The Archive

Detecting unanswerable user queries remains essential for the reliable deployment of real-world embodied agents. However, modern vision-language models (VLMs) often generate overly confident answers even when the available visual memory cannot support the query. Such overconfidence poses various task-dependent risks. The agent may provide misleading information to the user in Embodied Question Answering and select an arbitrary coordinate and physically guide the user there in spatial reasoning for navigation. Despite these high stakes, only a few prior studies directly address when and how an...

Do different LLM architectures encode high-level concepts in structurally compatible ways? We systematically characterize a geometric-functional universality dissociation: across multiple concept domains and architectural families, moderate geometric convergence coexists with near-perfect functional transfer. Using contrastive-difference CKA (CKA_Delta), a training-free diagnostic that computes kernel alignment on per-sample contrastive differences, we isolate concept-specific convergence from generic similarity -- achieving significant discrimination where standard CKA cannot. The dissociati...

A group made up of dozens of cybersecurity experts urged the White House to remove export control restrictions on Anthropic’s models Fable and Mythos, arguing that the order is going to limit the ability of cybersecurity defenders to secure their software and products.

Google has announced a $1.5 billion investment for 2026 and 2027 to expand its data center campus in Jackson County, Alabama. Operating since 2019 on a repurposed former…

"They screwed us": Personality clashes sent Anthropic's models offline Lots of "source familiar with the administration's thinking" and "source close to Anthropic" in this Axios piece, which is the best collection of behind-the-scenes gossip I've seen about the US government export control Mythos/Fable story so far. Logan Graham, Dave Orr and blog favorite Nicholas Carlini are supposedly meeting with the Commerce Department today in D.C. Good luck to them! This closing notes doesn't give me much optimism that we'll be getting Fable back any time soon: The bottom line : One option is to m...

Salesforce says it wants to use Fin's team and technology to improve Agentforce, its existing enterprise platform that businesses can use to build custom AI agents that automate tasks.

Today, I’m talking with Adam Bry, who is CEO of Skydio, the leading US maker of autonomous drones. Before we recorded this episode, I actually got to remotely operate one of Skydio’s drones in the Bay Area from Adam’s laptop in our podcast studio in New York and fly an indoor drone around our office. You can check out the full video of that on our YouTube channel. Beyond flying drones around the country, Adam and I talked about why Skydio is so focused on the enterprise market — I asked him a lot about working with police and military, but you’ll hear him say a lot of Skydio’s customers are u...

Indian IT services company HCLTech is investing $150 million in the Bengaluru startup.

NewCore argues the next challenge in enterprise security will be managing AI agents, not people.

Quick glossary for readers new to VLA/WAM terminology VLA Vision-Language-Action model: a robot policy that starts from a pretrained VLM backbone and adapts it... Quick glossary for readers new to VLA/WAM terminology VLA Vision-Language-Action model: a robot policy that starts from a pretrained VLM backbone and adapts it to generate actions from visual observations and language instructions. Large-scale VLM pretraining is a core part of the recipe. See Pi-0 and GR00T N1. WAM World-Action Model: a policy that starts from a pretrained world-model or video… Source

In April, for the first time ever, an Earth observation satellite found what it was looking for, all on its own.

Where are your agents right now?

Anthropic's belief in its own commitment to safety gives the company license to aggressively favor its business and even challenge the U.S. government.

What makes this combustible: at the very moment that tens of thousands of workers are being shown the door, a small cohort of AI insiders is becoming wealthy on a scale that's hard to comprehend.

[...] Instead, I picture a specific person and I just write for them. Often this person is "me, but 3 years ago" or a good friend. — Julia Evans , write for 1 person Tags: writing , julia-evans

The new office places Cohere at the centre of London’s AI growth story with a growing global research hub based out of the city.

Modernize FOI request systems with AI. Learn how AI agents can help public-sector teams reduce manual work, improve responsiveness, and preserve oversight.

Why AI hasn’t replaced software engineers, and won’t Arvind Narayanan and Sayash Kappor take on the question of AI job losses through the lens of a profession that is uniquely suited to AI disruption - software engineering. In this essay, we argue that there is enough evidence to reject the narrative that once AI capabilities reach a certain threshold, it will cause mass layoffs. Given that this is true even in a sector with very few regulatory barriers, most other professions are likely to be even more cushioned. The first good news is that the data still doesn't support the idea that AI is ...

According to a new report from Semafor, the White House's decision to impose export restrictions on Anthropic's Mythos was driven in part by fears that it had been accessed by a group linked to China. If the Chinese government actually had access to Mythos 5 or Fable 5, it would present a serious national security risk. The government could also attempt to reverse engineer the model through distillation, a method in which a "student" AI is trained on a more advanced model to replicate its behavior. The White House has not confirmed this report, and a post on X by Trump advisor David Sacks did...

OpenAI launches the Partner Network, investing $150M to help global partners accelerate enterprise AI adoption, deployment, and transformation.

Startups are trying to "ride that SpaceX IPO wave."

AI agents increasingly access external models, tools, and services through Agentic Routing Infrastructure (ARI) to manage the overhead of heterogeneous interfaces and fragmented subscriptions. Yet, the architecture of ARI introduces fundamental trust risks: it obtains plaintext access to agent queries and service responses, while leaving agents unable to verify that their queries are routed to intended service providers or that requests and responses remain untampered. To address this problem, we present TrustedARI, the first trust-native agentic routing infrastructure for agentic AI. Archite...

Recent advances in large language models (LLMs) have produced many specialized multimodal LLMs (MLLMs) that share common foundational LLMs, forming distinct model lineages. It remains unclear whether a fundamental behavioral link exists between the foundational LLMs and downstream variants. We investigate this question by quantifying head-level context-truthfulness scores. Across diverse LLM and MLLM lineages, including Vicuna-, Qwen2.5-, LLaMA2-, and Mistral-based models, we find that Truth Scores are strongly preserved within model families, even after instruction tuning or multimodal adapt...

The rapid progress of visual autoregressive (VAR) models has unlocked a transformative frontier for high-fidelity text-to-image synthesis, while heightening concerns over the safety alignment of generated content. Naive application of existing erasure techniques to VAR models causes catastrophic semantic collapse and visual artifacts, since they are predominantly designed for the homogeneous denoising steps of diffusion models. To address this foundational challenge, we first propose the Semantic Singularity Axiom, which posits that any target semantic concept embedded within a prompt is defi...

The deployment of NLP systems has raised concerns about harms they might produce, including representational harms. Recent literature has begun to conceptualize and measure one such harm, the harm of erasure. Nevertheless, the field lacks a clear and cohesive conceptual foundation for identifying and measuring erasure. Existing conceptualizations of erasure are often broad -- making it difficult to identify what is needed to establish and measure erasure -- or else specific to particular settings -- facilitating measurement for those settings but potentially challenging to adapt to other sett...

Constructing mathematically tractable function spaces that capture hierarchical compositional representations remains a central challenge in statistical learning theory. We introduce Brownian kernel ladders (BKLs), a recursively defined hierarchy of integral reproducing kernel Hilbert spaces generated through Brownian-kernel integral constructions. Starting from linear functionals, each layer is obtained by integrating Brownian kernels over probability measures supported on subsets of the previous layer, yielding a recursive function-space model in which depth is encoded directly through the ...

Large language models deployed as commercial APIs are vulnerable to model extraction attacks, while existing defenses either act too late or degrade utility for legitimate users. We propose \textbf{Knowledge Trap}, a defense that redirects extraction attacks toward low-transferability knowledge through a \emph{Honeypot Knowledge Graph} (HKG) and breadcrumb-guided exploration. Instead of blocking queries or perturbing outputs, Knowledge Trap consumes the attacker's limited query budget on knowledge with negligible downstream utility while preserving benign-user performance. Experiments in medi...

Traffic state prediction is a fundamental task in intelligent transportation systems. In practical applications, some regions suffer from limited traffic observations due to insufficient sensing infrastructure, making cross-domain knowledge transfer an important solution for data-scarce traffic prediction. However, existing cross-domain traffic prediction methods still face several limitations, including coarse-grained source-target adaptation, limited capability in handling unseen target-domain patterns, and insufficient modeling of continuous traffic dynamics under irregular or heterogeneou...

Discrete diffusion language models enable parallel token generation, offering a pathway to low-latency decoding. However, selecting tokens independently by marginal confidence limits effective parallelism: tokens that appear reliable in isolation can form incompatible configurations when several positions are updated at once. We introduce a training-free decoding framework that coordinates these parallel updates. At each forward pass, the method assigns a commit score to each masked position and refines these scores using pairwise interactions derived from the model's predictive distributions...

Compilation-based techniques represent an important stream of solvers for multi-agent path finding (MAPF) due to their modularity and adaptability for non-standard variants of the problem. While in the standard MAPF the task is to navigate all agents from their initial positions to given individual goal positions without any collision, variants where a different requirement for agents is used are also relevant. Such a variant is MAPF with unassigned agents (UA-MAPF) where some agents have the same setting as in the standard MAPF with initial positions and goals while the remaining agents have...