2026-09-25

A multi-agent system can reduce latency on complex tasks by executing work concurrently. Several pioneering harness frameworks support multi-agent systems. However, the scalability of current multi-agent harnesses is often constrained by a central orchestrator's capacity to allocate tasks and coordinate workers. To address this limitation, we introduce Agensh, a scalable self-organized multi-agent harness without a central orchestrator: concurrent workers execute a multi-agent cooperation loop,

Repository-level coding benchmarks have become the standard for evaluating coding agents, yet they inherently suffer from data leakage because they are built upon popular open-source repositories repeatedly used for training. Consequently, strong performance may reflect memorization of canonical repository cues rather than robust repository reasoning. We propose SchrodingerRepo (Schrödinger's Repository), an evaluation framework for testing coding agents under dynamically instantiated repository

AI agents are beginning to automate research and development across the AI stack, from improving training efficiency to optimizing inference. A natural next step is to improve the research efficiency of the agents themselves. When an AI research agent's own code is the object of optimization, each accepted rewrite becomes the agent that the next round edits. We refer to this loop as recursive self-improvement. Its significance lies in a long-standing trend, in which increased cumulative spending

We present Hunyuan-A13B, an open-source large language model based on a Mixture-of-Experts architecture. It contains 80 billion total parameters but activates only 13 billion during inference, balancing model capability, computational efficiency, and deployment cost. The model is pretrained on a rigorously filtered 20T-token corpus with enhanced STEM data curation, improving factual reliability and reasoning ability. High-quality supervised fine-tuning and large-scale reinforcement learning furt

These boards cost me $115 each and I have them connected using llama.cpp with Vulkan and RPC on Bazzite. The boards have roughly 27GB of combined GPU memory and communicate over 1gb Ethernet. For around $300 including psu I’m loving the performance. I have a few more and want to see what 6 looks like trying to run qwen 3.8 flash.

A pair of developers say that with very little prompting, Meta's Muse will share its entire filesystem with you. Peter James and Jonny L. Saunders have said they both independently coaxed Muse into zipping up and sharing the entire contents of its root filesystem, Ubuntu system files, app templates, and internal documentation. Saunders posted on […]

Release: commit-rewriter 0.2 Support for branches other than the default branch. Use uvx commit-rewriter --branch other to run against another branch. #3 Tags: git

Release: datasette 1.0a41 Alec Garcia added support for OpenTelemetry to Datasette in this release. I've also refactored all of Datasette's modal dialogs to a single Web Component, which is now documented for other plugins to use . Tags: javascript , datasette , web-components , alex-garcia , opentelemetry

人类演示一次，机器人即可实现跨场景任务复用

Hey everyone, Jovan from UkisAI here! Today, we are introducing Swift, a family of efficient reasoning LLMs based on Qwen, trained by penalizing tokens related to pathological overthinking patterns and restoring accuracy via RL (GSPO) and OPD . After amazing feedback and 350k+ downloads in 13 days on our Swift Qwen 3.8 27B we are releasing the entire model family as well as the highly requested GSQ-RCO quants for 27B and Flash-Next. This release includes: Swift1.5 27B , an improved version of ou

Reinforcement learning has become a central component of large language model (LLM) post-training, yet token-level credit lacks a generally accepted mathematical definition, leaving its relationship to commonly used training signals unclear. We formulate three regularity conditions, namely Completeness, Prefix Consistency, and Neutrality, and prove that they uniquely determine token-level credit. This characterization provides a unified basis for explaining phenomena across existing algorithms a

Transformer architectures exhibit cross-layer redundancies, yet post-training compression pipelines typically optimize layers in isolation or rely on heuristic grouping strategies that disregard layer-specific activation geometries. We introduce a principled, training-free framework that sequentially optimizes cross-layer weight pairings and shared-dictionary factorizations. Rather than forcing weights of adjacent layers to share a basis or heuristically merging activation statistics, our approa

With the release of ThinkingCap-Qwen3.8-27B , I thought it would be worthwhile to do a comparison between the original Qwen3.8-27B, the new ThinkingCap, and Swift-Qwen3.8-27B . Both Swift which I already reviewed , and ThinkingCap do exactly the same thing: they reduce the excessive reasoning loops that 3.8-27B is renowned for. In fact, their claims are almost identical: both models claim to reduce reasoning tokens by approximately 40%, with minimal degradation in performance. I wanted to put th

I posted previously my swe-verified django 100 tasks benchmark comparing different local models and quantization. No new models for now, but a fix in my evaluation workflow that was unfortunately not stable during the weeks/months of me using it. I redid the evaluation on all runs and here are some noticeable changes: Flash Next is still king, but the benefit of xhigh vs medium reasoning effort is now properly showing. Same for 3.8 27B (however in everyday tasks I personally still prefer using m

Spatial reasoning is essential for vision-language models (VLMs) to understand and act in the physical world. Reasoning in dynamic environments requires VLMs to perceive local state transitions caused by object motion and viewpoint changes and integrate them over long trajectories to maintain an updated spatial state, yet existing VLMs remain limited in both capabilities. Current spatial training primarily focuses on static questions about object attributes and spatial relations, providing limit

Google's experimental orbital data center will have four TPUs and only run for 15 minutes at a time.

Air-gapped file encryption packed into a single, self-decrypting HTML page. Repo: I was inspired by self-extracting archives. I wanted to share files with basically no dependencies. The goal was: 1. Something that didn't require any installation (assuming a web browser) 2. Have a single file with no network that could self-decrypt 3. Be fully auditable The second point is done by having (sort of(*)) reproducible builds and embedded OpenPGP signatures. The first point is made by cleverly manipula

Hello HN! I've spent years debugging Windows crashes with tools that were either friendly but limited (e.g. Visual Studio) or powerful but archaic (e.g. WinDbg). I developed patterns and methods for understanding what was going on, and decided to build it into a much more effective debugging tool called ForensicDbg. I built a modern interface to minimize the friction when debugging. All of the data shown to you is analyzed, interpreted, and presented to you clearly, so you can focus on what matt

Reinforcement learning (RL) is vital for optimizing video generation models, with a robust reward model (RM) serving as the cornerstone. However, existing video reward models often produce unstable scalar scores because they directly map complex, subjective video quality into a single score without explicit evaluation criteria. This leads to scalar drift, where the scoring scale collapses or shifts across different prompts, making the reward unreliable for RL. Drawing inspiration from profession

arXiv:2609.25284v1 Announce Type: new Abstract: A social agent's most basic decisions (should I react to this post? who should I reach out to?) are not purely content problems. The right action often hinges on the latent relationship between people -- tie strength, reciprocity, mutual connections -- rather than on which content is most salient. Standard LLM agent loops do not explicitly represent how new relational evidence should revise the agent's current social hypothesis, leaving them prone

Standard subword tokenizers either treat every orthographic variation of a word (such as hello, Hello, HELLO, and Héllo) as unrelated vocabulary entries, which fragments the embedding space, or discard this variation through lossy normalization. We present the Functionalizer, a lossless pre-tokenizer framework that factors orthographic and structural variations into a compositional opcode/operand prefix stream before tokenization: a canonical base token (operand) prefixed by parametric transform

This tutorial provides a complete coding guide to TypeSafe AI's Jev, a System One model designed for non-text, structured judgments. It covers installing the official Python SDK, using primitive question types (Choice, Score, Noul), implementing speculative fan-out, confidence-gated routing, and building async production workflows The post A Coding Guide to TypeSafe AI Jev: Typed Decisions, Calibrated Confidence, and Speculative Fan-Out with a System One Model appeared first on MarkTechPost .

Evaluating world models requires assessing both the quality of the worlds they generate and their consistency and responsiveness under exploration, interaction, and modification. We introduce HappyWorld-Bench, a comprehensive benchmark that evaluates whether generated worlds remain reliable as agents interact with them. Our design is built on a hierarchical capability framework of six world capabilities (W1-W6), from generative construction to unified world modeling, instantiated across three in

The notice would allow Oracle to delay payments should the facility miss its 2028 target to come online.

F-Droid's Android app store has been rebuilt from the ground up.

Prism's larger goal is open-weight AI that runs on devices and makes better use of the computing power they already have.

It's an experiment to see how the chips hold up in the harsh vacuum of the cosmos.

ElevenLabs powers the AI voice on the other end of a lot of customer service calls, and its CEO told me this week that businesses should probably tell you that — at least until getting a machine is what everyone expects anyway.

Guest Post: In science, thinking has gotten cheap but doing has not. This asymmetry is reshaping how research companies operate, largely inconspicuously.

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The user is frustrated — I rambled too much and didn't act. Let's just run the test suite and move on. No more forensics. One command, execute, then report. (Original memo is a casual internal monologue in English. Translating faithfully while preserving the informal, stream-of-consciousness register.) Got it. No more analysis. Running the suite now: (Casual English internal memo, stream-of-thought style, with the informal tone of the original Japanese preserved.) (Ugh, I'm going in circles. Sto

Vision-Language-Action models provide a strong foundation for general-purpose robot control, yet a vast majority of policies do not preserve and leverage episode-level information beyond the current observation. This limitation is consequential in history-dependent manipulation tasks that depend on information available only in past observations. Retaining past observations in context can aid in recovering this information, but at the significant cost of ever-growing, bloated context and inferen

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MIT Technology Review examined research from Jessica Wachter, a Wharton finance professor, and coauthor Jonathan Wachter, who based their estimate on spending by Alphabet, Microsoft, Amazon, Meta and Oracle. Their analysis says the sector would need a 2.7-fold productivity increase by 2030 after accounting for capital costs, depreciation and a 15% return. The paper warns that if the expected boom fails to appear, the buildout could become “the largest misallocation of capital in history.”