Engineering
Engineering research.
18 papers in engineering.
Lynx: the KV cache does not have to arrive whole
In disaggregated LLM inference the bottleneck is moving the KV cache, not computing on it: a single 128K request can be 23.5 GB, which is two seconds on a 100 Gbps link before decoding can start. A team at University College London and Huawei split the cache into a four-bit anchor stream carrying order of magnitude and a four-bit residual stream carrying precision, start decoding speculatively on the anchor, and verify when the residual lands. A reading of the quantizer that makes the bit-split work, the measured acceptance rate, and why the accuracy gain and the latency gain come from two different halves of the paper.
Read the paper →- October 2026EngineeringAGA-49
ScaleSweep, and what AbsMax leaves on the table in NVFP4
NVFP4 gives every 16 elements their own FP8 scale, and almost every quantiser picks that scale the same way: divide the block maximum by 6. Two researchers at Peking University prove the optimum lies within eleven FP8 bit patterns of that guess, search them, and recover 93.9 per cent of BF16 performance with weights, activations, KV cache and query states all at four bits. A reading of the format, the derived bounds, the cost at each configuration, and why the paper's own ablation suggests the bounds buy latency rather than accuracy.
Luis Hasanaj · Read → - October 2026EngineeringAGA-48
DeepSeek-V4.1-Flash and the 890-byte KV cache
DeepSeek brings the global KV cache to 890 bytes per token, a quarter of DeepSeek-V4-Flash, by serving forty transformer layers from four shared caches and storing them in FP4. A reading of the actual mechanism: the three CSA2 modes, the Causal Encoder-Decoder that halves prefill, and the bounded replay that is really a cache retention policy. We reconstruct the 890 bytes from the published configuration, and argue the paper's quality costs are asserted rather than measured.
Luis Hasanaj · Read → - October 2026EngineeringAGA-40
OpenShell puts the enforcement outside the agent
NVIDIA has open-sourced a runtime for AI agents whose central claim is about location rather than capability: enforcement happens outside the agent's process, where it cannot be prompted away. A reading of the four parts, the formal policy prover, the credential that never enters the workload, and the point at which the open source runtime becomes a reference design for hardware NVIDIA sells.
Luis Hasanaj · Read → - October 2026EngineeringAGA-37
Gemini 4 Argon: the output limit is the story
Google DeepMind's Gemini 4 Argon raises the output token ceiling from 64,000 to 1,000,000, and that single number changes more about how agent systems are built than any benchmark in the announcement. A reading of what the new ceiling costs, why shipping a model without cyber guardrails to a named cohort makes capability an identity problem, and the one optimisation DeepMind deliberately refused to take.
Luis Hasanaj · Read → - September 2026EngineeringAGA-30
OpenAI's dots: the boundary is the product
OpenAI launched always-on personal agents at DevDay, each running on its own cloud computer with a browser and access to more than 4,000 apps. The marketing is autonomy. The engineering is a carefully drawn permission boundary, and the single most important decision in it is that an agent left running while you are away can only read.
Luis Hasanaj · Read → - September 2026EngineeringAGA-35
Hardware-native AI: the alarm that sees
A position paper. The next operating system layer will be built into devices rather than shipped as an app on top of them, and home security is the clearest worked example: an alarm today is a deterrent that detects motion, while a camera that understands what it is looking at is a different category of thing. The interesting engineering is not the vision model. It is deciding how far up the response ladder a device may go without a person.
Luis Hasanaj · Read → - September 2026EngineeringAGA-26
TypeSafe's System One: code orchestrates
A new model class argues that the control flow belongs in your code and the model should be a typed function call returning a calibrated probability. It is the strongest published case yet that the deterministic core is the cheap, fast, reliable part, and that putting a language model in charge of the loop is a transitional architecture rather than the destination.
Luis Hasanaj · Read → - September 2026EngineeringAGA-24
GPT-6 Astra's system card is a build spec
Read as safety documentation it is reassuring. Read as an engineering requirement it is a list of things your infrastructure now has to do: a halt state you do not control, an approval gate a machine must not be able to satisfy, credentials the model will find, and oversight that no longer rests on reading the model's reasoning.
Luis Hasanaj · Read → - September 2026EngineeringAGA-21
GPT-6 Astra or Fable 5.1 for coding?
Both list at 10 dollars and 50 dollars per million tokens, so the headline price is a tie. The cache read rate is not, and on a coding agent that is where the bill actually lives. A head to head on the published coding benchmarks, the cost that the price list hides, and an honest answer about which we would reach for today.
Luis Hasanaj · Read → - September 2026EngineeringAGA-18
Tinker, Inkling and the return of fine tuning
Thinking Machines Lab is betting that models an organisation can adapt will beat the one size fits all models the big labs sell. What Tinker and Inkling actually are, why LoRA and per token pricing changed the economics, and the test that tells you whether your problem needs fine tuning or just better retrieval.
Luis Hasanaj · Read → - September 2026EngineeringAGA-15
AI agent security: the real attack surface
An agent with credentials, tools and memory is not a chatbot with a bigger prompt, it is software that acts. This is the attack surface that creates, read against the OWASP Top 10 for Agentic Applications 2026, two live CVEs in agent infrastructure, and the first reported cyber espionage campaign run mostly by hijacked agents.
Luis Hasanaj · Read → - September 2026EngineeringAGA-16
MCP, A2A, ACP, AP2: the agent protocol stack
Four protocols keep getting written about as rivals. They are layers. MCP gives an agent tools, A2A lets agents talk to each other, AP2 proves a human authorised a payment, and ACP completes the purchase. What each one actually does, who governs it now, and what a business has to implement to be sellable to an agent.
Luis Hasanaj · Read → - August 2026EngineeringAGA-08
The multimodal shopping assistant: an agentic architecture for open-ended discovery
An agentic architecture for a shopping assistant that reasons over text and images together, resolves references across a multi-turn conversation, and retrieves products from a retailer's own catalog, turning open-ended requests that keyword search fails into completed carts.
Luis Hasanaj · Read → - August 2026EngineeringAGA-10
Engineering for the EU AI Act: Article 50 transparency as system architecture
How we build the EU AI Act's Article 50 transparency duties into AI-native products as architecture rather than configuration: disclosure at first contact, provenance marking at generation, recorded human review and per-message jurisdiction, read against the timeline as amended by the 2026 Digital Omnibus.
Luis Hasanaj · Read → - May 2026EngineeringAGA-05
Transaction foundation models: transformer embeddings for tabular financial data
A transaction foundation model learns from large volumes of tabular financial data to produce transformer embeddings of individual transaction rows. Fed as features into existing ML models, those embeddings raise accuracy and cut false positives on fraud detection, authorization and risk scoring.
Luis Hasanaj · Read → - March 2026EngineeringAGA-03
Agentic commerce: a reference architecture for AI checkout under merchant control
A reference architecture for letting AI agents discover products, apply promotions, and complete secure checkouts on a customer's behalf, over a standardized agent-to-merchant protocol layer that keeps pricing, payments, and compliance under merchant authority.
Luis Hasanaj · Read → - January 2026EngineeringAGA-01
GPU Query Engine (GQE): a reference architecture for GPU-accelerated SQL
A reference architecture for a GPU-accelerated SQL engine, built on libcudf and NVCOMP, that pushes complex analytics query processing to speed-of-light on modern GPUs.
Luis Hasanaj · Read →