[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-ant-ling-3-flash-fin-finance-llm":3,"news-related-932ae5e5-3552-4f9d-a6fe-26eedca0bb2b":38},{"id":4,"title":5,"summary":6,"content":7,"original_url":8,"source_id":9,"tags":10,"translations":24,"news_slug":31,"published_at":32,"created_at":33,"modified_at":34,"is_published":35,"publish_type":36,"image_url":14,"view_count":37},"932ae5e5-3552-4f9d-a6fe-26eedca0bb2b","蚂蚁首个金融增强模型开源在即:Ling-3.0-flash-Fin 押注投研 Agent","蚂蚁百灵 8 月 28 日推出首个金融增强模型 Ling-3.0-flash-Fin:延续 Ling-3.0-flash 架构,124B 总参、5.1B 激活,通过金融语料持续预训练与工具优化强化投研能力,AA 智能指数从 38 升至 41,权重预告下周开源。","8 月 28 日,蚂蚁集团携手中金公司及行业专家推出蚂蚁百灵首个金融增强模型 Ling-3.0-flash-Fin。它延续 Ling-3.0-flash 的模型架构和长上下文能力,保持 124B 总参数、5.1B 激活参数的配置,在此基础上通过金融语料持续预训练、领域后训练和工具使用优化,强化对年报、财务工作簿、多份研究材料等复杂金融内容的处理能力。模型权重预告下周正式开源,OpenRouter 同步提供为期一个月的限时免费 API 调用,Vercel AI Gateway 免费期至 9 月 25 日。\n\n## 不换基座,只做领域增强\n\n这套做法和 DeepSeek-V4-Flash 的\"只换后训练不换权重\"思路一致:基座不动,靠金融语料持续预训练加领域后训练把模型往专业场景推。官方把能力收敛为四项——信息检索、研究推理、估值建模、研报撰写。官方演示里,模型直接处理了一份 Google 2026 年 Q2 财务表,文件包含 7 个工作表和 5000 多条公式,模型可以更新实际数据、调整公式、刷新跨表引用和图表;另一个任务里它连续调用 23 次工具,寻找 Google 月度 Token 用量的历次披露,并核对来源、日期和统计口径。\n\n## 自测成绩:部分任务超过旗舰,但别只看官方口径\n\n评测方面,模型在蚂蚁与中金联合打造的 FinFIRST 基准,以及 FinSearchComp Verified、Finance Agent、APEX-Agents、SpreadsheetBench、τ³-Banking 等金融智能体基准上进行了测试,覆盖金融信息检索、投资研究分析、金融长程任务执行、估值建模、银行业务应用。官方将其与 GPT-5.6-Sol、Claude Opus 5、Gemini 3.7 Flash、Kimi K3 等模型比较,结果并非每项第一,但在部分 Finance Agent 任务上超过部分旗舰模型。通用能力方面,AA Intelligence Index v4.1.1 得分从基础版的 38 提升至 41。其中 FinFIRST 由 50 余名金融专业人士参与设计,从结果、过程和证据三个层面评估答案质量,基准本身也将于近期开源。\n\n## 该泼的冷水:权重还没落地\n\n第三方观察者的提醒值得记下:截至目前,Ling 3.0 家族的其他成员(Flash 的 BF16\u002FFP8、Tiny 的 BF16\u002FFP8\u002FINT4)都在 Hugging Face inclusionAI 组织下开放,Fin 版尚无仓库,没有可下载权重,也没有 Artificial Analysis 独立页面或第三方复现数字——所有能力声称暂时只能对官方口径。规格上它是纯文本模型,上下文窗口 256K,单次最大输出 32768 tokens,支持 tools\u002Ftool_choice 与 reasoning 参数,推理模式默认开启。\n\n## 所以呢\n\n金融是合规要求最重的行业之一,基座 Ling-3.0-flash 以 MIT 许可开源,若 Fin 版按承诺在下周开放权重,金融机构就能在私有环境里跑一个投研定向模型,这比 API 调用的想象空间大得多。\"下周开源\"这四个字,就是接下来最值得盯的观察点。([IT之家报道](https:\u002F\u002Fnews.qq.com\u002Frain\u002Fa\u002F20260828A04QWW00))","https:\u002F\u002Fnews.qq.com\u002Frain\u002Fa\u002F20260828A04QWW00","74d16e24-139f-47d1-94f3-b8e597ef9160",[11,15,18,21],{"id":12,"name":13,"slug":13,"description":14,"color":14},"471c51be-e620-49df-bd6c-0b5504f53f00","ant-group",null,{"id":16,"name":17,"slug":17,"description":14,"color":14},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":19,"name":20,"slug":20,"description":14,"color":14},"7e89b5cc-57db-4f37-bc6d-28919a73931c","model-release",{"id":22,"name":23,"slug":23,"description":14,"color":14},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source",[25],{"id":26,"lang":27,"title":28,"summary":29,"content":30},"d5d89416-3abd-4c5b-8cc7-38897bb15633","en","Ant's First Finance-Enhanced Model Nears Open Source: Ling-3.0-flash-Fin Bets on Research Agents","Ant Group's Bailing team released Ling-3.0-flash-Fin on August 28: a finance-enhanced model keeping the Ling-3.0-flash architecture at 124B total \u002F 5.1B active parameters, tuned via financial continual pre-training and tool-use optimization, lifting its AA Intelligence Index from 38 to 41, with weights promised to open-source next week.","On August 28, Ant Group, together with CICC (China International Capital Corporation) and industry experts, released Ling-3.0-flash-Fin, the first finance-enhanced model from Ant's Bailing (Inclusion AI) team. It inherits the architecture and long-context capability of Ling-3.0-flash, keeping the 124B total \u002F 5.1B activated parameter configuration, then adds continued pre-training on financial corpora, domain post-training, and tool-use optimization to strengthen handling of annual reports, financial workbooks, and multi-document research materials. The model weights are promised to be open-sourced \"next week\"; OpenRouter offers a one-month limited free API window, and Vercel AI Gateway free access runs until September 25.\n\n## Same backbone, domain enhancement only\n\nThe approach mirrors DeepSeek-V4-Flash's \"swap post-training, not weights\" philosophy: keep the base model, push it toward professional scenarios via financial continual pre-training and domain post-training. Officially, capabilities are grouped into four areas — information retrieval, research reasoning, valuation modeling, and research-report writing. In the official demo, the model processed a Google 2026 Q2 financial spreadsheet containing 7 worksheets and 5,000+ formulas, updating actual data, adjusting formulas, and refreshing cross-sheet references and charts. In another task it chained 23 tool calls to track down every historical disclosure of Google's monthly token usage, verifying sources, dates, and statistical calibers along the way.\n\n## Self-reported results: beats some flagships, read with care\n\nOn benchmarks — FinFIRST (co-built with CICC), FinSearchComp Verified, Finance Agent, APEX-Agents, SpreadsheetBench, and τ³-Banking — the model covers financial information retrieval, investment research analysis, long-horizon financial task execution, valuation modeling, and banking applications. Compared against GPT-5.6-Sol, Claude Opus 5, Gemini 3.7 Flash, and Kimi K3, it is not first on every item, but on some Finance Agent tasks it surpasses certain flagship models. On general capability, its Artificial Analysis Intelligence Index v4.1.1 score rose from the base version's 38 to 41. FinFIRST itself, designed with participation from 50+ financial professionals, evaluates answers across results, process, and evidence, and will also be open-sourced soon.\n\n## The cold water: weights aren't out yet\n\nThird-party observers note a caveat: other Ling 3.0 family members (Flash in BF16\u002FFP8, Tiny in BF16\u002FFP8\u002FINT4) are all open on Hugging Face under inclusionAI, but the Fin variant has no repository yet — no downloadable weights, no Artificial Analysis page, no third-party reproduction. Every capability claim currently rests on the vendor's own numbers. On specs, it is a text-only model with a 256K context window and 32,768-token maximum output, supporting tools\u002Ftool_choice and reasoning parameters, with reasoning mode on by default.\n\n## So what\n\nFinance is among the most compliance-heavy industries. The Ling-3.0-flash backbone is MIT-licensed; if the Fin variant delivers on next week's open-weights promise, financial institutions could run an investment-research-tuned model inside their own private environments — a much bigger deal than API calls. \"Open-source next week\" is exactly the thing to watch. (Source: IT Home report)","ant-ling-3-flash-fin-finance-llm","2026-08-31T19:15:00Z","2026-08-31T19:13:21.121176Z","2026-08-31T19:13:21.121190Z",true,"agent",214,{"items":39},[40,45,50,55,60,65],{"id":41,"title":42,"news_slug":43,"published_at":44},"c024cc24-c861-4bba-9194-23d2c456c484","蚂蚁百灵开源万亿思考模型 Ring-2.6-1T：把思考深度做成可调旋钮","ant-ring-2-6-1t-reasoning-effort-knob","2026-05-15T02:00:00+00:00",{"id":46,"title":47,"news_slug":48,"published_at":49},"d941056b-c2e7-42e5-965a-a982c20b1169","Qwen3.8-Flash-Next 架构细节:Gated Residual 多分支残差 + QSA micro-block 稀疏注意力","qwen3-8-flash-next-cost-efficiency-architecture","2026-09-02T02:00:00+00:00",{"id":51,"title":52,"news_slug":53,"published_at":54},"741bd34c-7134-4e8e-ab45-4f53dc576a6b","腾讯 Hy4 登顶 9 月开源榜:79.87 分超 Qwen3.8 Max,Anthropic 包揽总榜前三","tencent-hy4-tops-open-source-benchlm-september","2026-09-01T17:10:00+00:00",{"id":56,"title":57,"news_slug":58,"published_at":59},"637f84e0-e6dc-490a-bba1-879f6527bdd5","Qwen3.8-Max 2.4T 开源:Gated DeltaNet 把长上下文成本砍到 1\u002F8","qwen3-8-max-2-4t-open-weights-gated-deltanet","2026-08-30T03:00:00+00:00",{"id":61,"title":62,"news_slug":63,"published_at":64},"33f3b08b-c8a2-43ec-81cf-85e2b918f913","腾讯开源 Hy4 preview:770B MoE、1M 上下文,模型首次参与自身训练","tencent-hy4-preview-770b-moe","2026-08-29T15:00:00+00:00",{"id":66,"title":67,"news_slug":68,"published_at":69},"3d36921f-3b84-4663-97a0-fee7d4eff795","汤森路透开源 Thomson-1.0-Small:持续学习改造 Qwen,3B 激活的 35B MoE","thomson-1-0-small-continual-learning","2026-08-28T19:10:00+00:00"]