Autogenesis gives agents auditable self-modification: identify capability gaps, generate and test improvements, integrate with full lineage and rollback — a deployment specification, not a demo.
Research formalizes 'diversity collapse': multi-agent LLM systems homogenize outputs due to structural coupling — brainstorming setups must be explicitly engineered for heterogeneity.
DeepMind's April 2026 paper diagnoses transformers as depth-bounded TC^0 circuits, pointing toward a continuous-depth ODE-based recurrent architecture with O(1) training memory.
NVIDIA's multi-agent LLMs self-evolve the ABC chip design toolchain, beating decades of human-tuned baselines — the first production self-improvement system on real semiconductor infrastructure.
Skill-RAG predicts LLM failure via hidden-state probing, retrieves only when needed, and routes failure types to specialized skills — beating RAG benchmarks on efficiency and accuracy.
DPM replaces per-agent stateful memory with immutable decision logs — enabling audit trails, multi-tenant isolation, and provenance for regulated enterprise AI deployment.
Trump fires all 24 NSF board members by email, ending 76 years of independent oversight of the $9B/year US science funding agency.
Kimi's Prefill-as-a-Service splits LLM inference into separate compute-heavy prefill and latency-sensitive decode services, achieving 1.54× throughput and 64% lower TTFT in tests.
Google's MIRAS framework unifies Transformers, Mamba, and Titans as instances of one optimization problem with four design choices, revealing a shared 'retention gate' abstraction.
NVIDIA Ising uses AI to shrink quantum computer calibration from days to hours and provides 3D neural network error correction faster than existing open-source methods.
Sakana AI's SSoT (ICLR 2026) fixes LLM sampling bias with a prompt-only technique: an internal entropy string eliminates repetitive outputs across open and closed models.
SSRL uses RL to teach LLMs to search their own knowledge internally—5.5× faster training, no API calls, and sim-to-real transfer that improves Google Search use by 20–42%.
New arXiv study finds AI agents gathered and then ignored evidence in 68% of cases, never updating beliefs in 71% — a serious challenge to agentic research autonomy claims.
Anthropic's Project Deal experiment ran live agent-to-agent negotiations among 69 staff: 186 deals, $4,000+ volume, Opus outperformed Haiku — but participants couldn't tell the difference.
Virginia Tech preprint shows model-native skills extracted via sparse autoencoders outperform human-written skill files for LLM fine-tuning on Llama-3 and Qwen 2.5.
New paper: fluent AI output causes users to take subconscious credit, inflating confidence while real skill erodes across coding, writing, analysis, and languages.
TACO reduces agentic terminal agent token overhead by ~10% on SWE-Bench by learning trajectory-derived compression rules for long-horizon reasoning.
Google DeepMind's Vision Banana shows image generation models can perform segmentation, depth, and normal prediction at SOTA without architectural changes.
A 1.7B model beats GLM-5 at 744B on Schema Guided Dialogue even with corrupted training data, reversing a 437x parameter-size gap.
CoInteract introduces spatially-structured co-generation for physically consistent human-object interaction in synthetic video—a key gap in generative video models.
New preprint: LLMs are enabling pro se federal court filings at unprecedented rates—the first empirical evidence AI is breaking effort-gated institutional systems.
MIT's new artificial muscle fibers use ionic pumps instead of motors—silent, scalable, and strong enough to bend robotic arms and shake hands naturally.
Anthropic's new monthly Economic Index Survey asks Claude users how AI is changing their work — anchored by an 81K-participant study showing AI amplifies both productivity and displacement worry.
MILKYWAY freezes GPT-5.4 and moves all learning into editable skill.md files rewritten by a harness editor agent — scoring 61% vs 44% on future-prediction benchmarks.
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