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File size: 2,205 Bytes
353843b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | """Test CCR markers and content preservation in compressed output."""
from __future__ import annotations
import json
import sys
sys.path.insert(0, ".")
from examples.context_compression_demo import build_retriever_chunks
from headroom import compress
def main():
chunks = build_retriever_chunks()
retriever_json = json.dumps(chunks, indent=2)
messages = [
{"role": "user", "content": "What are the types of reward hacking discussed in the blogs?"},
{
"role": "assistant",
"content": None,
"tool_calls": [
{
"id": "call_001",
"type": "function",
"function": {
"name": "retrieve_blog_posts",
"arguments": json.dumps({"query": "types of reward hacking"}),
},
}
],
},
{"role": "tool", "tool_call_id": "call_001", "content": retriever_json},
]
result = compress(messages, model="claude-sonnet-4-5-20250929")
compressed_tool = str(result.messages[2].get("content", ""))
print("=== Compressed tool output (FULL) ===")
print(compressed_tool)
print()
print(f"Tokens: {result.tokens_before} -> {result.tokens_after} ({result.tokens_saved} saved)")
print(f"Transforms: {result.transforms_applied}")
print()
# Check for CCR markers
if "hash=" in compressed_tool:
print("CCR MARKERS FOUND — LLM can retrieve originals")
else:
print("No CCR markers")
print()
# Check key content
key_terms = {
"reward tampering": False,
"sycophancy": False,
"specification gaming": False,
"proxy gaming": False,
"reward model hacking": False,
"distribution shift": False,
}
for term in key_terms:
key_terms[term] = term.lower() in compressed_tool.lower()
status = "FOUND" if key_terms[term] else "MISSING"
print(f" {term}: {status}")
found = sum(1 for v in key_terms.values() if v)
print(f"\n{found}/{len(key_terms)} key concepts preserved in compressed output")
if __name__ == "__main__":
main()
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