model releases

What Should Embodied Agents Remember? Benchmarking and Evaluating Memory Modality for Embodied Agents

model-releasesarxiv-cs-ro

arXiv:2610.12561v1 Announce Type: new Abstract: Memory allows large language model (LLM) agents to use past experience, often stored as textual records, when responding to requests. For embodied agent

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When Charts Deceive Web Agents: A Paired Benchmark for Misleading Visualization-Grounded Tasks

DGX agent

arXiv:2610.12823v1 Announce Type: new Abstract: Web agents increasingly browse, interpret, and act on web content on behalf of users. In many realistic workflows, however, the visual evidence they con

model-releasesarxiv-cs-cl

12 Oct 2026

When LiDAR Fades, Radar Takes Over: LiDAR Reliability-Aware Adaptive Localization Under Airborne Obscurants

model-releasesarxiv-cs-ro

arXiv:2610.13788v1 Announce Type: new Abstract: LiDAR-inertial odometry (LIO) is widely used for real-time localization and mapping, but its reliability can degrade substantially in subterranean envir

When Simulation Lies: A Sim-to-Real Benchmark and Domain-Randomized RL Recipe for Tool-Use Agents

model-releasesarxiv-cs-ai

arXiv:2605.11928v2 Announce Type: replace Abstract: Tool-use language agents are evaluated on benchmarks that assume clean inputs, unambiguous tool registries, and reliable APIs. Real deployments viol

When VLMs Destroy What They Know: Spatial Representations Fall Below Lexical Priors

model-releasesarxiv-cs-cl

arXiv:2610.13910v1 Announce Type: new Abstract: Vision-Language Models (VLMs) increasingly analyze and reason about images, yet it remains unclear whether their internal representations faithfully enc

Who&When Pro: Can LLMs Really Attribute Failures in AI Agents?

model-releasesarxiv-cs-ai

arXiv:2607.09996v2 Announce Type: replace Abstract: Automated failure attribution uses LLMs to identify where and why agentic systems fail. As agents become more capable, their failures become subtler

Why Heavy-Tailed Weight Spectra Predict Model Quality

model-releasesarxiv-cs-lg

arXiv:2610.13123v1 Announce Type: cross Abstract: A solid understanding of the predictive success of deep neural networks (DNNs) remains elusive. For example, while many data-dependent metrics exist t

With only ~$1,000 of training compute, we trained 𝗳𝗿𝗼𝗺 𝘀𝗰𝗿𝗮𝘁𝗰𝗵 a 1.7B coding agent that beats Claude 3 Opus and GPT-4o on SWE-ben…

model-releasesclem-delangue--x

With only ~$1,000 of training compute, we trained 𝗳𝗿𝗼𝗺 𝘀𝗰𝗿𝗮𝘁𝗰𝗵 a 1.7B coding agent that beats Claude 3 Opus and GPT-4o on SWE-bench Verified 🤯 At 25.5% pass@1, it sets a new record for nanoswe, our op

world’s first full length workshop on claude flows @cjav_dev and Harrison Stall are kicking off @aidotengineer bright and early on a monday …

world’s first full length workshop on claude flows @cjav_dev and Harrison Stall are kicking off @aidotengineer bright and early on a monday morning to a PACKED room, standing room only #AIEngineerNY @

model-releasesswyx--x

WoundAmbit: Bridging State-of-the-Art Semantic Segmentation and Real-World Wound Care

model-releasesarxiv-cs-ai

arXiv:2504.06185v3 Announce Type: replace-cross Abstract: Chronic wounds affect a large population, particularly the elderly and diabetic patients, who often exhibit limited mobility and co-existing h

Zai just debuted a legal specific model: GLM Legal, and it is the new #1 open-weight model on Harvey’s Legal Agent Benchmark and LegalBench.

model-releaseszhipu-ai--x

Zai recently launched a domain‑specific language model called **GLM Legal**. According to a tweet posted at 4:13 PM on Oct 12, 2026, GLM Legal became the leading open‑weight model on both **Harvey’s L

A new work suggesting that division of labor among self-improving AI agents can help them avoid reinforcing their own errors. I found this w…

model-releasesdavid-ha--x

A new work suggesting that division of labor among self-improving AI agents can help them avoid reinforcing their own errors. I found this work interesting from a metascience perspective. Science is a

Anatomy skill for building wireframe designs and interactive prototype machines is now an officially integrated optional skill in Hermes Age…

model-releasesnous-research--x

Anatomy skill for building wireframe designs and interactive prototype machines is now an officially integrated optional skill in Hermes Agent You can find it in the optional built in skills in the sk

b11553

server: add component tests (#30312) server: add component tests nits add codeowners Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/54782864 macOS/iOS: mac

b11554

vulkan: handle mul_mat_id duplicates in the prepass rather than looping (#29998) vulkan: compute every row of an expert in mul_mm_id when ids repeat vulkan: handle mul_mat_id duplicates in the prepass

b11555

cpu: fix AVX2 CPUs not using F16C when available (#30270) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/54796975 macOS/iOS: macOS Apple Silicon (arm64) ma

b11556

model : support for Prism Bonsai 2 27B (#29600) Runtime support for Prism Bonsai 2 27B Assisted-by: Claude Code address prism hadamard runtime feedback move hadamard tensors into method, define folded

b11557: rpc : turn GGML_RPC_DEBUG into a verbosity level and add logs (#29544)

rpc : turn GGML_RPC_DEBUG into a verbosity level and add logs GGML_RPC_DEBUG is now parsed as a number: 0/unset disables debug logs, 1-3 emit increasingly detailed output (events, per-command trace, t

b11558

opencl: fix image limit for q4_k dense bin kernels, q4_0 bcast and rms_norm (#30310) opencl: fallback when Q4_K weight images exceed device limits Co-authored-by: Hongqiang Wang wangh@qti.qualcomm.com

b11559

opencl: improve fa, allow dk512 for gemma-4, improve dk64 (#30266) opencl: enable Gemma-4 E4B GPU decode Co-authored-by: Hongqiang Wang wangh@qti.qualcomm.com opencl: extend Gemma-4 GPU decode to E2B

Building a 4x R9700 setup for a 10 person startup

Just wanted to share a build I am doing for a client. $18k. Specs: Threadripper 9970x 128gb DDR5 ECC 5600 4x AMD Radeon R9700 AI Pro 32gb(128gb total VRAM) 1600W PSU(GPUs are undervolted and under 210

Converting dense models into Mixture-of-Experts

For the past few weeks I've been trying out converting existing dense models to sparse Mixture-of-Experts models, with no pretraining from scratch. The Idea If you can turn a dense model into an MoE t

Every Model That Can Be Run On 10-16GB VRAM Ranked

It's been 4 years since c.ai first hallucination model, and yet, we're nowhere good enough at LLMs in terms of spontaneity/interesting hallucination features. Fortunately, last year brought new archit

Final revision of zzpanic vllm-radiance for 1 x R9700 GPU, qwen3.8-27b

Summary of project status metrics Since StillDeadcode has released his new radiance inference engine, the writing is on the wall for the old vllm-radiance builds, people have moved on to newer and bet

Gemma4 E4B EXL3 - Alternative to Llama.cpp on Jetson Orin

My Jetson Orin–optimized engine, little-gemma V1.0, substantially outperforms llama.cpp. Even after exhausting every practical GGUF option, however, it still falls short of the ideal performance level

I ran Qwen 3.8 Flash-Next on my AMD 7900 XTX at 500k context. All local and what a shift it has been.

I was running Qwen3.8-Flash-Next (base and later Swift-1.5 NVFP4) on a paid pod seat for about a month, and it started answering with short strings that echoed its own earlier reasoning. I assumed the

I tested different Qwen 3.8 27B quants

There is a lot of discussion which quant to use. Especially a lot of post how much speed you gain with FP4, but not much about quality. So i tested some of the models that I use on my R9700. Models: Q

I trained a 102M recursive BitNet-v2 model from scratch: 64K context, trained on less than 5B tokens

DISCLAIMER: the post and the model card was made with the assist of AI. Hiya, I’m releasing Recursive BitNet N-Gram 102M, a small experiment combining ternary weights, shared transformer layers, and h

Impressive by @river_ai_inc to beat @deepseek_ai rates - cache in particular is tough! Talented team there, full RL + inference stack should…

model-releasesemad-mostaque--x

Impressive by @river_ai_inc to beat @deepseek_ai rates - cache in particular is tough! Talented team there, full RL + inference stack should be real competition River Inference is live! We're serving

local semantic file search for Linux (Rust, llama.cpp, EmbeddingGemma 2)

indexes PDFs, Office files, scans, screenshots, audio and video, and finds them by content. A query like 'when did they decide to freeze hiring' opens the recording at that second. * EmbeddingGemma 2

microsoft/AesCode 8B and 32B

https://preview.redd.it/r2ledkyuntuh1.png?width=1684&format=png&auto=webp&s=9ed4518b2d0f20f44835ebbfac605581da186a57 AesCode generates information-rich visual artifacts such as slides, posters, and da

🧭 New model: Typed Decision, built on EmbeddingGemma 2 Give it a state + any schema of questions → typed, calibrated answers (choice · scor…

model-releasesclem-delangue--x

🧭 New model: Typed Decision, built on EmbeddingGemma 2 Give it a state + any schema of questions → typed, calibrated answers (choice · score · noul). No text generation, no output parsing. 76.4% on ty

Now you can grow Bonsai on your potato

No more excuses for GPU-poor folks not to start LLMing! Full 27B-class reasoning in ternary transformer weights, for llama.cpp (CUDA, Metal, CPU) ~9.3x smaller than FP16 (ideal) | 98.2% of FP16 intell

OMG! If you have a Mac with 64GB, try Qwen3.8-Flash-Next-oQ4e-mtp with oMLX!

I'm genuinely shocked! I was able to run Qwen3.8-Flash-Next-oQ4e-mtp on M3Max 64GB with oMLX! Even a couple of weeks ago, I wasn't able to get it to run. I just tried the latest commit for fun, and it

OpenMed 3.0 is out: Apache-2.0 clinical AI that runs fully local and never falls back to the cloud. 422 open issues if you want in on 3.1

Hey r/LocalLLaMA! Maintainer here. Some of you might remember OpenMed from our 1.8 post a few months back. Quick recap: it's an open-source (Apache-2.0) medical AI toolkit with one rule we never break

Qualcomm CEO Cristiano Amon says AI companies are 'starting to do phones' and asking Qualcomm to support running 100B-parameter local LLMs continuously by 2028 (Alex Heath/Sources)

Alex Heath / Sources: Qualcomm CEO Cristiano Amon says AI companies are “starting to do phones” and asking Qualcomm to support running 100B-parameter local LLMs continuously by 2028 — Cristiano Amon o

model-releasestechmeme

Qwen 3.8 27B Q5 vs Qwen 3.8 Next Q3_S for document analysis

I have an app that processes ~500 different documents every day. An agent analyzes them, sorts them, tags them, extracts various legal nuances, creates descriptions, etc. So far I've been using Qwen 3

Qwen3.8 Flash Next fixed my GNOME extension

I love Dash2Dock Lite, but Icedman is always a week or two before updates. I randomly ran pacman for the first time in weeks, not realizing GNOME had a major update. I told it to fix it in DeepSeek Ha

Running Next Flash IQ3_XXS at ~70 tok/s with 100k context or 2 instances of Qwen 3.6 35B A3B IQ4 at ~145 tok/s with 256k all on $500 of ex mining BC-250 boards

This will be my third update on the bc-250 cluster and for my first forray into local ai I have been having a blast. This has turned from a weekend experiment into something I think I will use in the

'Strata' for GLM5.3 Flash is here for some! Project Maya

I stumbled across this as I was currently having glm5.3 flash run only around 10tok/s basically unusable. With Maya i am running 30 tok/s now. GLM5.3 feels even with this low quant like a much more en

The NFC East winning 0 games on a week in which two teams played each other feels like a pretty good encapsulation of the division at the mo…

model-releasesanthropic--x

On October 11, 2026 Mina Kimes tweeted that the NFC East had earned zero wins for the week because the division’s only two games were a head‑to‑head matchup that produced no divisional victories, illu

This is an incredible exchange.

model-releasesanthropic--x

The post is a brief Twitter‑style interaction involving Dr S Maitra and MD Mary Talley Bowden, in which their conversation is described as “an incredible exchange.” A subsequent reply asks how many in

UPDATE: Qwen 3.8 27B 140 tok/s on single RTX 3090 Megakernel: KL divergence 0.0009 vs llama.cpp

This is a follow-up to my post from yesterday (https://www.reddit.com/r/LocalLLaMA/comments/1x2erdj/qwen3827b_on_a_single_3090_140_toks_on_code_with/) detailing the CUDA megakernel. Thank you all for

v0.40.3

model-releasesollama-releases

What's Changed ollama launch claude no longer shows a warning about Claude connectors by @hoyyeva in #18878 ollama launch codex no longer warns about service tiers by @hoyyeva in #18879 Paused backgro

vllm-ascend updates (GLM5.3-flash) on dual 310p Ascend cards

Alright, I learend a few valuable lessons since I posted about a week ago, and I've made more strides with the DDR4x Ascend 96GB 310P cards I purchased, so here we go, meet the new friendly and less v

[vllm] Is it possible to run Qwen3.8-27B-FP8 on a 5090?

Based on my experiments and feedback from claude, it seems extremely unlikely and even if I get it working, not practical because of minimum context. But just asking here as a last effort. I'm able to

We were able to fine tune an open weight model (Qwen3.5-9B) on proprietary internal data at @coinbase, and it out performed frontier models,…

model-releasesclem-delangue--x

We were able to fine tune an open weight model (Qwen3.5-9B) on proprietary internal data at @coinbase, and it out performed frontier models, at much lower cost and speed, for a specialized task (fraud

What setup do you use, which harness? and how do you customize it to run qwen 3.8 flash next swift with a high context length and a reasonable amount of tok/sec?

As i use a rather tiny setup 12gb vram gpu and 32bg ram, i was wondering: What setup do you use, which harness? and how do you customize it to run qwen 3.8 flash next swift with a high context length