AI Explained
Plain explanations of trending AI concepts, with live visualizations.
MemOps benchmarks agent memory as lifecycle operations — Memory lifecycle operations — What does it mean?
MemOps grades an agent's long-term memory as four operations — remember, forget, update, reflect — not one static store of facts.
Agent optimizer study shows regression control compounds gains — Regression control in continual optimization — What does it mean?
An agent optimizer keeps compounding gains only when regression control steers it past shortcuts that boost new tasks by erasing old wins.
MCPEvol-Bench measures agents as MCP servers change — Tool-interface drift benchmarking — What does it mean?
MCPEvol-Bench mutates real MCP servers so tool interfaces drift underneath an agent — even the best of 12 models still loses about 14% once the tools evolve.
Harness Handbook localizes agent behavior before edits — Behavior-centric harness map — What does it mean?
One behavior lives in the prompt, the state, the tool call, and the coordination code. File search returns files. A behavior map returns all 4.
Plan evaluator study exposes omission incentives — Deletion non-monotonicity — What does it mean?
Deleting a step a plan needs can raise its score — the scorer drops both the step's cost and its chance of failing.
Long-Horizon-Terminal-Bench grades agent progress densely — Partial-reward threshold — What does it mean?
Pass/fail turns a 231-episode run into one bit. A partial-reward threshold grades how far the agent got, and moving the line moves the score.
OpenAI trains GPT-Red to harden agents against prompt injection — Self-play red-teaming — What does it mean?
OpenAI trained an attacker model to invent prompt injections, then fed everything it found back into the defender's training — a red-teamer that never ships.
E3 cuts coding-agent scope before expanding context — Estimate-Execute-Expand — What does it mean?
E3 is a coding-agent strategy: start from the smallest scope, expand only when a check fails, cutting cost ~85% at the same success.
Interaction scaling grounds agent feedback loops — Instrument-grounded feedback loops — What does it mean?
An agent keeps improving when it acts, measures the result with a tool, and revises — not by thinking longer or trusting a review by eye.
GATS plans agent tasks with zero LLM calls during the search — World-model tree search — What does it mean?
GATS plans an agent's actions by searching a learned world model — the search makes zero LLM calls, and it solved every task in the paper's test.
Danus coordinates math agents with fact-graph memory — Verifier-gated fact graph — What does it mean?
Danus lets parallel math agents build long proofs by gating every claim through a verifier into one shared fact graph.
Agora auctions each agent reasoning step to expert models — Auction-based task allocation — What does it mean?
Agora puts each reasoning step up for auction so expert models bid by rectified competence and cost — one dial slides the whole system along the cost-quality frontier.
