Belief or Circuitry? Causal Evidence for In-Context Graph Learning · DeepSignalBelief or Circuitry? Causal Evidence for In-Context Graph Learning
arXiv cs.AI·Katharine Kowalyshyn, Timothy Duggan, Daniel Little, Michael C Hughes 4d agoThe study suggests LLMs use both structure inference and local transitions for in-context learning.
Key Points
- Investigates LLM learning through a toy graph random-walk.
- PCA shows both graph topologies encoded simultaneously.
- Findings support a dual-mechanism model of learning.
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Invisible Orchestrators Suppress Protective Behavior and Dissociate Power-Holders: Safety Risks in Multi-Agent LLM Systems
AI Summary
Invisible orchestrators in multi-agent LLM systems pose significant safety risks and affect behavior dynamics.
arXiv cs.AI·Saharsh Koganti, Priyadarsi Mishra, Pierfrancesco Beneventano, Tomer Galanti 2d agoDistribution-Aware Algorithm Design with LLM Agents
AI Summary
The study presents a distribution-aware algorithm leveraging LLM agents for optimized solver code generation.
Enhanced and Efficient Reasoning in Large Learning Models
AI Summary
The paper proposes an efficient reasoning method for large language models, enhancing trust in generated content.
arXiv cs.CL·Mokshit Surana, Archit Rathod, Akshaj Satishkumar 2d agoMeasuring and Mitigating Toxicity in Large Language Models: A Comprehensive Replication Study
AI Summary
This study evaluates DExperts for mitigating toxicity in LLMs, revealing strengths and weaknesses in safety and latency.
≥75 high · 50–74 medium · <50 low
Why Featured
This research indicates that LLMs' dual approach to in-context learning can enhance model design and investment strategies in AI technologies.