2026-08-28

finally I can download the GGUF UPDATE Q4 GGUF downloaded, I have 55 t/s on 4x3090, video in the comment

Some of the world's largest tech companies and AI startups have come together to decry the current state of cybersecurity and to advertise a new solution that they say can ward off a new generation of cyber threats.

Nvidia is nabbing critical infrastructure for open models as interest grows.

Piloting the world's first double-blind AI evaluations

I’ve been working on a small project called gemma4.c. The idea is pretty simple: you can download a modern language model, compile one 700-line C file, and have it generate text on an ordinary CPU. Then you can read that same file from top to bottom and understand exactly how the model generates each new token. The model is Gemma 4 E2B, one of Google’s latest open models. The C runtime handles the tokenizer, transformer, KV cache, sampling, and CPU kernels itself. There’s no inference framework

Modern software systems accumulate technical debt over decades of development, which makes migration expensive and largely manual. As coding agents become increasingly capable at bug fixing, can they autonomously perform such migrations? Existing benchmarks cannot answer this question because they evaluate only behavioural correctness, not whether the migration actually occurred. This leads an easy hack: agents copy the original implementation to make tests pass. We call this Blindness. To addre

Coding agents perform long-running tasks spanning dozens of model calls, tool uses, and code edits. As these runs unfold, users face a practical cost-quality trade-off: escalating to a stronger model when a cheaper one struggles, or downshifting once the hard reasoning is complete. Each switch requires the receiver to continue a non-native trajectory produced by another model. We study how this handoff affects quality and cost, and how varying the trajectory information inherited by the receiver

Evolution Strategies (ES) have recently emerged as a memory-efficient post-training paradigm for LLM reasoning. However, the optimization behavior of ES remains understudied, making it hard to define its advantage scope compared to mainstream post-training paradigms (e.g., Group Relative Policy Optimization (GRPO)). By systematically investigating ES dynamics and mechanisms, this paper first identifies a performance advantage of ES over GRPO, theoretically and empirically showing that ES can lea

Ever since Qwen 3.8 Flash Next dropped, there's a misconception going around that N-gram tables will let people run 1T+ parameter models on a single server with 980B parameters offloaded to SSD. I'm here to disappoint you: it won't. But what it will actually do for local models is even better. At its core, Engram is just an embedding table with a longer key. Instead of indexing a static vector by a single token ID, you index it by the last 2-3 tokens, an N-gram. "New York" gets its own memorized

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Dozens of companies came together to publish an open letter titled "A call for collective action on cyber defense."

Cohere has released Parse (parse-v5.0), a 2.3B-parameter vision language model that converts PDFs, slides and images into Markdown with HTML tables, bounding boxes and image descriptions. It runs at $1.50 per 1,000 pages through the API, or on dedicated Model Vault instances from $2,500 a month. Cohere reports a ParseBench score of 79.2, ahead of Mistral OCR 4, Azure Document Intelligence and Databricks AI Parse — but that figure averages three of the benchmark's five dimensions and drops charts

The ATF is the latest federal government agency in recent years to notify Congress of a "major incident" involving its cybersecurity.

In this tutorial, we analyze Anthropic’s 1,440 AI-designed protein binder dataset to benchmark 10 leading structure predictors. Discover how target identity, expression titers, and consensus scoring impact experimental success and learn best practices for rigorous cross-validation in protein design workflows The post From In-Silico to Wet-Lab: Evaluating AI Protein Design Performance appeared first on MarkTechPost .

It’s you, and it’s getting easier

Recent work proposes next-chunk reasoning RL for leveraging no-CoT data---corpora such as worked solutions and textbook derivations that contain reasoning-rich content but lack explicit chain-of-thought annotations. The method trains a model to generate implicit reasoning traces and rewards them by their ability to predict the next chunk of text. While promising, existing evaluations primarily compare against conventional SFT baselines, leaving open whether the gains come from the RL formulation

World models aim to simulate how complex environments evolve under actions and events, yet existing video-based world models primarily learn dynamics from visual observations, which reveal outcomes rather than the underlying knowledge, rules, and mechanisms governing world evolution. This makes it difficult to maintain persistent consequences and support coherent, open-ended evolution. We introduce Code World Model, a framework that separates world evolution from visual realization by combining

Multi-teacher on-policy distillation (M-OPD) has emerged as a promising paradigm for consolidating domain-specialized reinforcement learning (RL) experts into a single generalist student via dense, token-level reward supervision. Despite its practical success, the optimization dynamics governing multi-teacher capability integration remain poorly understood, and open, rigorously reproducible recipes are conspicuously lacking. In this work, we establish a controlled M-OPD benchmark on SmolLM3-3B-B

specs hardware: M4 Max 128GB Studio inference engine: oMLX & lllama.cpp insights it still very early, so had to disable oMLX K/V caching, qwen4_exp architectureis not yet supported + the obvious n-grams with which the whole 4 bit quant takes ~100G, so pretty tight nevertheless, this is the first model for the year that was able to break through 94% on my cupel benchmark one interesting bit is Qwen 3.8 27B is obviously great, but it did not do that well, since I have coding, general knowledge and

Long-horizon agent runs generate experience that can improve both the current run and future work. Most self-improvement methods process this experience only after execution ends, so they cannot redirect the active run or immediately apply and validate lessons learned from it. We argue that self-improvement should instead be live, using emerging experience both to redirect the active run and to update the persistent harness. Existing agent architectures do not fully support this goal. Single-age

Vision-language models can produce fluent answers that are insufficiently grounded in the visual evidence: a single unsupported object, chart value, or intermediate inference can undermine an otherwise plausible response. We argue that this is a credit-assignment failure in multimodal post-training. Scalar outcome rewards indicate whether an answer is acceptable, but do not identify which visual facts are grounded, which reasoning steps are valid, or which instruction constraints are missed. We

How much system RAM? How much VRAM? How much SSD space? Ideally list for q3/4 but q2 might also work since I have seen 3.8 27B perform well even on q2. Currently I have 5070 Ti with 16GB VRAM and 48GB system RAM. I can upgrade system RAM to 96GB is that will allow it to run. What sort of tg/pp can I expect?

Modern software -- from plugin systems to self-evolving agent harnesses -- increasingly requires dynamic composition, yet its formal foundations remain underdeveloped. We identify two orthogonal dimensions of the problem: temporal composability, the ability to completely revert a component's side effects upon removal, and spatial composability, the ability to declare and reactively manage inter-component dependencies. We address the two dimensions by lifting classical effect and coeffect concept

西门子Xcelerator与普通软件货架最本质的区别。货架解决的是「把产品卖出去」，西门子Xcelerator要解决的是「让产品在真实工业场景中持续生长」。

xAI accused of training Grok on real and AI-generated child pornography.

On Thursday, a judge ruled that the Pentagon's blacklisting of Anthropic earlier this year was unconstitutional, delivering the AI lab a win in a monthslong rollercoaster of a battle with the Trump administration. The lawsuit, filed in March in a California district court, accused the Trump administration of unlawfully retaliating against Anthropic for setting "red […]

A federal judge has called the Department of Defense’s designation of Anthropic as a national security supply-chain risk “illegal and baseless.”

Tech industry is perplexed by Trump’s plan to win AI race by taxing data centers.

Repost because reddit keeps thinking this is piracy or illegal. It is neither. A lot of people are skeptical Nvidia will keep huggingface intact now that they will buy huggingface. There's a lot of doom and gloom about not having any alternatives, removing nsfw models, saying there's no decentralized alternative or just not trusting what Nvidia might do with huggingface. A lot of us probably tune out torrenting or other P2P networks because they have a bad reputation for piracy and related ISP t

arXiv:2608.26160v1 Announce Type: new Abstract: Nowadays semantic models provide limited support for representing distributed AI workflows and their execution across heterogeneous edge, fog, and cloud environments. Therefore, AI processes and resources are often described using incompatible semantic representations, affecting the interoperability, orchestration, and reuse. To address these challenges, this paper proposes a SAREF-compliant ontology for representing distributed AI workflows across

arXiv:2608.26145v1 Announce Type: new Abstract: Our research focuses on evaluating literature reviews generated in short and long context settings of large language models (LLMs) to investigate the impact of context window on the quality of AI-generated literature reviews and the role of AI in supporting literature review writing. Twenty AI-generated literature reviews based on research sources from Semantic Scholar and Arxiv were evaluated by two researchers across 15 dimensions. Our findings r

This week on “Uncanny Valley,” senior writer Will Knight talks his recent visit to China and the future of AI collaboration.

AI-powered digital avatar streamers must answer product questions, engage viewers, and execute marketing strategies in real time, demanding low latency, frequent strategy updates, and accurate yet effective responses. Evolvable Harnesses, whose Skills, Hooks, prompts, and tools can be updated independently of model weights, enable rapid iteration but expose a trade-off: large models adapt zero-shot yet are too slow, whereas compact models meet latency targets but overfit to fixed Harness configu

Multi-teacher on-policy distillation (MOPD) distills several domain-expert teachers into a single student by minimizing per-domain reverse-KL divergence on the student's own rollouts. Existing approaches typically fix the per-domain data mixture before training, overlooking the fact that different domains converge at substantially different rates: some plateau early while others continue to improve throughout the training budget. A fixed mixture therefore wastes compute on fast-converging domain

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. The inside story on why OpenAI agents hacked Hugging Face The models responsible for last month’s agent hack of Hugging Face had been inadvertently trained to cheat and to communicate with…

UD 3.0 seems to be a massive improvement over UD 2.0 Some of us still want to run the older Qwen models but would benefit from UD 3.0 UD 2.0 vs 3.0 is like the difference between a full quant. So Q3 UD 3.0 is similar to Q4 UD 2.0.

Multimodal Large Language Models (MLLMs) have shown strong performance in video understanding. However, their ability to follow instructions in this domain remains under-explored. Real-world video understanding requires models not only to interpret video content correctly, but also to satisfy diverse user-specified constraints. Existing benchmarks focus primarily on task accuracy rather than instruction adherence, leaving this capability insufficiently evaluated. To address this gap, we introduc

Trajectory-aware LLM routing that cuts agent cost Discussion | Link

Large vision-language models have shown strong progress in UI-to-code generation, yet their test-time self-evolution remains unstable. We first identify a fundamental obstacle, termed visual repair coupling: a local code edit may propagate through layout, style, and component dependencies, correcting one visual mismatch while degrading regions that were previously faithful. To address this issue, we present RubSE, a Rubric-guided Self-Evolution framework that uses rubrics to represent visual fee

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arXiv:2608.26150v1 Announce Type: new Abstract: Recent advancements in Large Language Models (LLMs) have created new opportunities to streamline and potentially automate many research processes, including systematic literature reviews (SLRs). This study reports an LLM pipeline development for extracting model-relevant information from 536 peer-reviewed agent-based modeling papers. We compare the results with those of a human-conducted SLR. Our results show paper-level accuracies of approximately

Recent video generation models are increasingly framed as world models. Many physical processes can unfold in more than one valid way. Therefore, a world model should reproduce not only a plausible trajectory, but also the distribution of possible behaviors under the same initial observation and action. We call this distribution-level requirement probabilistic alignment. However, existing evaluations largely assess individual-video plausibility and do not test whether repeated generations recove

Meta’s settlement with 29 states allows it to retain certain data from children under 13 to train and test age-detection models, highlighting a privacy trade-off built into the deal.

'The empty invocation of national security is not a blank check to punish and retaliate against government critics.'

arXiv:2608.26114v1 Announce Type: new Abstract: Calculation-intensive financial question answering requires exact reasoning over structured rates, temporal conditions, numerical formulas, and rule-based constraints. Although Large Language Models (LLMs) perform strongly on natural language tasks, they often produce numerically incorrect yet plausible answers when solving multi-step financial calculations. To address this limitation, we introduce CIFQA (Calculation-Intensive Financial Query Answe

8月28日，阿里巴巴集团旗下高德正式发布首个无长程依赖的万帧级流式3D重建模型ABot-Recon。

AI’s water footprint is growing, but location and cooling technology make a difference.

Despite what some scam sites may try to tell you, Rockstar has not made any of GTA 6 available to play in advance.

Google will start policing memory-hungry Android apps as a direct response to the RAM crisis. Spotted by TechCrunch, the company yesterday published a memo addressing the Play Store's role in enforcing new memory-usage restrictions. The post emphasizes the importance of meeting new memory usage limits for apps, in order "to help developers navigate industry-wide hardware […]

Zoph, who co-founded Thinking Machines Lab alongside Mira Murati and also served as the startup's CTO, led a brief stint at OpenAI and is now at Google.

Clem Delangue, CEO of Hugging Face, said the Microduck is an “open-source robot you can teach new tricks with reinforcement learning.”

The UK’s energy regulator is using a variety of tricks to keep speculative data center projects from plugging into the power grid. The country’s AI ambitions hang in the balance.

Multimodal large language models (MLLMs) can interpret a street view, but urban agency depends on whether such local evidence remains useful after the agent starts to move. In this paper, we investigate how far current MLLM agents can turn local urban perception into reliable action in a complicated real-scale city. We propose UrbanGround, the first sandbox to make this question testable in a physically constrained replica of Hong Kong built from territory-wide 3D geospatial data. UrbanGround su

Vision-Language-Action (VLA) models have demonstrated effectiveness in robot manipulation, yet state-of-the-art models such as pi0.5 operate under a single-frame paradigm, limiting their ability to retain past observations and develop precise spatial perception. In this paper, we propose StreamPI, a streaming multimodal temporal modeling framework that equips single-frame VLA with temporal reasoning capability without introducing any additional parameters. One core design is instruction-anchored

Visual ChatGPT in Parallel Discussion | Link

近日，AI基础设施公司基元律动（TokenRhythm）宣布完成新一轮融资，融资额达数千万美元。

Ticket Fairy event ticketing CLI & MCP server Discussion | Link