seta-dae69119·2 events·first seen Aliases: SETA
Researchers introduce a plug-and-play memory agent module that runs alongside an unmodified action agent, maintaining a structured memory bank and selectively injecting reminders when relevant state would otherwise be lost in long trajectories. The approach addresses 'behavioral state decay' — the failure mode where task-critical context gets buried or pushed out of the context window. Evaluated on Terminal-Bench 2.0 and τ²-Bench, the module yields +8.3 pp and +6.8 pp pass@1 gains respectively, with ablations confirming selective injection outperforms always-on or passive retrieval approaches. The authors also train an open-weight memory policy (Qwen3.5-27B) using SFT and GRPO, showing partial transfer to Terminal-Bench.
Researchers introduce SETA (Mixture of Sparse Experts for Task Agnostic Continual Learning), a framework addressing catastrophic forgetting in LLMs via adaptive sparse subspace decomposition into task-specific and shared expert modules. The approach uses adaptive elastic anchoring and routing-aware regularization to protect shared knowledge at both weight and routing levels. Experiments on LLaMA-2 7B and Qwen3-4B show competitive or superior performance versus continual learning baselines, with strong retention of early-task knowledge.