The latest AI news, in focus.

3, 7, 14, 17, 42: Instruction Tuning Installs and Sharpens Number-Choice Heuristics in Language Models

arXiv:2610.36178v1 Announce Type: new Abstract: We measure, without sampling error, the probability a language model assigns to each integer from 1 to N when asked for a random number. For a fixed prompt the model defines one probability vector, which we compute by summing over each integer's scored spellings rather than estimate by sampling, removing the noise that limits earlier studies [1, 4]. We evaluate 18 open-weight model lines at five ranges up to N = 100, under 29 decoding configurations, on two inference stacks; 15 of them have matched base and instruction-tuned checkpoints that both give usable answers. Instruction tuning makes the distribution less uniform in all 15 pairs. It installs one heuristic, round-number avoidance, and sharpens another, endpoint avoidance. Base checkpoi

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.

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…

Build specialized multimodal models with LFM3

Build specialized multimodal models with LFM3

Can we trust LLMs to judge whether a research idea is novel?

Can we trust LLMs to judge whether a research idea is novel?

$\Psi$-0.6: Adaptive Streams of Thought in a Unified Vision-Language World Model

arXiv:2610.32177v1 Announce Type: new Abstract: Humans reason about the physical world by flexibly combining different forms of inference as a problem unfolds. Achieving this flexibility is a major open challenge in AI: capabilities often remain separated across specialized models, limiting how they can be composed and jointly adapted to a task. We introduce $\Psi$-0.6, a unified vision-language world model that adaptively composes perception, language, geometry, and simulation within a shared reasoning process we call streams of thought. These streams interleave images, text, and explicit representations of geometry and motion, all of which the model can generate and interpret through a shared token interface. $\Psi$-0.6 surpasses leading open-weight models zero-shot on multi-view spatial

How Much Do You Trust A Dog? Quantifying Hint-Following in Reasoning Language Models

arXiv:2610.32055v1 Announce Type: new Abstract: Large language models (LLMs) often exhibit sycophancy, altering their answers to align with user suggestions even on objective factual and reasoning tasks. Yet the quantitative form of this influence--- and whether it is calibrated to source reliability--- remains poorly understood. In this work, we investigate this behavior through hint-induced answer shifts on MMLU questions, evaluating prompts of the form ``[Source] thinks the answer is (X)''. We systematically vary the source to which the hint is attributed (a professor, a friend, or a dog) across three open-source model families, Qwen, Gemma, and SmolLM. We show that the change induced by the hint follows a predictable structure well-characterized by an exponential tilt: the hint induces

SpecMem introduces a real-time tool-call memory management mechanism, enhancing the agent’s response speed.

On October 26, 2026, arXiv published a preprint paper titled “SPECMEM: Accelerating Agentic Tool Calling via Live Memory Management” (ID 2610.31840v1), introducing a new system called SpecMem that significantly reduces tool-call latency by enabling cross-session, online, user-level memory management for tool invocations.

Why it mattersThis study addresses the high latency issue in deploying current tool‑calling agents, which arises from autoregressive decoding. It proposes key techniques such as verifiable write‑back, LRU eviction, and side‑effect gating, and validates their effectiveness on real query streams involving multiple users and sessions—achieving up to 1.74× measured speedup and a 27% average increase in accepted tokens. This represents an important advance in the field of AI system engineering.

Can Test-Time Memory Evolution Outperform Test-Time Training for Scientific Discovery?

arXiv:2610.32046v1 Announce Type: new Abstract: Recent \emph{test-time training} (TTT) frameworks have enabled open-weight large language models (LLMs) to outperform inference-only frameworks powered by frontier models on scientific discovery tasks. However, TTT introduces additional computational cost for online weight updates during the discovery process, making computational efficiency an important consideration when scaling to larger models. This motivates us to explore an efficient \emph{test-time adaptation} method for scientific discovery. We introduce \emph{Test-Time Memory Evolution} (TTME), a plug-and-play test-time adaptation method, as an alternative to TTT. TTME continually updates the external memory by distilling the experience to guide subsequent discovery without updating

Research reveals that open-source large models commonly exhibit list-filling phenomena in structured extraction.

An empirical study published on October 24, 2026, on arXiv (paper ID 2610.30997v1) found that when a JSON extraction schema specifies a minimum number of items in the list but the actual document content falls short, eight open-source weighting models generally treat this minimum value as a generation target rather than a constraint, resulting in 55.4% of output lists exceeding the true count (compared to a baseline of 34.5%). The filler items are often drawn from the original text, and about half pass the source‑consistency check; common mitigation strategies prove only marginally effective.

Why it mattersThis study employed a preregistered protocol to systematically evaluate eight open-source models and six hosted-service models across 558 real-world documents and nine annotated datasets. With a rigorous methodology and publicly available data—both the code and results have been released on GitHub—it reveals a critical yet often overlooked deficiency in structured information extraction that undermines the reliability of downstream tasks: schema‑induced non‑faithful filling behavior.

Several leading financial institutions in Europe have adopted open-source large models to build proprietary intelligent systems.

At the GTC Berlin conference, European financial institutions including Revolut, Starling Bank, Lloyds, and Santander announced that they are building production‑grade AI capabilities based on NVIDIA’s accelerated computing platform and open‑source weight models—such as PRAGMA, the Nemotron series, and the Kumo family—covering core use cases like trading analytics, speech recognition, fraud detection, and data pipeline acceleration, while emphasizing data sovereignty, cost control, and compliant deployment.

Several European institutions have deployed the NVIDIA Earth-2 open-source model to advance localized weather and climate prediction applications.

NVIDIA has released the Earth-2 open-source AI model family for Europe, including the region-specific nowcasting model StormScope EU, and is collaborating with organizations such as AXA, Vaisala Xweather, the University of Manchester, and TotalEnergies to conduct real-world validation, covering tasks like storm risk assessment, air quality modeling, solar power generation forecasting, and radar terrain inversion.

NVIDIA’s open-source models are helping to bring AI projects in the public sector to fruition across multiple European countries.

NVIDIA, in collaboration with governments and technology partners across multiple European countries, is advancing sovereign, controllable AI applications—leveraging open-source large models such as Nemotron and synthetic persona datasets—in Germany, Poland, the United Kingdom, France, Greece, and other nations. These efforts span areas including medical image analysis, employment services, digital government assistants, classified information processing, and localized language modeling.

NVIDIA releases a portfolio of open-source models across multiple domains, driving the development of sovereign AI in European industries.

NVIDIA announced the expansion of its open-source model family to encompass areas such as language, robotics, bioinformatics, and quantum computing, unveiling the Nemotron 3.5 series, the Kumo structured-data model, the Cosmos world model, the Alpamayo 2 Edge autonomous-driving model, the BioNeMo RFD4-Proteina protein-design model, and the Ising quantum-computing model. All these models are now available to developers and enterprises, with access to weights and tools, supporting localized customization and independent deployment.