Unverified50% confidenceDecision RuleExact time
For 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.
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 complete75% 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.74% similarUnverifiedCLM'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.73% similarUnverifiedCLM introduces a new prompt injection attack surface: injected malicious instructions can persist in the context file and survive across multiple rounds, even actively preventing themselves from being deleted, which the paper's authors list as an unresolved risk.70% similarUnverifiedScenarios such as long-process regression and nightly batch execution should take the CLI+Skill path, whose advantages include persistent state storage, lower Token consumption, and scripts that can be checked into version control.68% similar
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