2026-10-17

Meta released Muse Spark Open, an 816B-parameter open-weight MoE model with 42B active parameters and a 1M-token context. It leads open models on MCP-Atlas and runs on 2 M5 Ultra Mac Studios. The post Meta Releases Muse Spark Open: 816B Open-Weight MoE With 1M Context appeared first on MarkTechPost .

UPDATE llama.cpp support merged Beam is an open model built by Reflection. Beam takes text as input and generates text as output. This release includes the instruction-tuned model in BF16, FP8, and NVFP4 numerics. Beam uses a sparse mixture-of-experts (MoE) architecture with 501B total parameters and 23B active parameters. It supports a context window of 1M tokens and was developed predominantly for English. Primary capabilities Token-efficient reasoning: Reaches comparable solution quality to l

Large language model agents solve tasks through long sequences of tool calls, and the prevailing design has a single strong model generate every step, although many steps are routine enough for a much smaller model. Existing ways of sharing this work keep the strong model as the generator: routers and collaborative agents decide which model handles each query or step, and speculative methods check a small model's drafts one step at a time, usually against the strong model's own action. We introd

The capabilities of an agent swarm depend on both its individual members and how they coordinate. Yet existing multi-agent systems often train agents within fixed orchestration structures, separating the learning of individual capabilities from the organization of collective behavior. We introduce market-based post-training (MPT), a framework for jointly learning action policies and self-orchestration in decentralized agent swarms. Agents express their willingness to participate through bids, wh

Diffusion language models are the future!

Hey r/LocalLLaMA ! We're excited to share ORL (On-Policy Residual Learning) , our new research on making diffusion language models (dLLMs) significantly faster without sacrificing generation quality. With ORL and our custom inference engine dlmrt , we've achieved: 2,788 tokens/s on a single RTX 5090 — LLaDA-2.1-mini + ORL, NVFP4 7,708 tokens/s on 8× B200 — DiffusionGemma 26B MoE + ORL, NVFP4 Up to 6.9× higher end-to-end throughput on our QwenDLM-4B model, at comparable or better quality 1.74× fa

The actions could have resulted in criminal charges had humans been behind the keyboard.

The data comes from Vercel's AI gateway, a unified API service similar to OpenRouter.

World and action modeling are closely related, yet their reciprocal benefits under unified causal training remain unclear. We introduce OneWM, a simple framework for unified causal training that treats actions as both conditions for world prediction and targets for policy learning within a shared video-action architecture. Using this framework, we investigate whether learning forward dynamics conditioned on specified actions improves action learning, and whether learning to generate actions in t

arXiv:2610.20665v1 Announce Type: new Abstract: Large language model (LLM) agents can solve complex interactive tasks, yet reasoning and tool calls take time. In continuously changing environments, an otherwise correct action may arrive too late. We introduce RealtimeBench, with 180 tasks across six game types, to evaluate agents while games continue running during inference. Paired evaluations show that GPT-6.1-sol outperforms the faster Jev when games are paused, but falls behind when they kee

It's clear that today’s SOTA (state-of-the-art) models remain ahead of open-source models. However, if we filter the benchmarks correctly, a different picture emerges compared to what we saw just a few months ago. Today, open-source models are outperforming Sonnet 5, a significant shift, considering it took many months to surpass the performance levels of Sonnet 4.6 or 3.7. (Dario is nervous) Honestly, I had expected "Spark Open" to be on par with version 1.2; nonetheless, when the benchmarks ar

arXiv:2610.20675v1 Announce Type: new Abstract: Agents pair reasoning models with harnesses that organize model calls, execute tools, load skill instructions, and retain history in memory. Understanding agent performance requires linking execution conditions to outcomes, behavior, and resource use. We introduce AlphaDiana, a measurement system with modular interfaces for datasets, agents, environments, and scorers. It preserves harness-native control loops while controlling file reuse and memory

arXiv:2610.20809v1 Announce Type: new Abstract: A self-improving agent has to decide which new skills to keep, and in practice it keeps whatever earns a high reward, convinces a judge model, or compresses its library, even when the skill is wrong. That is fine until the answers have to be exactly right: over a table of transactions, a total is either right or wrong. We ask a harder question: can an agent grow what it can compute with no reward, no judge, and no answer key, and still be exactly r

arXiv:2610.20780v1 Announce Type: new Abstract: In LLM-based multi-agent systems (MAS), a local mistake can propagate as other agents build on it, turning a correctable error into a failed task. Diagnosing completed runs or repeatedly re-executing them to search for corrections does not provide a learned policy for intervening while execution is still in progress. We present Agent Runtime Inspection and Amendment (ARIA), a framework for learning when and how to repair ongoing multi-agent executi

Two engineers shipped Stack Overflow for Agents in a few weeks with Codex, and its ChatGPT plugin helped drive up to 10× growth in agent creation.

There's also a base model. As all OpenMoss releases, they're Apache licensed. Wonder how this compares to Qwen Image 2.1 and Flux Klein 3.

NVIDIA’s open multimodal model for specialized agents Discussion | Link

Long-context training of large language and multimodal models couples structured attention masks (document, prefix-LM, share-question, sliding-window, block-sparse) with multi-node context parallelism, making the mask a dominant bottleneck. FlashMask's column-wise sparse mask compresses them from O(n^2) to O(n) via four query-axis boundary vectors, covering broad structured patterns. Implemented for Ampere GPUs, it neither exploits FlashAttention-3's TMA/WGMMA warp-specialized pipeline on Hopper

Replit CEO Amjad Masad envisions a future of “self-driving companies,” where AI agents handle day-to-day tasks and people focus on higher-level decisions.

Humans interpret multimodal observations of the world and forecast how it will evolve. Recent multimodal generative systems have demonstrated similar capabilities: they can integrate multimodal context and synthesize visual outputs that depict changes in the world. To characterize this emerging trajectory, we organize model capabilities into a four-level hierarchy and discover that higher-level tasks remain relatively underexplored despite model progress. To bridge this gap, we explore model per

Data agents use code to process heterogeneous files and derive analytical insights. Scaling their training faces costly environment curation, privacy and intellectual-property restrictions, and difficulty verifying open-ended findings without known generative truth. Our core insight is to specify the world behind the files before generating the files themselves. We introduce Data World, a multi-agent engine that synthesizes environments, tasks, and rubrics from explicit mechanisms. Independent a

Reinforcement learning can align visual generators with reward signals beyond their pretraining objectives, but existing score-function methods typically introduce stochasticity by perturbing the reverse denoising trajectory, leading to costly rollout-based optimization. We instead ask whether stochastic exploration can be introduced inside the generator while preserving its original sampler. Routing Policy Optimization (RPO) answers this question by turning attention routing into a stochastic e

Language models are increasingly trained against several verifiable rewards at once, such as the correctness and the format of a tool call, or the individual constraints of a complex instruction. Reward-decoupled normalization (GDPO) normalizes each reward separately so that different reward combinations keep distinct advantages, but it still spreads every reward's advantage evenly over the whole response, although each verifier checks only a few positions. We propose MRTPO, which assigns the ad

arXiv:2610.20667v1 Announce Type: new Abstract: Rapid advances in AI capabilities are causing benchmarks to saturate. Once several models approach perfect scores, a benchmark loses its power to differentiate between them, and with it its utility for informing AI safety cases and frontier policies. Yet benchmark items produce far more information than the scores we typically extract from them: a full transcript of how each model solved the task. We investigate whether these transcripts can be use

Liquid AI released d1-ultra, a text and image decision model that returns calibrated probabilities in 1 forward pass. It scores 65 on the Artificial Analysis Decision Index and costs $0.042 per million input tokens. The post Liquid AI Releases d1-ultra: A Multimodal Decision Model That Scores 65 on the AA Decision Index appeared first on MarkTechPost .

arXiv:2610.20786v1 Announce Type: new Abstract: Multi-modal agents have made significant progress in interacting with open worlds. However, their ability to self-evolve remains limited. Supervised fine-tuning enables imitation but not active experience organization, while outcome-based reinforcement learning promotes exploration yet underuses intermediate trajectory information. Therefore, we propose EvolvingBrain, which uses an adaptive skill system as the brain for agent self-evolution, consis

arXiv:2610.20681v1 Announce Type: new Abstract: Formal inequality proving requires mathematical reasoning and exact computation, often connected through intermediate lemmas and goal reformulations. We introduce IneqAgent, an agentic framework combining LLM reasoning with symbolic computation for inequality proving in Lean4. Lemma-level Monte Carlo Tree Search coordinates lemma construction, goal reformulation, and symbolic backend selection. Complementary sum-of-squares, Bernstein, and successiv

I am a LegalTech Attorney leading a developer team from Costa Rica. While trying to load credits onto my Anthropic API account (claude.com), I experienced a critical internationalization (i18n) and UX failure that has placed an unfair financial burden on me due to a severe oversight in their payment pipeline. The platform prompted me for a payment. Since I am in a Spanish-speaking region where localized systems and keyboards handle dots and commas differently, I typed 5.04. Anthropic’s input sys

I know everyone's excited about 3.8, but honestly I'm still refining 3.6. And I hate the way 3.8 talks, so... I keep seeing all these posts about what's the use of open source, so I figured I'd submit my own answer with this post. Qwen 3.5 27B was my first agent, starting my journey around May 31st, and I've learned a lot in these 4+ months, so I wanted to share with other people just starting their journey because there are clearly a LOT of new users out there, and people want help but sometime

Basically title. All labs seem to have agreed 24-27b is the minimum size they will go, nothing below that. I wonder when will we get the next sub 14b model from a SOTA lab

Cowork agents navigate dependencies across tools and files while adapting to changing user requirements and edits to shared workspaces. Scaling such environments requires maintaining consistency among task objectives, executable resources, user behavior, and success criteria. We introduce Cowork-Gym, a framework that synthesizes Cowork environments through a build-execute-revise loop that uses execution evidence and review feedback to validate and repair generated environments. To capture realis

Drama3 support in audio.cpp was contributed by . Many thanks for the great work! So far, follow-up optimizations have primarily focused on CUDA. The model also runs on Vulkan and Metal. There's still plenty of room for improvement in performance and VRAM efficiency and correctness. On Vulkan, one request hit the token limit. I also found some unexpected silence in multi-speaker generation. There will be more iterations before the model makes it into a minor release. If you're interested in helpi

Hi r/LocalLLaMA , we're releasing Tyger, a small model that handles full-duplex turn-control for voice agents. What it is Base: NVIDIA Nemotron 3.5 ASR Streaming 0.6B + 14M causal GRU on top. Every 80 ms, with no lookahead, it picks one of three states: - answer : the user finished their turn - stop : the user cut in, so the agent stops talking - chime in : the agent says “mm-hm”, “oh, really” and keeps listening It hears both the mic and the agent's own playback, so the agent doesn't mistake it

Meta “streamlined” its commercial TOS. Legal experts warn it’s an AI power grab.

Masad said the decision was made for a number of different reasons, including cost. Replit announced a partnership with OpenAI in August, but there was never any indication that it was no longer with Anthropic.

MIT professor Sherry Turkle warns that people have become dangerously enmeshed with chatbots.

The ChatGPT browser extension lets you compare information from your open tabs.

arXiv:2610.20685v1 Announce Type: new Abstract: Symbolic regression searches for an equation that explains observed data. Numerical error is commonly used to guide the search, but it alone can favor an accurate surrogate over a candidate with the right structure. We propose L2G-SR, a framework for LLM-based symbolic regression that guides search with a learned structural verifier. We construct structural supervision signal using tree edit distance (TED), accounting for commutative operators and

arXiv:2610.20669v1 Announce Type: new Abstract: Reward-guided discrete diffusion enables test-time alignment with downstream objectives, but applying Sequential Monte Carlo (SMC) guidance at every denoising step is computationally expensive. Active guidance steps require additional reward evaluations and, for gradient-based proposals, value-gradient computations. Sparse guidance reduces this cost by guiding only a subset of timesteps, but commonly used schedules are fixed heuristics whose effect

arXiv:2610.20676v1 Announce Type: new Abstract: Correctness feedback suppresses the very evidence that specification-completeness auditing is designed to uncover: programs that satisfy a written contract but violate intended behavior. We demonstrate this objective reversal on 12 Verus/Rust tasks across four model families under matched call budgets. Independent no-feedback discovery more than doubles certified-witness yield relative to IntentShield's corrective-feedback loop (59.0% versus 27.1%)

arXiv:2610.20805v1 Announce Type: new Abstract: A reasoning trace retained at answer time plays two roles: it can raise accuracy, and it can compete with the current evidence when the two disagree. We disentangle these roles by crossing verified evidence versions with the traces generated from each and answering from the same traces and evidence under two configurations: continuation of the model's own thinking and a separate answer call. Across three model families (gpt-oss, Gemma4, Qwen3) on H

arXiv:2610.20679v1 Announce Type: new Abstract: Representation-based safety defenses for vision-language models (VLMs) often learn to separate harmful from benign inputs, but separation alone does not establish what a risk score means or when it should be trusted. We introduce Counterfactual Neighborhood Risk Fields (CNRF), an evidence-grounded framework for selective VLM safety. CNRF constructs context-matched benign-harmful counterfactual pairs, uses their representation differences as capabil

KDE celebrates 30 years with a new Plasma release, a farewell to X11, and a movie.

Every coding agent, every machine, one command center. Discussion | Link

Every coding agent you run, in one place. Discussion | Link

Whenever it comes to Strata and low bit QFN discussion, everyone is talking about Unsloth, ISTA-lab and nobody mentions Agantai with his GYRO series of quants - The quants allegedly have simultaniously better KLD, top 1 and lesser size than the ones from ISTA. Also enhanced loop resitance is advertised. Bold claims and signle contributor were the things why I was initially reluctunt to check the claims myselfm hovewer that potential space savings were impossible to resist so I downloaded GYRO-M

Alibaba's Qwen team released a new repository on Wednesday, October 14, 2026. Its goal is to measure how well visual agents can create 3D scenes in Blender based on real-world references. I think this is a good sign. Companies often build benchmarks in the areas where they plan to improve their models. So it's reasonable to expect Qwen to focus on agent skills in 3D design and on using tools like Blender through MCP. Qwen may well publish its upcoming models' results on this benchmark, but this