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Agents2026-08-21 · source-backed
RGA-Designer trains a reward model scoring both task correctness and structural compactness, then fine-tunes a graph generator against it to design communication topologies. arXiv For fan-out agent teams where inter-agent chatter dominates the bill, topology is a cost lever most people leave on the framework default and never touch.
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arXiv 2608.11879 benchmarked Mem0, Hindsight and Mastra Observational Memory across conversations up to 400 turns and 665 LoCoMo questions. Cost models built on conversation length miss badly because internal memory behavior dominates. Break-even against just replaying the ful...
RAGAS-style evaluation checks correctness against a frozen snapshot, which means routine document updates and corrections can silently break production without moving a dashboard. This ASE 2026 paper defines 11 mutation operators perturbing at both the pre-chunk index level an...
On a verifiable protein-function characterization task routed across tools, model choice swamped federation topology, RL-versus-LLM harness, and prompt expertise: Opus at roughly 92 to 94%, o4-mini at 40 to 50%. Federation across institutional boundaries cost almost nothing (a...
Recuris (arXiv 2608.24876) keeps a Working Memory tracking current task progress separate from an Experiential Memory of learned skills, so skill selection indexes against what the task needs now rather than the whole history. It improves 35 of 37 model-benchmark pairs, gains...
The attack needs no instruction, trigger, or retriever optimization, just plainly worded false assertions generated in one pass against a LongMemEval corpus. A four-stage screening pipeline that reaches 0.832 recall on indirect prompt injection rejected none of the poisoned me...
A 1.5B distilled model trained with GRPO chooses NoThink, Short, or Long at response start, using a shaped reward that makes each mode pay off at a different length plus hard per-mode token caps. Accuracy held at 0.782 against 0.796 baseline while mean length fell from 4,796 t...
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