Google used LLM agents to discover new quantum algorithms with 290x speedups over human baselines. 25 researchers from Google dropped an insane paper on discovering formally verified quantum programs with AI. Normally, if an LLM writes a complex algorithm, you have to worry about hallucinations. they can cheat, weaken theorems, or fake their performance numbers. so deepmind built a zero-trust environment. they unleashed 7 different autonomous agent teams to write quantum programs AND simultaneously write the mathematical proofs in the Lean theorem prover to prove they actually work. every submission was checked in a clean sandbox against a frozen specification. the ai couldn't lie.. it either worked and mathematically proved its efficiency, or it failed. to make it even crazier, they gave the agents a shared version-controlled wiki. if one agent team hit a dead end, they posted their notes to the wiki so the other ai teams wouldn't repeat the mistake. the results of this multi-agent collaboration are actually mindblowing: - they cut the computational cost of the arcsin quantum subroutine by over 290x - they reduced the cost of simulating nuclear fusion stopping power, photochemistry, and drugs by 2.49x to 10.9x - on one photochemistry problem, the ai teams iterated and beat each other's records 7 different times, eventually combining techniques from three separate teams to get the final verified circuit fault-tolerant quantum computers are still years away because the algorithms require way too much compute. but these ai agents are drastically shrinking the size of the software needed to run them. we are literally watching autonomous ai agents mathematically optimize the software for quantum computers that don't even fully exist yet.. the future is moving so fast.

In another reckless expansionist provocation, the Philippine government has delimited the so-called "baselines of the territorial sea" of China's Huangyan Dao, a move that flagrantly violates China's sovereignty and international law and is doomed to fail.

Meta Superintelligence Labs is letting the model manage its own context instead of bolting on another hand-engineered harness. On long-horizon agent tasks it beats the fixed-harness baselines while using fewer FLOPs. @muse

We asked co-director Simon Johnson how things have changed since his book "Power and Progress" (co-authored with @DAcemogluMIT) came out in 2023. Is it too late to redirect the path of AI? Watch the full AMA video with @baselinescene:

One of the interesting properties we’ve observed around schema-guided, complex document extraction tasks 📑 is that coding agent harnesses are good baselines (in terms of cost/accuracy) for longer documents. We tested Claude Code and Codex, along with specialized OCR tools (including LlamaParse) and raw VLMs. * On short documents, specialized OCR tools are generally a fraction of the cost of coding agents, with equivalent or higher accuracy * On longer documents, coding agents are a bit closer to the cost/accuracy Pareto curve (see bottom graph) It’s an interesting result, though ultimately not surprising. Complex document extraction is a specialized reasoning task, and coding agents are effectively generalized reasoning harnesses. Over long documents, coding agents have more room to use a variety of tools to search snippets of the document instead of loading the entire document into context. They can also make use of prompt caching to reduce total token cost even as it expands multi-step reasoning. On the flip side, they do generate a baseline degree of token usage that proves to be wasteful for shorter docs compared to specialized extractors. This specific graph is in our Appendix D in the ParseBench paper, come check it out! ArXiv: ExtractBench:

A team from Zhejiang University and vivo has introduced FlashRelight, an AI system that can relight portrait videos. It's more than just applying a filter. The goal is to change the lighting while preserving a person's identity, expression, and motion. FlashRelight takes a two-stage approach: First, a delighting model suppresses the original lighting and shadows. Then, a relighting model generates new dynamic lighting across the video. You can control the lighting in three ways: • Envmap sequences: Precise frame-level lighting control. • Text prompts: Describe color, direction, movement, and flicker. • Reference videos: Transfer lighting effects from another video. The team built a hybrid dataset with 34k+ paired videos: • ~24K synthesized from static OLAT lighting data • 10k+ generated by Wan2.2 This gives the model varied lighting and motion without costly dynamic light-stage capture. On envmap-based relighting, FlashRelight outperformed all baselines across all five evaluation metric For short videos, ads, or virtual-human content, this could make it easier to adjust a portrait’s lighting in post-production.

TL;DR: Cortex bakes the gains an LLM gets from context into its weights, so they persist after the context is removed. After 50 sequential factual edits it recovers 88.9% of the gain while keeping 99.8% of original performance. Title: Cortex: Consolidating Context into Parametric Memory URL: Key points ・🧠 Inspired by hippocampus-to-neocortex consolidation; unifies knowledge editing, skill learning, behavior steering, and suppression as one problem ・🔮 The model generates queries anticipating how the context will be used and learns from them ・🧩 Updates only the FFN: low-rank updates (Synaptic) or new added channels (Structural), both mergeable into the weights ・👩‍🏫 A frozen teacher that sees the context distills its outputs and hidden states into a student that doesn't ・📊 Tested on 4B-120B dense and MoE models; batch editing hits 80-91% CR vs. 36-46% for the strongest baselines ・🤖 On ALFWorld, the consolidated model even beats supplying the context directly ・⚠️ Knowledge suppression still harms neighboring knowledge 💡 A practical path beyond RAG and prompting: folding experience into the weights. #LLM# #ContinualLearning#