pith:A4YI2J37
AgentEscapeBench: Evaluating Out-of-Domain Tool-Grounded Reasoning in LLM Agents
LLM agents handle short tool sequences but lose substantial accuracy when required to track deep chains of dependencies across novel procedures.
arxiv:2605.07926 v2 · 2026-05-08 · cs.AI
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\pithnumber{A4YI2J375WJRRNHU6DCBBGSGXT}
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Record completeness
Claims
Experiments with sixteen LLM agents and human participants show that performance drops sharply as dependency depth increases: humans decline from 98.3% success at difficulty-5 to 80.0% at difficulty-25, while the best model drops from 90.0% to 60.0%.
That the escape-room tasks with explicit DAG constraints and incremental state revelation accurately capture the core challenges of out-of-domain tool-grounded reasoning without introducing benchmark-specific artifacts or overly artificial constraints.
AgentEscapeBench shows LLM agents' success rates drop from 90% to 60% as tool-dependency depth increases from 5 to 25 steps, while humans drop only from 98% to 80%.
Formal links
Receipt and verification
| First computed | 2026-05-21T01:05:20.639499Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
07308d277fed9318b4f4f0c4109a46bcdc086e6710acb73e1deee590fb307d83
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/A4YI2J375WJRRNHU6DCBBGSGXT \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 07308d277fed9318b4f4f0c4109a46bcdc086e6710acb73e1deee590fb307d83
Canonical record JSON
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