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ARXIV:2604.02155 · AGENTS · SUBMITTED 03 APR · 20:50 UTC · FRESHNESS STALE
ARXIV:2604.02155AGENTSSUBMITTED 03 APR · 20:50 UTCFRESHNESS STALEXuan Qi · arXiv
A novel CoT prompting strategy that significantly improves function-calling agent accuracy and reliability by focusing on efficient function routing, with a structural guarantee against hallucination.
Opportunity summary
Pain A novel CoT prompting strategy that significantly improves function-calling agent accuracy and reliability by focusing on efficient function routing, with a structural guarantee against hallucination.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
A novel CoT prompting strategy that significantly improves function-calling agent accuracy and reliability by focusing on efficient function routing, with a structural guarantee against hallucination. Chain-of-thought (CoT) reasoning is widely assumed to improve agent…
How much should a language agent think before taking action? Chain-of-thought (CoT) reasoning is widely assumed to improve agent performance, but the relationship between reasoning length and accuracy in structured tool-use settings remains poorly…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Chain-of-thought (CoT) reasoning is widely assumed to improve agent performance, but the relationship between reasoning length and accuracy in structured tool-use settings remains poorly…
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A novel CoT prompting strategy that significantly improves function-calling agent accuracy and reliability by focusing on efficient function routing, with a structural guarantee against hallucination.
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10.48550/arXiv.2604.02155A novel CoT prompting strategy that significantly improves function-calling agent accuracy and reliability by focusing on efficient function routing, with a structural guarantee against hallucination.
Abstract
How much should a language agent think before taking action? Chain-of-thought (CoT) reasoning is widely assumed to improve agent performance, but the relationship between reasoning length and accuracy in structured tool-use settings remains poorly understood. We present a systematic study of CoT budget effects on function-calling agents, sweeping six token budgets (0--512) across 200 tasks from the Berkeley Function Calling Leaderboard v3 Multiple benchmark. Our central finding is a striking non-monotonic pattern on Qwen2.5-1.5B-Instruct: brief reasoning (32 tokens) dramatically improves accuracy by 45% relative over direct answers, from 44.0% to 64.0%, while extended reasoning (256 tokens) degrades performance well below the no-CoT baseline, to 25.0% (McNemar p < 0.001). A three-way error decomposition reveals the mechanism. At d = 0, 30.5% of tasks fail because the model selects the wrong function from the candidate set; brief CoT reduces this to 1.5%, effectively acting as a function-routing step, while long CoT reverses the gain, yielding 28.0% wrong selections and 18.0% hallucinated functions at d = 256. Oracle analysis shows that 88.6% of solvable tasks require at most 32 reasoning tokens, with an average of 27.6 tokens, and a finer-grained sweep indicates that the true optimum lies at 8--16 tokens. Motivated by this routing effect, we propose Function-Routing CoT (FR-CoT), a structured brief-CoT method that templates the reasoning phase as "Function: [name] / Key args: [...]," forcing commitment to a valid function name at the start of reasoning. FR-CoT achieves accuracy statistically equivalent to free-form d = 32 CoT while reducing function hallucination to 0.0%, providing a structural reliability guarantee without budget tuning.
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PROBLEM
A novel CoT prompting strategy that significantly improves function-calling agent accuracy and reliability by focusing on efficient function routing, with a structural guarantee against hallucination. Chain-of-thought (CoT) reasoning is widely assumed to improve agent performanc...
METHOD
How much should a language agent think before taking action? Chain-of-thought (CoT) reasoning is widely assumed to improve agent performance, but the relationship between reasoning length and accuracy in structured tool-use settings remains poorly understood.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Chain-of-thought (CoT) reasoning is widely assumed to improve agent performance, but the relationship between reasoning length and accuracy in structured tool-use settings remains poorly understood. Code...
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
brief reasoning (32 tokens) dramatically improves accuracy by 45% relative over direct answers, from 44.0% to 64.0%
Explicitly stated in abstract with specific numeric results (44.0% to 64.0%)
partial
extended reasoning (256 tokens) degrades performance well below the no-CoT baseline, to 25.0% (McNemar p < 0.001)
Explicitly stated in abstract with specific numeric results and statistical significance
partial
At d = 0, 30.5% of tasks fail because the model selects the wrong function from the candidate set; brief CoT reduces this to 1.5%
Directly stated in abstract with specific error percentages
partial
long CoT reverses the gain, yielding 28.0% wrong selections and 18.0% hallucinated functions at d = 256
Directly stated in abstract with specific error percentages
partial
Oracle analysis shows that 88.6% of solvable tasks require at most 32 reasoning tokens, with an average of 27.6 tokens
Explicitly stated in abstract with specific percentages and averages
partial
a finer-grained sweep indicates that the true optimum lies at 8-16 tokens
Directly stated in abstract based on finer-grained sweep analysis
partial
FR-CoT achieves accuracy statistically equivalent to free-form d = 32 CoT while reducing function hallucination to 0.0%
Directly stated in abstract with specific performance claims
partial
Our central finding is a striking non-monotonic pattern on Qwen2.5-1.5B-Instruct
Central finding explicitly stated in abstract with supporting evidence
partial
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A novel CoT prompting strategy that significantly improves function-calling agent accuracy and reliability by focusing on efficient function routing, with a structural guarantee against hallucination.
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