eng Kee Seafood
• Chef Daniel Chavez – Chef-Owner, Canchita Peruvian Cuisine & Latinada Festival
• Joel Lim – Chief Sommelier & Wine Director, Buona Terra

4. Onsite Distributors Facilitating Trade Conversations

To kick start industry collaboration, distributors available onsite include:

• EJRT ASIA(SINGAPORE) PTE., LTD.
• The Wine Distribution Company
• Epicurean Nomads Private Limited
• Inter Rice Asia Pte Ltd
• Shiki SG Pte. Ltd.

Closing Thoughts

As one of Asia's most dynamic gastronomic cities, cosmopolitan Singapore is home to diverse global cuisines and a sophisticated consumer base who are increasingly open to premium beverages and are receptive to cross-cultural flavours. Japanese sake, with its long history of brewing artistry and varying styles, is a premium and versatile pairing partner across cuisines. As Singapore and Japan mark 60 years of diplomatic ties, this initiative reinforces culinary diplomacy: using food and beverage as a bridge for deeper cultural and economic exchange.

Full press kit in the link here: bit.ly/BeyondBordersSake

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MEDIA FACT SHEET: Beyond Borders: "The Global Language of Sake"