AI 资讯 · 每日前沿动态

DeepSeek融资大战：5000亿估值，500亿融资，一场疯狂而虚幻的金钱游戏 - 虎嗅网

Google News 中国大模型DeepSeek融资大战：5000亿估值，500亿融资，一场疯狂而虚幻的金钱游戏虎嗅网

· 大模型,融资,Google,DeepSeek,游戏,招聘HR

Spider Bench: comparing 9 vision models on 2,000 spider photos [P]

Reddit r/MachineLearning

I tested nine vision models on the same 2,000 spider photos. The highest exact-species accuracy was 49.85%. The tasks, predictions, scoring code, and run settings are public.

The setup:

Data: 2,000 photos covering 671 species and subspecies, sampled from a filtered collection of research-grade iNaturalist observations. The species list came from a Polish checklist; the photos were taken worldwide.
Task: Pick one of 20 scientific names. Each list contains the expected species, up to nine alternatives from the same family, and other-family names to fill the remaining places.
Controls: Every model received the same prepared image bytes and the same candidate lists in the same order. Sampling and candidate selection used fixed seeds.
Scoring: Matches against the iNaturalist labels, divided by all 2,000 assigned photos. Failed, missing, and invalid answers count as incorrect.

These are the nine high-effort runs used in my write-up:

Model	Exact-species accuracy
Gemini 3.8 Flash	49.85%
GPT-6 Astra	47.50%
Claude Fable 5.1	43.10%
Muse Spark 1.3	39.10%
GLM 5.3 Flash	36.75%
GPT-5.6 Sol	33.65%
DeepSeek V4.1 Flash	26.40%
GPT-5.6 Terra	22.80%
GPT-5.6 Luna	21.50%

The top two were separated by 47 photos. Additional runs at other effort settings are included in the repository.

I also grouped predictions by taxonomy. Gemini’s answers matched the expected genus 63.65% of the time and the expected family 93.40% of the time, including exact-species matches in both figures.

Code, frozen tasks, predictions, and results

Benchmark protocol

My write-up with charts and example predictions

submitted by /u/d_kielbasa
[link] [comments]

2026-09-21 10:13:13 · 大模型,具身智能,开源,OpenAI,Google,Anthropic,DeepSeek,智谱,扩散模型,模型评测,招聘HR,榜单评测

ChronosAttack: Adversarial Tool Scheduling Attacks on LLM Agents

arXiv cs.LGarXiv:2609.27857v1 Announce Type: cross Abstract: Large language model (LLM) agents often process external tool responses as they arrive, making response timing part of the decision process. We introduce ChronosAttack, a delay-only scheduling attack that changes when authentic tool responses arrive without modifying, adding, removing, or accelerating them. Bounded delays can change the order of the same evidence and alter the final decision. We evaluate ChronosAttack on GPT-5.6 Sol, Gemini 3.6 Flash, DeepSeek V4 Flash, and Claude Sonnet 4.6. GPT-5.6 Sol and Claude show strong targeted shifts in vulnerable settings, Gemini shows large shifts in the opposite direction, and DeepSeek is more stable under the tested schedules. We also find that sequential agent state is not always required and that a single scheduling inversion can cause a large decision change. Synchronization and order-consistency defenses reduce attacker control over observation order. These results show that tool-response timing can itself form an attack surface in asynchronous LLM agents.

2026-09-24 04:00:00 · 大模型,AI应用,OpenAI,Google,Anthropic,DeepSeek,Agent智能体,强化学习,招聘HR,论文

Scaling of Capability and Efficiency at Inference Time in Large Reasoning Models

arXiv cs.LGarXiv:2609.27166v1 Announce Type: new Abstract: Capability and efficiency are two key dimensions of reasoning in large language models (LLMs). Capability refers to the ability to solve a given problem correctly, whereas efficiency refers to the ability to do so with limited resources. When LLMs use Chain-of-Thought (CoT) reasoning to solve problems of controlled hardness, both the number of problems solved correctly and the number of tokens required to reach a correct answer depend on problem hardness and model size. However, how these factors jointly shape capability and efficiency remains poorly understood. Here, we use hierarchical Bayesian models to evaluate the capability and efficiency of LLMs from the DeepSeek-R1-Distill model family across four classes of arithmetic and algorithmic reasoning problems. At a fixed model size, the probability of correctly solving an instance decays approximately exponentially with instance size, our proxy for problem hardness. The decay scale grows sublinearly with model size, indicating that larger models are more capable, but that capability gains diminish with scale. Output length grows as a power law with instance size, which serves as a proxy for difficulty. However, the parameters of this power law do not vary systematically with model size, suggesting that larger models do not become more efficient. Together, these findings reveal potential limitations of naive scaling as a strategy for developing more capable AI systems: capability improves with diminishing returns, while efficiency shows little to no improvement.

2026-09-24 04:00:00 · 大模型,算力芯片,Google,DeepSeek,推理思考,微调蒸馏,论文

Can LLMs Catch a Rigged Backtest? A Clean-Control Calibration Benchmark

arXiv cs.CLarXiv:2609.28090v1 Announce Type: new Abstract: Backtest auditing is a calibration problem: high flaw recall is not useful when the model falsely flags matched clean strategies. We build a 96-item paired benchmark in which every flawed backtest has a clean control that holds strategy, dates, code style, labels, and reporting scaffold fixed while changing one methodology detail. A deterministic scorer separates flaw recall, clean-control false positives, evidence localization, and fix relevance. Over 1440 cached audits from four text endpoints, the primary DeepSeek auditor reaches 100.0\% closed and clean-aware code recall, but open prompts over-flag 93.8\% of clean code controls, and clean-aware all-three specificity is 87.5\% even where recall saturates. A clean-aware warning drops DeepSeek code false positives from 20.8\% (95\% CI 11.7--34.3) to 0.0\% (0.0--7.4) at unchanged recall, while the budget anchor still flags 38/48 clean controls under the same prompt. Reporting recall alone would rank three of these four models identically; reporting the clean-control rate separates them by 79 points.

2026-09-24 04:00:00 · 大模型,DeepSeek,扩散模型,强化学习,模型评测,提示工程,招聘HR,论文

Delegated Misalignment: How Multi-Agent Structures Amplify LLM Safety Risks

arXiv cs.CLarXiv:2609.27900v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in multi-agent systems where a principal agent decomposes tasks and delegates them to subordinate agents that may invoke external tools. Safety alignment, however, is still evaluated almost exclusively under a single-agent threat model, treating safety as a property of the individual LLM. We show that this assumption breaks down: \emph{individual safety alignment fails to transfer to multi-agent settings}. Two failure mechanisms emerge under delegation: \emph{responsibility diffusion} on the principal side and \emph{role-bias compliance} on the subordinate side, jointly converting language-level refusal into actionable harm. We refer to this phenomenon as \textit{delegated misalignment} and study it through a three-condition protocol across 6 frontier LLMs on 49 hazardous tasks. Delegation amplifies end-to-end harm substantially: DeepSeek-V3.2's full-execution rate rises from 30.6\% to 77.6\% once delegation is introduced, and the same model behaves very differently across roles (GPT-5: 22.5\% as a single agent vs.\ 61.2\% as a subordinate). Ablations further show that standard single-layer defenses each fail on their own and can even backfire. We call on the community to move beyond per-model alignment and toward composite safety mechanisms before multi-agent LLM systems are deployed at scale.

2026-09-24 04:00:00 · 大模型,AI应用,OpenAI,DeepSeek,Agent智能体,扩散模型,模型安全对齐,招聘HR,论文

SkillGym: Internalizing Human Skills into LLMs for Real-World Problem Solving

arXiv cs.CLarXiv:2609.27717v1 Announce Type: new Abstract: Human-written agent skills encode rich workflows for real-world problem solving, but are typically used as external inference-time instructions rather than internalized as reusable model capabilities. We introduce \texttt{SkillGym}, a framework that transforms these skills into executable, verifiable training environments for large language model agents. Its skill-to-task pipeline instantiates concrete tasks, verifies outcomes with code-based checkers, and assesses empirical skill dependence through contrastive executions. We construct and release 2,756 environments across 12 categories and collect 8,364 successful trajectories from multiple models and harnesses, averaging 49 tool calls and over 60k logged text tokens. These resources support supervised fine-tuning on verified workflows and reinforcement learning with outcome-based rewards. Under Claude Code, supervised fine-tuning improves Qwen3.5-35B-A3B by 199 Elo on GDPval-AA v2, 19.10 percentage points on Terminal-Bench 2.1, and 28.13 and 12.38 points on SkillsBench v1.1 with and without skills, respectively. Our 35B \texttt{SkillGym-Agent} reaches 51.47\% on skill-assisted SkillsBench, exceeding reported scores for Claude Sonnet 4.6, GPT-5.4 Mini, and DeepSeek V4 Pro. Without skills, it also surpasses skill-assisted bases under Codex and Claude Code, suggesting reusable procedural competence.

2026-09-24 04:00:00 · 大模型,AI应用,OpenAI,Anthropic,阿里巴巴,DeepSeek,代码生成,Agent智能体,搜索RAG,强化学习,微调蒸馏,招聘HR,论文

MWE-ECL: Recoverable Long-Range Context Does Not Always Override Local Lexical Priors

arXiv cs.CLarXiv:2609.27590v1 Announce Type: new Abstract: Long-context evaluations often test whether a model can recover distant evidence, but recoverability does not guarantee behavioral influence. We test the prediction that a distant discourse anchor can remain explicitly recoverable yet fail to change the locally preferred reading of a familiar multiword expression; such failures should concentrate when the model's no-anchor default conflicts with the anchor, while prior-correct decisions remain largely preserved. We introduce Multiword Expression Effective Context Length (MWE-ECL), a bilingual diagnostic whose matched anchor-retrieval, no-anchor prior, and interpretation prompts measure explicit recoverability, model-observed defaults, and anchor-conditioned decisions, respectively. Across eight English deployment panels on a shared 0-128K grid, retrieval-control accuracy on prior-conflict items is 0.989-1.000, prior-conflict override spans 0.806-1.000 (0.809-1.000 after conditioning on correct retrieval), and preservation of prior-correct decisions remains 0.977-1.000. A same-call control querying retrieval and interpretation in one prompt reproduces the gap for DeepSeek V4 Pro (1.000 retrieval versus 0.900-0.920 interpretation), showing that separate invocations are not its sole explanation; smaller or absent gaps in the other two models bound its generality. For DeepSeek V4 Flash, separate prompt-fit tests retain perfect retrieval with lower interpretation at 512K and 1M, while foil-consistent cues shift the no-anchor prior far more than retrieval; cross-model cue effects are heterogeneous. A separately reported 10-family Chinese subset shows similar descriptive gaps, but imperfect retrieval for some models prevents an integration-only attribution. MWE-ECL therefore evaluates whether explicitly recoverable distant context changes a competing local semantic decision.

2026-09-24 04:00:00 · 大模型,DeepSeek,扩散模型,提示工程,长上下文,论文

Automated Extraction of Records of Processing Activities (RoPA) Using Hybrid RAG and Locally Deployed Large Language Models

arXiv cs.CLarXiv:2609.27359v1 Announce Type: new Abstract: Vietnam's Personal Data Protection Law (Law No. 91/2025/QH15) and Decree No. 356/2025/ND-CP, effective January 1, 2026, require organizations to establish and maintain Records of Processing Activities (RoPA). Manual RoPA preparation is labor-intensive, while cloud-hosted large language models (LLMs) may conflict with data-sovereignty requirements. We propose RoPA Manager, a system for automated RoPA information extraction using hybrid retrieval that combines lexical ranking over tsvector, dense-vector search, Reciprocal Rank Fusion (RRF), and locally deployed LLMs. We introduce a Vietnamese RoPA benchmark with 32 organizations, 77 processing activities, 12 field groups, and 4,338 reference values. Evaluation is reported at three distinct levels. The automated scorer, tested on perturbed data without invoking an LLM, achieved F1 = 0.9493 [0.9436, 0.9548]; this measures scorer robustness rather than end-to-end extraction accuracy. End-to-end extraction achieved token coverage of 50.04-55.25% against the reference labels. Two independent experts reviewed 1,558 reference values (35.9% of the benchmark), found no incorrect values, and achieved 99.68% agreement with PABAK = 0.9936. Value-level precision was not measured. Across 32 paired scenarios on a 24 GB GPU, locally deployed Qwen3.5-27B-GPTQ-Int4 showed no statistically significant difference from cloud-based DeepSeek-V4-Flash (difference 0.20 percentage points in favor of DeepSeek, 95% CI [-0.93, 1.32], p = 0.72), while Gemma-4-31B performed significantly worse (p < 0.01).

2026-09-24 04:00:00 · 大模型,算力芯片,AI应用,阿里巴巴,DeepSeek,搜索RAG,模型评测,招聘HR,论文

Avoid common mistakes with popular AI tools by studying this new masterclass, just $16

Mashable

TL;DR: Learn how to use AI more effectively with the Ultimate AI Assistant Masterclass, now $15.99 (reg. $39).

Opens in a new window

Credit: Eduonix Learning Solutions

The Ultimate AI Assistant Masterclass

$15.99
$39 Save $23.01

Most AI assistants are free to use, but getting useful answers out of them takes more than typing a question into a box. A vague reply and a precise one usually come down to how the request is written. The Ultimate AI Assistant Masterclass shows you how to use AI effectively, and lifetime access just went on sale for $15.99 (reg. $39).

This bundle includes six self-paced video courses, each focusing on AI platforms such as ChatGPT, Google Gemini, Microsoft Copilot, Perplexity AI, DeepSeek, and Tabnine AI. Your training starts with prompt engineering fundamentals and then covers guidance on specific work, from research and content creation to coding and daily productivity. No prior experience is needed, so the courses work whether you’re new to these tools or already using them every day.

Throughout every course, there’s a throughline of learning better principles in prompt engineering. That includes learning how to phrase a request so an assistant returns accurate, usable results that you can apply to practical work. There’s even a Perplexity course that focuses on research and real-time search, along with a Gemini course that works through multimodal tasks across text and images. You’ll also get access to courses for advanced reasoning with DeepSeek, productivity improvements with Copilot, and content creation with GPT. If you work with code, make sure to check out the Tabnine courses to help you speed up your workflows without compromising quality.

Once you’ve redeemed your purchase, you have access to all course materials indefinitely.

Get the Ultimate AI Assistant Masterclass while it’s on sale for $15.99.

StackSocial prices subject to change.

2026-09-23 09:00:00 · 大模型,AI应用,OpenAI,Google,Microsoft,DeepSeek,代码生成,多模态,推理思考,搜索RAG,扩散模型,提示工程,招聘HR,网络安全,榜单评测

Gallup poll reveals widespread global AI optimism, not doom and gloom

Mashable

a person holds an iPhone displaying icons for Copilot, Gemini, Meta AI, Claude, ChatGPT, Grok, DeepSeek, Perplexity and Doubao in a folder titled

People around the world are largely optimistic about artificial intelligence, according to a new Gallup poll. At the same time, a majority of respondents reported they've never actually used the new technology.

The Gallup-Microsoft AI survey, published Sept. 23 as part of the polling company's ongoing research with Microsoft, found that positive feelings about AI outweigh negative ones in 34 of the first 37 countries to complete fieldwork this year. The United States, Egypt, and Palestine were the only exceptions. Gallup says the study will eventually cover 140 countries.

Across the 37 countries, 72 percent of AI-aware adults reported at least one positive emotion toward the technology, compared with 41 percent who reported at least one negative emotion. Curiosity was the most common response, with a median of 64 percent saying AI makes them feel curious. Worry was the most common negative emotion, at a median of 32 percent.

Views on AI's broader impact were also mostly positive. In 29 countries, more people said AI would mostly help their country than harm it. The contrast between the United States and China was particularly revealing: 93 percent of Chinese respondents said AI will help their country. The United States ranked near the bottom, at 36 percent, just above Egypt and Bangladesh.

Gallup noted that optimism doesn't always track with usage. In Nigeria, for example, 68 percent of AI-aware adults expect AI to help the country, although only 20 percent have used it.

In the United States, a Pew Research Center AI survey published in September 2025 found that 62 percent of Americans interact with AI several times a week, including 31 percent who do so several times a day or "almost constantly."

Gallup's poll also found low trust in AI accuracy, and as Mashable has reported, AI hallucinations and inaccuracies remain a stubborn problem. A median of 36 percent of respondents said they trust AI completely or a lot to provide accurate information, with majorities expressing that level of trust in only four countries: Israel, Vietnam, China, and Nigeria. Trust tended to rise with use. In the U.S., 45 percent of daily users trust AI's accuracy, compared with 8 percent of people who have never used it. However, Gallup cautioned that the survey can't determine whether trust drives use or vice versa.

Wealthy Western countries stood out for their worry over AI. Gallup found that 74 percent of AI-aware Americans say AI makes them feel worried, the highest share in any country surveyed, followed by the Netherlands and Canada. But within those countries, daily users were generally less worried than people who use AI less often or not at all.

2026-09-23 17:51:31 · 大模型,AI应用,OpenAI,Google,Anthropic,Meta,Microsoft,DeepSeek,xAI,代码生成,搜索RAG,模型安全对齐,招聘HR

AI Agents Teamed Up to Cheat at Blackjack. Their Collusion Is Getting Harder to Spot

WiredA clandestine card-counting operation suggests we may need new ways to spot agent-to-agent deception.

2026-09-23T18:30:00.000Z · 大模型,AI应用,具身智能,开源,OpenAI,Google,Anthropic,Meta,阿里巴巴,DeepSeek,Agent智能体,扩散模型,招聘HR,合作,榜单评测,开发者生态

DeepSeek新论文公开Agent训练！梁文锋署名

量子位每秒能产生5000+个沙盒

· 大模型,AI应用,DeepSeek,Agent智能体,论文

GPT-6 Sol 降价 50% 的秘密：消失的 Terra，一场模型梯队平移

雷锋网

奥圣上台，梁圣危机了么？

作者丨高允毅

编辑丨岑峰

OpenAI 也走性价比路线了？别被它的“偷梁换柱”给骗了。今早，硅谷又热闹了，迎来双雄对决。Anthropic 推出 Claude Opus 5.5，OpenAI 紧接着推出 GPT-6 Sol 与 Luna。和以往模型更新不同的是，行业的讨论点聚焦到了“大家怎么都变便宜了”。Claude Opus 5.5 较上一代 Opus 5，成本降低 40% ，拥有 Fable 级性能。而 GPT-6 Sol 降得更狠，性能持平 GPT-5.6 Sol，价格直接腰斩，每百万 Token，输入 2 美元，输出 10 美元，性价比看似要一步踩进 DeepSeek 的腹地。但如果仔细对照，就会发现 OpenAI 所谓腰斩，是抹掉了一个型号 Terra。我们都知道，OpenAI之前的模型档位是：旗舰 Sol、中端 Terra、轻量 Luna。现在变成了 Astra、Sol、 Luna。Terra 哪去了？网友戏称变成 Sol了，把整个梯队往上提了一格。

再重新对比一下 GPT-5.6 Terra，每百万 Token 输入 2 美元、输出 12 美元，和 GPT-6 Sol 对照看，几乎没什么变化。这波降价潮的真相，也就不言自明了。

三家账本摊开看，谁在真降价？

当下AI赛道价格战白热化，各大厂商纷纷下调模型定价，但降价的成色、逻辑和含金量天差地别。Anthropic 降价最有诚意，DeepSeek 仍有绝对的成本优势，但差距在缩小，OpenAI 更像是商业定价叙事为主，工程优化为辅的策略型降价。

先说 Anthropic。Anthropic 走的是

2026-09-23 12:47:00 · 大模型,OpenAI,Anthropic,DeepSeek,传媒内容,招聘HR,模型发布,产品更新,榜单评测

【钛晨报】“十五五”民政工作怎么干？四大重点任务，养老排在第一位；马斯克预测未来世界：20年后可能有1000亿台人形机器人；高瓴创投原合伙人严文韬正式入职DeepSeek

钛媒体

（本文作者为钛媒体官方账号，钛媒体经授权发布）

【钛媒体综合】9月23日，国务院新闻办举行“开局起步‘十五五’”系列主题新闻发布会。民政部部长李常官介绍，“十四五”时期，中国兜底民生保障全面加强，常态化保障3936万名低保对象、488万名特困人员、57万名孤儿和事实无人抚养儿童基本生活，“两项补贴”惠及1198万名困难残疾人和1675万名重度残疾人。养老服务保障水平持续提升，基本养老服务制度全面建立，5300多万名老年人享受老年人补贴，养老服务机构和设施达到39.5万个。专项社会服务能力稳步增强，婚姻登记实现“全国通办”，群众办事便利度显著提升，公益性殡葬设施短板加快补齐，“身后事”办理让家属更加省心、暖心。相关社会治理创新发展，83万余家社会组织在经济社会发展中发挥积极作用，“乡村著名行动”深入实施，规范命名乡村地名92.7万条。

“十五五”时期，中国将持续推进普惠性、基础性、兜底性民生建设。重点推进四方面工作。

一是着力增进老年人福祉。深入实施积极应对人口老龄化国家战略，健全城乡三级养老服务网络，加大养老机构护理型床位建设，推动养老机构护理型床位占比达到73%，加强失能失智老年人照护服务，因地制宜发展农村养老服务，保障养老服务质量安全，推动全体老年人享有基本养老服务。同时，大力发展银发经济，促进老年人社会参与，加强老年人权益保障，建设老年友好社会。

二是提升社会救助和社会福利水平。健全科学精准认定社会救助对象的制度机制，加强动态监测和分层分类救助帮扶。完善困境儿童福利保障体系，加强流动、留守儿童关爱保护，推动儿童福利机构“开门办院”，让更多困境儿童共享机构专业服务资源，减轻家庭负担。完善残疾人社会保障制度和关爱服务体系，加强重度残疾人托养照护服务，加快发展康复辅助器具产业。

三是优化社会事务管理服务。巩固优化婚姻登记“全国通办”，深化婚俗改革，倡导文明健康婚俗礼仪。强化殡葬行业公益属性，建立健全基本殡葬服务制度，建立殡葬行业跨部门综合监管机制，持续推进公益性殡葬设施建设，深化殡葬移风易俗。

四是加强和创新社会治理。提升地名管理服务法治化、规范化水平，推动区划地名历史文化传承保护工作。优化社会组织结构布局，健全监管机制，引导社会组织更好发挥积极作用。促进和规范慈善事业发展，持续实施“阳光慈善”工程，壮大慈善规模，提升慈善组织公信力。

钛媒摘声：

大模型是历史上发展最快的技术浪潮，更强更通用。当前，Agent的进化，正从对话到助手，从被动到主动，从个体到群体。Agent正发展为Agent经济，而这也是token经济。智能体正取代人类成为互联网主角。五年后非人类流量将是人类流量的1000倍。
——源码资本投资合伙人、美国国家工程院外籍院士张宏江
当出现安全隐患时，AI公司无需达成全行业协议即可放慢开发速度，市场压力将使“对齐”（即确保AI系统遵循用户意图）成为一种竞争优势。每家实验室都有责任，也有动力按照安全训练模型所需的速度推进。我的看法是，信任和对齐能力正迅速成为区分不同AI智能体和模型的最重要能力。任何不重视对齐的实验室都将落后。
——Meta首席执行官马克·扎克伯格

国内公司：

高瓴创投原合伙人严文韬正式入职DeepSeek

9月23日，高瓴创投原合伙人严文韬已正式加入DeepSeek，出任CFO。公开资料显示，这位被梁文锋选中的大将出生于1991年，毕业于复旦大学。2013年至2020年间，他先后任职于腾讯投资、H Capital，2020年加入高瓴创投，后晋升为合伙人。严文韬过往参与投资的代表项目包括字节跳动、智谱、MiniMax、小红书、微牛、极兔等。

闲鱼公布调查结果：“涉未成年人淫秽”系不实消息，已就引流行为报案

9月23日，闲鱼调查结果公布：经全面清查，在平台公开展示信息中未发现淫秽信息，更不存在涉未成年人淫秽信息；近期媒体报道中涉及的违规聊天截图，经核实来自其他平台，并非发生在闲鱼，闲鱼已就相关事件向公安机关报案。面对跨平台引流等行业性难题，闲鱼表示，将全力履行平台主体责任，主动配合各级主管部门，持续升级技术识别能力，加大人工巡查力度，同时倡议，各互联网平台联手建立针对黑灰产的反引流联盟，以技术手段和多平台协同，阻断利用网络平台为违法犯罪引流的行为。

豆包工作功能更新，任务模式新增“目标”和“计划”模式

9月23日，豆包工作宣布上新，任务模式新增“目标”和“计划”两种模式，此外支持任务队列、Markdown文件编辑功能、新增深色模式。目前，以上功能已在豆包工作电脑端全量上线。

腾讯WorkBuddy企业版发布办公套件化升级最新进展，管理员统一管控Credit用量

9月23日，腾讯WorkBuddy企业版发布套件化升级最新进展，腾讯文档、乐享、网盘、安全与效能平台及AI设计智能体Ardot首批接入。企业按原价格获五大产品权限，实行一个订阅、一个Agent底座、一个管理后台；网盘由5GB/人扩至50GB/人，各端共享账号、资产与上下文，管理员统一管控Credit用量。套件以Agent Harness统一供能，文档转长期记忆、乐享做知识治理、Ardot精修设计。自2026年3月上线，WorkBuddy已迭代50余版，覆盖政务、教育、零售、金融等50多行业。

网易云音乐鸿蒙版正式上线

9月23日，网易云音乐鸿蒙版全面上线，功能体验更完整，核心听歌体验、社区互动体验全适配；同时完成手机、折叠屏、平板多终端适配，用户可无缝体验跨端听歌。据悉，鸿蒙版APP由网易云音乐与鸿蒙深度合作联合开发，8月底上线尝鲜内测版。过去3周，双方根据用户反馈，快速迭代8个版本，上新具体功能1000+。同时，网易云音乐鸿蒙版特别定制鸿蒙专属“沉浸光感”播放器，融合鸿蒙星环与网易云音乐经典的黑胶播放器元素。

国外企业：

马斯克预测未来世界：20年后可能有1000亿台人形机器人

9月23日，特斯拉CEO埃隆・马斯克接受央视财经专访。谈及机器人，他展望未来美好图景：每个人都能拥有专属机器人，它们可以照料年迈的父母，看护孩童，还能担任孩子的一对一私教，承担各类工作。未来世界，一个人可以操控成百上千台，甚至上万台实体机器人和数字智能体。“我的预测是十年内至少有10亿台人形机器人；未来15年或许达到100亿台；未来20年可能达到1000亿台。”

SpaceX据悉停止猎鹰9号2028年以后预订，业内正掀起运力抢购热潮

9月23日，据报道，消息人士称，SpaceX已停止接受2028年以后的猎鹰9号火箭预订。政府官员和业内人士警告，随着猎鹰9号退出市场，火箭发射运力可能在2030年前出现供不应求的局面。一些公司正自行研发火箭，或者考虑收购一家火箭公司，以保证自己的发射日程。现有的发射商也在抓紧扩大投资，以承接SpaceX外溢的订单。

苹果出手限制“摇一摇”广告

9月23日，大量iPhone用户反馈，苹果在最新发布的iOS 27.2 Beta 2测试版系统中，针对中国区用户悄然新增了一项名为“Restrict Motion Data”（限制运动数据）的设置选项。该功能位于“设置—隐私与安全—运动与健身”目录下，开启后用户可手动选择需要限制的应用，被加入名单的App将无法获取加速度计、陀螺仪等运动传感器数据的权限，从而从系统底层切断“摇一摇”开屏广告的触发源头。

实测发现，iPhone在升级到iOS 27.2 Beta 2版本后，AppStore必须登录国内ID才能在设置里看到“Restrict Motion Data”选项，并启用该功能，而启用后重新切换回国外ID功能就会失效，此前用国区账号设定的限制名单也会同步消失。也就是说，这是苹果针对中国市场“特供”的一项用户体验保护措施，功能跟着账号走，而非跟着设备走。

三星与KT和SK Telecom携手，为韩国AI RAN项目提供支持

9月23日，三星电子今日宣布，已与韩国电信（KT）和SKTelecom签署合同，为其在韩国科学技术信息通信部（MSIT）“超人工智能网络”计划下的人工智能无线接入网（AIRAN）项目提供支持。三星将分别担任KT的全球独家供应商和SKTelecom的主要供应商。这些项目计划于2026年10月启动，将在从造船厂到石化设施等各种工业环境中部署5G独立组网(SA)专用网络，以验证AIRAN能力并为未来的部署生成现实世界的见解。

政策风向：

央行：9月28日至10月8日开展隔夜逆回购操作，每日操作量不超过10000亿元

9月23日，央行公告，为更好匹配银行体系短期流动性需求，中国人民银行将在9月28日至10月8日开展隔夜逆回购操作，采用固定利率、数量招标，每日操作量不超过10000亿元。

央行：9月24日将开展8000亿元MLF操作，期限为1年期

9月23日，央行发布9月中期借贷便利招标公告称，9月24日，中国人民银行将以固定数量、利率招标、多重价位中标方式开展8000亿元MLF操作，期限为1年期。

五方面重点发力，中国将进一步优化婚姻管理服务

9月23日，在国务院新闻办公室今天举行的“开局起步‘十五五’”系列主题新闻发布会上，民政部副部长舒惠好介绍，“十五五”时期，民政部门将聚焦群众需求，进一步优化婚姻管理服务，在以下五个方面重点发力：

一是提升登记服务水平。完善全国婚姻基础信息库，提升数据质量和信息核验效率，让“全国通办”运行更加顺畅。出台男女双方均非内地居民的中国公民在内地办理婚姻登记办法，进一步规范和便利港澳台居民、华侨来内地办理婚姻登记。

二是拓展辅导服务供给。健全婚姻家庭辅导服务体系，推动服务资源向基层社区延伸、向线上平台拓展。鼓励各地引入更多专业力量，为群众提供婚前辅导、婚姻家庭关系调适等全链条服务。

三是优化登记场所建设。推进婚姻登记机关规范化、标准化、便利化、信息化、专业化、人文化“六化”建设，常态化开展结婚颁证、集体婚礼等特色服务，增强婚姻登记的仪式感和神圣感。

四是培育文明婚俗风尚。持续深化婚俗改革，着力破除大操大办、高额彩礼、低俗婚闹等陈规陋习，弘扬文明简约的婚俗新风。

五是规范婚介行业发展。联合相关部门开展整治婚介机构乱象专项行动，严厉打击虚假宣传、诱导消费、婚托婚骗等违法违规行为。完善婚介机构监督管理的政策措施，建立健全行业监管长效机制。支持各地发展公益婚恋服务平台，为青年提供交友渠道，助力婚介行业健康有序发展。

中国将适度超前、系统推进新一代通信网建设，推动算力设施扩容提质

9月23日，“十五五”时期，中国将把握新一代通信网络建设的战略机遇，适度超前、系统推进新一代通信网建设。工业和信息化部副部长余晓晖表示，要坚持应用牵引、适度超前、系统推进新一代通信网建设，推进宽带网络向双万兆演进，推进低轨卫星互联网系统建设，统筹优化国际海陆网络布局，推动算力设施扩容提质，强化算网协同和算力互联，更好支撑一体化算力网建设。

股市行情：

恒生指数公司推出恒生半导体产业指数等八条新指数

9月23日，恒生指数公司推出恒生半导体产业指数等八条新指数。

山东路桥：控股股东及一致行动人拟增持4.5亿至9亿元股份

9月23日，山东路桥公告称，公司控股股东高速集团及其一致行动人高速投资控股拟自公告披露之日起6个月内，以专项贷款和/或自有资金通过集中竞价方式增持公司股份，增持总金额不低于4.5亿元且不超过9亿元。其中，高速集团增持金额不低于3亿元且不超过6亿元，高速投资控股增持金额不低于1.5亿元且不超过3亿元。本次增持不设价格区间。

奥联电子：控股股东将变更为骏图天泽，股票复牌

9月23日，奥联电子公告称，公司股票自2026年9月24日开市起复牌。控股股东瑞盈资产拟将所持16.20%股份协议转让给骏图天泽，另将5%股份转让给衢州信安广合。交易完成后，控股股东将变更为骏图天泽，实控人变更为陈扬。

行云科技：全资子公司签订8.72亿元智算资源租赁服务合同

9月23日，行云科技公告称，公司全资子公司长沙湘树云科技有限公司与VE客户签署《智算资源租赁服务合同》，湘树云向VE客户提供算力服务，协议服务期限为5年，含税总金额为8.72亿元。本次合同履行期限较长，收入将分期确认，存在双方因经营情况变化、财务状况恶化等丧失履约能力的风险。目前公司对应服务器的采购工作已完成，正在接收验收中，预计在2026年9月30日前完成对VE客户的交付。若合同顺利实施，预计对公司未来的经营业绩产生一定影响。

其他重要内容：

经合组织上调2026年全球经济增速预期至2.9%

9月23日，当地时间23日，总部位于法国巴黎的经济合作与发展组织（经合组织）发布最新一期中期经济展望报告，预计2026年全球经济增速为2.9%，较今年6月预测值上调0.1个百分点；2027年全球经济增速预计为3.0%，较6月预测值下调0.1个百分点。报告显示，在中东地缘冲突带来持续负面影响的背景下，全球多数经济体仍展现出韧性。较为充足的石油库存、海湾地区以外的增量供应，叠加各国政府的定向支持政策，有效对冲了多重下行风险。同时，AI相关产业的持续景气也拉动了投资、生产和贸易增长。报告预计，二十国集团国家总体通胀率今年将小幅上行至4.1%，2027年伴随能源价格回落，整体通胀率将下降至3.6%，但核心通胀回落节奏仍然偏慢。

未来四年“双一流”高校本科招生数还将扩容7.6万人

9月23日，教育部发展规划司

2026-09-23 23:20:00 · 大模型,算力芯片,AI应用,具身智能,自动驾驶,融资,政策监管,Meta,字节跳动,腾讯,华为,DeepSeek,xAI,智谱,对话助手,Agent智能体,办公效率,设计创意,医疗健康,金融,教育学习,扩散模型,模型安全对齐,政务,工业制造,零售电商,传媒内容,营销广告,招聘HR,模型发布,产品更新,收购并购,合作,财报,开发者生态

‌⁣‌‌⁤⁡⁤‬⁡‌⁤‬‬⁣‌⁢‬⁣‬⁤‬‬‍⁤⁤⁢⁣⁢‍⁡‬‬⁢WorkBuddy 把办公 Agent，做成了人人可搭的「赛博乐高」

爱范儿

演示 Demo 里，一份漂亮的 PPT 出炉，任务就算完成了。到了办公室，往往还要接上一句：「这个再改改。」

你上个月的的修改意见，AI 能记住吗。同事补进来的材料，它也得接着用。下个月做同类汇报，别再从头解释一遍……

最近沙利文发布的《2026 年全球桌面 AI 智能体市场研究报告》，把上手、协作和长期使用都纳入了评价指标。腾讯 WorkBuddy 同时位列其中的中国个人端与企业级桌面智能体榜单第一。

两个榜单背后，是一款办公工具迟早要碰到的两种要求。

新手希望今天就能交出结果，别把用 AI 变成另一份工作。团队则要更关心这套方法能不能大家一起用，权限怎么分，改错了怎么办？

APPSO 此前把 DeepSeek Harness 比作「赛博乐高」，它给开发者自由组合插件的空间。

但其实桌面 Agent 也有着和乐高相似的使用过程，先帮助新手完成一项任务，再围绕同一份任务不断调整，最后把个人摸索的方法变成团队可以复用的流程。

那么职场人怎么能拥有一个属于自己「赛博乐高」，APPSO 希望通过一系列实测，看看 WorkBuddy 在从个人走向团队的过程里，它的能力边界在哪里。

好用的 Agent，应该让人少学一点

第一次打开 WorkBuddy，任务、助理、项目、专家、自动化、资料库和 Skill 等入口同时出现在眼前，用户难免犹豫该从哪里开始。

「专家·技能·连接器」广场提供了一条更直接的路径，因为这里的能力按照用户要做的事情呈现。分析数据、准备招聘、策划营销活动、撰写内容等等办公场景，都能找到对应的 AI 专家。

打工人未必知道任务背后需要什么模型、工具和执行步骤，却很容易判断，自己今天需要的是一位数据分析师，还是一位选题顾问。

我们首先选择了一个与编辑工作直接相关的角色，让「科技频道选题评估师」判断围绕 iPhone DUO 的选题是否值得做，并分别面向硬核极客、数码爱好者和普通读者，给出不同的报道角度。

WorkBuddy 核查发布日期与相关事实后，再结合已有内容完成评分和选题建议，避免后续策划建立在错误前提上。

随后，我们上传一份虚拟扫地机器人 Q3 的销量 CSV，要求数据分析专家找出销量环比下降最明显的两个品牌，计算客单价变化，并解释降价是否带来了销量。

WorkBuddy 据此生成柱状图和分析材料，把分散的数据整理成可以直接审阅的结果。

为了观察专家广场能够覆盖多大的任务跨度，我们又选择了几类差异明显的场景。「直播电商教练」需要为磁吸散热充电宝设计一段 45 秒口播，兼顾前三秒 Hook、「边充边冷」卖点与小黄车引导。

「资深营销策划师」则要在 20 万元预算、10 家媒体和三小时时长的约束下，安排一场包含签到、体验、宣讲与伴手礼发放的 AI PC 沙龙。

这些专家的作用，是把模型能力和提示词经验整理成明确角色，让用户从「要完成什么」开始，而不用先研究背后的工具组合。

不过，AI 开始操作文件以后，另一个顾虑也随之出现。如果指令没表达清楚，或者修改结果不符合预期，原文件还能不能找回来？

其实 WorkBuddy 5.0 及以上版本还上线了「安全中心」，越权操作实时拦截、敏感操作审批、删除文件自动进入回收站、修改文件可版本还原等核心能力默认开启。

▲ 左下角设置-安全中心

对普通用户而言，文件修改留下恢复的余地，会直接影响他们是否愿意把真实工作交给 Agent。低门槛因此也包含另一层意思，除了更容易开始，还要让用户有调整和纠错的空间。

让 AI 进入上下文，让决定权留在人手里

打工人都知道，生成第一份结果只是真实工作的起点。

数据会更新，判断会变化，内容也会反复调整，我们很多时间往往消耗在搬运上下文上。

于是我更常用WorkBuddy 「人机双写」功能，来和AI一起修改文件，它支持腾讯在线文档以及本地 Word、Excel、PPT 和 Markdown 文件这些主流格式。

在模拟新品传播稿中，我们圈出环保包装段落，要求它保留「可降解」和「减碳 30%」两项信息，只调整表达。

修改直接同步到原文件，其余内容保持不变，省去了复制、粘贴和核对版本的过程。

接下来的业务复盘报告测试，更能体现持续协作的价值。

随后，我们手动更新了 Word 业务报告中的数据，再让 WorkBuddy 补充相关段落。它使用了最新数字，只修改指定位置，并延续了原有格式；

在 PPT 中选中某段话术后，它也能保留项目符号和页面结构完成改写。

同样的体验，也出现在跨格式的任务中。我们提供了一份员工商业行为与合规准则的测试文档，选取其中关于数据安全与资产保护的章节，要求 WorkBuddy 提炼成新员工培训演示大纲，并保存到资料库里。

实测中，它成功输出了逻辑清晰的 PPT 大纲。我们随后沿用这份大纲，继续让它生成 PPT，并对演示中的特定话术进行人机双写修改。

整段工作有一个连续的推进过程，从阅读材料、重组内容，到生成演示文件，再到调整具体表达，前一个阶段的产物都能成为下一步的起点。

好的协作不会夺走人的控制权，只会减少人反复解释的成本。

人机双写的控制感，来自 AI 始终基于文件的最新版本处理内容，而修改范围与最终表述仍由用户决定。

让 Agent 从个人助手，进化为组织OS

虽然现在办公 Agent 产品已经不少，但很多公司和团队在引入 AI 后常常会遇到一个问题：明明有人能用 AI 提效 10 倍，但公司用上 AI 后可能效率提升却不明显。

团队的 AI 能力，不能只看最会用的那个人。

所以我们尝试用 WorkBuddy 给团队搭建一套「组织 OS」，希望把找线索、选题、写稿、审稿和知识积累接起来。让每天的工作，成为整个团队下一次可以调用的上下文。

资料库先把编辑部的记忆建起来

我们做了十多年的内容，旧文章里有不少值得重新引用的洞察。把全文存进去还不够，还要筛选关键观点、论证逻辑和有价值的表达，保留出处，方便写稿时核对。

历史文章、长期跟踪的选题资料，以及确认过的写作偏好，分别整理。临时改稿的意见，也要判断是否值得成为长期规则。

我发现最近 WorkBuddy 更新资料库「团队空间」，很适合迎来搭建团队的知识库。它让这些内容可以由多人共同维护。

个人文档能移入团队空间，再按项目和目录分类。采访材料、选题记录和 Agent 产物都能放在一起。

我们可以让资深编辑负责筛选和写入，新人先调用核实过的资料，管理者负责成员与权限。Agent 也只能访问当前用户有权查看的内容。

有新人进入团队，就能从过去十多年精选过的积累开始，不用先把所有旧文章翻一遍，然后自己判断哪些值得学习。

从人机双写，到人-人-机协作

AI 领域每天都有大量论文和新技术产品涌现，面对过载的信息，作为AI媒体如果不能利用好 AI ，同样很容易跟不上。

我们也会借助 AI 先整理事实、旧文引用和待确认项。至于选题值不值得写、从哪个角度写，还是由编辑决定。

在这里，人机双写还有个挺适合团队的进阶用法，我们拿最近新款 Mac mini 的实测内容来试试，这个项目需要多人分工，刚好让同事和他们的 Agent，一起改稿。

编辑把初稿弄完之后，就通过资料库分享出去，主编以及参与实测的同事一起查看，大家直接在原文上批注。

2026-09-23 09:02:23 · 大模型,AI应用,具身智能,政策监管,腾讯,DeepSeek,对话助手,Agent智能体,搜索RAG,办公效率,设计创意,教育学习,扩散模型,提示工程,图神经网络,零售电商,传媒内容,营销广告,AI for Science,招聘HR,模型发布,产品更新,榜单评测,论文,开发者生态

早报｜iOS27测试版新功能可阻止摇一摇广告/5999起，小米18 Pro发布/宾利发布首款纯电车Torcal，888马力

爱范儿

小米 18 Pro 系列发布，平板、穿戴与三筒洗衣机同场上新

DeepSeek 发新论文，公开 Agent 训练沙箱 DSec

iOS 27.2 Beta 2 加入运动数据限制，可阻止「摇一摇」广告跳转

蔚来 ES9 交付达 3 万台，用时 119 天

iPhone 18 Pro 用户反映夜景照片出现「绿影」

🪟微软向 DARPA 开放 Majorana 2 量子系统，支持现场独立评估

闲鱼公布涉未成年人违规引流调查：称站内未发现淫秽信息，已报案

WPP 在伦敦启用 AI 广告制作中心，先服务可口可乐、联合利华等客户

京东方玻璃基封装载板试验线贯通，24 层产品送交客户验证

美国调查 Comma.ai 辅助驾驶设备，涉及 5 起碰撞事故

李飞飞：AI 安全不能只由开发者自我评估

骁龙 X2 携手 Googlebook 进军 Linux

宾利首款纯电动车 Torcal 亮相，S 版最大功率 888 马力

蚂蚁密算发布「密算一号」，企业可在受控条件下调用云上 AI

🪄千问发布 Qwen-Audio 3.1，新增音频创作与场景理解模型

仓颉编程语言 1.2.0 发布，强化跨平台、编译器与工具链

豆包工作升级任务模式，新增「目标」与「计划」两种执行方式

Mirage 发布 Tesseract，让 AI Agent 直接编辑分层视频项目

蚂蚁开源 Ming-Image 设计模型，可生成版式并拆分图层

‍阶跃星辰开源 Step Code，支持终端编程与长任务委托

吉利发布 2.25 兆瓦智充站，测试车辆 4 分半补电至 70%

百威英博调整产品策略，小包装啤酒和功能型无酒精饮品将增多

Instacart 宣布将接入 Meta Muse，购物清单可转为生鲜订单

YouTube 推出 Shorts 连续剧功能，短剧可按季和集播放

《活久久》定档 12 月 31 日，张艺凡与陈立农主演

《雷·冈恩》首曝预告，布拉德·伯德拍未来都市侦探故事

小米 18 Pro 系列发布，平板、穿戴与三筒洗衣机同场上新

小米昨晚发布 18 Pro、18 Pro Max 两款手机，并带来平板 9 系列、Watch S5 41mm 和 Watch S5 透明版。主要产品与版本信息如下：

小米 18 Pro：12GB + 256GB 版 5999 元起，配备第六代骁龙 8 至尊版、6.4 英寸屏幕、7000mAh 电池；后置 2 亿像素主摄、2 亿像素潜望长焦与 5000 万像素超广角。

小米 18 Pro Max：12GB + 256GB 版 6999 元起，采用第六代骁龙 8 超级至尊版、8500mAh 电池，影像组合同样为双 2 亿像素加 5000 万像素超广角。两款手机均配有一体式纯平背屏、硬件级智能防窥和澎湃 OS 4，发布后开售。

透明特别版：18 Pro 的 16GB + 1TB 版定价 9999 元，18 Pro Max 同配置定价 10999 元；两款采用红色透明视觉设计。

平板与手表：平板 9 / 9 Pro 分别搭载第四代骁龙 8s、第五代骁龙 8，起售价分别为 2999 元、3799 元。Watch S5 41mm 起售价 1299 元，支持与小米汽车、智能门锁联动；Watch S5 透明版限量 5000 台，售价 2499 元。

手环与家电：手环 11 三彩釉陶瓷版采用景德镇工艺制作，支持睡眠 HRV 监测；米家三筒洗衣机健康洗 Pro 14kg 系列采用一个大筒、两个小筒的分洗结构，同场推出滚筒单洗与洗烘版本。

小米称，18 Pro 系列的「传奇一瞬」影像功能提供端侧相机风格和云端处理，背屏首批有超过 100 款应用卡。

2026-09-24 00:17:26 · 大模型,AI应用,开源,政策监管,Google,Meta,Microsoft,字节跳动,阿里巴巴,DeepSeek,小米,京东,语音音频,代码生成,Agent智能体,搜索RAG,设计创意,医疗健康,模型评测,端侧AI,图神经网络,零售电商,传媒内容,营销广告,招聘HR,模型发布,产品更新,合作,论文,开发者生态

DeepSeek Elastic Compute (DSec): Sandbox Infrastructure for Effective Agentic Training at Scale

Lobsters

Comments

· 大模型,AI应用,DeepSeek,Agent智能体,招聘HR

AgentHazard: A Benchmark for Evaluating Harmful Behavior in Computer-Use Agents

arXiv cs.AIarXiv:2604.02947v2 Announce Type: replace Abstract: Computer-use agents extend language models from text generation to persistent action over tools, files, and execution environments. Unlike chat systems, they maintain state across interactions and translate intermediate outputs into concrete actions. This creates a distinct safety challenge in that harmful behavior may emerge through sequences of individually plausible steps, including intermediate actions that appear locally acceptable but collectively lead to unauthorized actions. We present \textbf{AgentHazard}, a benchmark for evaluating harmful behavior in computer-use agents. AgentHazard contains \textbf{2,653} instances spanning diverse risk categories and attack strategies. Each instance pairs a harmful objective with a sequence of operational steps that are locally legitimate but jointly induce unsafe behavior. The benchmark evaluates whether agents can recognize and interrupt harm arising from accumulated context, repeated tool use, intermediate actions, and dependencies across steps. We evaluate AgentHazard on Claude Code, OpenClaw, and IFlow using mostly open or openly deployable models from the Qwen3, Kimi, GLM, and DeepSeek families. Our experimental results indicate that current systems remain highly vulnerable. In particular, when powered by Qwen3-Coder, Claude Code exhibits an attack success rate of \textbf{73.63\%}, suggesting that model alignment alone does not reliably guarantee the safety of autonomous agents.

2026-09-23 04:00:00 · 大模型,算力芯片,AI应用,Google,Anthropic,阿里巴巴,DeepSeek,月之暗面,智谱,代码生成,Agent智能体,模型评测,模型安全对齐,招聘HR,论文

FeatLens: Feature-Guided Dynamic Code Graph Construction and Retrieval for Repository-Level Code Generation

arXiv cs.AIarXiv:2609.26480v1 Announce Type: cross Abstract: Recent code generation research has moved from isolated function completion toward repository-level generation in existing codebases. To implement a target function correctly, an LLM must identify reusable repository dependencies such as existing functions, APIs, and cross-file definitions. Existing retrieval methods provide such context through code similarity search, persistent whole-repository graphs, or LLM-driven graph exploration, but often incur high graph construction, reasoning, and token costs. Feature-oriented methods offer a natural view of software functionality, yet they mainly support requirement decomposition, planning, or feature editing rather than code dependency retrieval. This paper presents \textbf{FeatLens}, a feature-guided dynamic code graph construction and retrieval approach for repository-level code generation. FeatLens builds a feature index that links natural-language feature descriptions to function-level code entities. Given a generation task, it dynamically constructs a task-specific seed graph from the feature index and applies semantic-structural graph reasoning with personalized PageRank to select a compact reasoning graph. This design replaces persistent whole-repository graph maintenance and LLM exploration with deterministic and lightweight dependency retrieval. Experiments on DevEval and EvoCodeBench show that FeatLens achieves the best DR@15 among sparse, dense, and graph-based baselines (0.501 and 0.460). On DevEval generation, it obtains the highest DIR@1, reaching 52.91\% with DeepSeek-V3.2 and 53.58\% with GPT-5-mini, while maintaining competitive Pass@1 and producing shorter code. Compared with the strongest graph-based baseline, FeatLens reduces graph nodes by 61.0\%, edges by 86.2\%, and total token overhead by 45.9\%, with no LLM tokens used during retrieval.

2026-09-23 04:00:00 · 大模型,OpenAI,DeepSeek,推理思考,扩散模型,强化学习,微调蒸馏,招聘HR,论文,开发者生态

继续滚动加载更多…