开发者的 技术雷达

arXiv:2610.41172v1 Announce Type: cross Abstract: Retrieval-augmented generation is becoming agentic. A search harness writes queries, reads results, reformulates, and decides when to stop. This largely solves accuracy: a harness with only BM25/grep and MiniMax M3 answers 85% of BrowseComp-Plus and 91% on multi-hop FRAMES in our runs, without compiled or vector indexes. It exposes a context problem: an undisciplined loop repeats queries, rereads documents, and fills the window with unused passages. The missing layer is knowledge compilation: a configurable ingestion pipeline where LLMs enrich documents with context no chunk contains alone--navigation hierarchies with claim-evidence leaves, entity graphs, timelines, mind maps, ontologies, wikis, and memory. Compilation is not a better retriever: under a competent harness, structural artifacts gave no detectable accuracy gain on FRAMES, and a PageIndex-style outline degraded at scale. It is a supply layer. A navigation tree whose leaves are claim-evidence pairs--the mechanism we measure--cuts query tokens by 91% when evidence suffices and adds 1.9% when it does not, a 38% average reduction; this implies a 2% break-even sufficiency rate and a measured rate near 43%. A decision gate cuts latency from 140 s to 30 s, though substitution's token saving is bought with retrieval rounds and carries its own latency cost. Other artifacts--ontology, graph, wiki, timeline, mind map, memory--pay off in deployment (governance, integration, onboarding, temporal orientation, continuity) where accuracy benchmarks cannot see them. We describe RAGFlow's pipeline and harness (open source at https://github.com/infiniflow/ragflow), which turns the loop into five disciplined stations and keeps the context entering the model the smallest sufficient set; together they form an agent-native database: a store that hands the agent compiled, governed context instead of a ranked list of passages.

arXiv:2610.41199v1 Announce Type: cross Abstract: Cameras and vision sensors are widely used for product and process inspection in manufacturing environments. Traditionally, this process required thousands of images of both nominal and defective products to train such models. Recent research shows that vision transformer (ViT) models, such as CLIP, can reduce this requirement to 50-100 images per nominal and defective class. However, collecting even 50 defective images can be difficult in high-yield or low-volume manufacturing processes. In this paper, we examine the utility of two recently proposed structured decision models (Jev and Clef-Flash) in applications using as few as k=2 reference images per class at inference. In the process, we provide three main contributions: (a) a framework for k-shot learning of these models for image classification/inspection applications; (b) an agentic approach for automatically refining the text descriptions that guide image classification in these decision models; and (c) an approach to calibrate the resulting defect probabilities to improve their reliability for inspection decisions. We test these contributions in five manufacturing case studies. Using two reference images per class, both Jev and Clef-Flash outperform CLIP's test accuracies with the same references (0.75-1.00 [Jev] and 0.71-0.99 [Clef-Flash] vs 0.67-0.84 [CLIP]). Furthermore, we show that our agentic refinement of text descriptions significantly improves accuracy across 6 of 8 model-application combinations, and our calibration approach reduces the Brier score in 13 of the 15 model-application configurations. To support the reproducibility of our work, we provide our replication code and have created the R "decisions" package for evaluating additional models. We also provide a publicly available web app that allows practitioners to inspect their own images with no code.

arXiv:2610.41210v1 Announce Type: cross Abstract: Real-world image editing requires coordinating multiple visual transformations with content preservation and reference constraints, yet aggregate scores can readily obscure unmet local requirements. Effective evaluation must therefore identify local failures and translate them into priorities for subsequent model improvement. We introduce WeGenEditBench, comprising 2,740 evaluation instances and 148 task types across single-image, text-intensive, and multi-reference editing. Pre-generated checklists shared across models decompose each request into atomic requirements; item-level deductions and visual evidence are established first and then summarized into scores for edit execution, preservation, visual quality, and task-specific correctness. To support the experience-acquisition component of recursive self-improvement (RSI), we develop WeGenEvoStudio, a unified multi-agent harness for RSI-oriented learning that integrates model-conditioned failure diagnosis, retrieval-grounded data construction, supervised adaptation, and re-evaluation. Among 15 evaluated configurations, GPT-Image-2 leads single-image editing with an Overall score of 4.076, while Seedream-5.0-Pro leads text-intensive editing at 4.291. Evolution experiments across four generation and editing benchmarks demonstrate substantial targeted gains: on their report-selected Top-5 single-image targets, FLUX.2 [dev] improves Overall from 1.083 to 3.517, while Qwen-Image-Edit-2511 gains 13.2%. Together, our benchmark and harness provide a foundation for developing more reliable image models through interpretable evaluation and feedback-driven learning.

arXiv:2610.41231v1 Announce Type: cross Abstract: Coarse-to-fine 3D-native generation relies heavily on the first-stage sparse structure, which determines the global orientation and overall geometry for subsequent refinement. However, existing coarse generators suffer from two key limitations: their orientation is difficult to control due to ambiguous semantic canonicalization, and their structures are often inconsistent with input images due to weak 2D-3D grounding. We propose Coarse3D, a sparse-structure generation model that produces orientation-controllable and structurally faithful voxel geometry from arbitrary unposed images. Coarse3D introduces three designs: a first-frame anchored canonical space that aligns generation with the first input view while preserving object-centric normalization; a dual-tower DiT that couples generation with auxiliary feed-forward reconstruction to learn spatially grounded features; and spatial-prior guided attention that regularizes cross-attention with pixel-to-voxel correspondence. Coarse3D can directly replace the coarse stage of existing pipelines. Experiments show that it substantially improves sparse structure quality and consistently enhances final 3D generation, achieving state-of-the-art performance.

“世界模型，重点不是渲染的画质是否好看，而是模拟器是否能忠实还原现实物理规律和行为的因果”。11月3日，在深圳举行的百度AI Day·Apollo专场上，百度Apollo首次披露面向物理AI的智能体驱动全模态世界模型Apollo World，并提出世界模型 “2+3”判断标准。目前，该模型已用于萝卜快跑全球化测试与验证，助力无人驾驶规...

模型能够成功接入，并不代表调用体验已经足够顺畅。 在实际使用中，一个 API 模型往往不仅由算法工程师维护，还会被系统开发、业务分析等不同角色调用。输入参数较多时，需要反复对照接口文档填写；返回结果包含多层 JSON 结构时，又需要额外确认每个字段具体代表什么。 这些问题不会直接导致模型“不能用”，但会增加模...

Jync v1.11.0 · MongoDB 专属迁移 「专属迁移」新增 MongoDB → MongoDB 与 MongoDB → KingbaseES 两种整库一次性复制，与实时同步、ETL 完全隔离；ES / OpenSearch → Easysearch 的行为保持不变。 新增 MongoDB → MongoDB：使用随包内置的原生 mongodb-driver-sync，自动发现集合并按模式筛选；复制集合选项（cappe...

Cisco 总裁兼首席产品官 Jeetu Patel 给了一个 27 岁工程师无限量的 AI token。原话是在一场小型圆桌晚宴上说的："我给了他无限的 tokens，因为他要去重构代码。所以我说，去吧，随便用。" 这人叫 Padmanabhan Krishnamurthy，Patel 管他叫 Paddy。2023 年他联合创办了实时翻译公司 EzDubs，2025 年 Cisco 收购后他随之加...

GFlow（极风工作流平台）基于 RuleGo 规则引擎构建，核心理念是：AI 先审 · 人再签 · 签完自动办——大模型先审风险，低风险自动直通；会签、加签、任意退回，中国式审批一个不少；审批通过那一刻，入账、通知、回写业务系统由规则链自动执行。 本次 v1.4.1 围绕流转留痕、IM 集成、开放集成、表单打印、安全合规五大方...

更新日志 1.【新增】同城急送插件，对接快递100聚合运力与达达配送，支持门店端查看骑手、回调日志与发货同步 2.【新增】小票打印公共可视化设计，多门店/飞鹅小票统一设计能力，并支持多商户 3.【新增】门店小票增加大屏叫号号码展示，打印测试同步支持大屏叫号 4.【新增】热标签打印可视化配置，支持商城、多商户、多门...

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