NEW paper worth reading: MetaCogAgent MetaCogAgent equips a multi-agent system with metacognition so each agent decides whether it should answer or delegate. In other words, it aims for self-aware task delegation rather than fixed routing. The bottleneck in multi-agent systems
@dair_ai
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Meta paper: Agentic Discovery of Neural Architectures
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NEW paper from Meta: Agentic Discovery of Neural Architectures. This is a hot new area of research! Keep an eye on it.
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Paper: GPT-5.4 Nano with Critic-Comparator Reaches SWE-bench Parity
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NEW paper worth reading. GPT-5.4 nano plus a critic-comparator orchestration loop hits 76.4% on SWE-bench Verified, matching standalone Gemini 3 Pro and Claude Opus 4.5 Thinking. The trick is to select from k=8 weak-model proposals using execution and proof signals. What does
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Top AI Papers of the Week (May 11–17)
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The Top AI Papers of the Week (May 11 – May 17) – AEvo
– δ-mem
– AutoTTS
– AI Co-Mathematician
– Lighthouse Attention
– Is Grep All You Need?
– A Geometric Calculator Inside a Neural Network Read on for more: -
Technical Survey of Multi-Agent AI Systems and Self-Evolution
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// Beyond Individual Intelligence // One of the more useful multi-agent surveys I've read this year. 200+ papers mapped along three axes: collaboration mechanisms, failure attribution, and self-evolution. The self-evolution chapter is the cleanest field map of where memory,
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Comparison of Agentic Search vs. Vector Search
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Great paper discussing agentic search vs. vector search.
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AEvo: A Method for Iterative AI Agent Self-Improvement
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// Harnessing Agentic Evolution // Pay attention to this one if you run iterative agentic search loops. (bookmark it) AEvo splits the self-improvement loop into two jobs: > One proposes the next candidate. > The other watches what worked, what failed, and edits the procedure
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δ-mem: An efficient online memory mechanism for LLMs
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// δ-mem: Efficient Online Memory for LLMs // One of the more elegant memory mechanisms I've seen this month. Most long-term memory work either inflates context or retrains the model. This paper shows a tiny external state, coupled directly into the attention computation, can
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New research challenges the assumption that multi-agent systems improve LLM reasoning
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Do multi-agent systems make LLM reasoning better? Most AI devs assume that it should. But this new paper shows that this is often not the case. It ran 22,500 deterministic trajectories across GAIA, SWE-bench, and Multi-Challenge with three frontier models. Agents frequently
