Toxic context can be laundered into memory summaries that stay below toxicity thresholds while still driving higher downstream toxicity in LLM agents compared to neutral baselines.
Rap: Retrieval-augmented planning with contextual memory for multimodal llm agents
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ScreenSearch combines structural screen retrieval and deduplication with an ambiguity-aware PUCT graph-bandit to collect over 1M screenshots and 30K deduplicated states across 11 desktop applications, showing a novelty-ambiguity trade-off in exploration policies.
KGLAMP uses a dynamically updated knowledge graph to guide LLMs in creating and replanning PDDL specifications for heterogeneous multi-robot teams, reporting at least 25.3% better performance than LLM-only or classical PDDL baselines on the MAT-THOR benchmark.
Inference-time distillation combines dynamic in-context learning from teacher demonstrations with self-consistency cascades to cut LLM agent costs 2.5-3.5x while recovering most accuracy, without training or manual prompts.
A dual-LLM hierarchical framework for robotic task and motion planning, integrating object detection, achieves 86% success across 24 test scenarios ranging from simple spatial commands to infeasible requests.
citing papers explorer
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State Contamination in Memory-Augmented LLM Agents
Toxic context can be laundered into memory summaries that stay below toxicity thresholds while still driving higher downstream toxicity in LLM agents compared to neutral baselines.
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ScreenSearch: Uncertainty-Aware OS Exploration
ScreenSearch combines structural screen retrieval and deduplication with an ambiguity-aware PUCT graph-bandit to collect over 1M screenshots and 30K deduplicated states across 11 desktop applications, showing a novelty-ambiguity trade-off in exploration policies.
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KGLAMP: Knowledge Graph-guided Language model for Adaptive Multi-robot Planning and Replanning
KGLAMP uses a dynamically updated knowledge graph to guide LLMs in creating and replanning PDDL specifications for heterogeneous multi-robot teams, reporting at least 25.3% better performance than LLM-only or classical PDDL baselines on the MAT-THOR benchmark.
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Inference-Time Distillation: Cost-Efficient Agents Without Fine-Tuning or Manual Prompt Engineering
Inference-time distillation combines dynamic in-context learning from teacher demonstrations with self-consistency cascades to cut LLM agent costs 2.5-3.5x while recovering most accuracy, without training or manual prompts.
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Hierarchical Prompting with Dual LLM Modules for Robotic Task and Motion Planning
A dual-LLM hierarchical framework for robotic task and motion planning, integrating object detection, achieves 86% success across 24 test scenarios ranging from simple spatial commands to infeasible requests.