2026-09-13

Dario Amodei said that "we owe it to humanity to try."

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Before co-founding Kepler, Vinoo Ganesh led Spark at Palantir and built Project Frontline — a pioneering program for Forward Deployed Engineers. He takes us through the best practices of FDEs.

arXiv:2609.11018v1 Announce Type: new Abstract: The term agent in artificial intelligence lacks a standard definition, complicating the evaluation, comparison, and reproducibility of AI agent research. We address this ambiguity through a survey organized around five dimensions of agenticness: environmental interaction, learning and adaptation, autonomy, goal-directed behavior, and temporal coherence. For each dimension, we examine how the underlying capability has been conceptualized across prio

arXiv:2609.11030v1 Announce Type: new Abstract: AI agents increasingly act through tools and delegated authority, but general incident repositories rarely capture the mechanisms needed to compare public failures with agent-security evaluations. We present the Agent Incident Registry (AIR), a source-linked catalog containing \N{} records of agent-related events disclosed from \Yfirst{} through \Ylast{}. Each record includes supporting evidence, a stable identifier, and missingness-aware labels fo

arXiv:2609.10964v1 Announce Type: new Abstract: Agentic LLM workflows consist of sequences of model turns interleaved with tool interactions, so their end-to-end completion time depends not only on inference speed but also on when ready turns are released. Most runtimes release each turn immediately upon readiness. Under contention, this eager release policy can accumulate released but unfinished work; once submitted, those turns can no longer be reordered by the workflow-level policy, increasin

On-Policy Self-Distillation (OPSD) has emerged as a popular paradigm for large language model (LLM) self-improvement, allowing models to act as their own teachers by leveraging privileged information such as ground-truth solutions. However, recent findings indicate that OPSD can severely degrade the performance of LLMs on complex reasoning tasks: By forcing the student to imitate an artificially confident reasoning trace conditioned on privileged information, OPSD inadvertently suppresses expres

I find this new model at HF: "Built for demanding work. A 262 144-token context window, adjustable reasoning effort, tool calling, and text, image and video understanding. Architecture Agnes-3.0-Flash is a hybrid-attention decoder: three of every four layers run a gated delta rule (recurrent, with per-layer state independent of sequence length), and the fourth runs standard global attention. Only 18 of the 72 layers therefore hold a KV cache that grows with context." Context length 262 144 token

Blog Post : Per-tensor layout maps for GGUF quantization Reddit thread : New tensor type layouts for my GGUF uploads EDIT : Model card has updated things such as Graph, table, text, etc.

Kimi routed some PLA requests to Claude for distillation purposes without warning the PLA users. There is rumor that 16 Moonshot employees were arrested for this leak.

First off, I know that GLM, Qwen, and DeepSeek absolutely dominate in terms of SOTA Open Source models, and that’s what I use in my personal projects and for school, however, I’m also responsible for deploying local AI on my organization’s H100s, and we are forbidden by management from running any Chinese models. This is obviously not an ideal situation, but it is what it is, and there is nothing I can do to change this unfortunately. Again, if it were up to me I would deploy GLM 5.3 Flash in a

Hola all. Do you guys mind sharing your LLama.cpp config and system setup details for Qwen3.8 Flash Next? Model's quite big and tryining many combinations of llama.cpp options takes lots of time, so looking for other people setup details. I've attached my current config at the bottom, so if anyone sees something that could be improved please shout. My current best result: - PP within 130...200 tps (limited by cpu?) - TG within 14..22 tps (~15tps on average) Hardware: - Dual RTX 3090 (48GB VRAM)

Hey everyone! I'm curious to hear from people that use a combination of cloud-based frontier models and local ones for development. I'm planning to set something similar up and wanted to hear about actual examples of this in action. Currently my plan is to use my chatgpt plus subscription purely for planning and judging with Astra, and then run a local qwen3.8-27b model for the actual coding gruntwork - i.e Astra plans -> qwen implements -> Astra critiques the implementation -> qwen fixes and so

As you all know, Qwen3.8 Flash Next on mainline llama.cpp is still in a pretty experimental stage, but a lot of community forks are trying to get it to work better. There's also a closed-source solution called Halogen ( ) that boasted 1.2k t/s prefill numbers when the community fork barely reached 400. Since I dislike closed source and I like open source, I decided to take the challenge and bring llama.cpp up to the same performance level and I'm happy to report that after burning through a few

Revolut said it notified affected customers and alerted the relevant government agency, law enforcement, and financial regulators.

Twenty-five leading mathematicians signed an open letter arguing that AI labs are threatening their intellectual work.

触觉、记忆、Ego数据、自进化……这个世界模型全都有

arXiv:2609.10724v1 Announce Type: new Abstract: Sustained deployment of generative AI agents requires more than isolated task success. Agents must remain useful across repeated interactions, changing conditions, and dependencies on people within shared workflows, especially as technical, human, and operational disruptions accumulate over time. We propose operational resilience and considerate participation as two complementary aspects of evaluating such agents: the former captures how agents rec

Hi! I run both models on MTPLX on my m5 max, and since I have 128GB of ram I run the q8 27b. I think MTPLX only lets me run "optimized for speed" which it says is a dynamic q4 with 8 bit attention. Both of them honestly are very speedy! For coding (in pi agent in nodejs) I've just noticed that 27B feels stronger with harder tasks. But I've read so many people on here say qwen-next is better so I was wondering if maybe I'm just doing or thinking about it wrong? (and p.s. its sooo amazing that ali

AuK-Flash: Fast 4-Step Speech Generation and Editing arXiv : Full Paper : GitHub : Project : Introduction AuK is a 1.5B foundation model for speech generation and editing. Trained on millions of hours of diverse audio data, AuK supports zero-shot and instruction-based TTS, content and acoustic editing, paralinguistic editing, speech enhancement, and source separation through a unified natural-language instruction interface. AuK has two variants: Model Description Weight AuK Base model for high-q

For those using these models for coding in larger projects where things can get complex, do you find yourself using the 8-bit quants if you have enough memory? Or do you stick with UD-Q6_K_XL? The 6-bit is faster, noticeably so on my setup. And I keep seeing people say it's imperceptible. I've been doing tests myself, and well, I can't tell, but maybe that's just because I'm an idiot. That said, can you tell? Have you ever done some tests to see?

Q2 is there and Q4 is uploading as I type. Has his github been updated yet? How do you run this?

Note: this declaration was drafted by Mathematicians, and is mostly addressed to the mathematical community. It'd be interesting to discuss, among others, if what is written in the declaration may also apply to other communities---and, specifically, the AI/ML one.

President Donald Trump is weakening environmental regulations in the name of speeding up the construction of AI data centers, raising health risks for Americans, a cadre of former EPA officials said this week in a briefing and new report. They are urging - perhaps futilely - the president to adopt a "Data Center Health Protection […]

arXiv:2609.10584v1 Announce Type: new Abstract: Bounded-suboptimal search seeks a solution within a factor $w$ of optimal while reducing search effort. Focal Search (FS) uses heuristic guidance within FOCAL, the frontier nodes eligible under the threshold $w f_{\min}$, but its deterministic policy may leave $f_{\min}$ unchanged for many expansions. We introduce Probabilistic Focal Search (PFS), which follows the FS guided choice with probability $p$ and expands a minimum-$f$ OPEN node with proba

arXiv:2609.10873v1 Announce Type: new Abstract: Independent evaluation can reject harmful policy updates yet also prevent useful continual learning. We argue that update admission must be assessed through both error control and retained learning opportunities at a stated interaction budget. We identify a concrete failure: a range-based confidence gate cannot certify unchanged old-task behavior within otherwise substantial budgets. A standard paired-binomial construction reduces this burden when

arXiv:2609.10728v1 Announce Type: new Abstract: Large language models are unreliable at arithmetic, which is a problem for clinical calculators where a single numerical error changes the recommendation. The standard response is to hardcode each calculator as a validated function, one at a time. We test an alternative: the model does not calculate. Instead, it writes case-specific Python that a restricted local executor runs as a deterministic solver, and the model's task reduces to deciding how

Image tokenizers define the ``visual language'' of unified multimodal models, yet are commonly studied through isolated metrics or generation-/understanding-only evaluations. These evaluations do not fully capture how visual tokens behave when modeled jointly with text. We build a controlled pure-autoregressive testbed and track task-specific validation losses during multimodal continual pretraining across text, image, text-to-image (T2I), and image-to-text (I2T) prediction. We examine how these

We introduce MetroLLM-Bench, a 955-case benchmark for testing language models as the policy layer of a transit kiosk. It covers six real metro systems, ranging from 37 to 414 stations, and eleven categories that include routing, fare calculation, disruptions, accessibility, and adversarial input. In each case, the model must call structured tools and submit a machine-renderable terminal state containing an outcome, a per-ticket fare quote when applicable, and a kiosk action. Fourteen determinist

arXiv:2609.10656v1 Announce Type: new Abstract: Selecting LoRA rank for diffusion fine-tuning requires balancing quality and compute cost. We present a controlled study on CIFAR-10 using a DDPM U-Net with ranks {2,4,8,16,32}, fixed optimization settings, and a reproducible local-folder pytorch-fid protocol. We report FID, trainable parameters, runtime, and GPU memory, then validate trends with extended-budget DDPM runs (20 epochs; ranks 4/8/16) and a Tiny DiT backbone (10 epochs; ranks 4/8/16).

Autoregressive video world models enable interactive, long-horizon exploration, but flexible control remains challenging. Exploring a source video from new viewpoints requires the generated rollout to remain synchronised with the recorded event, place observed content in the requested view, plausibly complete newly exposed regions, and recover previously generated appearance on revisits. Existing methods typically address these requirements through task-specific modules or additional training. W

Reasoning language models have made substantial advances on a variety of complex tasks, yet their capabilities remain overwhelmingly English-centric: models primarily reason in English regardless of the language they are prompted in. This is inaccessible for non-English-speaking users, risks losing the intent of the original question, and forgoes knowledge more readily expressed in the target language. In this work, we advance L2 reasoning, the ability of a model to reason consistently in the la

My comment on Feeling sad about AI — Hacker News. I'm not sure how useful it is to say this, but I think a lot of people (myself included, a few years ago now) have been through this moment of existential crisis and come out the other side. The initial reaction, when some coding agent does a piece of work that would have taken you a week in an hour and does it well , is to be very disheartened by it. Once you come to terms with the idea that translating an exact specification into decent code is

Reports of the demise of coders may have been exaggerated.