World Wire

Perplexity has released Photon , an in-house retrieval and ranking engine written in Rust. It replaces an open-source engine Perplexity had forked for its AI-native search stack. Photon now handles retrieval and ranking for all production traffic. It also powers a new Fast Search mode in the Perplexity Search API. Perplexity reports single-call latency of 160 ms at p50 and 230 ms at p95. Is it deployable? Yes, as a hosted API. Set search_type: "fast" on POST /search and pay $1 per 1,000 requests. Photon itself is not open source, so the engine cannot be self-hosted. Why Perplexity Replaced its Old Engine The old engine hit 3 limits as the index grew: Tail latency: Production p99 sat near 800 ms. The dataset exceeded RAM, so mlock was not an option. Cold reads triggered major page faults that stalled queries. Merge spikes: During disk index fusion, p99 climbed to about 1.2 s for 10 to 15 minutes. Slow recovery: Deploying and syncing an extra c...

Video world models can render convincing clips that still break physics. Butter spreads like paint. Balls pass through walls. A team from NVIDIA, MIT and the University of Oxford argues the fix can come from language itself, not from extra visual, latent or numerical signals. Their framework, Physis-Lang , treats physical language as a shared, optimizable representation. The same text drives data curation, model training and inference. On the public Physics-IQ Verified leaderboard snapshot dated September 29, 2026, Physis-Lang on Cosmos3-Super ranks first at 48.2 ± 1.4. The Cosmos3-Nano version ranks second at 43.3 ± 1.5. A video world model can make a convincing clip and still get the physics wrong. Our researchers just released Physis-Lang, an open self-evolving framework that adds physics reasoning to video captions. The captions explain why and how a scene unfolds. We use them to fine-tune… pic.twitter.com/agIHuIB6N2 — NVIDIA AI (@NVIDIAAI) September 29, 2026 W...

Liquid AI has released d1 , a decision model built for structured choices instead of text generation. You give it context and a set of typed questions. It returns calibrated probabilities across a fixed set of outcomes in a single call, with zero generated tokens. The target is the work many teams still send to general LLMs: classification, ticket routing, scoring, moderation, reranking and LLM-as-judge checks. Is it deployable? Yes, today, as a hosted API. d1 runs on the Liquid API under the model name d1:free . Liquid’s model library lists it as API only and not trainable, so there are no GGUF, MLX or ONNX weights to self-host. What is a Decision Model? A decision model evaluates a situation and returns a typed answer from options you define before the call. It does not write text. In every response, usage.output_tokens is 0. Liquid AI’s migration guide gives a simple rule: if the answer is one of N known options, use a decision model. If the model must c...