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Last synthesis: 2026-09-20. 8,991 pending.
Search and filter through extracted claims from AI researchers.
Showing 1441-1460 of 4537 claims
"Large language models can generate executable programs, which makes it possible to search directly over procedural content generators rather than individual levels."
"Starting from a classical reconstruction algorithm, an LLM-driven multi-agent search modifies only the components that the user has opened for editing, without prescribing the mechanism to be discovered. Candidates that violate the physical constraints of the task are rejected outright, and the remaining ones are scored by execution. The output is neither an agent system nor a neural network, but a standalone white-box algorithm that requires no agent or neural network at inference time."
"For interpolation without added noise, the search discovered a residual-gated, phase-aligned dip-consistency projection; Evo-POCS improves the SNR over classic POCS by 3.49 dB on average across missing ratios from 30% to 70%."
"For simultaneous interpolation and denoising, it discovered a reliability-grouped singular-value shrinkage; Evo-MSSA improves the average reconstruction SNR by more than 7 dB over classic MSSA and by more than 3 dB over a stronger rank-reduction baseline."
"To the best of our knowledge, this is the first study to formulate the design of a seismic reconstruction operator as a constrained, LLM-driven program evolution task."
"Agentic algorithm evolution can thus complement deep learning in discovering explicit, inspectable, and deployable seismic processing algorithms."
"Deep-learning methods encode the reconstruction rules in learned weights rather than in an explicit operator that can be inspected and modified."
"As LLMs get more refined as tools, rather than conversational assistants, I think we are actually going backwards on our goals of having models produce inspiring writing."
Geoffrey Irving expects full-blown superintelligence in roughly two to three years
"Geoffrey — formerly a safety researcher at OpenAI and Google DeepMind and chief scientist at the UK AI Security Institute — expects full-blown superintelligence in roughly two to three years."
Nobody has a strong argument that current alignment plans will work for superintelligence
"Geoffrey thinks that combination could work. The alarming part is that nobody has a strong argument that it will."
Governments should slow AI development while we work out which alignment methods can be trusted
"When should governments slow the race toward superintelligence? According to Geoffrey Irving, the careful answer is sometime in the past. The useful answer is now."
Anthropic's AI detector will be trivial to defeat by iteratively modifying text until it passes
"My hunch is this will be trivial to defeat: generate text, run it through Anthropic's own AI detector tool, then modify words until that AI detector passes"
"The Pentagon has requested a 24,000% increase in the budget for DAWG, a recently established autonomous warfighting group whose previous budget was $225m, now requesting $54.6 billion for FY2027."
"Maven Smart System, Palantir's AI-assisted targeting platform (a $1.3 billion Pentagon contract), helped CENTCOM strike more than 13,000 targets in the first 38 days of the 2026 Iran campaign"
"specifically, trained overseers are needed to check if model reasoning contains evidence of deception, but in classified deployments on military networks, most frontier lab researchers (without clearance) would not be able to participate in this monitoring, and we don't have evidence that military engineers are being trained to develop this expertise."
"When OpenAI encountered the first Artifactory security incident, patched the vulnerability and rebuilt the server, it was a coincidence that this wiped out the first message board. OpenAI had no idea that the agents had been in communication."
"Quite a few instances of multiple distinct AIs coordinated, including debating what hacks and exploits to do. In zero cases did any of the AIs tell a human."
"It is bad news in that OpenAI did not look for or detect the message board, even after the initial security incident, whereas so many AI instances found the message board. OpenAI failed to do ordinary scans for unusual activity, even after the initial incident."
"This subagent training may cause models to be more susceptible to memetic spread of misalignment, as well as more likely to seek out communication with peers in the first place."
"But a sufficiently strong drive to help other agents, instilled by subagent training, might just outweigh the drive to be aligned."