AI Explained
Plain explanations of trending AI concepts, with live visualizations.
SigmaScale learns its SVD scaling matrices — Learned scaling for truncated-SVD compression — What does it mean?
SigmaScale learns two scaling vectors under an activation-aware loss so truncated-SVD throws away less — shrinking LLM weights by rank, not bits.
MiniMax M3 ships open-weight 1M context — MiniMax Sparse Attention (MSA) — What does it mean?
MiniMax M3 (open-weight, 1M context) runs on MiniMax Sparse Attention — block-sparse KV gather that cuts per-token compute ~20× at one million tokens.
EmbedFilter — Unembedding matrix as a feature lens — What does it mean?
LLMs make blurry text embeddings because a subspace in their unembedding matrix injects frequent words — EmbedFilter projects it out in one linear transform.
Self-evolving agents collapse over iterations — Continual experience internalization — What does it mean?
Self-evolving agents can degrade as they learn from their own runs. Three design choices decide whether they keep improving or collapse.
MLEvolve: self-evolving agents beat AlphaEvolve — Progressive Monte Carlo Graph Search — What does it mean?
Progressive Monte Carlo Graph Search lets MLEvolve share discoveries across branches — SOTA on MLE-Bench in half the usual budget.
Code2LoRA gives code models per-repo knowledge — Hypernetwork-generated LoRA adapters — What does it mean?
Code2LoRA's hypernetwork stamps out a repo-specific LoRA adapter in one pass — repo knowledge with zero prompt tokens at inference.
AdaPlanBench tests agent planning under incremental constraints — Adaptive replanning under hidden constraints — What does it mean?
AdaPlanBench hides each task's rules until a plan violates one — the best of 10 LLMs clears just 67.75%, and user constraints are harder than world constraints.
Tangram speeds multi-turn serving up to 2.6× — Per-head KV cache budgets — What does it mean?
Tangram sizes each attention head's KV cache to what it actually keeps, not one uniform budget — lifting multi-turn serving up to 2.6×.
Google ships Gemma 4 QAT checkpoints — Quantization-Aware Training — What does it mean?
Gemma 4 ships QAT 4-bit checkpoints: training on the low-bit grid dodges the accuracy cliff that naive post-training rounding hits.
MatMul-only matrix inversion makes quantized Gated DeltaNet 5x faster — Truncated-Neumann triangular inverse — What does it mean?
Gated DeltaNet's chunk solve hides a sequential matrix inverse — a truncated Neumann series turns it into parallel MatMuls, ~5x faster.
AutoLab benchmarks frontier agents on long-horizon R&D tasks — Iterative experiment-loop evaluation — What does it mean?
AutoLab grades agents on the propose → run → measure → refine loop; across 17 models, sustained iteration — not the first answer — predicted success.
Token Budgets paper — Affine-typed budget ownership — What does it mean?
Token Budgets models an agent's token cap as a use-at-most-once resource — so a budget overrun fails to compile instead of overspending at runtime.