Exclusive | Pentagon Used Anthropic’s Claude in Maduro Venezuela Raid - WSJ
Exclusive | Pentagon Used Anthropic’s Claude in Maduro Venezuela Raid WSJ
Could not retrieve the full article text.
Read on Google News - AI Venezuela →Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
claude
Don't write for LLMs, just record everything
Some people have argued the advent of LLMs has dramatically increased the value of having a public writing footprint. The first reason given is that this might help secure a meaningful form of immortality. The second reason given is that this might make future LLMs trained on public writing corpora more useful to you, personally, in mundane ways [1] . I think that the first one doesn't check out, the second one is possible but a long-shot, but you can get a lot of the anticipated benefits of the second one by dropping the "public" bit and doing something a little unorthodox. Contra Immortality I don't know if gwern believes in this specific story : two years ago, he wrote a comment which contained the sentence: This seems like a bad move to me on net: you are erasing yourself (facts, value
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Models
TABQAWORLD: Optimizing Multimodal Reasoning for Multi-Turn Table Question Answering
arXiv:2604.03393v1 Announce Type: new Abstract: Multimodal reasoning has emerged as a powerful framework for enhancing reasoning capabilities of reasoning models. While multi-turn table reasoning methods have improved reasoning accuracy through tool use and reward modeling, they rely on fixed text serialization for table state readouts. This introduces representation errors in table encoding that significantly accumulate over multiple turns. Such accumulation is alleviated by tabular grounding methods in the expense of inference compute and cost, rendering real world deployment impractical. To address this, we introduce TABQAWORLD, a table reasoning framework that jointly optimizes tabular action through representation and estimation. For representation, TABQAWORLD employs an action-condit
Contextual Control without Memory Growth in a Context-Switching Task
arXiv:2604.03479v1 Announce Type: new Abstract: Context-dependent sequential decision making is commonly addressed either by providing context explicitly as an input or by increasing recurrent memory so that contextual information can be represented internally. We study a third alternative: realizing contextual dependence by intervening on a shared recurrent latent state, without enlarging recurrent dimensionality. To this end, we introduce an intervention-based recurrent architecture in which a recurrent core first constructs a shared pre-intervention latent state, and context then acts through an additive, context-indexed operator. We evaluate this idea on a context-switching sequential decision task under partial observability. We compare three model families: a label-assisted baseline
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!