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An easy way to try local AI with braindead-local-intelligence

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· 大模型,算力芯片,具身智能,开源,OpenAI,NVIDIA,阿里巴巴,Mistral,长上下文,端侧AI,招聘HR,开发者生态

Robustness of Chatbot LLM Judgments of Presidential Actions and Hypothetical Reforms

arXiv cs.CLarXiv:2610.39398v2 Announce Type: replace Abstract: Chatbots are increasingly consulted about public affairs, and their judgments of policies are useful only if they are robust. We measure this robustness in the setting of an ordinary user, through consumer chat interfaces. For 30 days, five chatbot configurations (Mistral, Gemini Flash, Claude Sonnet, and Claude Opus at two effort settings) were asked twice a day to score the long-term effect on the United States of 40 actions of the last four presidential administrations and of 15 hypothetical reforms, each with a confidence and a short comment. The judgments are robust: all configurations agree on the sign and the ranking of the administrations' mean scores, and two different configurations asked on the same day give opposite signs to the same action in about 4% of cases. Responses from different days differ more than same-day responses (which shared the order of the items); the difference grows slowly over the month, except where the service itself changed. We identify three such changes, which shifted the judgments while leaving little or no trace in the interface. Treating the comments as a retrieval task, we find them specific to each item and consistent across runs and chatbots, with lexical and embedding similarities agreeing. We release the dataset of 16,500 judgments.

2026-11-02 05:00:00 · 大模型,Google,Anthropic,Mistral,对话助手,强化学习,向量数据库,招聘HR,论文

BYOD: Build Your Own DLM - Efficient Autoregressive-to-Diffusion Language Model Conversion

arXiv cs.CLarXiv:2610.41574v1 Announce Type: new Abstract: Unlike autoregressive (AR) models, diffusion language models (DLMs) permit parallel token updates, flexible generation, and bidirectional attention, but leading systems rely on costly pretraining or full-model fine-tuning. We present Build Your Own DLM (BYOD), a generalizable LoRA-only recipe for parameter-efficient AR-to-DLM conversion. Without architecture-specific tuning, BYOD induces diffusion generation in Gemma 2 9B, Llama 3.1 8B, Qwen2.5 7B, and Ministral 8B; to our knowledge, this is the first reported AR-to-DLM conversion of a Mistral model. The adapters comprise 4.1-5.8% of final parameters while every base-model parameter remains frozen; each 25,000-iteration conversion needs at most 102.4M tokens, processed in 3.1-4.0 hours on one Google Colab GPU. All four models generate from fully masked sequences, use bidirectional context in a controlled attention test, and retain substantial parent-model knowledge. BYOD-Gemma is the strongest BYOD conversion on our nine-task evaluation, reaching 50.6% average accuracy compared with 51.9% for LLaDA 8B Instruct in the same harness. Open-generation quality at low network function evaluation (NFE) budgets remains the principal limitation: BYOD perplexity is 8.3-11.1 versus 4.1 for LLaDA and 2.2-3.0 for the AR parents when generating 128 tokens in 64 steps. To enable further experimentation and inspection, we provide a standalone training and evaluation implementation and interactive demos for all four conversions. BYOD thus provides a practical route for extending diffusion generation to other existing model families while identifying the gap that efficient conversion must next close.

2026-11-02 05:00:00 · 大模型,算力芯片,AI应用,Google,Meta,阿里巴巴,Mistral,搜索RAG,Transformer,扩散模型,微调蒸馏,预训练,论文

Advancing Clinical Trial Outcome Prediction with Domain-Specific Large Language Models

arXiv cs.AIarXiv:2610.41710v1 Announce Type: new Abstract: Clinical trial failures are a major barrier to efficient drug development, incurring substantial costs and delaying patient access to effective therapies. Structured approaches such as the Hierarchical Interaction Network (HINT) predict trial outcomes by explicitly modelling interactions between drugs, diseases and protocols, but make limited use of the free text in trial registrations. We evaluate two domain-specific language models, BioBERT (108M parameters) and BioMistral (7B parameters), for phase-level outcome prediction on the TOP benchmark (12,477 trials), representing each trial as text built from ten ClinicalTrials.gov fields. We compare fine-tuning (full fine-tuning for BioBERT, low-rank adaptation for BioMistral) with probing, in which the pretrained model is frozen and only a lightweight classifier is trained on its embeddings. On the held-out test set of each phase, all four language-model variants achieved higher ROC-AUC (0.613--0.772) and PR-AUC (0.649--0.901) than the published HINT results, corresponding to relative PR-AUC gains of 10--20\%. F1 differences were small and mixed, and no model, including HINT, clearly exceeded the F1 of a trivial classifier that predicts success for every trial. Under the configurations tested, probing performed comparably to fine-tuning: fine-tuned models scored highest on ROC-AUC and PR-AUC in Phases I and II, and frozen BioMistral embeddings in Phase III (ROC-AUC 0.772; PR-AUC 0.901), although most differences between variants were below 0.03 and were not tested for significance. These results suggest that pretrained biomedical representations already encode signal relevant to trial outcome, and that probing is a computationally cheaper alternative to fine-tuning for this task.

2026-11-02 05:00:00 · 大模型,Mistral,微调蒸馏,预训练,模型评测,向量数据库,榜单评测,论文,开发者生态

Multi-Test Nation Bias Benchmarking of LLMs with Explicit Unbiased Baselines

arXiv cs.AIarXiv:2610.41486v1 Announce Type: new Abstract: While prior work has highlighted that systematic nation-level bias in large language models (LLMs) can pose operational risks for international relations (IR) applications, many existing evaluations still lack a clearly specified unbiased reference (ground truth), limiting fully quantitative and cross-setting measurement. Using an expanded corpus of 722 official UN Security Council (UNSC) records, we measure nation-level bias with five complementary tests, thereby extending prior approaches to cover a broader set of bias expression channels: opinion ranking (WOW), association sorting (AK+-), vote simulation (PVS), opposition attribution (F-Oppose), and framing (Headliner). To enable grounded bias evaluation, we explicitly define an unbiased status for each test; in particular, for WOW and AK+- where ground truth is difficult to specify, we propose an action-based pseudo unbiased status derived from observable voting behavior to construct a quantifiable baseline. Benchmarking 4 LLMs (GPT-4o-mini, Llama 3.3-70B, Mistral-22B-Small, and Qwen3-30B) across the five permanent members of the UNSC, we find a test-agnostic macro-pattern: relative to the reference baselines defined for each test, model outputs lean toward the opposition (red) side for China and Russia and toward the supportive (blue) side for the U.K., the U.S., and France. These results underscore the need to audit IR-facing LLM deployments with grounded, multi-test benchmarks tied to explicit reference points.

2026-11-02 05:00:00 · 大模型,算力芯片,OpenAI,Google,Meta,阿里巴巴,Mistral,扩散模型,强化学习,模型评测,网络安全,论文

Confident Fools or Careful Searchers? A Behavioural Study of When LLMs Search Beyond Their Static Internal Memory

arXiv cs.AIarXiv:2610.41124v1 Announce Type: new Abstract: Agentic LLM assistants must repeatedly decide whether to answer from memory or to call a tool such as web search. Unnecessary searches cost latency and money; missing ones yield confident but unsupported answers. Prior work mostly measures verbalised confidence or trains new adaptive-retrieval methods, and rarely audits the search decisions that deployed products actually make. We introduce a benchmark of 400 multiple-choice questions that combines widely known inside-knowledge (IK) questions, which should be answered directly, with outside-knowledge (OK) questions about facts from news published after the models' training cut-offs (to our knowledge), which should trigger search or, without tools, abstention. Each item offers an abstain option, and two action-level scores, NAS and KIS, reward searching on OK questions and restraint on IK questions. Across five commercial LLMs with and without their vendors' web search, GPT-5 mini and Grok 4 Fast search on over 90% of OK but only 1% of IK questions, whereas Mistral Small searches on just 20% of OK questions, often abstaining instead of using its tool; one product's search indicator proved unusable. Searching is partly triggered by explicit temporal cues: on uncued OK questions, the NAS of the two selective models falls from about 0.72 to 0.57--0.58. The scores thus measure the search triggering of specific products, not metacognition in a strict sense.

2026-11-02 05:00:00 · 大模型,AI应用,OpenAI,Meta,xAI,Mistral,Agent智能体,扩散模型,强化学习,模型评测,论文

France’s AI Strategy: Turning Models, Compute, and Public Investment Into National Capability

Hacker Noon

France's potential role in AI rests on its ability to organise a chain of capabilities. Model developers can produce useful technologies, computing institutions can provide infrastructure, and public administrations can establish sustained demand. National capacity grows when these activities become connected through arrangements that work under practical constraints. The decisive question is whether French institutions can secure the resources they need, adapt them to their purposes and remain responsible for the resulting decisions.

That question matters because the visible scale of an AI strategy can obscure its operational content. In February 2025, the Élysée reported €109 billion in announced investments associated with AI infrastructure and deployment projects. [1] This signalled commitments by investors. It was neither a measure of installed computing capacity nor a statement that the French state had already spent that amount. Assessing France requires following projects and access arrangements beyond the announcement.

Models that users can adapt

In the Invention layer, an important distinction concerns what users can actually obtain and modify. Mistral's documentation lists Ministral 3 8B, released in December 2025, as generally available under Apache 2.0. [2] An available model with permissive licensing gives developers a different set of options from a service accessible only through a proprietary interface. It can support adaptation and deployment decisions closer to the institution using it.

That possibility is not equivalent to a complete operating capability. An administration still needs suitable infrastructure, technical staff, evaluation procedures and authority to use the relevant data. A model that performs well on a general benchmark may require considerable work before it can support an administrative task reliably. The French opportunity is to develop those supporting capacities around models that institutions can understand, test and replace.

Mistral's August 2026 announcement provides a useful illustration of access becoming more specific. The company said its regional endpoints were generally available, allowing customers to select inference in Europe or the United States. It also disclosed that limited, safeguarded transfers to subprocessors outside the selected region could occur. [3] This is evidence of a defined service offering, with stated limits. A European processing location alone cannot establish complete control over the surrounding technical and contractual relationships.

Computing infrastructure under shared authority

Industrialisation requires infrastructure that can support demanding work at useful scale. Alice Recoque illustrates both the ambition and the institutional complexity. EuroHPC signed its procurement contract in November 2025. The planned system will be owned by EuroHPC and hosted in France, with GENCI managing the project and CEA operating the machine. The Jules Verne consortium includes the Netherlands and Greece, and computing resources are to be allocated jointly in proportion to investment. [4]

France therefore occupies a significant organisational position without acquiring exclusive national disposal of the system. The planned architecture also combines European components with AMD processors and accelerators. [4] European coordination can enlarge access and influence while retaining external technological dependencies. The relevant policy issue is whether those dependencies remain manageable through procurement, support and continuity arrangements.

Project timing is equally consequential. GENCI's current project page identifies the end of 2027 as the expected operational date. [5] This profile therefore treats Alice Recoque as contracted future capacity. It does not add its promised performance to a present French inventory. The distinction affects decisions by laboratories and public organisations that need resources before the new system becomes available.

The state as an organised user

Operationalisation becomes more tangible in Albert API. DINUM, France's interministerial digital directorate, operates this shared gateway to generative models. Its official description covers access to open-weight and proprietary models, alongside functions such as document classification, retrieval and optical character recognition. It identifies Albert API as an instance of the state-developed OpenGateLLM software. [6]

A shared interface can help administrations avoid rebuilding the same connections and can make changing providers less disruptive. Its strategic value depends on the surrounding institutional arrangements: which data may be processed, who evaluates performance, how errors are corrected and who can suspend a failing service. DINUM's page states that an approved version for internal state data is still being finalised. [6] Claims about universal readiness for sensitive public workloads would therefore exceed the evidence.

This is where governance connects Invention, Industrialisation and Operationalisation. The state can shape demand through procurement specifications and common infrastructure, while retaining responsibility for each public use. An architecture that permits model substitution, independent evaluation and continuity of service would make national capability less dependent on the fortunes of a single supplier. Achieving this requires administrative expertise as well as technical investment.

The evidence to watch

Four developments would make France's trajectory easier to judge. First, documented acceptance and access milestones for Alice Recoque would establish when contracted infrastructure becomes usable. Second, reports on completed and energised facilities would distinguish delivered investment from announcements. Third, Albert API's authorisation status and service records would clarify which workloads it can responsibly support. Fourth, evaluations of public deployments would reveal whether AI improves accuracy, processing times or service quality at an acceptable cost.

France could play a substantial European role by connecting model development, shared computing and public demand into dependable arrangements. The strongest evidence would be institutions that can continue to operate, evaluate and adapt their systems when suppliers, prices or political conditions change. That would demonstrate a capacity to mobilise AI for public purposes, extending the significance of the companies and infrastructure located within French territory.

How to read this profile

This is original country analysis applying the GOAI capability conversion framework. It is not a peer-reviewed country assessment or a scored ranking. Proposed applications and indicators are analytical judgments; company and government claims are attributed to their sources.

Sources

All sources were accessed on 20 October 2026. Service characteristics reflect their providers' descriptions; expected dates remain forecasts.

Élysée. France's AI strategy and announced investments. 11 February 2025.
Mistral AI. Ministral 3 8B documentation. Model release: 2 December 2025; documentation checked on the access date.
Mistral AI. Regional inference, open models and European infrastructure. 11 Au

2026-10-31 05:00:02 · 大模型,Mistral,扩散模型,强化学习,模型评测,招聘HR,网络安全,开发者生态

Mistral AI Releases Weights of 1-Trillion-Parameter "Large 4" for Free, Opening Path to On-Premises Deployment for Enterprises - BigGo Finance

Google News LLMMistral AI Releases Weights of 1-Trillion-Parameter "Large 4" for Free, Opening Path to On-Premises Deployment for Enterprises BigGo Finance

2026-10-30 01:10:38 · 大模型,Google,Mistral,招聘HR

Comparing Human Role-Players and LLM-Simulated Clients in Online Counselling Training: An Analysis of Counselling Patterns

arXiv cs.AIarXiv:2610.40563v1 Announce Type: new Abstract: This study investigates the capabilities of Large Language Models to simulate counselling clients in educational role-plays in comparison to human role-players. Initially, we recorded role-playing sessions, where novice counsellors interacted with human peers acting as clients, followed by role-plays between humans and clients simulated by Mistrals Mixtral 8x7b using 4-bit quantization. These interactions were analysed with a counselling communication pattern system at sentence level. We investigated two key questions: (1) to what extent LLM-generated responses replicate authentic conversational dynamics and (2) whether counsellors' communication behaviour differs when interacting with human versus LLM-simulated clients. The findings highlight both similarities and differences in the application of counselling patterns across scenarios, showing the potential of LLM-based role-playing exercises to enhance counselling competencies and to identify areas for further refinement in virtual client simulations.

2026-10-31 04:00:00 · 大模型,Mistral,微调蒸馏,论文

From Leniency to Suspicion: How an Open Post-Training Recipe Changes Whom LLM Fact-Checkers Believe

arXiv cs.AIarXiv:2610.38532v1 Announce Type: new Abstract: LLMs increasingly fact-check online content with the poster's identity in view. However, it remains unclear which post-training changes how much a model's truth judgement depends on the poster's social class, and how such a change happens inside the model. This paper investigates job-based doubt, i.e., judging a claim false more often when the poster has a lower-status job, using fact-checked claims with randomised poster profiles in base and post-trained checkpoints of six model families. Our results reveal that: 1) post-training creates such doubt only with the open T\"ulu-3 recipe (OLMo-2 32B, T\"ulu-3 70B), not with Meta's, Alibaba's or Mistral's; the effect is under one percentage point but statistically reliable, exceeding training drift and replicating on independent claim sets; 2) in OLMo-2 32B, the model does not start noticing the poster's job more, but starts interpreting it differently: a lower-status job pushes the base model toward "true" and the post-trained model toward "false"; and 3) this flip is carried by a single residual-stream direction and happens at supervised fine-tuning. The mechanism was confirmed on 400 new claims with pre-specified predictions. None of seven deployed commercial models doubts lower-status posters more. Our work underscores the need to audit speaker bias per training recipe.

2026-10-31 04:00:00 · 大模型,Meta,Mistral,扩散模型,微调蒸馏,网络安全,论文

Mistral releases open weights for Mistral Large 4, providing sovereign control for the company’s ‘Le Chonk’ AI model - SiliconANGLE

Google News LLMMistral releases open weights for Mistral Large 4, providing sovereign control for the company’s ‘Le Chonk’ AI model SiliconANGLE

2026-10-29 15:20:00 · 大模型,Google,Mistral,招聘HR

Mistral AI publicly releases Large 4, targets cybersecurity users - Reuters

Google News AI (英文)Mistral AI publicly releases Large 4, targets cybersecurity users Reuters

2026-10-29 15:00:07 · 大模型,OpenAI,Google,Mistral,推理思考,招聘HR

We're releasing the weights of Mistral Large 4

Hacker NewsComments

2026-10-29T12:00:07.000Z · 大模型,Mistral,招聘HR

Paris-based Mistral releases "Le Chonk," a strong coding model that still trails China overall

The Decoder

Mistral has released the weights for Large 4, aka "Le Chonk," a trillion-parameter model trained entirely in Europe. Mistral says it beats its open rivals at coding, and independent testing shows it's fast and fairly cheap. It still can't match China's top models, scoring 26 points to the 32 posted by DeepSeek V4.1 Flash and GLM-5.3.

The article Paris-based Mistral releases "Le Chonk," a strong coding model that still trails China overall appeared first on The Decoder.

2026-10-29 15:25:27 · 大模型,DeepSeek,Mistral,智谱,招聘HR,榜单评测

Mistral’s New ‘Le Chonk’ AI Model Is Big, Open and Now Available for Download

CNETMistral Large 4 is a state-of-the-art model for cyberdefense, manufacturing, finance-related tasks and multimodal use.

2026-10-29T13:32:56-04:00 · 大模型,Mistral,多模态

Mistral’s Le Chonk is now a top open model for coding, ranking alongside Codex GPT-6.1 Terra

ZDNetMistral has released the weights for Large 4, along with some impressive Artificial Analysis scores.

2026-10-29T10:00:00-04:00 · 大模型,OpenAI,Mistral,代码生成,榜单评测

Mistral Says Its AI Model ‘Le Chonk’ Has Leapfrogged China in the Open-Weight Race

WiredThe French AI lab claims its latest release matches or outperforms even the best models from DeepSeek and Z.ai in coding and cybersecurity.

2026-10-29T14:00:00.000Z · 大模型,开源,OpenAI,Google,Anthropic,DeepSeek,Mistral,招聘HR

Mistral releases weights for its trillion-parameter AI model, targeting cybersecurity

TechCrunchMistral says its new model can afford organizations a kind of control over its cyber defenses that closed models can't.

2026-10-29T17:00:00+00:00 · 大模型,Mistral

万亿参数模型 Mistral Large 4 开放权重，用更少的 token 打平了最高分

开源中国Mistral 把 Mistral Large 4（内部代号 Le Chonk）的权重开源出来了：1 万亿参数、原生多模态、开源权重里编码能力最强。这是到目前为止开放权重阵营里体量最大的一批模型，而且是从零开始在欧洲的基础设施上进行训练。先看它凭什么说自己是第一。Cognition 的私有基准 FrontierCode 2.0 测的是端到端代码质量和"仓库维...

· 大模型,开源,Mistral,代码生成,多模态,模型评测,基础设施

Google announces Gemini 4 Carbon, its new frontier AI model

Mashable

An illustration of an ice berg surrounded by the number 4

Google announced Gemini 4 Carbon, a new frontier AI model that arrives just two weeks after Gemini 4 Argon reached the Gemini app.

In a blog post published Oct. 28, Koray Kavukcuoglu, SVP at Google DeepMind, the company’s frontier AI lab, called Carbon Google’s strongest model yet. (Kavukcuoglu is leading Google DeepMind now that former CEO Demis Hassabis has stepped aside.) The post points to cybersecurity and long-horizon tasks, meaning complicated jobs that take many steps or run for a long time, as Carbon’s biggest strengths.

Already, Gemini 4 Carbon has the top spot in the Artificial Analysis Intelligence Index, beating out Claude Opus 5.5.

artificial analysis intelligence index ranking as of oct. 28, 2026

Credit: Screenshot: Artificial Analysis

The pace at which AI companies are releasing new frontier models has accelerated sharply in recent months, as the frontier AI labs race to have the best model available. Anthropic released Claude Opus 5.5 on Sept. 22, OpenAI shipped GPT-6 Astra earlier that month, and Google announced Argon on Sept. 30 before releasing it to Google AI Pro and Ultra subscribers on Oct. 13. Each lab has claimed a new lead within weeks of the last.

As with Argon, Google is announcing Carbon well before most people can use it. Carbon is starting with a small group of cybersecurity defenders, and Google hasn’t said when it will reach everyone else. (Last week's PHP2Shell incident shows just how much havoc new AI models could wreak on the internet at large.) Announcing early lets Google claim the top spot on the leaderboards now. It also gives everyone a look at the benchmarks before the next round of releases.

Gemini 4 Carbon: Benchmarks

In Google’s comparison table, Carbon posts the top score on 15 of 17 benchmarks against Argon, GPT-6 Astra, Claude Opus 5.5, and Claude Fable 5.1. All figures in this section are as reported by Google.

Credit: Google

The two benchmarks Carbon doesn’t win are worth noting. GPT-6 Astra still leads on FrontierSWE 2, a software engineering test. Claude Opus 5.5 leads on OSWorld-2.1, which measures how well a model can operate a computer.

A big change from Argon is that Google now reports Terminal-Bench 4.0 scores run by Artificial Analysis, an independent testing firm, rather than its own. For quick context, Terminal-Bench 4.0 measures how well an AI model can complete real technical tasks — like installing software or fixing broken code — by typing commands into a computer’s terminal on its own. It's one of the hardest agentic tests for LLMs.

This is notable because the scores companies reported for each other’s models didn’t always line up with independent testing. On Terminal-Bench 4.0, Carbon jumps about eight points over Argon and moves ahead of Opus 5.5.

Google also says Carbon can work on difficult engineering problems for days at a time. Its web development skills improved enough to climb to fourth place on WebDev Arena, a leaderboard where people vote on AI-built websites.

The Artificial Analysis Intelligence Index

The Artificial Analysis Intelligence Index is one of the most closely watched independent leaderboards for AI models. Artificial Analysis runs a range of tests itself and combines them into a single composite score.

On the newly updated version 5.0, Carbon (high) scores 59, one point ahead of Claude Opus 5.5 (max).

A graph showing the various AI benchmark scores

Gemini 4 Carbon's score of 59 is leaps above Argon's 50. Credit: Artificial Analysis

The scores can’t be directly compared to the previous version of the index, where Argon (High) scored 53. A one-point lead is also thin enough that the next release from OpenAI or Anthropic could easily erase it. Carbon and Meta’s Muse Wonder Full are both flagged on the leaderboard as having no publicly available API, so they can’t yet be used outside their companies’ own access programs.

Gemini 4 Carbon: Safety and safeguards

Google says Carbon uses the same safety system as Argon, which includes monitoring the model’s reasoning, and adds more work on alignment, or keeping the model behaving as intended. According to Google’s own testing, Carbon is also better behaved than its predecessor.

Google says Carbon never successf

2026-10-28 19:30:50 · 大模型,AI应用,具身智能,OpenAI,Google,Anthropic,Meta,阿里巴巴,DeepSeek,xAI,Mistral,月之暗面,智谱,快手,文生视频,Agent智能体,推理思考,扩散模型,模型评测,模型安全对齐,招聘HR,榜单评测,开发者生态

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