model releases

Speaking of fine-tuning, we’ve got support on @axolotl_ai. Start training North Micro Vision right away, no hardware required. Find their docs here: https://docs.axolotl.ai/docs/models/cohere-north-mi

arXiv:2608.10692v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as mobile assistants, where a key challenge is leveraging personal information scattered across

arXiv:2608.11075v1 Announce Type: new Abstract: Event cameras capture intensity changes asynchronously with high temporal resolution, requiring novel preprocessing methods for downstream tasks. Unlike

arXiv:2608.10439v1 Announce Type: new Abstract: Streaming video generation holds strong potential for world modeling, where future frames must be inferred online sequentially to form a continuous vide

arXiv:2608.10810v1 Announce Type: cross Abstract: Emotion understanding in discourse requires reasoning beyond surface sentiment because speakers often convey affect through indirect, implicit, polite

arXiv:2608.10837v1 Announce Type: cross Abstract: The strong performance of foundation models for tabular tasks comes at substantial inference costs. Distilling models into task-specific architectures

arXiv:2608.10258v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly provide conversational health information that may influence treatment decisions, yet existing benchmarks do

arXiv:2606.16188v2 Announce Type: replace Abstract: Diffusion models excel in Real-World Image Super-Resolution (Real-ISR) due to their powerful generative priors but suffer from slow iterative sampli

arXiv:2509.25143v2 Announce Type: replace-cross Abstract: Existing medical reasoning benchmarks for vision-language models primarily focus on analyzing a patient's condition based on an image from a s

arXiv:2608.10932v1 Announce Type: cross Abstract: Understanding camera motion is fundamental to video perception, with applications in spatial intelligence and controllable video generation. Multimoda

arXiv:2608.11191v1 Announce Type: cross Abstract: GUI Visual Grounding is a fundamental capability for GUI agents. Existing models typically freeze their parameters after deployment, limiting their ab

Ran the model with quants (Q5) and MTP by bartowski with llama.cpp server. It takes ~24GB ram running on M5 Pro with 48GB at about 65t/s. On some tasks it was quite the overthinker. Overall, the quali

arXiv:2608.10145v1 Announce Type: new Abstract: LeWorldModel trains a latent world model with a prediction loss and a single anti-collapse regulariser, and reports approximately 87% of goals reached o

arXiv:2505.11635v2 Announce Type: cross Abstract: Many real-world tasks, from associative memory to symbolic reasoning, benefit from discrete, structured representations that standard continuous laten

The model punches above its weight, outperforming Gemma 4 E2B and Ministral 3 3B across a broad range of visual understanding benchmarks and delivers particularly strong results on document understand

The most dangerous document extraction failure isn't a wrong value. It's a missing row that looks like nothing is wrong. We released ExtractBench yesterday: 370 enterprise docs, 14 systems. The hardes

arXiv:2608.09941v1 Announce Type: new Abstract: While 4-bit weight quantization is critical for deploying Small Language Models (SLMs) on edge devices, evaluations of the resulting performance degrada

arXiv:2606.15821v2 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) have produced many specialized multimodal LLMs (MLLMs) that share common foundational LLMs, fo

Today, we’re adding another member to our model family. Meet North Micro Vision. Our smallest vision-language model yet, ideal for sophisticated document understanding. Available open-source under an

arXiv:2608.10268v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly mediate legal determinations over what human rights are realized, and how. Yet, no evaluation benchmark exis

arXiv:2608.10447v1 Announce Type: cross Abstract: Large language model-based recommender systems are increasingly adopting slow-thinking models that generate step-by-step reasoning before making predi

arXiv:2608.10346v1 Announce Type: cross Abstract: Although advancements in face landmark detection (FLD) methods continue to push performance boundaries, they overlook two major functional limitations

arXiv:2608.10176v1 Announce Type: new Abstract: Public service chatbots are expected to deliver recommendations from an underlying public service directory, while also making sure that the recommendat

Try Grok 4.6 on tough real-world tasks! imo GDPVal is probably the most important benchmark, it measures the performance of models on real world tasks Big leap in performance here to top it at a great

Try Qwen-Image-3.0 on @openart_ai! 🎨👀 Qwen Image 3.0 is now on OpenArt ✨ The most Real Qwen image model yet. Native text across 12 languages, precise 10px type, and full interfaces like web pages, gam

arXiv:2608.10007v1 Announce Type: cross Abstract: The current state-of-the-art (SOTA) deep randomized neural networks, such as deep Random Vector Functional Link (dRVFL) and ensemble deep RVFL (edRVFL

arXiv:2608.10042v1 Announce Type: cross Abstract: Tool-use LLMs are increasingly asked to act on users' behalf, but existing benchmarks usually focus on profile recall, style imitation, generic tool u

arXiv:2603.18446v2 Announce Type: replace Abstract: Long-context inference remains challenging for large language models due to attention dilution and out-of-distribution degradation. Context selectio

arXiv:2608.11047v1 Announce Type: new Abstract: While existing benchmarks have made substantial progress in evaluating LLMs across STEM domains, financial reasoning over structured data remains compar

What's Changed Models that don't set a repeat_penalty now default to 1.0 (off) instead of 1.1, matching other engines and speeding up speculative decoding; set a per-model parameter if an older model

arXiv:2608.10875v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly deployed as personal assistants. Existing evaluations, however, mostly use short, self-contained re

arXiv:2608.10408v1 Announce Type: new Abstract: Vision-language models (VLMs) have shown strong capabilities in generating visualization code from textual or visual specifications. However, real-world

arXiv:2607.16173v2 Announce Type: replace Abstract: Open-vocabulary 3D maps let robots answer language queries about what and where, but they assume a static world and cannot answer queries about how

arXiv:2608.10682v1 Announce Type: new Abstract: Single-frame surround-view reconstruction faces severe geometric instability and rendering artifacts due to minimal inter-camera overlap. While existing

arXiv:2608.10359v1 Announce Type: cross Abstract: As information increasingly traverses linguistic boundaries, users require concise cross-lingual representations of long-form content. Nevertheless, l

We are in an insane run of open-weight drops. Every modality, open source is winning. This is what an open source AI summer ☀️ looks like: 🧠 LLMs & Reasoning → DeepSeek-V4-Flash-0731 (my king 👑): 304B

We have her dash camera which shows they are lying. The agents are wearing body cameras and should have dash cameras of their own. If what they say happened was true they wouldn’t be issuing statement

We wrote a 36-page ArXiv whitepaper on ExtractBench 🧑‍🔬 , our effort to create the most comprehensive, schema-guided, real-world document extraction benchmark. It’s extremely detailed and covers every

We’re excited to announce DiG-bench, a new benchmark for discovery! Over the last few weeks we’ve been testing frontier AI models on our novel discovery games and seeing how they score. Each game is a

Warning: Kinda long post. If you don't like reading, please skip for your own sanity. Also, I've got nothing to sell, just a tinkerer, so I just want to share ideas and learn from you guys too. When I

arXiv:2608.09942v1 Announce Type: cross Abstract: It is widely assumed that chain-of-thought (CoT) prompting universally improves LLM reasoning. We investigate this through the conceptual framework of

arXiv:2608.11024v1 Announce Type: new Abstract: Attribute hallucination---where vision-language models (VLMs) correctly identify an object but mischaracterize its properties---is prevalent yet mechani

arXiv:2608.10220v1 Announce Type: new Abstract: Humanoid locomotion in highly confined environments requires navigating dense environmental obstacles and complex self-collision bounds while maintainin

arXiv:2608.11095v1 Announce Type: new Abstract: Agentic coding READMEs like CLAUDE.md grow without bound in real repositories, stopping only when the repository retires or someone rewrites the file wh

Why does CLAUDE.MD keep growing? If you maintain a CLAUDE.md or an AGENTS.md, this one is worth your time. (bookmark it) This work traces why these files grow without bound. Appending an instruction i

With the smartest person I know, @HarshSensei, we're building evsys-sdk for the community, this open-source repository allows anyone to build their own continual learning system with first-class suppo