AI Explained
Plain explanations of trending AI concepts, with live visualizations.
MAESTRO prunes MoE experts with routing-aware Markov chains — Markov-chain expert pruning — What does it mean?
MAESTRO models MoE routing as a Markov chain, ranks experts by their long-run share of traffic, and prunes the rarely-visited ones — keeping up to 10.61% more performance at 50% compression.
DominoTree speeds speculative decoding with conditional draft trees — Conditional draft-tree scoring — What does it mean?
DominoTree scores speculative-decoding draft trees by how well each token follows the last, not one token at a time — up to 6.6× on Qwen3-4B.
TF-Engram uses SSD-backed phrase memory for train-free LLM recall — Predictive prefetching that hides SSD latency — What does it mean?
TF-Engram stores phrase memories across GPU-DRAM-SSD and prefetches them so a slow SSD read overlaps decode instead of stalling it.
OpenAI finds about 30% of SWE-Bench Pro tasks broken — Benchmark task validity — What does it mean?
OpenAI audited SWE-Bench Pro and found ~34% of tasks broken — a benchmark's pass rate only means something if its tasks are valid.
Mistral releases Robostral Navigate for single-camera robot nav — Pixel-waypoint action space — What does it mean?
Robostral Navigate steers a robot from one RGB camera by predicting a pixel in the image to move toward — no depth sensor or LiDAR, and it still hits 76.6% on R2R-CE.
Multi-agent attacks slip past per-agent monitors (FakeLab) — The fragmentation effect — What does it mean?
Split a malicious goal across a team of agents and each trace looks harmless, so a monitor that checks agents one at a time can miss it.
DeLS-Spec adds short-context heads to block drafting — Long-short logit fusion — What does it mean?
DeLS-Spec fuses a short-context head into a block drafter, discounting common words, so a fast block draft finally picks up the local context its parallel guesses skipped.
AWS maps MCP tool-design tradeoffs for agents — Tool design as context engineering — What does it mean?
AWS reframes MCP tool design as context engineering — tighten schemas, trim responses, lazy-load detail, and at the limit hide the whole workflow behind one endpoint.
Agentic Botnets exploits hallucinated repo and skill names — Adversarial hallucination squatting — What does it mean?
Attackers pre-register the fake repo and skill names an LLM hallucinates, then hide malicious prompts there — up to 85% repo / 100% skill hallucination rates.
ToolFailBench separates skip, ignore, and fabricate failures — Tool-use failure taxonomy — What does it mean?
ToolFailBench sorts agent tool-use failures into four modes, and adds trap tasks to catch tools that shouldn't have been used.
Vera tests LLM-agent safety with executable evidence checks — Evidence-grounded verification — What does it mean?
Vera judges an AI agent's safety from the real evidence it leaves in a sandbox — files, tool calls, commands — not the model's own say-so.
SIS turns off-policy RL tokens into on-policy updates — Selective Importance Sampling — What does it mean?
Selective Importance Sampling accepts the agreeing tokens as on-policy (ratio 1), taming the variance when RL reuses off-policy rollouts.
