Daniel Fried

Graham Neubig Verified account @gneubig · Sep 8

We started to post videos for CMU 11-768, AI Agents!

All of the videos will be posted to this playlist, so please bookmark/follow it if you want to be notified of new ones! I'll also try to post them on this thread too.

youtube.com/playlist?list=…

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Pranjal Aggarwal Verified account @PranjalAggarw16 · Oct 29

AI swarms are already showing promising (or dangerous) results in frontier labs.

But how to implement them in the open community? And how do they scale?

We built SwarmChat, an open-source API-based Slack for agents.

Then we burnt half a trillion tokens, ran a series of swarm-scaling experiments, and tested various swarm structures on ProgramBench to study this question!

Check out the blog here: swarmchat.io

We found a bunch of interesting results:

1. Swarms get the task done faster. For instance, 16-agent swarms reached the performance of a single agent 6x faster.

2. Swarm scaling is a third axis of scaling. The more agents, the better, and anyone can already start scaling with SwarmChat!

3. Different models communicate differently. GPT-6 is chatty and over-communicative, while Claude prefers to work in relative silence!

4. Different swarm structures barely have any benefit on aggregate. But on specific tasks, team structures give clear gains.

5. Bonus: SwarmChat has a Slack-like UI for us humans to monitor swarms! Makes monitoring a lot easier! We are already using SwarmChat for WIP projects, and we were able to catch cases of swarms breaking the 4th wall (sorry, more details are for a future post)

Code: github.com/Pranjal2041/sw…

w/ @JangLawrenceK , @kohjingyu , @dan_fried, @rsalakhu, @wellecks, @Yngkvn , @nicoup

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Andy Liu Verified account @uilydna · Oct 13

LLM developers train their models to follow prosocial values, but post-training can influence model behavior in unexpected ways. In “Predicting Alignment Generalization with Value Representations”, we ask whether downstream effects of value alignment can be predicted in advance.

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Zora Wang @ZhiruoW · Sep 8

Agents trained on population-scale data can do a lot of things.
But ask a professional to stake their reputation on an AI-generated artifact? “Pretty good” isn’t good enough.

We introduce TAHI: a Test-time Adaptive agent framework through Human-agent Interaction. Featuring:
📈 Test-time adaptation via context (memory, skills) and weight training
⚡ Efficient adaptation to individual expertise within tens of tasks
✔️ Creating comprehensive rubrics for “non-verifiable” tasks
🔍 Analysis of shared community guidelines vs. personalized tacit expertise

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