Research
Everything Intel keeps, in one place — every brief we run, every department, readers' own, and every finding behind them. Browse below, or see the same body of work as a connected graph on the live map.
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Everything Intel has read, newest first. Each title opens at its original publisher.
- Research✓ verified
Think it, and the robot does it: BrainCo shows a brain-to-robot platform that reacts in under 200 milliseconds
Brain-computer interfaces have mostly lived in research demos and rehab clinics; a sub-200ms decode loop that plugs into off-the-shelf robots is what turns "thought control" from a lab trick into a control scheme other robotics teams can actually build on.
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AI systems are out-counterexampling human mathematicians — with machine-checked proofs
Generate-then-machine-verify turns AI output from a claim into a checkable artifact — a pattern that generalizes directly to code.
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Google DeepMind and Isomorphic Labs publish their playbook for keeping AI biology models safe — and useful
Biology is where AI capability and AI risk get most concrete — this is what the leading lab's actual mitigation posture looks like.
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NVIDIA Isaac Lab-Arena: an open framework to evaluate general-purpose robot policies
A robot policy that works in a demo can fail in the world — shared, large-scale evaluation in simulation is the missing benchmark layer physical AI needs before it is trusted off the lab floor.
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Making an autonomous work loop survive the seams: how an agent loop was designed to resume its own goal from disk after a killed session (built + reviewed, dry-run pending)
If you're building an agent that has to keep working across session death, context compaction, or a model downgrade, the hard part isn't retrieving state — it's proving the loop resumes the RIGHT state and can't run away, ship on its own, or grant itself a fresh budget every restart. This is a worked, honestly-graded design for exactly that: a durable state cell, a cumulative budget that survives restarts, a halt-and-hand-back rule instead of a silent spin, and a never-self-ship gate — with the parts that are green-in-tests kept clearly separate from the parts still pending a live dry-run.
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Cross-session agent memory in 2026: the platform now ships natively what most teams still hand-roll
If you're building an agent that needs to remember anything across sessions, check whether Anthropic's native memory tool already does what you were about to hand-roll — and if you're already building one, the field's clearest fix for the write-back/consolidation gap is a scheduled, importance-triggered pass, not more discipline.
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You can't reliably detect AI-written code — the detectors aren't trustworthy enough to stand alone
Don't hang a pass/fail — or an accusation — on an AI-code detector. Treat its output as a hint to look closer, never as the verdict.
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The one genuinely new 2026 tool: a cryptographic 'receipt' that proves a tool actually ran
There's finally a cheap way to prove a tool ran and what it returned — but only when you run the tool. A receipt a user hands you for a tool they ran themselves proves nothing.
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In 2026, the most AI-resistant way to check understanding is still a live human conversation
No tool can tell you whether someone actually understands their work. In 2026, the reliable signal is still the oldest one: ask them to explain it, live.
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Can you prove an AI agent actually did the work? What today's tools can and can't verify
If your team accepts work an AI helped produce, this is the honest map of what's actually checkable in 2026 — and where a human still has to be the judge.
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The tools that flag 'this looks AI-written' are biased against non-native English writers
Automated 'is this AI?' signals don't just misfire — they misfire unevenly, penalizing people who write in a second language. That's a fairness problem, not just a noise problem.
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'My AI read your repo and it checks out' is a claim, not proof
Anything an agent reports about work done on a machine you don't control is unverified by default. If it matters, re-run the check yourself instead of trusting the summary.
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Anthropic + AE Studio publish "modular pretraining" for gating dual-use model capabilities (GRAM)
Early research, not a shipping feature: it points toward a future where specific model capabilities can be switched off without retraining, but it is a lab result explicitly not in any production Claude today — nothing to adopt yet.
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Anthropic + Glasswing partners propose an industry jailbreak-severity score
A common severity scale would change when a lab (or a regulator) decides a model must be pulled — directly relevant to how agent products get governed.
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Microsoft Research: agent skills as trainable parameters (SkillOpt) + token-efficient agent memory (Memora)
Concrete, buildable levers: systematically optimizing your skill/instruction files (rather than the model) and compressing agent memory can raise quality and cut token cost — directly applicable if you author skills or run long-horizon agents. Figures are the authors' reported results; validate before quoting.
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The UK is trying to halve householder planning decision times with a Gemini-built assistant
A paperwork backlog that has throttled British house-building for decades is being attacked as a document-understanding problem — which is the shape a surprising number of 'unsolvable' administrative problems turn out to have. The 50% is a target, not yet a result.
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Google DeepMind launches a Robotics Accelerator, putting Gemini robotics models in startups' hands
When a frontier lab puts its vision-language-action models directly in startups' hands, embodied AI stops being a lab demo and starts becoming an ecosystem — the same pattern that scaled language-model apps.
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SABER benchmark: leading coding agents violate safety in over half of tasks
Coding agents doing real repo work is exactly AI Uni's build model — a reminder that autonomous edits need guardrails measured on outcomes, not refusals.
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Anthropic publishes a Zero Trust framework for enterprise AI agents
A useful audit checklist even outside Anthropic's own stack — the seven control domains it names are a reasonable starting list for anyone standing up agents with real write access.
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A survey and paper list mapping agent-memory architectures, so you don't have to design one blind
Agent memory is the least-settled piece of most production agent stacks — a maintained survey and paper list is the fastest way to see which memory architecture actually matches your agent's failure mode before building a bespoke one from scratch.
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Agentic Context Engineering (evolving contexts for self-improving LMs)
The academic framing of what the substrate-batch + landscape-brief + KB-principle loop does informally. Indexed via a curated list — read the underlying papers before teaching specifics.