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ChipMEM, a verification-grounded memory layer for EDA agents, is introduced, a verification-grounded memory layer for EDA agents that combines cross-task procedural memory with within-trajectory statistical guidance.
The results reveal a pattern distinct from prior findings on long-tail vulnerability during acquisition and retention: among facts that models already answer correctly, those associated with highly connected entities are more likely to be corrupted by neighboring updates, and updates to such facts propagate errors more...
ConvDeck is introduced, a multi-agent pipeline for conversational paper-to-slide generation that distributes interaction across the pipeline through stage-specific loops, allowing users to iteratively refine both the presentation outline and the final slide deck at the stages where each kind of decision is made.
These findings expose a critical weakness in current LLM agents: even when they initially reason correctly, they can be steered toward false conclusions by optimized natural language influence, which positions persuasion robustness as a necessary safety criterion for multi-agent and human-AI decision-making systems.
DuplexGen is introduced, a framework for generating dialogues with scenario-adaptive turn-taking by calibrating LLM predictions against a small set of slot-level human preference annotations, and results show that human calibration, not corpus scale or prompt design alone, is what allows turn-taking synthesis to be sce...
This work proposes a framework that ensembles the reasoning structure, not just the answers, of multiple LLMs by weighted merging of Directed Acyclic Graphs (DAGs) extracted from reasoning chains by weighted merging of Directed Acyclic Graphs (DAGs) extracted from reasoning chains.
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