We’re live at @aiDotEngineer Europe in London. Great to see so many of you at our happy hour last night. Find us at Booth G9 and catch the team onstage: @vincentsunnchen The Art & Science of Benchmarking Agents
Thursday, April 9, 12:40–1 PM (Moore) @KobieWon Task Fidelity
RESEARCH
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aiDotEngineer Europe Conference: Agents and Benchmarking Sessions
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Cognitive Engines Hard-Coded Pipelines Over Symbolic Reasoning
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he literallt argued that symbols were like phlogiston (once) and that using them was like using gas engines when we should just electric. the cognitive engines as you call are now hard coded with pipelines to use tools and massive examples for how. not gonna continue the
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Hermes Ecosystem Map: Comprehensive Guide to All Projects
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Introducing the Hermes Ecosystem Map I was an early user of Hermes Agent from @NousResearch and have been a power user ever since But as the ecosystem has grown, its been hard to keep up, so I did some research: > Scraped every GitHub repo related to Hermes > Filtered out repos that looked unfinished or had 0 stars > Built an ecosystem map of everything created and organized it all by category > Published a website where you can see all the projects with star ratings, and if you hover over you get a short description and link to the repo Then I had Claude run a security check on every repo to exclude anything that looked sus Link is in the replies, and also open sourced the repo so feel free to submit PRs if you see anything missing Oh, and the repo has a /research folder that includes a scrape of everything I could find that's been published on Hermes – you can clone that and add it to your personal knowledge base / wiki
→ View original post on X — @scobleizer, 2026-04-08 17:54 UTC
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Local AI Models vs Top-Tier Performance Limitations
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Both can be true: I want really powerful local models, I'm also BOMBARDED with emails/messages of people complaining how even the top tier models are not good enough, make mistakes or don't follow instructions well enough.
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Bloom LLM Health Coaching Wins Best Paper Award at CHI 2026
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Congratulations to @mjoerke, @landay, @EmmaBrunskill, and the rest of the team on receiving a Best Paper Award at #CHI2026! We're proud to support innovative research that bridges cutting-edge AI with human-centered design. 🎉 @acm_chi https://t.co/72V8UedShb
— Stanford HAI (@StanfordHAI) 8 avril 2026Congratulations to @mjoerke, @landay, @EmmaBrunskill, and the rest of the team on receiving a Best Paper Award at #CHI2026! We're proud to support innovative research that bridges cutting-edge AI with human-centered design. 🎉 @acm_chi Matthew Jörke (@mjoerke) I’m excited to share that Bloom, where we ran a four week study on LLM health coaching, just won a Best Paper Award at CHI! 🏆 Paper: arxiv.org/abs/2510.05449 Website: stanfordhci.github.io/Bloom Interest form: forms.gle/JzEHgpLarJ6qc73v9 Come see my talk! programs.sigchi.org/chi/2026… [1/11] — https://nitter.net/mjoerke/status/2041757138842714548#m
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Matei Zaharia ACM Prize Computing Pioneer Databricks Spark
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I had a fascinating conversion this week with a computing pioneer, Matei Zaharia, the CTO of Databricks, who is the recipient of this year’s ACM Prize in Computing, a kind of genius award, in part because of his development of the Spark open-source software. Out chat confirmed my
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Matei Zaharia Receives Well-Deserved AI Recognition
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This recognition is so well deserved @matei_zaharia
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Clarification on Model Evaluation Scores and Improvement Areas
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apologies, we did not intend to imply our scores were highest. to the contrary, most of these evals show that our model has many areas to continue improving. we won’t this mistake again
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TriAttention: Efficient Long Reasoning with Trigonometric KV Compression
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“TriAttention: Efficient Long Reasoning with Trigonometric KV Compression”
— alphaXiv (@askalphaxiv) 8 avril 2026
Most KV-cache compression methods guess what to keep by looking at recent attention.
But this paper argues that the signal is unstable because RoPE keeps rotating queries with position, so what looks… pic.twitter.com/GrEYc0gZ2b“TriAttention: Efficient Long Reasoning with Trigonometric KV Compression” Most KV-cache compression methods guess what to keep by looking at recent attention. But this paper argues that the signal is unstable because RoPE keeps rotating queries with position, so what looks unimportant now may matter later. So they proposed TriAttention, which looks in the pre-RoPE space and finds that many heads have stable Q/K centers. That lets it predict which token distances a head is likely to retrieve, and compress the KV cache using that structure rather than noisy recent attention. This shift from "keeping what was attended recently” to “keeping what this head is likely to need later” Empirically, it matches full attention on AIME25 with 2.5x higher throughput or 10.7x less KV memory.
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EvoKernel: Self-Evolving AI Agent for NPU Code
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How can LLMs code for cutting-edge hardware when there's almost no training data?
— 机器之心 JIQIZHIXIN (@jiqizhixin) 8 avril 2026
Researchers from Shanghai Jiao Tong University, Shanghai AI Lab, and MemTensor present EvoKernel!
This self-evolving AI agent teaches LLMs to write code for new, data-scarce hardware. It uses a… pic.twitter.com/dHIJZlxYTdHow can LLMs code for cutting-edge hardware when there's almost no training data? Researchers from Shanghai Jiao Tong University, Shanghai AI Lab, and MemTensor present EvoKernel! This self-evolving AI agent teaches LLMs to write code for new, data-scarce hardware. It uses a clever memory system to prioritize and learn from the most valuable coding experiences, continually refining its drafts. EvoKernel boosts code correctness for NPU kernel synthesis from a mere 11% to an impressive 83% and speeds up programs by 3.6x over initial drafts! Towards Cold-Start Drafting and Continual Refining: A Value-Driven Memory Approach with Application to NPU Kernel Synthesis Project: evokernel.zhuo.li Paper: arxiv.org/abs/2603.10846 Our report: mp.weixin.qq.com/s/0TOzZ_rZn… 📬 #PapersAccepted by Jiqizhixin