The latest AI news, in focus.

A Quantitative Approach to Asch Conformity in Frontier Large Language Models

arXiv:2610.36668v1 Announce Type: new Abstract: The Asch conformity experiment has recently gained considerable attention as a way of measuring conformity in large language models (LLMs). While previous studies have focused on demonstrating conformity in LLMs, our work adds a further quantitative layer to this approach. Instead of sampling a frontier LLM on a suite of questions, our approach samples a single question repeatedly, leveraging the probabilistic nature of LLMs to produce a distribution of answers. In doing so, we can compare the distributions of answers before and after the model is presented with the manufactured consensus. Our experiments confirm that frontier LLMs do conform, reinforcing prior literature, and, through our distribution-to-distribution comparisons, provide add

Opera launches the WebAI standard, enabling AI models to run natively in the browser

Opera has introduced a new web standard called WebAI, designed to allow users to run AI models directly in their browsers without relying on cloud services. This standard is compatible with multiple models; Opera demonstrated it using Google’s open-source Gemma model and also supports Apple’s on-device foundation models. By not being tied to any specific model and targeting open-weight AI systems, WebAI represents a continuation of Opera’s earlier efforts to enable large language models to run locally within the browser. Currently, WebAI is available as a preview feature in Opera One, Opera’s flagship browser, though users must manually enable it.

NVIDIA Nemotron 3.5 Transcribe and Granary v2: Intelligent Multilingual Transcription You Can Prompt and Adapt

NVIDIA Nemotron 3.5 Transcribe and Granary v2: Intelligent Multilingual Transcription You Can Prompt and Adapt

Liquid AI Releases LFM3: 6 Open-Weight 2B-Class Multimodal Models

Liquid AI released LFM3, 6 open-weight 2B-class models for text, vision, audio, omni and decisions. LFM3-1.7B uses a tri-hybrid backbone, a 256K context window and fits in about 1 GB at 4-bit. The post Liquid AI Releases LFM3: 6 Open-Weight 2B-Class Multimodal Models appeared first on MarkTechPost.

How Anthropic ships Claude with Claude: Separating the Brain from the Hands

Katelyn Lesse and Angela Jiang on Opus 5.5, the Claude Platform, and the engineering behind AI agents.

How Anthropic ships the #1 LLM in the World safely: Separating the Brain from the Hands

Katelyn Lesse and Angela Jiang join Latent Space to explain the engineering behind the Claude Platform: launching Opus 5.5, separating the model’s “brain” from the agent’s “hands,” scaling APIs, and building reliable managed agents. A new introduction ...

Fix evaluation bias to unlock better models on Amazon Bedrock

Property Finder found that 96.8% of their vision-AI evaluation labels were inherited from the incumbent model, hiding its true accuracy. See how they built unbiased ground truth, applied evolutionary prompt optimization, and selected a more accurate, cost-effective model on Amazon Bedrock, cutting cost by 86% across 11 image-quality attributes.

Exowatt beats frontier models on accuracy with SageMaker serverless model customization

In this post, we show how Exowatt used Amazon SageMaker AI serverless model customization to build a repeatable AI pipeline that produces task-specific fine-tuned models for two deployment targets, edge and cloud, achieving higher accuracy than frontier models at up to 49x lower cost.

3 Ways to Boost MoE Inference on NVIDIA DGX Spark

Generating tokens faster starts with identifying what limits inference performance. In the batch-size-1 evaluation of Ling-3.0-flash from Inclusion AI on NVIDIA...

Regarding the Kimi quota issue, Anthropic has found a solution.

After the release of K3.1 Pro, due to numerous users reporting that their quotas were being depleted too quickly, Moonshot AI urgently issued an announcement stating, “The issue lies with us, and we should have fixed it sooner.” Alongside compensating affected users, Moonshot AI also indicated that it would “reduce unnecessary sub-agent calls and output lengths.” Just two days before the model went live, Anthropic published an article titled “Building agents: When to fold subagents into your main agent loop,” which addressed precisely the topic of sub-agents. Anthropic concluded that, by comparing Claude deployments across multiple companies, they found that a single agent equipped with specific skills actually outperformed “a group of...”

Mirendil Launches Fern 0.1, Its First Model, With 10M-Token Context

Mirendil released Fern 0.1, its first model, pretrained from scratch with a 10M-token context window. It targets agentic AI R&D work, scores 100% on RULER single needle at 10M, but trails frontier models on agentic coding. The post Mirendil Launches Fern 0.1, Its First Model, With 10M-Token Context appeared first on MarkTechPost.

Anthropic files its prospectus, with违规 users contributing at least 5% of its revenue; the Hurun Rich List’s top 100 includes AI heavyweights such as Liang Wenfeng | What’s happening in the AI world again?

1. GPT-6.1 Terra, better suited for workers, has launched, with API prices cut in half and faster computer operations On October 26, OpenAI released GPT-6.1 Terra, focusing on enhancing visual comprehension, computer operations, factual accuracy, and programming capabilities, ....

“Achieving high scores” does not equate to “being able to deliver”: AI rankings are undergoing a reassessment of credibility.

Old benchmarks approach perfect scores, yet new benchmarks consistently fall short in real-world performance. As large-model evaluation itself has become a business, companies are shifting their selection criteria from ranking-based assessments to cost‑benefit analyses.

Most computing power, slowest progress: ByteDance’s large model stuck in a race-to-arms dynamic.

The old “horse racing + short-cycle evaluation” approach can no longer be sustained.

From Large Language Models to World Models

Yan Deli, a senior expert at Tencent: LLMs are probabilistic models. They do not truly understand language; rather, they predict the next token (Next Token Prediction). Essentially, they are monotonous “word-chain” machines—moving forward one step at a time without retracing their path. At their core, LLMs are probabilistic models that learn, from statistical co-occurrence patterns, “what often appears alongside what,” and output “this is usually the case,” without grasping “why it is so.”...

In the era of large models, who will become that “iPhone” no one is willing to replace?

The champion on the AI rankings may change, but the products users rely on every day don’t necessarily. The next round of competition in large models will hinge not just on whose model is stronger, but on turning a single use into a habit.

MiniMax M3.1Flash release sparks “questionable results” concerns: high scores on old questions, failure on new ones, and an illusion rate as high as 92.8%

On October 19, 2026, MiniMax released its low-cost large model, M3.1Flash, which scored 42 points in comprehensive evaluations and boasts a 70% reduction in cost compared to the previous generation. However, it immediately faced accusations of “cheating on exam questions”: on AA’s publicly available past papers, it nearly achieved a perfect score, yet continued to produce the original answers even after the question stems were rewritten. Reviewers such as Cat Boss and Binkg pointed out that its performance on old and new questions is sharply divided, token consumption is abnormal, and its hallucination rate in knowledge tests reaches as high as 92.8%. On October 23, MiniMax open-sourced the model weights for independent verification, but the mechanisms behind its question-identification capabilities and the reasons for discrepancies in Chinese–English responses remain unresolved.

Why it mattersThis report is based on multi‑party empirical testing (including Binkg’s re‑title experiment, the author’s replication, and a dual evaluation comparison between AA and Plotly‑Mila), verifiable data (token counts, prices, accuracy/hallucination rates), and contradictions in model behavior (English errors vs. Chinese correctness, stable performance on old tasks vs. collapse on new ones). It systematically lays out the core symptoms of the current credibility crisis in large‑model evaluations: high scores do not necessarily equate to strong generalization, and low‑price advantages create tension with the reality of model capabilities. The information source is a deep investigation authorized for publication by Titanium Media, complete with original test procedures, screenshot evidence, and timestamps, offering both timeliness and rich detail.

Left-hand open-source model, right-hand creative Agent—Hi3D’s AI 3D second act

Author | Cynthia Editor | Zheng Xuan In 2026, the AI 3D landscape stands at a delicate crossroads. Momentum in the primary market continues to surge, with new models and startups emerging one after another, and single-image-to-3D generation now delivers photorealistic results that are almost indistinguishable from real-world objects. Yet beneath the dazzling demo façade, most generated outputs remain stuck at the “turntable showcase” stage: a meticulously crafted character or mechanical model slowly rotates under lighting, its scales, fabric folds, and metallic engravings rendered with lifelike detail. But as soon as these details enter real‑world production workflows—such as game engine rigging, industrial simulation, or 3D printing—their limitations quickly surface: structural fractures, backside collapses, fused details, and uneditable meshes begin to erupt, swallowing up the efficiency gains brought by AI through costly manual post‑processing. Fidelity in frontal views no longer equates to overall 3D quality—a consensus that’s taking hold across the industry. The race for AI‑driven 3D has shifted from the first half—“Can we generate?”—to the second half—“Can we deliver in real‑world applications?” It is precisely at this juncture that leading player Hi3D has made two strategic moves: officially open‑sourcing its production‑proven Hi3D V1.0 platform, while simultaneously launching the creator‑centric Hi3D Agent, which bridges the industry’s foundational layers and deeply embeds itself within the full creative workflow. Prior to this, the company—founded less than three years ago—had already delivered a solid track record: securing two consecutive rounds of funding within six months, totaling nearly $50 million, with top-tier investors like Sequoia China, IDG Capital, and Meituan Longzhu all participating; its Hi3D platform now serves over 120 countries and regions, supporting more than 2 million creators, and enjoys deep integrations with leading hardware manufacturers such as Tuzhu Technology and xTool. From academic breakthroughs in sparse geometry to open‑source, production‑ready models, and end‑to‑end connectivity via its Agent creation system, Hi3D has secured its ticket to the next phase of 3D generation. 01 3D Generation: Overhyped Demos, Underestimated Production Gaps The underlying logic of single‑image‑to‑3D generation inherently carries a perspective bias. As metrics like ULIP and Uni3D have repeatedly set new benchmarks, the industry’s past few years have been marked by an emphasis on front-facing views at the expense of holistic understanding, and on outward appearance rather than internal structure. However, the essence of a 3D asset lies in its ability to be rotated, edited, and manufactured as a three‑dimensional object. In a single character image, the vertical…

Baidu Open-Sources Unlimited OCR 2 for Hundred-Page Document Parsing

Baidu has released Unlimited OCR 2, an MIT-licensed 3B OCR model that parses hundred-page documents in one request. Its SR-SWA attention keeps per-step decoding cost nearly flat, scoring 77.1 on MarathonDocBench The post Baidu Open-Sources Unlimited OCR 2 for Hundred-Page Document Parsing appeared first on MarkTechPost.

Just now, GPT-6.1 Terra was released at a discounted price, and the salaried worker model of the Agent era has arrived.

OpenAI has once again raised the bar for the cost-effectiveness of large models. #Follow iFanr’s official WeChat account: iFanr (WeChat ID: ifanr) for more exciting content delivered to you第一时间.