#long-horizon-reasoning News & Analysis

6 articles tagged with #long-horizon-reasoning. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles

AIBullisharXiv – CS AI · 3d ago7/10

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Meta-Cognitive Memory Policy Optimization for Long-Horizon LLM Agents

Researchers introduce Metacognitive Memory Policy Optimization (MMPO), a novel training method that improves how AI language model agents manage memory across long-horizon tasks. The approach uses Belief Entropy—a self-supervised metric measuring uncertainty about task state—to provide fine-grained supervision during memory summarization, enabling agents to maintain 97.1% performance even with 1.75M-token contexts.

AIBullisharXiv – CS AI · 4d ago7/10

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Do Language Models Need Sleep? Offline Recurrence for Improved Online Inference

Researchers propose a sleep-like mechanism for transformer language models that periodically consolidates context into persistent fast weights, reducing the computational burden of long sequences. The method shifts heavy computation offline while maintaining fast inference speeds, showing significant improvements on reasoning tasks that standard transformers struggle with.

AIBullisharXiv – CS AI · May 127/10

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When to Re-Commit: Temporal Abstraction Discovery for Long-Horizon Vision-Language Reasoning

Researchers introduce a learnable approach to commitment depth—the number of primitive actions executed before replanning—in vision-language models for long-horizon reasoning. Their adaptive policy outperforms fixed-depth baselines and surpasses GPT-4.5 and Claude Sonnet on puzzle-solving tasks, achieving higher solve rates with fewer actions.

🧠 GPT-5🧠 Claude

AIBullisharXiv – CS AI · May 127/10

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Slipstream: Trajectory-Grounded Compaction Validation for Long-Horizon Agents

Researchers introduce Slipstream, a system that validates LLM agent trajectory compression by running compaction asynchronously alongside continued agent execution, enabling independent validation of summarized context. The approach improves task accuracy by up to 8.8 percentage points while reducing latency by 39.7% on long-horizon coding and web-browsing tasks.

AINeutralarXiv – CS AI · Apr 146/10

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Belief-Aware VLM Model for Human-like Reasoning

Researchers propose a belief-aware Vision Language Model framework that enhances human-like reasoning by integrating retrieval-based memory and reinforcement learning. The approach addresses limitations in current VLMs and VLAs by approximating belief states through vector-based memory, demonstrating improved performance on vision-question-answering tasks compared to zero-shot baselines.

AIBullisharXiv – CS AI · Mar 37/109

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From Verbatim to Gist: Distilling Pyramidal Multimodal Memory via Semantic Information Bottleneck for Long-Horizon Video Agents

Researchers have developed MM-Mem, a new pyramidal multimodal memory architecture that enables AI systems to better understand long-horizon videos by mimicking human cognitive memory processes. The system addresses current limitations in multimodal large language models by creating a hierarchical memory structure that progressively distills detailed visual information into high-level semantic understanding.