Import AI 457: AI stuxnet; cursed Muon optimizer; and positive alignment

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Stuxnet before Stuxnet: …Fast16 bugs software likely used in weapons programs… Here’s a fascinating investigation of a ~20+ year old computer virus called fast16.sys. This software is interesting because it “selectively targets high-precision calculation software, patching code in memory to tamper with results. By combining this payload with self-propagation mechanisms, the attackers aim to produce equivalent inaccurate calculations across an entire facility.” If any of you have read the Three Body Problem, this might sound familiar - in that (fictional) book, aliens intent on taking over the Earth use a technology called a Sophon to disrupt high-energy physics experiments all over the world, making it impossible for humanity to advance certain types of science.

More details on the virus: When the researchers at SentinelOne did their teardown of the virus they found something quite unusual: “Most patched patterns correspond to standard x86 code used for hijacking or influencing execution flow. One injected block is different. It’s a larger and complex sequence of Floating Point Unit instructions dedicated to precision arithmetic and scaling values in internal arrays. This code is a standalone mathematical calculation function unrelated to code flow hijacking or any other typical malicious code injection.” Further investigation deepened the mystery: “We converted the patching rules into hexadecimal YARA signatures and ran them against a large, period‑appropriate corpus. The results showed a very low hit rate: fewer than ten files matched two or more patterns. Those matches, however, shared a clear theme. They were precision calculation tools in specialised domains such as civil engineering, physics and physical process simulations.”

Enter Aurora: In response to this the researchers build and make available Aurora, “a leverage-aware optimizer for rectangular matrices”. In tests, this optimizer works, though they only run it at small scales. “We train 1.1B-parameter transformers on ~100B tokens and compare Aurora against Muon and NorMuon, each using PE-8. Aurora achieves the lowest final loss of all methods, reaching a smoothed loss of 2.26 at step 24k, which is a clear improvement over Muon (2.31) and NorMuon (2.33),” they write. “Aurora’s loss improvement translates to consistent gains on standard benchmarks... Strikingly, Aurora improves MMLU scores by 10 points over Muon. We hypothesize that since MLPs are predominantly responsible for memorization, Aurora’s gains are most visible on memorization-intensive benchmarks like MMLU.” Alexander Doria, a researcher with Pleias, has already independently validated this , with Aurora outperforming Muon and AdamW on a 600M-parameter model.

What they did; Prime Intellect tested out Codex (running GPT 5.5) and Claude Code (Opus 4.7) on the nanoGPT speedrun optimizer track. NanoGPT challenges systems to train a 124M-parameter GPT-style model. This challenge tasks systems to “lower the number of steps needed to reach a target validation loss while only changing the optimizer, schedules, initialization, and some hyperparameters.” “The agents did ~10k runs, burning around ~14k H200 hours. Both agents beat the human baseline and set new records in every session,” Prime Intellect writes. “We found that agents are very good at optimizer search, hyperparameter sweeps, and stacking methods together, but they struggle to come up with new ideas on their own and need upstream human records to keep improving.” The agents also tended to keep adding stuff onto their systems rather than more elegantly refining things. “The agents tend to add components and rarely run pruning rounds or try removing previous methods. They do not have a good mental model of how components interact,” they write.

Definitions: Positive alignment is “the development of AI systems that (i) remain safe and cooperative and (ii) actively support human and ecological flourishing in a pluralistic, polycentric, context-sensitive, and user-authored way.”

Motivation: “In the last decade, negative alignment has understandably prioritized failure-mode reduction. However, if we want AI systems that improve human outcomes in the environments where they will actually be used, we may benefit from an additional research program that treats alignment as constructively supportive of human aims, and that operationalizes this support with the same technical acumen that safety has brought to harm prevention,” they write. “As AI becomes embedded in education, medicine, governance, and everyday sensemaking, a solely negative posture risks optimizing our information ecology for risk avoidance rather than human development. It may reduce catastrophic errors while leaving society in a local optimum of superficial and ‘soulless’ assistance.”

Targeted tools: “The strongest overlaps point to three high-precision engineering and simulation suites from the mid-2000s: LS-DYNA 970, PKPM, and the MOHID hydrodynamic modeling platform, all used for scenarios like crash testing, structural analysis, and environmental modeling,” they write. “LS-DYNA in particular has been cited in public reporting on Iran’s suspected violations of Section T of the JCPOA, in studies of computer modeling relevant to nuclear weapons development… by introducing small but systematic errors into physical‑world calculations, the framework could undermine or slow scientific research programs, degrade engineered systems over time or even contribute to catastrophic damage.”

Why this matters - this is how a superintelligence might prevent others from coming into existence : fast16 is a subtle, hard-to-find bug which has been designed to degrade an actor’s ability to do certain types of science. You might imagine that a superintelligence could view “AI non-proliferation” as being just as important as nuclear states view “nuclear non-proliferation”. Read more : fast16 | Mystery Shadow Brokers Reference Reveals High-Precision Software Sabotage 5 Years Before Stuxnet (Sentinel LABS) .

Uh oh, the Muon optimizer kills neurons: …Maybe Aurora is finally the optimizer to beat?... Researchers with Tilde Research have done a tear-down of the Muon optimizer and found that it has some odd bugs that can damage the quality of models trained with it. “Muon’s update inherits row-norm anisotropy on tall matrices which can cause a significant portion of neurons in MLP layers to permanently die,” they write. “Muon can result in neuron death in MLP layers, whereby some neurons receive persistently small updates early in training and fail to recover”.