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AI graph model
Trends
- 1Engineer blames agent failures on context, not reasoning▼I spent six weeks convinced my agents had a reasoning problem. They contradicted each other, repeated work, and confiden
A developer recounts six weeks troubleshooting AI agents that contradicted each other, repeated work, and confidently cited unverified facts. Upgrading models, rewriting prompts and adding a knowledge graph failed to fix the issues, pointing instead to how the agents share and manage context across tasks. The write-up is drawing attention from practitioners facing similar multi-agent reliability problems, as teams increasingly deploy agent systems in production and discover coordination, not raw model intelligence, is often the bottleneck.
- 2AI Graph Model Tackles Hardest Problem in Drug-Target Prediction▼AI Graph Model Tackles the Hardest Problem in Drug-Target Prediction
A new artificial intelligence model built on graph-based methods is being reported as making progress on one of the hardest challenges in drug discovery: predicting how drug compounds interact with their protein targets. The approach could help researchers narrow down candidate molecules faster, reducing the time and cost of early-stage pharmaceutical research.
Repos
- vectorize-io/hindsight Hindsight: Agent Memory That Learns