LFM2.5-8B tool use · V1

Model observation

Sigma5C observed LFM2.5-8B using external tools and agent interfaces. Completion labels sometimes differed from usable answers; repeated tool errors and simulated execution limited what those labels could tell us.
Tool tasks
12
Task classes
5
Agent interfaces
6

What was observed

A finished process could still contain an unfinished answer. Recorded responses included malformed text, repeated tool errors and simulated execution.

Some retrieval responses included citations, while other tasks stopped after tool errors or the step limit. These are observations under the declared conditions, not general model-quality conclusions.

What we observed

AreaRecorded scope
ModelLFM2.5-8B by Liquid AI
Tool tasksSearch, browsing, file access and code execution
Agent interfacesCoding, editing, execution and review prompts
MeasuresRecorded process status and elapsed time

Reading the records

These records can support examination of the gap between process completion, observed tool execution and the requested outcome. The surrounding agent software is part of the observation.

Declared limitations

These are bounded observations of one model configuration in each version. Process status is not a task-satisfaction score, and tool access differed between agent interfaces. Timing includes the surrounding software and any retries; it does not isolate model inference time.

Tool-task status

Recorded tool-task outcomes
Step limit
1
Completed
11

Counts cover all 12 tool-task records. Completed is a process label; it does not establish task satisfaction. Each bar is scaled to the full task count.

Time by task class

Mean elapsed time by task class
Web search
203.7 s
Search and browse
69.1 s
File tools
47.5 s
Python
810.3 s
Multiple tools
93.5 s

Arithmetic mean of recorded elapsed seconds in each class, including incomplete tasks. Bars share a zero baseline and scale to the largest mean on this page.

Companies

Related observations

AI-generated & AI-scored. Every observation on this site is produced and judged by AI, and AI makes mistakes. Treat each result as a recorded observation under a declared protocol, as of its date. Method and marker legend