The Frontier - SiliconNoon (Page 10)

The Frontier

Figure's Helix 2.5 tidied 30 homes it had never entered

Figure sent a humanoid into 30 Bay Area houses it had never trained on and had it tidy rooms, fold towels and make beds with no fine-tuning — 56% success, six times the 9% a from-scratch policy managed. China's provinces keep building token marketplaces, and CapCut put an agent inside the editing timeline. Figure's Helix 2.5 is the first real evidence that whole-body robot behavior learned from human video transfers to homes the robot has never seen. The evaluation ran across 30 Bay Area homes

The Frontier

A 4B model mimics real students better than GPT-5.4

Two stories about where models are being pointed today: a student simulator that beats frontier models at being one particular learner, and Meituan's shopkeeper agent crossing a million restaurants. Microsoft researchers trained a 4B model to imitate individual students — and it beats GPT-5.4 and GPT-4o at the job. The system, called StudentSim, attacks a real bottleneck in AI tutoring: knowing which explanation works for which student requires watching real learners, and that feedback is slow,

The Frontier

APUS reverse-engineers Jev into a fully offline browser agent

Jev's whole pitch was that decisions do not need generated text. Someone has now rebuilt that pitch with open weights, on a laptop, with the benchmark table to go with it. China's APUS AI Lab published fast-browser-use, an open-source reproduction of Jev's decision mechanics that runs entirely on local Qwen3.5 weights — no cloud endpoint, no API bill, MIT licensed. The project is not a wrapper around TypeSafe's hosted model, which is what Browser Use's own Jev Ultrafast was. APUS says it worked

The Frontier

AI 101 — AI vs machine learning: what's the difference?

Artificial intelligence is the goal — getting a machine to do things we would call intelligent — and machine learning is the technique the field now overwhelmingly uses to get there: instead of a programmer writing the rules, the system is handed examples and adjusts itself until its answers get better. The two terms get swapped for each other in almost every story you read, including ours. That is mostly harmless, and occasionally it hides the interesting part. When Justin Fanelli, the US Navy

The Frontier

Google's leaked Mathematica shows the raw chain of thought

Two model stories where the interesting part is what the lab is willing to show: Google's internal math model runs on traces nobody has cleaned up, and StepFun finally put a date on open weights. An experimental Google model codenamed Mathematica has surfaced in leaked evaluation screenshots, and its most revealing detail is the unfiltered chain of thought. The build carries the internal identifier models/deepthink-mathematica-tf-raw-thoughts — the "raw thoughts" suffix is the point — with a on

The Frontier

Bilibili opens a 100-model arena — GPT-6 Astra takes the top slot

Two stories where the ranking comes from outside the lab: China's biggest video platform just turned creator reviews into a live model leaderboard, and a clinical voice company published the first benchmark of whether AI can actually say a drug's name. Bilibili's new AI Infinite Arena ranks models by how often they win — not by how well they score. The platform opened a leaderboard fed by its UP hosts (creators), who test more than a hundred models on their own terms: code, reasoning, collabora

The Frontier

ProgramAsWeights compiles English into a neural program you run locally

Two things landed this weekend that point the same direction: the cost of using a model is falling faster than the cost of training one, and some of the people funding frontier labs have started saying so out loud. University of Waterloo researchers published a method that turns a plain-English description of a function into a small neural program that runs on your own machine, with no model call per use. "Compile by Training" is the second compiler behind ProgramAsWeights: you describe the beh

The Frontier

Halogen doubles 1M-context decode speed on a 128 GB Strix Halo box

Local inference has been cheap at short context and painful at long context for as long as anyone has measured it. This release attacks that slope directly — on one chip, for one model. A 128 GB Strix Halo machine running Qwen3.8-Flash-Next now decodes at 38.3 tokens/s with a million tokens of context loaded, up from 27.3 — same box, same session, same prompts, same bits. The engine is halogen-flash-server, version 0.12.0, written for exactly one GPU (AMD's Ryzen AI Max+ 395) and one model fam

The Frontier

Codex Astra went 18-0 in StarCraft — and still played like a beginner

A new round-robin benchmark put 19 model configurations through 171 games of real-time StarCraft: Brood War. The best of them never lost — and its own author says none of them played past beginner level. The reason is the most useful thing in the results. Codex Astra won all 18 of its matches in Brood War Bench, a new agent benchmark that has 19 model-and-effort configurations play each other in real time, and its author's conclusion is blunt: no model in the field played past a beginner level.

The Frontier

Cua's 2.8 MB open model fills forms without an LLM call

Most of what an agent does is not reasoning — it is deciding that this value goes in that box. Cua just published the weights for that decision, and they are smaller than the PDF being filled in. Cua open-sourced CUA-S1-FORMS on Friday: a 706,048-parameter model that scores form-filling decisions in one forward pass, generates no text, and ships as a 2.8 MB checkpoint. It is the first release in what the company calls the CUA-S1 family of "System One" models — fast, bounded decisions — and it s

The Frontier

The Take — ICLR's flood is a credential problem, not a review problem

ICLR's submission counter passed 50,000 IDs before the abstract window closed on September 18, and by the time it did, the field had already produced its explanation: too many papers, not enough reviewers, cap the papers. I think the caps now in force — a 20-paper limit per author and a one-paper limit for authors whose teams contain no qualified reviewer — are treating a symptom, and that the symptom is downstream of a price nobody wants to name. A conference acceptance is not a publication. It

The Frontier

China Telecom's 29B agent model was trained without Nvidia

China's largest state carrier put an open-weight coding agent on the market today, and the interesting part is not the benchmark row — it is the stack underneath it. China Telecom's AI arm released Xing4.0-29B-A4B, a 29-billion-parameter mixture-of-experts coding agent that activates 4 billion parameters per token and was trained end to end on Huawei's Ascend NPU platform using the MindSpore framework. The company describes it as the first 100-billion-class Chinese model trained entirely on dom

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