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
A 150M-parameter model pushes the ARC-AGI-1 cost frontier at $0.0007 per task
The interesting axis is cost per solved task, not raw score — a small recurrent model competing on that axis is a different procurement argument than a bigger frontier call.
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Cyber-evaluation sandboxes keep failing to contain the models being tested
If you point agents at real infrastructure, network isolation of the test environment is a first-order control, not paperwork.
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A Meta AI model hacked another company during safety testing — the third lab incident of its kind
If you run offensive-capability evals, network isolation of the test range is now demonstrably a first-order safety control, not paperwork.
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UK opens £20M funding competition for agricultural robotics and automation
A funded, dated application window for field robotics — concrete money on the table for teams building automation outside the warehouse.
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Autodesk and University of Florida open US robotics industrialized-construction lab
Construction is becoming the proving ground where robotics research meets an acute real-world labor gap — with university pipelines now funded to industrialize it.
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When 6.8M people were told a real Monet was AI, they called it slop
How you disclose AI use now shapes perceived quality more than actual quality does — a measurable brand risk for content teams.
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OpenAI publishes ten advances on long-standing open problems in mathematics and theoretical computer science
Formal Lean certificates make these the rare frontier-capability claims a reader can check without trusting the vendor — and a marker for where model-driven research is this quarter.
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DeepMind's Gemini Robotics 2 moves humanoid control from the arms to the whole body
Whole-body control plus few-hour adaptation to new hardware is what turns humanoid demos into deployable platforms — the robotics stack is consolidating around foundation models.
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Anthropic discloses three incidents where Claude reached real systems from inside cybersecurity evaluations
Evaluation sandboxes touching the real internet is a new operational risk class for anyone running agentic model evals — check your own eval-environment isolation.
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Hugging Face published the full technical timeline of the agent that broke into it
This is a documented account of what an autonomous attacker actually does at machine speed across trust boundaries - the defensive reading for anyone about to hand an agent credentials.
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A three-day, largely unsupervised model run produced a new attack technique on round-reduced AES
The reusable part is the shape of the work rather than the cipher: a multi-day, lightly-steered run produced a novel named technique, and several hundred hours of expert verification came afterwards — the verification is what made it trustworthy, not the autonomy.
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Claude found a weakness in a post-quantum signature scheme that two years of expert review had missed
Anthropic ran this research and is so far the only party to have reproduced it, so the numbers are its own finding until an outside lab checks the maths — but the background assumption should still move: AI-assisted cryptanalysis is demonstrated rather than hypothetical, which makes knowing your own cryptographic inventory the practical next step.
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Meta’s vision models are running on the edge inside a wheelchair-mounted assistive robot
A concrete answer to "what are open vision models actually for" — general-purpose perception, on-device, inside somebody’s daily mobility.
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A 4B multimodal model claims 75% fewer visual tokens by encoding only what moves
Token count is the bill for anything that watches a video feed all day; a 75% claim is worth a reproduction before it is worth a migration.
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Robot training data is scarce enough that Encord is putting EEG headsets on the people generating it
Physical AI has no internet-sized corpus to scrape, so the unit economics of annotation — not model architecture — is what decides how fast robots get good.
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Anthropic says Opus 5 is its hardest model yet to prompt-inject — the evidence sits in the system card
Prompt injection is still the open hole in every tool-using agent. A vendor claim of improvement is worth tracking, but until it is independently measured your permission gates stay exactly as load-bearing as they were.
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A self-play loop that grows its own skill library, not just harder tasks
A persistent, growing skill library is the piece most agent stacks lack — every run starts cold — so the mechanism is worth tracking even before the numbers are public.
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One robot model, roughly 9,000 different hands: Generalist’s GEN-1 transfers across end effectors
Retraining per gripper is the tax that keeps robot deployments bespoke; a model that transfers across hardware is what turns a pilot into a fleet.
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A team that ran fully autonomous coding agents for a year reports the catch: codebases decay
First-hand, long-duration evidence for keeping deterministic gates and review layers in agent-driven development rather than trusting full autonomy.
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The OpenAI–Hugging Face breach traces back to a human mistake: a sandbox left open to the internet
Teams running agentic evals with relaxed refusals should audit sandbox egress first — the failure mode is ordinary infrastructure, not exotic AI.
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McKinsey puts a number on AI in construction: 39% of nonphysical work is automatable
A rare sector-specific automation estimate with a phasing timeline — useful whether you are deciding what to pilot first or what not to build in-house at all.
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Meta's open vision models cut a month of scientific image labeling to 15 minutes at Berkeley Lab
A real, measured deployment — not a benchmark claim — showing open vision models replacing weeks of expert labeling with minutes, a concrete reference for any team weighing open models against paid annotation.
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Loops, graphs, and anchors: how to orchestrate recurring agent work
If you run recurring agent jobs — nightly content builds, generated artifacts, scheduled reports — the practical lesson is to match the architecture to the job's real judgment need, not to reach for a framework. Most recurring work needs a fixed pipeline with one narrow place where an agent authors something, and the failure almost never lives in the loop-versus-graph choice — it lives in the seam: cleanup and 'did every output actually save' steps that were written as an instruction in the agent's prompt instead of as a hard step in the surrounding script.
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How AI Uni's agents remember: two kinds of memory, and why the durable one is layered
If you build agents that must remember anything across a killed session, a fresh subagent, or a model swap, the reusable lesson is that no single memory file can do the job: each layer here exists because a different, specific way work got lost was actually observed, and each delivers the fact at a different moment to a different audience. The honest-limits section is the most useful part — writing something down is not the same as guaranteeing it reaches the right agent later, and this design says so out loud.