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mar · Quotidien tech · Numéro 114

2026-11-03

TL;DR du jour

共采集 30 条候选情报(LLM 综述不可用,以下为规则排序结果)。

À la une

1

FrameForge: Harnessing Priors of Video Foundation Models for Unified Image Generation

Unified conditional image generation requires a single model to preserve different aspects of a visual input while producing a target image that follows textual guidance. Pretrained video models provide a promising foundation by encoding visual transformations across time, yet existing approaches typically assign the condition and target to fixed temporal positions. We observe that condition-target relationships vary substantially across generation tasks, while video models naturally encode visual transformations at different temporal scales. This motivates us to model conditional generation through input-adaptive virtual temporal relations. We propose FrameForge, which captures two complementary aspects of such relations. Mixture of Temporal Experts (MoTE) models how far the target lies from the condition by dynamically selecting temporal RoPE experts with different virtual intervals. Chain of Temporal Reasoning (CoTR) models how the condition is transformed into the target through a latent intermediate state, with a reasoning-bottleneck attention mask encouraging information propagation through this pathway. Experiments on DreamBench, COCO2017, and GEdit-Bench show that FrameForge achieves superior performance in subject-driven generation, canny-to-image generation, and image editing among the compared methods.

2

Official Statement: Qwen Models Will Stick to Open Source to the End!

There were some rumours the last few days that Qwen4 will be the last open-weight series of Qwen, and future ones would be API only. The following statement contradicts the rumours and clearly states the stance that Qwen is taking regarding their future releases. Qwen Head Liu Dayi Heng Clearly States : Future Qwen 4.0 and Qwen 5.0 Will Continue to Be Open Source! He believes that the value of AI should not belong only to a few people, but should become a foundational capability that users around the world can access, use, and create with, enabling more people to afford AI, utilize AI effectively, and participate in creating AI, ultimately achieving true AI for all. As of now, the Qwen series has surpassed 3 billion cumulative global downloads, with over 460 open-source models released and more than 300,000 derivative models, firmly holding the top spot worldwide.

3

R^2Orch: Recursive Self-Improvement for Agent Orchestration via Self-Play within a Skill-Internalization Loop

In recent years, agentic orchestration has been widely applied to solve complex human tasks. However, existing orchestration learning still faces key challenges, including hand-designed curricula, advantage collapse when a task's rollouts all reach the same outcome, and unreliable evaluation signals that judge each skill by one noisy score. To address these challenges, we propose R^2Orch, a new paradigm of Recursive Self-Play Orchestration: an inner loop of self-play that improves the orchestrator on self-chosen tasks, and an outer loop that improves how the inner loop improves. To support this paradigm, we introduce Recursive Learnability, a training objective that rewards a Proposer for choosing tasks whose rollouts keep reaching different outcomes as skills are withdrawn. Built on this objective, we further propose Skill Withdrawal, in which the outer loop withdraws each skill once a controlled evaluation within self-play shows that the orchestrator performs as well without it. Moreover, the two loops form one cycle: failures are distilled into skills, skills into parameters, and withdrawals lead the Proposer to new failures, so the orchestrator moves toward zero-skill deployment. Experimental results on twelve datasets show that R^2Orch consistently outperforms baselines across question answering, math, code, and interactive tasks. Our code is available at https://github.com/beita6969/R2Orch.

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Actu IA

Foundation models are becoming remarkably reliable at high-level reasoning, yet they remain too slow and costly to serve as fine-grained controllers. Conversely, fast policies like VLAs and whole-body controllers offer more efficient reflexes, but struggle to generalize. While pairing a deliberative brain (foundation models) with a reactive cerebellum (fast policies) seems natural for embodied systems, two obstacles remain: methods are built and scored on incompatible stacks, and their asynchron

🤖REA unifies deliberative and reactive policies in a single runtime with asynchronous coordination, achieving high success rates with reduced control overhead across simulated and real robot tasks.

Progress toward artificial general intelligence (AGI) has largely relied on scaling a resource-intensive executive, System-2, through larger models, more data and compute, longer reasoning, and more agents. We ask whether the next stage may come instead from co-designing it with System-1, a lightweight, semantically aware predictor that evaluates candidate interactions before execution, so that System-2 acts only on those worth their cost. Our answer is a qualified yes. We study conditional inte

🤖Co-designing a resource-heavy reasoning system with a lightweight predictive evaluator can reduce execution costs by filtering interactions, with efficiency governed by the density of useful interactions.

Dev & open source

I was reminded today of AI Adoption Rate Trending Down for Large Companies , a chart produced by Apollo Global Management in September 2025, which appeared to show a slowdown in U.S. corporate AI adoption based on numbers collected by the U.S. Census Bureau. At the time I used that chart to demonstrate GPT-5 data analysis , and then to experiment with Claude's new Code Interpreter . I decided to try running those numbers again to see what had changed over the past year, and in doing so learned t

llm 0.37Simon Willison1 minAutres

Release: llm 0.37 There's a lot of stuff in this new release of my LLM CLI tool and Python library, representing around two months of accumulated features and fixes. I added gpt-6.1-sol and gpt-6.1-terra , plus a new llm openai endpoint add command to make it easier to configure additional models that use OpenAI-compatible endpoints. I switched the default model (if you have not set your own default) to gpt-6-luna - half the price of the previous default, gpt-5.6-luna . LLM's support for embeddi

Anthropic has updated Claude Code's system prompt to prevent the AI tool from being used to re-create existing video games, as reported by Tom's Hardware. Retro gaming is currently being overrun with AI-made decompilations, recompilations, and ports, but while Anthropic will still let people use Claude Code to make games, it doesn't want people to […]

Échos de la communauté

Hi HN, we're Aarush and Arnav from The Subvocal Company (YC F26) ( ). We’re building smart glasses that let you talk to your computer without talking out loud. You can mouth words silently and discreetly and the glasses decode them to text at the speed of speech. Here's a video of Dave from YC trying it out: (Edit: you don't actually have to move your lips or even open your mouth for this to work! Since the RF can see inside your mouth the system can be fine tuned on "internal speech". This isn'

GitHub Trending

Sponsor Star thedotmack / claude-mem Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More

Sponsor Star D4Vinci / Scrapling 🕷️ The BEST Web Scraping framework that handles everything from a single request to a full-scale crawl! Don't be shy, join here: https://discord.gg/EMgGbDceNQ and follow here for daily tips and tricks: https://x.com/Scrapling_dev

Star hyprwm / Hyprland Hyprland is an independent, highly customizable, dynamic tiling Wayland compositor that doesn't sacrifice on its looks.

Sponsor Star Donchitos / Claude-Code-Game-Studios Turn Claude Code into a full game dev studio — 49 AI agents, 72 workflow skills, and a complete coordination system mirroring real studio hierarchy.

Star FlashML-org / FreeToken FreeToken brings datacenter-scale model serving to your desktop. Run massive models locally, fast and efficiently.

Star shiyu-coder / Kronos Kronos: A Foundation Model for the Language of Financial Markets

kornia/kornia★ 12171

Sponsor Star kornia / kornia 🐍 Geometric Computer Vision Library for Spatial AI

Star JustVugg / colibri Run frontier MoE models on hardware you already own — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 🐦

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