2026-09-05

Benchmark scores: Above, GPT-6 uses a harness for ARC-AGI-3, and is at about 60% without one: Prior to the launch, OpenAI President Greg Brockman said "I think it’s not unreasonable to feel that we are now in the AGI era". GPT-6 is now joining a growing list of models that greatly exceed the human baseline on GDPval-AA v2: If we have AGI, why do human knowledge/remote workers still have jobs? Is it just a matter of time until the economy replaces a large number of humans with LLMs, or are LLMs l

After Qwen3.8 27B came out, I decided to benchmark the models that could fit in my GPU (RTX 5080) on my actual code ( C code), the results were not completely unexpected but some quants were definitely underwhelming. TLDR : Best overall: bartowski/Qwen3.8-27B-IQ4_XS . Best uncensored: huihui-ai/Huihui-Qwen3.8-27B-abliterated-UD-IQ4_XS . For a bit more context: jpetrina/Qwen3.8-27B-IQ4_XS-pure or uncensored: Bucoid/Qwen3.8-27B-Uncensored-IQ4_XS_4BPW (sorted by Mean KLD) Model Mean KLD Same top p

arXiv:2609.03236v1 Announce Type: new Abstract: Tool-using LLM agents spend wall-clock time not only on model inference but also in serial action--observation turns, where each tool call, environment transition, and observation can delay subsequent decisions. We introduce \textbf{Speculative Macro Commit} (SMC), a runtime mechanism for a two-tier agent system: a large authoritative actor model produces the official trajectory, while a faster speculative drafter model continuously predicts and ex

arXiv:2609.03494v1 Announce Type: new Abstract: Long-output reasoning has made the key--value (KV) cache a critical memory bottleneck for efficient LLM serving. Existing KV compression methods usually rely on a predefined per-request budget and adjust only which KV states are retained, leaving the total capacity fixed throughout decoding. However, reasoning workloads exhibit substantial demand variation: different requests require different KV capacities, and the attention demand of an individua

Coding agents are now commonly evaluated on the SWE-bench family of benchmarks, whose tasks are built from curated GitHub issues: long, structured, and information-rich. Real user requests, however, are typically far shorter and less structured. To characterize this gap, we define a six-category information taxonomy and four dimensions of linguistic style, and apply them to real user prompts from SWE-chat and problem statements from SWE-bench Verified and Pro. We find that requests carrying only

Agent performance depends jointly on the model parameters and the executable harness code that manages context and control flow. Optimizing either component in isolation can leave the system bottlenecked by its frozen counterpart: weight updates can change which harness is effective, while harness updates can change which model capabilities are exposed. Existing joint-adaptation methods optimize weights and textual prompts but leave the broader harness fixed. We propose Weight-Harness Alternatin

星尘智能（Astribot） 基座模型团队发布能异步执行的在线强化学习框架 SmoothRL

Streaming video understanding requires multimodal large language models (MLLMs) to process continuous visual inputs and respond to user queries under strict causality and bounded memory. Existing approaches typically compress historical observations into an external memory bank and retrieve query-relevant evidence as additional visual context. Though effective, this store-and-retrieve paradigm keeps historical evidence as external visual context, preventing it from being internalized into a comp

arXiv:2609.03515v1 Announce Type: new Abstract: Decoding-time KV cache compression research focuses heavily on designing better token scoring functions, while the temporal rule that aggregates scores across decode steps is often treated as an implementation detail. Under aggressive KV compression, we find that exponential-moving-average (EMA) aggregation makes approximately order-preserving scorer modifications largely indistinguishable at the eviction-set level. Value-norm and entropy variants

The August edition of my sponsors-only monthly newsletter is out. If you are a sponsor (or if you start a sponsorship now) you can access it here . This month: We got more details on OpenAl's accidental cyberattacks One-shotting Raccoon Heist games with Fable 5 and Sol 5.6 Claude auto mode Understanding ChatGPT Work Model releases Miscellaneous bits and bobs My projects What I'm using at the moment Here's a copy of the July newsletter as a preview of what you'll get. Pay $10/month to stay a mont

Reinforcement Learning from Verifiable Rewards works well when a task has a programmatic checker, but most long-horizon agent domains have none. We work in the outcome-blind setting, where ground-truth success signals are not available. Multi-criteria rubrics are a popular way to supply such a reward; they are scored once per trajectory, but a single scalar is a poor signal across tens of steps. We propose DRACO: Distributing Rubric-based Advantage for Credit Optimization. It generates rubrics d

Hello everyone, Brandon from NetBird. NetBird 0.78 introduces Draft Mode in the Control Center. The Control Center is the network map that shows your peers, groups, policies, networks, and resources. Draft Mode makes it so you can use that map into an editor, so you can restructure access by dragging connections on a canvas, then review the exact API calls before anything is applied to your live network. NetBird is an open source, WireGuard based zero trust networking platform that connects your

It performs well across visual perception, STEM reasoning, document intelligence, multimodal agent tasks, frontend coding, and medical report interpretation.

I recently converted my old laptop into a ubuntu server running Casaos on top. I installed home assistant, adguard and vaultwarden. I heard about Immich and next cloud and got excited and started looking for A: storage and B: the option to build a server with 8TB. I was shocked when i saw the price for PC parts especially ram and storage. it makes me think it might be best to pay google for my google one but wow I wish i could self host everything. This economy wont make be be great at all

The era of AI inference has arrived. Imagine a healthcare system analyzing millions of data points in real time to accelerate life-saving medical research, or an intelligent assistant instantly resolving thousands of complex customer needs at once. These real-world breakthroughs rely on advanced infrastructure acting as the engine of continuous intelligence, powering real-time services while…

arXiv:2609.03460v1 Announce Type: new Abstract: As generative AI makes polished prose cheap to produce, users can no longer rely on fluency as a proxy for truth. We call this failure mode the Fluency Trap: users trust fluent hallucinations while also discounting accurate content once it is disclosed as AI-generated. Binary ``Made with AI'' labels respond with authorship disclosure, but they do not show what supports a claim. We propose Provenance Density, an evidence-visualization interface that

A once-overlooked block of unicode that's invisible to humans is gaining ever wider use.

But you'll need a powerful system with 64GB of RAM to use it.

This epyc server I am using twelve cards with 64 GB memory, plus 256GB ram. Looking at the most capable models in open source, GLM 5.3 seems to be the only option, but with Astra releasing it will likely be fairly behind. GLM6 looks like it will be at least double in size, maybe even triple. Qwen-max and Kimmi are already way too big to even consider. Even the deepseek V4 Pro is too big. Should I just give up on this frontier dream sell the excess GPUs and settle For flash models with far fewer

5000支队伍同台做题，写尽行业四年变化

arXiv:2609.03416v1 Announce Type: new Abstract: LLM-empowered paper-code discrepancy detection has received growing concern since the scaling of research submissions exceeds the manual review capability. However, the limited context capacity and one-sided discrepancy detection of existing single-agent LLM paradigms lead to an inferior recall performance in detecting discrepancies. In this paper, we propose Dude, the first Dual-Detection Multi-Agent System for paper-code discrepancy detection. We

MLLM-based embedding models remain limited in compositional retrieval, often failing to distinguish scenes containing the same concepts but different attribute-object bindings. Yet the same backbone can resolve such distinctions when used as a cross-attentive reranker, motivating us to distill its compositional judgments into the embedding model. We propose CORE, which synthesizes candidate lists spanning five compositional matching levels and introduces a Rank-KL objective that trains the embed

How good/bad is it against comparable MoEs the same size? How does it compare against Qwen 3.6 35BA3B? Since we don't have 3.8 35B this seems like an upgrade if we look at some benchmarks like terminal bench, but they don't have SWE bench pro on the benchmarks table, and i don't really know anything about this lab, I'm wondering if it trades blows with models like tiel coder or if it's some benchmaxxed model like ornith? At a single glance it looks really decent but haven't tried it in depth yet

I previously made a post a while ago about wanting to leave Proxmox for my own version of Arch using packages such as Cockpit, Podman, and other tools. Though I decided to use Debian 13 instead for this task. I only had a very basic Proxmox setup at first and did not really like the way I had configured my server. This made me wonder if I could use a more bare bones distribution like Arch or base Debian and configure my system the way I liked it to try and challenge myself more. If anyone has an

Hey all, i just launched my Eink Bike computer project and think it is cool. Another tidbit, in the crazy things that AI has done... It has helped create a ANT (common sensor wireless protocol used in workout/biking) implementation for ESP32 by messing around with undocumented registers:

arXiv:2609.03209v1 Announce Type: new Abstract: We study a governed approach to enterprise analytics: a language model interprets the question, while deterministic policy selects and runs a pre-approved analytical program that returns both results and evidence. We show that this restriction can remain expressive within a defined analytical class, using relational operations plus aggregation, comparison, windows, ranking, and similarity. Fixed meaning, policy, data, and execution rules also make

Nscale, which recently struck a $45 billion deal with Anthropic, is in talks to raise additional funds in anticipation of an upcoming IPO.

Public-market scrutiny will intensify pressure on the Claude maker’s unusual attempt to balance profit and purpose.

Broadcom admits it put “too big a focus on VCF.”

Online 3D reconstruction models perform poorly on long videos. This happens because regressing poses relative to a fixed first-frame anchor forces extrapolation far beyond the training distribution. Small drifts accumulate and amplify into significant geometric collapse. However, we observe that per-frame depth remains stable throughout this failure. The backbone's local geometry remains intact; only the global pose head breaks down. Motivated by this decoupling, we introduce Scal3R. This approa

While diffusion base models such as GPT-Image-2 and Nano-Banana exhibit remarkable visual expressiveness, their end-to-end generation inherently yields flattened bitmaps with error-prone text, precluding layer-wise post-editing. Conversely, code-based visual generation via Coding Agents provides precise layout control and decoupled layers, yet remains constrained by a lack of global aesthetic intuition and the difficulty of coding complex visual assets. To address this, we propose Editable Visua

Gemini Spark can edit and curate photo albums, create shared collections, turn photos into calendar events, and handle other Google Photos tasks for AI Pro and Ultra subscribers.

A senator's letter confirms the U.S. military moved to prevent the tracking after foreign adversaries used location data to target troops.

国产卡跑出高品质 AI Token 生产级性能

arXiv:2609.03503v1 Announce Type: new Abstract: With the rapid development of the Internet of Things, computation intensive directed acyclic graph (DAG) tasks have become increasingly common in cloud-edge-end collaborative environments. However, cloud, edge, and end nodes are highly heterogeneous in computing capacity, network bandwidth, and energy consumption, which makes the efficient scheduling of tasks with complex dependencies an NP-hard problem. Traditional heuristic algorithms and convent

arXiv:2609.03423v1 Announce Type: new Abstract: Full-duplex voice agents must continuously decide when to listen, backchannel, interrupt, handle speech overlaps, take the floor, and yield. Existing benchmarks largely test these behaviors through explicit turn-management instructions, while deployed agents are often configured through roles or personas from which the appropriate conversational behavior must be inferred. We introduce DuplexSpeechBench-IFEval (DSB-IFEval) for evaluating implicit in

Personalized assistants should not only comply with user requests but also assess whether those requests are appropriate given the user's current circumstances. However, prior work has primarily focused on accurately executing requests, overlooking the need for assistants to account for context and engage in conflict-based refusal. Furthermore, while existing work on conflict or safety detection relies on explicitly provided factors, real-world scenarios often involve implicit factors that must

Hey everyone, been a while! A few months ago, Gemma graced us with models that served as a much needed downpour from a year-long drought. I'm so happy to see us thrive once again. The difference between v1 and v1.1 is quite simple: v1 was an early attempt, an overdue release that excelled in prose and writing, while requiring some handholding to get over quirks like stuttering. v1.1 is a more refined approach where stability meets quality. My community is split, so I figured I'd just release bot

OpenAI introduces Daybreak for Frontline Defenders. A $1 billion commitment expands access to frontier cyber AI, training, and support for essential services.

The malleable OS for the age of agents Discussion | Link