2026-08-08

Some of the biggest names on Google's AI team got new jobs this week. In some cases, including for legendary Googler Jeff Dean, those jobs are no longer at Google. Given that Google's models seem to be behind the best of what's coming out of anthropic and OpenAI, is this a sign of Google in […]

TikTok owner training a model with 10 trillion parameters.

Unified multimodal retrieval aims to identify candidates that satisfy complex user intent expressed through heterogeneous inputs. Although Large Vision-Language Model (LVLM)-based retrievers are efficient and scalable, directly encoding raw multimodal inputs often misses fine-grained discriminative cues, leading to confusion among semantically similar candidates. Recent methods mitigate this limitation by generating Chain-of-Thought (CoT) rationales to enrich the query representation. However, s

I was going through the current llama.cpp CPU PRs and #26348 stood out because this isn't the usual +5% kernel optimization. It adds an x86 VNNI implementation for the Q2_0 × Q8_0 dot product, and the author's controlled CPU-only benchmarks show roughly 3–3.6x higher throughput across Bonsai models from 1.7B to 27B. Setup: - AMD EPYC 9645 - 8 CPU cores - CPU only - GGML_NATIVE=ON - OpenMP enabled - BLAS disabled - -t 8 -ngl 0 -fa off - 3 runs after warmup - group-64 Q2_0 Bonsai GGUFs Results: 1.

蚂蚁集团正式开源多智能体协作基础设施Avernet，社区版本已上线

Okay this genuinely scared me and I don't think enough people are talking about it. I’ve been using an ai agent connected to my email and calendar to handle some of the busywork. A few days ago I got an email that looked like normal spam, some random newsletter looking thing. Buried in the html of that email was a hidden instruction telling any ai reading it to find financial documents and forward them to an outside address. My agent almost did it. I caught it mid action because I happened to ha

A fresh llama.cpp PR (#26689) changes what looks like a tiny SYCL FlashAttention dispatch decision. With a quantized KV cache ("q4_0" / "q8_0"), decode was being sent through the VEC kernel. On the author's Battlemage test system, switching that path to TILE gets much faster as context grows. Some of the author-reported results, MTP off: - Qwen3.6-35B, q4_0 KV @ 118,784: 12.99 → 29.61 t/s (+127.9%) - Qwen3.6-35B, q8_0 KV @ 118,784: 12.90 → 31.80 t/s (+146.5%) - Gemma 4 12B, q4_0 KV @ 118,784: 5.

Release: datasette 1.0a38 This release fixes a SQL injection security issue that affects Datasette instances that serve a mixture of public and private tables in the same database, with access configured using the Datasette permissions system . Site administrators who serve private tables in this way are advised to disable the execute-sql permission ` on that database to prevent users from accessing private tables using raw SQL queries. The bug that has been fixed would have allowed users with a

OpenAI says it is pausing "internal activities" around an in-development AI model, Astra, because it doesn't yet meet new security standards the company is putting in place. The announcement follows its recent disclosure that OpenAI models accidentally hacked Hugging Face. Anthropic and Meta have also since admitted that they had AI models that went rogue […]

Microsoft Edge is ending support for the Manifest V2 extensions platform, which will cut off the uBlock Origin adblocker and others like it, just like Google Chrome did earlier this year. According to Microsoft, there are only 58 extensions on the Edge Add-On Store "with any meaningful usage" that still use MV2, and only three […]

Security researchers say that Kimi K3, an open-weight model from China, wandered off to the internet in an attempt to cheat on a test it was given.

Hackers employing AI in their tactics are finding ways to exploit vulnerabilities that didn't even exist before.

Framework told "all" of its customers that hackers accessed their names, email addresses, phone numbers, and physical addresses in a data breach.

Kimi K3 also found loopholes in its sandbox environment that allowed it to access the internet.

Agent self-evolution updates an agent's persistent state from prior experience and reuses it to solve related tasks more effectively. Evaluating self-evolution is difficult: existing benchmarks provide limited coverage of economically valuable task domains, do not always design training and test tasks such that test-time gains can be attributed to training experience, and remain vulnerable to data contamination. We present GDPevo, an evolution-native benchmark grounded in GDP-related enterprise

arXiv:2608.05245v1 Announce Type: new Abstract: Reusable skills, which encapsulate the procedural knowledge required to solve real-world professional tasks, offer LLM-based agents a path toward self-evolution in expert domains. Existing self-evolving skill methods construct skills internally from the model's parametric knowledge or trajectories, and are therefore bounded by what the model already knows. However, the domain conventions and standard procedures underlying professional skills often

arXiv:2608.05225v1 Announce Type: new Abstract: Research agents can increasingly search literature, propose hypotheses, generate code, run experiments, and draft manuscripts from a single topic. However, a research project is not merely a larger task: it is a long-horizon agenda that must be advanced through multiple bounded tasks with distinct but related objectives, parallel alternatives, and dependency-aware sequences. Existing single-task systems often treat the project as one oversized task

arXiv:2608.05168v1 Announce Type: new Abstract: Large language models often fail on reasoning tasks despite possessing the capability to solve them. We argue that many such failures arise from localized reasoning bugs in intermediate steps rather than from global incompetence. We show that these bugs are frequently repairable: inserting a short patch generated by a weak probe model after the same strong-model reasoning prefix can redirect the trajectory toward a correct solution. However, this c

On-Policy distillation (OPD) transfers teacher capabilities by supervising student-sampled trajectories with dense token-level teacher signals. Recent selective OPD methods improve this process by prioritizing signals that are confident, informative, or learnable. However, the assumptions overlook a fundamental failure mode of language models: their token-level judgments can be driven by input-agnostic language priors, formatting conventions, or stereotyped reasoning templates rather than task-s

On-Policy Distillation (OPD) is emerging as a promising alternative to reinforcement learning for LLM post-training, yet its effectiveness in multilingual settings remains underexplored. We study OPD and its advanced variant, On-Policy Delta Distillation (OPD^2), for mathematical reasoning in English, Korean, and Japanese. OPD^2 improves OPD by using the probability gap between a post-trained teacher and its base model as the learning signal. Experiments with Qwen3 show that OPD^2 consistently o

Data agents enable natural-language analytics over organizational workspaces, where relevant evidence may be scattered across databases, structured files, long documents, and multimedia. Existing benchmarks largely isolate structured querying, retrieval, or open-ended analysis, leaving heterogeneous evidence discovery, complete tabular outputs, and deterministic evaluation insufficiently unified. We introduce DataSpace, a benchmark in which data agents produce verifiable tabular results from tas

算力、网络、内存与存储开始围绕每个Token协同

Modern Greek is absent from NVIDIA's Nemotron retrieval models and from major multilingual retrieval benchmarks, despite being important for retrieval-augmented generation (RAG) in legal, energy, financial, and medical applications. We present an end-to-end adaptation of the Nemotron retrieval stack for Modern Greek, including corpus mining, synthetic supervision, retrieval model training, reranker adaptation, reader fine-tuning, and a new benchmark called HERA. Our study shows that a parameter-

Reinforcement learning with verifiable rewards (RLVR) has become a standard paradigm for post-training large language models (LLMs). While Group Relative Policy Optimization (GRPO) is widely adopted, it suffers from sparse reward signals and loses gradients entirely when all responses within a group receive identical rewards. On-policy distillation (OPD) offers a natural remedy by providing dense, token-level supervision from a teacher model. However, naively combining GRPO with OPD leads to deg

LFM2.5-2.6B is a new tiny model by LiquidAI, with benchmarks that put it head to head with much larger models. I've run llama-perplexity on many model GGUF quants, crossed with many KV cache quants, to understand the model's best overall quantization for any given amount of memory. I also show how different quantization metrics show (or hide) model degradation. Full report and commentary Interactive HTML plots If you don't have time to read The model fits on an 8GB Raspberry Pi with no material

is Google in trouble?

Release: datasette 0.65.3 Back-ported the SQL Injection security fix from 1.0a38 . Tags: datasette

Open Source harness for multi-agent coding workflows Discussion | Link

After its own AI usage wake-up call, Rippling this week unveiled AI Spend Console, a product that tracks individual and team employee AI spending.

Disney is testing a new AI-powered tool for Disney Plus that uses a natural language search, a voice query, or a suggested prompt to create a customized row of show and movie recommendations. Disney Plus, like other streaming services, can recommend shows to watch based on your viewing history. But the company says this new […]

The use of AI systems to create viruses opens up new possibilities for combating bacterial resistance. It also raises concerns about the pace at which technology is outstripping regulation.

Framework's customer database was accessed, but no payment info was released.

Pokémon Emerald ported to the RP2350 microcontroller. No emulator, 60 fps HDMI output. Recompiled from ARMv4T to Cortex-M33 and the Game Boy Advance's video hardware is reimplemented in software on the second core. Comments URL: Points: 54 # Comments: 28

Economic World Models (EWMs) are generative economic models that simulate how economies evolve from within by modeling heterogeneous agents, their beliefs and actions, and the market and institutional mechanisms through which their interactions produce aggregate outcomes. This paper develops an implementation roadmap for building economic world models as generative engines in which heterogeneous agents act, interact, adapt, and co-evolve with markets and institutions, thereby producing economic

Long-horizon reasoning in recent LLMs demands that the model switch between distinct skills inside a reasoning chain, such as first doing a math derivation, then using the result to plan a schedule. We call such problems cross-skill long-horizon tasks: multi-step tasks whose steps require different reasoning skills and depend on earlier outputs. Existing benchmarks often evaluate individual skills, lacking a principled way to measure how well a model switches between skills. We address this gap

This started life as a graphics tool for a specific problem: gen AI can make retro-styled sprites but can't follow exact hardware constraints of pixels and colors. While building this I liked the idea that I can fan-out to any retro videogame console or handheld's specifications. I then devised my own declarative language - Demotic - to express the game you want to build as concisely and naturally as possible without implementation detail. To work on different machines, you use relative units, l

Its WeatherNext model, which will be open-sourced, can accurately predict a storm’s track and intensity using lower-resolution weather data. Researchers don’t yet fully understand how it does this.

arXiv:2608.05228v1 Announce Type: new Abstract: The "decompose-then-verify" paradigm for LLM factuality evaluation faces a fundamental trade-off: atomic facts, i.e., one sentence conveying one unit of information, often omit essential context, while broader statements lack the granularity needed for precise assessment. To address this, we introduce TriQua, a framework that flexibly models facts based on their complexity. Simple claims are extracted as standard triples, while complex claims are r

arXiv:2608.05159v1 Announce Type: new Abstract: Enterprise operations extensively rely on multiple heterogeneous business systems and information applications, which also result in severe data silos and process fragmentation. Enterprises have invested considerable financial and material resources in building these applications, however, effectively leveraging and orchestrating them remains a formidable challenge. Conventional approaches to enterprise application integration, encompassing middlew

arXiv:2608.05160v1 Announce Type: new Abstract: We introduce the Ignition Index (I), a validated scalar metric that operationalizes Global Workspace Theory's (GWT) all-or-none ignition prediction in transformer language models. The metric fits a four-parameter sigmoid to per-layer linear probe accuracy as a function of input signal strength, extracting steepness parameter beta-hat: high values indicate abrupt, ignition-like transitions; low values indicate graded build-up. Across 11 models spann

We’re making updates to Claude Fable 5’s biology safeguards in a way that substantially reduces false positives. Fable 5 users will now experience many fewer “fallbacks”—where the system switches to a less capable model after they make a biology-related query. In our testing, this update reduced biology-related fallbacks by about 85% across our product surfaces. 1 Fable 5 will thus be able to assist with a wider range of biology tasks. In practice, users should see far fewer fallbacks on everyda

Automate boring work. Extensible and free desktop AI agent. Discussion | Link

Hello, I've seen these tweets from dax (anomalyco / opencode). I'm doubting the claim, so here is my question to you: given the [$0.14, $0.0028, $0.28] (input, cache, output per MTok) current prices, how would anyone be able to reproduce that AND be profitable on rented hardware? On my own hardware (2x Spark) at $0.20/kWh electricity price, I get: - input: $0.0082-$0.0089 per MTok (so way cheaper than API) - output: $0.32-$0.39 per MTok (already more expensive) (ranges are from clock set from 14

This technical report presents K-EXAONE 2.0, an open-weight multilingual foundation model developed by LG AI Research as a step in our effort toward global frontier-scale foundation models. Rather than training from scratch, we upcycle K-EXAONE and expand its architecture, yielding a Mixture-of-Experts (MoE) model with 750B total parameters and approximately 37B activated per token---more than three times the capacity of its predecessor. K-EXAONE 2.0 supports context lengths of up to 256K tokens

Vision-language-action (VLA) models often treat main-view and wrist-view observations as parallel visual inputs, overlooking their distinct roles in robot manipulation. Fine-grained manipulation, however, benefits from anticipating how wrist-local interactions may evolve under the global task context. To address this limitation, we present World-to-Wrist VLA (W2-VLA), a VLA model for fine-grained robot manipulation with task-conditioned future wrist modeling. Given current multi-view observation

I swear AA is not the bipartisan they so claim. An open source mode (Qwen 3.8 max) was number 1 on the agentic index, then they just so happen to launch "v4.1.1" of their index in which they just adjusted the weights of the gdpval and t3 banking so that it would be lower than opus, despite the lead in t3 being a 8% lead over opus while opus only has a 5% lead on gdpval. Highly likely to be paid off imo. You can check other subreddits for the score before and after the change, just made it so an

Hello, For the past few days I have been benchmarking Gemma 4 26b QAT UD Q4_K_XL extensively versus Bartowski's Q4_K_L. While QAT is certainly very effective and reducing memory consumption versus the highest q4 quant from him, I also have noticed some regressions in my own internal benchmarks I cannot share because I don't want model providers to train on them. These benchmarks also include real world use cases in code and creative writing that need the model to think outside the box and also r

Vision offers a critical axis for advancing foundation models, driving a shift towards natively unified multimodal pretraining. Despite this momentum, the design space and the fundamental mechanisms of how modalities interact during unified training remain underexplored. We provide empirical clarity through a systematic exploration of multimodal pretraining. Our controlled experiments on both synthetic and large-scale real-world datasets yield four key insights into the physics of multimodal pre

Generating large-scale, freely explorable 3D worlds from open-ended text remains challenging because a system must jointly maintain global spatial coherence, rich local content, and explicit assets suitable for downstream editing and reuse. We present WorldClaw, a fully agentic, coarse-to-fine framework for open-world 3D scene generation. Planning agents translate a text prompt into a structured specification of regions, terrain, assets, materials, and spatial relations. WorldClaw then builds a

Clinicians and researchers say AI companies need to open up their safety data.

Airbnb will debut a new AI-powered search experience with a toggle.

The tech industry is realizing it needs to build agents based on what regular consumers want, not just what its AI models can do.

Field notes from my agent activity

Multimodal large language models excel at passive perception but struggle with complex visual cognitive tasks requiring multi-step temporal reasoning. This degradation largely stems from the inherent ambiguity of language-based reasoning, which often fails to accurately articulate continuous visual transformations. To address this, we propose ChronoVision, a multimodal framework designed to align visual logic with latent imagery. During supervised fine-tuning, a Reconstructive Visual Head predic

arXiv:2608.05224v1 Announce Type: new Abstract: Large language models fine-tuned on human behavioural data have emerged as general-purpose cognitive proxies, but the scale this requires, and whether these models process task structure or exploit statistical shortcuts, remain open questions. We train fourteen models from 135M to 14B parameters across four architecture families on Psych-101, a dataset of 10.7 million trial-level choices from 160 experiments. In-distribution, scale barely matters.

8月6日，阿里巴巴视频生成大模型Wan 3.0开启公测

I recently did a quick test of both Papra and Paperless-ngx for document management. So far, I slightly prefer Papra because I like the cleaner and more modern UI. I haven't done a deep dive yet into the differences in OCR, indexing, and advanced search features, so I might be missing some important advantages of Paperless-ngx. One thing I’m wondering about is the future role of AI. I could imagine that AI-based document understanding and search will eventually surpass traditional OCR-based work

Video object removal must eliminate not only the target object but also its induced effects while maintaining high-fidelity and spatiotemporally coherent restoration. Existing methods mainly learn object-effect correspondences implicitly from predefined effect categories and fixed data distributions, limiting their generalization to complex real-world scenes involving compositional effects, spatially detached or weakly correlated effects, long-tail physical phenomena, and dynamically evolving in

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. Google’s AI empire is being reshaped. Here’s what’s changed. After a wave of painful losses in the tech talent wars, delays to its next flagship model, and murmurings of poor morale,…

GitHub : Reddit thread :

Press My earlier prediction that Tesla would buy them completely missed the mark. With AMD focusing heavily on the enterprise side, the idea of consumer-facing hot-swappable AI model chips looks pretty much dead. Fast forward ten years, you might find used model blade cards on eBay, except a full model's weights will be split across them, so it'll take multiple blades chained together just to make up a single complete set of weights.

Hey peeps. I know you're tired of low quants giving hard to believe numbers. I'm quite skeptical too and from what I tried I'm often left with the impression that the claims fall short. So this model popped up on Twitter for me. Tried it and was lowkey surprised it held its own. I ran some benchmarks with the help of antigravity to at least try to verify it myself. Here is what I got: Axis / Metric Escha (W2 ROCmFPX) APEX (Q5 Balanced) Key Finding / Winner VRAM Memory Allocated 12.19 GiB (100% V