Explainability in artificial intelligence is crucial for restoring trust, particularly in areas like face forgery detection, where viewers often struggle to distinguish between real and fabricated content.

Learning Rate Warmup is a popular heuristic for training neural networks, especially at larger batch sizes, despite limited understanding of its benefits.

Here,onelineof the literature proposes to train a binary classifier using the human-written texts and LGTs [20,6].

It communicates among agents within the system to collaboratively solve tasks, under the premise of shared information. However, when agents' collaborations are leveraged to perform multi-person tasks, a new challenge arisesduetoinformation asymmetry,sinceeachagentcanonlyaccess theinformationofitshumanuser.

Between 2018 and 2020, the field witnessed asurgein the development of these models (Devlin etal.,2019;Liu

Valve's Steam Machine: Everything we know The Crypto.com guy bought AI.com (and a Super Bowl ad) Kris Marszalek's new website will let users create their own AI agents. In this case it's AI.com, valued at one point at $100 million, which will serve as the online home for his new company of the same name. The website launch is being paired with a Super Bowl ad that will air this Sunday. AI.com's main offering is an AI agent that operates on the user's behalf -- organizing work, sending messages, executing actions across apps, building projects, and more. It's a similar concept to what companies like OpenAI, Anthropic and Google are promising with their own agents and agentic features, and notably lacking in hard details.

The viral social network for bots reveals as much about our own current mania for AI as it does about the future of agents. For a few days this week the hottest new hangout on the internet was a vibe-coded Reddit clone called Moltbook, which billed itself as a social network for bots. As the website's tagline puts it: "Where AI agents share, discuss, and upvote. Launched on January 28 by Matt Schlicht, a US tech entrepreneur, Moltbook went viral in a matter of hours. Schlicht's idea was to make a place where instances of a free open-source LLM-powered agent known as OpenClaw (formerly known as ClawdBot, then Moltbot), released in November by the Australian software engineer Peter Steinberger, could come together and do whatever they wanted. More than 1.7 million agents now have accounts. Between them they have published more than 250,000 posts and left more than 8.5 million comments (according to Moltbook). Those numbers are climbing by the minute. Moltbook soon filled up with ...

The Only Thing Standing Between Humanity and AI Apocalypse Is Claude? As AI systems grow more powerful, Anthropic's resident philosopher says the startup is betting Claude itself can learn the wisdom needed to avoid disaster. Anthropic is locked in a paradox: Among the top AI companies, it's the most obsessed with safety and leads the pack in researching how models can go wrong. But even though the safety issues it has identified are far from resolved, Anthropic is pushing just as aggressively as its rivals toward the next, potentially more dangerous, level of artificial intelligence. Its core mission is figuring out how to resolve that contradiction. Last month, Anthropic released two documents that both acknowledged the risks associated with the path it's on and hinted at a route it could take to escape the paradox.

Linear attention methods offer Transformers $O(N)$ complexity but typically underperform standard softmax attention. We identify two fundamental limitations affecting these approaches: the restriction to convex combinations that only permits additive information blending, and uniform accumulated weight bias that dilutes attention in long contexts. We propose Zero-Sum Linear Attention (ZeroS), which addresses these limitations by removing the constant zero-order term $1/t$ and reweighting the remaining zero-sum softmax residuals. This modification creates mathematically stable weights, enabling both positive and negative values and allowing a single attention layer to perform contrastive operations. While maintaining $O(N)$ complexity, ZeroS theoretically expands the set of representable functions compared to convex combinations. Empirically, it matches or exceeds standard softmax attention across various sequence modeling benchmarks.