Papers with SEAM
Beyond Experience Retrieval: Learning to Generate Utility-Optimized Structured Experience for Frozen LLMs (2026.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) are largely static and often redo reasoning or repeat mistakes. Prior experience reuse relies on external retrieval, which is similarity-based, can introduce noise, and adds latency. |
| Approach: | They propose a lightweight plug-in that stores experience in its parameters and generates a structured, instance-tailored experience entry in a single forward pass to guide a frozen LLM executor. |
| Outcome: | Experiments on mathematical reasoning benchmarks show consistent accuracy gains across executors with low overhead. |