search
A 4B search agent matches 30B by grading its own failed attempts News
ABSeeker trains a 4-billion-parameter web-search agent on 8,500 examples by working backwards from the known answer to score each individual search step, letting useful steps inside failed runs earn credit -- and matches agents roughly seven times its size.
Approximate nearest neighbor search: how a vector database finds a needle in a billion haystacks Lesson
Approximate nearest neighbor search is the algorithm that makes vector search fast enough to be useful - it finds the closest matches to a query embedding without comparing it against every item in the database, trading a small, tunable amount of accuracy for speedups of a hundred times or more. Every vector database and every retrieval-augmented system runs on it, and the accuracy it gives up is the hidden knob behind a lot of 'the retrieval just missed it' bugs.
Reddit's US daily users slipped while everything else grew News
Reddit reported 130.3 million daily users and 61% revenue growth for the second quarter, but its US daily user count fell slightly from the previous quarter and the company said search referrals were choppy.
BM25 and lexical search: the keyword formula that keeps beating neural retrieval Lesson
BM25 is a decades-old formula that ranks documents by how often a query's rare words appear in them, adjusted for document length - and it remains the baseline that modern AI retrieval systems have to beat, often unsuccessfully.
A decades-old keyword ranker beat the search agent once the document pile passed 10 million tokens News
In a controlled study that grew the same corpus across 28 nested sizes, the agent that browsed files won at small scale but spent 39 times more query tokens, and BM25 - a 1990s keyword ranking formula - overtook it around 10 million tokens and led by nearly 20 points at full scale.
AI search agents get better when relevance tells them where to look, not what to read News
Researchers at Tencent rebuilt relevance as a guide for how a search agent traverses a corpus rather than as a ranked list of documents, cutting the agent's tool calls by roughly a sixth while raising accuracy.
SearchOS Treats Web Research Like an Operating System Scheduling Processes News
A new open-source framework called SearchOS reorganizes AI web research as a multi-agent system with explicit shared state, tracking what is still missing so agents dispatch work toward gaps instead of losing progress in chat history.
Retrieval-Augmented Generation: giving a model an open book Lesson
A language model only knows what it learned in training, and it cannot cite sources. RAG fixes both by letting the model look things up in a real document store at answer time, then write its answer from what it found.
T-Search Tool
An open agentic retriever you can try in the browser - it runs multi-round evidence gathering for questions that need several searches chained together rather than one lookup.
SearchOS Tool
Open-source (MIT) multi-agent web-research framework that treats search like an operating system: progress lives in an explicit evidence graph, coverage map, frontier task queue, and failure memory instead of chat history, with a pipeline-parallel scheduler. Ships a CLI/TUI, web frontend, installer, and replayable sessions.
ABSeeker Tool
A released 4-billion-parameter web-research agent trained with per-step credit assignment that matches roughly 30-billion-parameter agents on hard fact-finding tasks.