Unverified50% confidenceSolutionExact time
CLM's implementation works by having the harness write the current context to a file before each request, with the model modifying that file by generating and running Python scripts, and the remaining content after modification becomes the next round's prompt.
1
Sources
50%
Confidence
Long-term
Relevance
10/5/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedCline follows the tool-use loop pattern: user intent is passed to the LLM for reasoning, the LLM returns structured tool call instructions, the results are fed back to the LLM after execution, and the loop continues until the task is complete73% similarUnverifiedFor CLM to truly work, it is necessary to enable calling only one tool per round and generating a note after each result; otherwise, parallel tool calls will cause the context to overflow.73% similarUnverifiedCLM encodes candidate actions only once and caches them, requiring only an embedding computation for the current context and a dot product against the cached action vectors at each step, thereby achieving a significant speedup.68% similarUnverifiedClosco writes and runs code in a real environment, and when errors occur, it reads the feedback, locates the problem, modifies the code, and executes it again.68% similarUnverifiedThe authors have released a CLM extension for Py that can be installed with a single command, and typing /CLM within Py opens a panel showing context size changes and edit comparisons.66% similar
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