Data Story: A Deep Dive into Deepseek V4 (What we know so far)

Editor's Note (March 2026): DeepSeek V4 has not been officially released as of publication. While a "V4 Lite" briefly appeared on DeepSeek's platform in early March 2026, the full model remains unreleased. Multiple sources — including Dataconomy (citing Chinese tech outlet Whale Lab) and the Financial Times — have reported imminent launch dates that have since passed. This article analyses three research papers published by DeepSeek between late December 2025 and mid-January 2026 — on Engram conditional memory, manifold-constrained hyper-connections (mHC), and DeepSeek Sparse Attention — that are widely expected to form the architectural basis of V4. However, DeepSeek has not officially confirmed which of these innovations will be incorporated into V4's final architecture. The connections drawn here are informed by the papers' authorship (including CEO Liang Wenfeng), code leaks, and observable platform changes, but remain technically speculative until an official release and technical report are published. We will update this article with confirmed specifications, benchmarks, and real-world performance data when DeepSeek V4 launches.

DeepSeek V4's anticipated architectural innovations — Engram conditional memory, manifold-constrained hyper-connections, and DeepSeek Sparse Attention — wouldn't just change how large language models compute. If integrated as expected, they would redefine what counts as good data and how datasets should be structured for the next generation of AI.

Introduction

Most coverage of DeepSeek V4 focuses on the headline number: a reported one trillion parameters. That's the wrong number to pay attention to. According to multiple technical analyses, DeepSeek V4 is expected to activate only a fraction of those parameters per forward pass — consistent with DeepSeek models' Mixture-of-Experts approach, where DeepSeek V3 activated 37 billion of its 671 billion total. If these reports are accurate, DeepSeek didn't just scale up. They redesigned the relationship between what a language model memorises and what it computes.

The deeper story is about data. The research papers widely associated with DeepSeek V4 embed a specific theory: that factual knowledge and reasoning capabilities are fundamentally different types of intelligence, each requiring different data, different storage, and different processing. The architecture described across these papers is, in effect, an argument about how datasets should be built.

The case for V4's architecture rests on three papers published between late December 2025 and mid-January 2026. Manifold-constrained hyper-connections (mHC) solves a training stability problem that would otherwise make an extremely large-scale model impractical. Engram introduces conditional memory that separates static knowledge retrieval from dynamic neural computation. DeepSeek Sparse Attention (DSA) with a "Lightning Indexer" cuts long-context compute roughly in half. Together, they represent a philosophical shift: instead of asking "how do we make the model bigger," DeepSeek appears to have asked "how do we make each parameter do the right kind of work" — and the answer starts with training data.

Key Takeaways

• Engram separates static knowledge (O(1) hash lookup) from dynamic reasoning (MoE), with an optimal 20–25% memory / 75–80% compute allocation
• This split implies datasets must be curated differently: knowledge-dense data feeds memory tables, reasoning-dense data feeds MoE experts
• mHC constrains signal amplification from 3,000x to under 2x, enabling stable training at 6.7% overhead — a prerequisite for any data to be useful at scale
• DeepSeek V3's training pipeline already separated knowledge from reasoning data; the papers associated with V4 encode this distinction structurally
• DeepSeek's open-weight strategy commoditises the base model, shifting competitive advantage to fine-tuning data quality

What Did DeepSeek V3's Training Pipeline Establish?

DeepSeek V4 inherits — and is expected to extend — a training infrastructure and data philosophy refined across three prior generations. DeepSeek V3 was a strong mixture-of-experts language model with 671 billion total parameters, 37 billion active per forward pass, trained on 14.8 trillion high-quality tokens. The entire training process cost $5.6 million — a figure that stunned the industry when the company's flagship model achieved performance comparable to closed-source models like GPT-4 and Claude 3.5 Sonnet at a fraction of their training costs.

Several innovations made this possible. Multi-Head Latent Attention (MLA) compresses key-value caches into a low-rank latent space, dramatically reducing memory footprint and enabling pre-training on longer sequences without proportionally increasing computational resources. An auxiliary-loss-free strategy for load balancing maintains balanced expert utilisation without competing against the primary training objective — eliminating a source of performance degradation in prior DeepSeek models. And multi-token prediction as a secondary training objective effectively provides more signal per forward pass, improving training efficiency without additional training data while also enabling speculative decoding to achieve efficient inference at 1.8x reduced latency.

The pre-training corpus tells the data quality story directly. DeepSeek V3's base training corpus deliberately increased mathematical and programming content relative to DeepSeek V2, reflecting a strategic bet that reasoning-dense and code-dense data yield disproportionate model performance gains. For DeepSeek-Coder-V2, the composition was explicit: 60% source code, 10% mathematical text, 30% natural language. The dataset was curated through aggressive near-duplicate detection, linguistic and semantic filtering, and domain-balanced remixing — using exclusively plain web pages and e-books, without synthetic data during initial pre-training.

DeepSeek V3's benchmark performance established that an open-weight model, trained at an economical cost, could match or exceed leading closed-source models. On coding benchmarks, it matched GPT-4 Turbo on HumanEval. On mathematical reasoning, it competed with Claude 3.5 Sonnet. Comprehensive evaluations across MMLU, ARC, and HellaSwag showed it within points of the best proprietary models. The lesson: training efficiency and model performance are not in tension. You don't need $100 million — you need to spend wisely on data composition, training stability, and post-training quality.

How Did DeepSeek's Approach Evolve from V2 to V4?

DeepSeek's model development follows a deliberate progression where each generation addresses a specific bottleneck — and each bottleneck traces back to data. DeepSeek V2 introduced multi-head latent attention and an efficient architecture for MoE routing, establishing that competitive model performance was possible with far fewer active parameters. DeepSeek V3 scaled MoE to 671 billion parameters, refined the data mix, and introduced the post-training pipeline — supervised fine-tuning with R1-generated reasoning data, reinforcement learning with rule-based and human-feedback rewards — that remains the most transparent alignment documentation from any frontier lab. DeepSeek R1 pushed the reasoning model frontier, demonstrating that long chain-of-thought reasoning trained with reinforcement learning could compete with the best proprietary models on mathematical and coding benchmarks.

DeepSeek V4 appears poised to synthesise these lessons into a single architecture. If Engram is integrated as widely anticipated, it would encode the V3 data-mix insight — different data types belong in different computational pathways — directly into model structure. mHC would ensure the training process doesn't collapse under its own scale. And DeepSeek Sparse Attention would enable the long context scenarios that reasoning-intensive tasks demand.

What Problem Does Engram Actually Solve?

Standard transformers treat every token the same way: each passes through the full computational pipeline, whether the model is retrieving a well-known fact or reasoning through a novel problem. Looking up "the capital of France" shouldn't require the same compute as debugging a recursive algorithm.

Engram addresses this by introducing "conditional memory" — a new axis of sparsity parallel to the strong mixture-of-experts backbone. Where MoE solves "how to calculate less per token," Engram solves "don't calculate what you can look up."

The mechanism works through multi-head hashing. For each token, the system extracts suffix N-grams from surrounding context, hashes them through multiple heads to avoid collision, and retrieves pre-computed embeddings from a large table. A context-aware gating mechanism decides how much weight to give retrieved memory versus the neural backbone's computation, achieving efficient inference for knowledge retrieval while preserving the full depth of the reasoning model for complex tasks.

The critical insight is what happens to transformer layer dynamics. By offloading local pattern recognition to memory lookup, early layers finish their "pattern work" faster. Mechanistic analysis using LogitLens reveals that early Engram layers behave like much deeper layers in a standard MoE model — freeing compute for deeper reasoning chains. Counterintuitively, reasoning benchmarks improved even more than knowledge benchmarks: complex reasoning jumped from 70% to 74%, while knowledge tests went from 57% to 61%.

In the Engram-27B research model, this translated to gains against an iso-parameter, iso-FLOP baseline: +3.4 on MMLU, +5.0 on BBH, +3.0 on HumanEval, and a jump from 84.2 to 97.0 on Multi-Query Needle-in-a-Haystack — consistent benchmark performance gains across both knowledge-intensive and reasoning-intensive tasks. How these results translate to a production-scale V4 model remains to be seen.

How Does Memory-Compute Separation Change What "Good Data" Means?

This is where the architectural ideas behind DeepSeek V4 become a data engineering story. In a standard transformer, all data flows through the same pipeline. In Engram's architecture, the model has two ingest pathways with different optimal properties.

The memory table is populated with static, context-invariant patterns: named entities, common phrases, API signatures, short code patterns. This data needs to be comprehensive, factually accurate, and entity-rich. Errors here become permanent — a wrong fact in the lookup table doesn't get "reasoned away" by the neural backbone.

The MoE experts are trained on data requiring compositional reasoning: multi-step inference chains, mathematical problem solving, debugging logic, code generation from ambiguous specifications. This data needs to be process-rich, showing not just correct answers but reasoning paths. DeepSeek V3's training pipeline reflected this: reasoning data was generated using their R1 reasoning model, then validated by human annotators checking both correctness and reasoning quality.

The U-shaped scaling law makes the stakes concrete. The Engram paper discovered that optimal allocation is roughly 20–25% of sparse parameters to memory and 75–80% to computation. Deviate in either direction and performance degrades — too much memory means insufficient reasoning capabilities; too much compute wastes cycles on pattern matching that a hash table handles in constant time. In the 27B research model, this meant reducing routed experts from 72 to 55 and reallocating freed parameters to a 5.7-billion-parameter embedding module. Total model size stayed constant, but model performance improved across the board. If V4 applies this principle at trillion-parameter scale, the implications for data curation would be significant.

This two-pathway requirement is a data labeling problem as much as a data sourcing one. The memory table needs entity-rich, factually verified content — the kind of work where a wrong label becomes a permanent wrong answer. The reasoning pathway needs process-rich data with validated chains of inference, where annotators must judge not just correctness but reasoning quality. These are fundamentally different labeling tasks requiring different expertise: domain specialists who can verify factual accuracy for knowledge data, and experienced practitioners who can evaluate multi-step reasoning for compute data. Platforms built around domain-expert annotation with measurable quality metrics — consensus scoring, honeypot benchmarks, annotator-level performance tracking — become structurally important when the architecture itself enforces a quality threshold for each pathway.

What Are Engram's Known Limitations?

The Engram paper is transparent about several constraints worth noting — particularly for anyone extrapolating from the 27B research model to a production-scale V4.

Validated only at 27B scale. All results come from Engram-27B and Engram-40B models trained on 262 billion tokens. The paper does not present results at the trillion-parameter scale that V4 reportedly targets. Whether the U-shaped allocation law, the benchmark gains, and the mechanistic effects (early-layer depth compression) hold at 10–40x scale is an open question. The paper itself acknowledges that Engram-40B — the larger of its two models — doesn't dominate Engram-27B on every task, attributing this to under-training and noting that the training loss gap was still widening at the end of their token budget. This suggests the scaling properties of larger memory tables need further investigation with longer training runs.

Hash collisions are a design-level concern. Engram uses multi-head hashing to map N-grams to embedding tables, which inherently produces collisions — different N-grams mapping to the same slot. The paper addresses this with context-aware gating that suppresses irrelevant retrievals, but collisions remain a fundamental property of the approach. A follow-up paper (Engram-Nine, arXiv:2601.16531) specifically investigates whether high-frequency key collisions are a primary bottleneck, introducing a collision-free hot-tier extension. The fact that this follow-up exists confirms that collision management is an active area of refinement rather than a solved problem.

Memory captures only local, static patterns. Engram retrieves embeddings based on short suffix N-grams (maximum size 3 in the paper's configuration). This means it captures local, stereotyped patterns — named entities, formulaic phrases, short code idioms. Knowledge that requires longer-range context to identify, or that doesn't decompose into short token sequences, won't benefit from the lookup table. The paper is explicit that Engram is designed for patterns that are "local, static, and highly stereotyped" — it's not a general-purpose retrieval mechanism.

Layer placement involves a modelling-versus-latency tradeoff. The paper's ablation study shows that early layer placement (layer 2) is optimal for modelling performance — but the system design requires enough preceding computation to mask the latency of prefetching from host memory. This creates a tension: the best position for performance isn't necessarily the best position for inference speed. The paper notes this requires "hardware-algorithm co-design" and the optimal placement must satisfy both constraints simultaneously. At larger scales with different hardware configurations, this tradeoff may shift.

Static after training. Engram's memory tables are trained alongside the model and fixed at inference time. They can't be updated without retraining. For domains where factual knowledge changes frequently — financial data, regulatory frameworks, rapidly evolving codebases — the static lookup table may become stale faster than the reasoning backbone. The paper doesn't address post-training memory updates, fine-tuning strategies for the lookup tables, or how to refresh factual knowledge without a full retraining cycle.

These limitations don't undermine Engram's contributions — the benchmark gains are consistent and the mechanistic analysis is rigorous. But they define the boundaries of what the paper actually demonstrates, which matters when the industry is extrapolating from a 27B research model to claims about a trillion-parameter production system.

What Happens When Static Knowledge Is No Longer GPU-Bound?

One of Engram's most underappreciated innovations is offloading. The memory tables — storing over 100 billion parameters of static knowledge in the research model — can move entirely to host (CPU) RAM with less than 3% overhead, thanks to deterministic prefetching. Unlike MoE routing, which is dynamic and unpredictable, Engram lookups are determined by input N-gram context and can be prefetched before needed.

As Tom's Hardware reported, this breaks a fundamental constraint: the amount of factual knowledge a model can store is no longer bound by GPU memory. CPU RAM is dramatically cheaper than HBM, and Engram's deterministic access pattern minimises latency. This decouples computational resources for reasoning from storage capacity for knowledge.

The implication for data strategy is significant. If storage cost drops to near-zero, the value of comprehensive, high-quality factual datasets increases. The bottleneck shifts from "how much can the model fit" to "how good is the data we feed the lookup table." For enterprises deploying DeepSeek models on their own infrastructure, an Engram-based model with adequate CPU RAM could store far more domain-specific knowledge than a standard language model of equivalent GPU footprint — making specialised cloud deployment applications viable without proportionally larger costs.

Why Did DeepSeek Need to Reinvent Residual Connections?

Training a trillion-parameter model isn't just about GPUs. The deeper and wider a network gets, the more signal amplification causes gradients to explode. When a training run diverges, everything invested — compute, time, and the entire curated dataset — is wasted. At the scale DeepSeek V4 is reported to target, a single diverged run represents millions in lost computational resources.

Hyper-Connections (HC), proposed by ByteDance in 2024, attempted to solve this by expanding residual stream width. But HC broke the identity mapping property that makes residual networks trainable. At 27 billion parameters, unconstrained HC caused signal gains exceeding 3,000x.

DeepSeek's mHC paper fixes this by projecting the residual mixing matrix onto the Birkhoff Polytope — the manifold of doubly stochastic matrices — using the Sinkhorn-Knopp algorithm. Where unconstrained HC produced amplification of 10^3 to 10^5, mHC holds it below 2x. The overhead is just 6.7% of training time — cost-effective training stability compared to restarting a diverged trillion-parameter run. Benchmark improvements were consistent: BBH improved from 43.8 (baseline) to 48.9 (HC) to 51.0 (mHC).

The data quality angle is about prerequisites. mHC doesn't directly affect training data, but determines whether curated data can be used effectively at all. A model that diverges at 500 billion parameters never benefits from the high-quality tokens prepared for the full run. As the South China Morning Post noted, CEO Liang Wenfeng personally uploaded the mHC paper to arXiv — signalling that DeepSeek views training stability as a strategic capability, and lending credence to the inference that mHC is intended for V4.

What Does DeepSeek Sparse Attention Add?

The third pillar addresses long-context compute cost. Standard attention scales quadratically with sequence length, making million-token context windows prohibitively expensive.

DeepSeek Sparse Attention with a "Lightning Indexer" was revealed through an accidental code leak in DeepSeek's FlashMLA repository, where developers discovered 28 references to "MODEL1" across 114 files. The sparse attention mechanism reportedly reduces compute by approximately 50% for long-context processing. If V4 does ship with a reported 1-million-token context window as widely anticipated, DeepSeek Sparse Attention would be what makes that practical for reasoning over entire codebases or extended documents.

From a data perspective, efficient sparse attention changes what's feasible in training. Long-context performance depends on high-signal long documents — technical manuals, multi-file codebases, book-length texts. With DeepSeek Sparse Attention cutting processing costs, training on longer sequences becomes economically viable, increasing demand for high-quality long-form datasets.

Why Is DeepSeek V4 Expected to Be Optimised for Coding Tasks?

DeepSeek V4 is widely reported as a coding-first model, and the coding story is really a data story. Code exposes the difference between knowledge that should be memorised and reasoning that must be computed.

Some coding work is pure recall: API signatures, library imports, boilerplate syntax. Other parts require genuine reasoning: debugging logic errors, choosing architectural patterns, handling edge cases. Engram's memory-compute separation maps onto this distinction naturally — static code patterns would go to the lookup table, while the reasoning model's chain-of-thought capabilities handle novel problem-solving.

For tool use — where language models call external APIs, execute code, and orchestrate multi-step workflows — this separation would offer a natural advantage. Tool use requires both memorisation (knowing which tools exist and what parameters they accept) and reasoning (deciding which tool to call, in what sequence). DeepSeek V3 already demonstrated strong coding benchmarks, and the DeepSeek-Coder-V2 variant showed that deliberate data composition choices yield disproportionate returns on coding tasks. If V4 integrates Engram, this advantage becomes structural.

How Does DeepSeek V3's Post-Training Pipeline Inform V4's Data Strategy?

DeepSeek V3's post-training pipeline — the most transparent alignment documentation from any frontier lab — reveals a data curation philosophy that directly foreshadows the architectural separation described in the Engram paper.

During supervised fine-tuning, DeepSeek curated 1.5 million instruction-tuning instances. For reasoning-intensive tasks, data was generated using the R1 reasoning model, producing explicit long chain-of-thought reasoning. But R1-generated data had systematic issues — overthinking, poor formatting, excessive length — corrected through human review checking both answer correctness and reasoning quality. For non-reasoning tasks, data came from DeepSeek-V2.5 with human verification, tuning the output style for clarity. This two-track approach mirrors what Engram does architecturally: R1-generated data trains the compute pathway, human-verified factual data trains the knowledge pathway.

The reinforcement learning stages were equally deliberate. Where possible, DeepSeek used rule-based reward models — mathematical problems with deterministic solutions, coding tasks with compiler feedback — because rule-based validation is harder to game than learned rewards. When automatic validation wasn't possible, human feedback provided the signal. This maintains control over reward quality, preventing reward hacking across heterogeneous domains.

An important finding: models trained with reinforcement learning on verified reasoning data showed improvements that transferred across domains. Reasoning performance gains on mathematics improved coding benchmarks and vice versa, suggesting shared underlying reasoning capabilities with compounding returns.

DeepSeek also used knowledge distillation to transfer R1's reasoning into V3 — using R1-generated chain-of-thought data as supervised targets. The quality of distillation depends entirely on R1's output quality, which is why human annotators verified both answers and reasoning paths. This ensures the pipeline transmits genuine reasoning capability rather than superficial pattern matching.

How Does DeepSeek V4 Compare on Training Efficiency?

DeepSeek V3's entire training process cost $5.6 million. Comparable proprietary models reportedly cost $100 million or more. The architectural innovations in the papers associated with V4 — mHC's stability overhead, Engram's CPU offloading, DeepSeek Sparse Attention's compute reduction — are designed for further cost-effective training.

The infrastructure matters for data utilisation. DeepSeek V3 achieved full computation-communication overlap through DualPipe scheduling, addressing the communication bottleneck in cross-node MoE training. Higher training throughput means more passes through curated data, so data quality improvements compound faster.

This cost efficiency has a counterintuitive implication. When training compute is expensive, labs face a tradeoff between data curation and more training runs. When training costs drop by an order of magnitude, data curation becomes a larger fraction of total budget. According to NxCode's analysis, DeepSeek V3's API pricing sits around $0.27 per million input tokens — roughly 50x cheaper than Claude Opus 4.6 — while achieving competitive benchmark performance. If model performance reaches parity at a fraction of the computational resources, the marginal value of data quality improvements is proportionally higher.

What Does DeepSeek V4's Open-Weight Strategy Mean for Data Quality?

DeepSeek has released every major model under permissive open-weight licences, and DeepSeek V4 is widely expected to follow suit. When the model weights are freely downloadable, the model itself is commoditised. What differentiates applications built on it is fine-tuning data quality — properly curated, accurately labelled, and structured for the task.

Engram would add a new dimension: in a memory-augmented architecture, domain adaptation may require understanding both your knowledge landscape (what the model should look up) and your reasoning patterns (what it should compute). For enterprises deploying open-weight DeepSeek models on their own infrastructure, domain expertise becomes the primary differentiator. The base model is free. The data to make it work for your domain is not.

The open-weight model strategy also enables deployment configurations that closed-source models can't match. Organisations handling sensitive data can run DeepSeek V4 entirely within their own infrastructure, with no data leaving their security perimeter. Combined with Engram's CPU-based memory tables, this would create a model where the base is public but domain-specific knowledge remains private.

This is where data labeling becomes a strategic function rather than an operational cost. When the base model is free and the architecture demands structured, high-quality data split across knowledge and reasoning pathways, the organisations that build the best fine-tuning datasets win. That requires more than crowd-sourced annotation at scale — it requires domain experts who understand what "correct" means in context. A radiologist validating diagnostic AI labels, a compliance officer verifying KYC annotation rubrics, a senior engineer evaluating code reasoning chains: the annotator's expertise directly determines whether fine-tuning data improves the model or introduces systematic errors. In architectures that separate memory from reasoning, the labeling partner's domain depth matters as much as throughput.

What's the Current Release Status?

As of mid-March 2026, DeepSeek V4 has not been officially released. A "V4 Lite" appeared briefly on DeepSeek's platform on March 9, 2026, suggesting an incremental rollout strategy. Dataconomy, citing Chinese tech outlet Whale Lab, reports an April 2026 launch. The Financial Times previously reported a March release. Multiple earlier launch dates — including mid-February 2026 around Lunar New Year — have passed without a full release.

What is publicly available are the foundational papers. The Engram paper was published with open-source code. The mHC paper has been independently reproduced by community implementations. The core architectural ideas are already available for evaluation, regardless of when the full model ships.

Conclusion: The Model Is Commoditised — The Data Isn't

The most interesting thing about DeepSeek V4 isn't the trillion parameters. It's the argument — embedded in the research papers widely associated with V4 — that different kinds of knowledge require fundamentally different processing, and by extension, fundamentally different data.

Engram's U-shaped scaling law makes this concrete: there is a mathematically optimal ratio between what a model memorises and what it reasons about. The memory pathway demands comprehensive, accurate, entity-rich data. The reasoning pathway demands process-rich, long chain-of-thought data with verified quality. mHC ensures all this carefully curated data doesn't go to waste in a diverged training run.

DeepSeek has shown that frontier-level open-source models can be trained for a fraction of what leading closed-source models cost. But cost-effective training doesn't mean data is cheap — it means data matters more. When architecture and compute are optimised, the remaining variable is data quality. The trajectory from DeepSeek V2 through V3, and now the research pointing toward V4, tells a consistent story: each generation got smarter about how it uses data, not just how much.

For anyone building, fine-tuning, or deploying large language models, the quality and structure of your data is the single highest-leverage investment you can make. And quality at this level is not a solo effort — it requires the right combination of domain expertise, structured quality workflows, and infrastructure that makes every human judgment auditable and measurable. The models that perform best in production are rarely those trained on the most data; they're trained on the most carefully curated data, validated by people who understand what "correct" actually means in context.

Ready to Build Training Data for the Next Generation of Models?

The architectural principles described in the Engram paper make the case that knowledge data and reasoning data need different curation pipelines, different expert reviewers, and different quality standards — whether or not V4 integrates Engram exactly as anticipated, this direction is where frontier model design is heading.

If you're fine-tuning open-weight models for your domain — whether that's financial compliance, medical diagnostics, legal reasoning, or code generation — talk to our team about how structured, expert-driven data labeling works in practice.

Resources

Technical Papers

• Engram: Conditional Memory via Scalable Lookup — Introduces conditional memory as a new sparsity axis for large language models (arXiv:2601.07372, January 2026) — https://arxiv.org/abs/2601.07372
• mHC: Manifold-Constrained Hyper-Connections — Solves training stability at trillion-parameter scale via Birkhoff Polytope projection (arXiv:2512.24880, December 2025) — https://arxiv.org/abs/2512.24880
• DeepSeek V3 Technical Report — Documents the 14.8T token training pipeline, data curation strategy, and post-training methodology — https://arxiv.org/html/2412.19437v1

Code Repositories

• Engram (Official DeepSeek) — Paper PDF and demonstration code — https://github.com/deepseek-ai/Engram
• mHC Community Implementation — PyTorch drop-in with NanoGPT training scripts — https://github.com/tokenbender/mHC-manifold-constrained-hyper-connections

Journalism & Analysis

• South China Morning Post — "DeepSeek kicks off 2026 with paper signalling push to train bigger models for less" — https://www.scmp.com/tech/big-tech/article/3338427/deepseek-kicks-2026-paper-signalling-push-train-bigger-models-less
• Dataconomy — "DeepSeek V4 And Tencent's New Hunyuan Model To Launch In April" (citing Whale Lab) — https://dataconomy.com/2026/03/16/deepseek-v4-and-tencents-new-hunyuan-model-to-launch-in-april/
• TechNode — "DeepSeek plans V4 multimodal model release this week, sources say" (citing Financial Times) — https://technode.com/2026/03/02/deepseek-plans-v4-multimodal-model-release-this-week-sources-say/
• Tom's Hardware — "DeepSeek touts memory breakthrough, decoupling compute power and RAM" — https://www.tomshardware.com/tech-industry/artificial-intelligence/deepseek-touts-memory-breakthrough-engram

Technical Deep-Dives & Comparisons

• NxCode — "DeepSeek V4: Everything We Know About the 1T-Parameter Coding Model" — https://www.nxcode.io/resources/news/deepseek-v4-engram-memory-1t-model-guide-2026
• NxCode — "DeepSeek V4 vs Claude Opus 4.6 vs GPT-5.4: Coding Model Comparison" — https://www.nxcode.io/resources/news/deepseek-v4-vs-claude-opus-vs-gpt-5-coding-2026
• WaveSpeedAI — "DeepSeek V4: Everything We Know About the Upcoming Coding AI Model" — https://wavespeed.ai/blog/posts/deepseek-v4-everything-we-know-about-the-upcoming-coding-ai-model/
• HuggingFace — mHC paper page with community discussion — https://huggingface.co/papers/2512.24880
• Medium (Tao An) — "DeepSeek's MODEL1 Leak Reveals V4's Architectural Blueprint" — https://tao-hpu.medium.com/deepseeks-model1-leak-reveals-v4-s-architectural-blueprint-28e2bdcc7f37

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