Brief Is Better: Non-Monotonic Chain-of-Thought Budget Effects in Function-Calling Language Agents

Brief Is Better: Non-Monotonic Chain-of-Thought Budget Effects in Function-Calling Language Agents | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/brief-is-better-non-monotonic-chain-of-thought-budget-effects-in-function-calling-language-agents

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-03
Score updated: 2026-04-03
Score fresh until: 2026-05-03
References: 0
Source count: 0
Coverage: 33%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID brief-is-better-non-monotonic-chain-of-thought-budget-effects-in-function-calling-language-agents | Route /signal-canvas/brief-is-better-non-monotonic-chain-of-thought-budget-effects-in-function-calling-language-agents

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/brief-is-better-non-monotonic-chain-of-thought-budget-effects-in-function-calling-language-agents

MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Brief Is Better: Non-Monotonic Chain-of-Thought Budget Effects in Function-Calling Language Agents

PDF: https://arxiv.org/pdf/2604.02155v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-04-03T20:50:40.241Z

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Watch and verify: Brief Is Better: Non-Monotonic Chain-of-Thought Budget Effects in Function-Calling Language Agents

/buildability/brief-is-better-non-monotonic-chain-of-thought-budget-effects-in-function-calling-language-agents

Watchwatch

Subject: Brief Is Better: Non-Monotonic Chain-of-Thought Budget Effects in Function-Calling Language Agents

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
brief reasoning (32 tokens) dramatically improves accuracy by 45% relative over direct answers, from 44.0% to 64.0%
Implicationpartial
Explicitly stated in abstract with specific numeric results (44.0% to 64.0%)
Verificationpartial
partial
Evidencepartial
extended reasoning (256 tokens) degrades performance well below the no-CoT baseline, to 25.0% (McNemar p < 0.001)
Implicationpartial
Explicitly stated in abstract with specific numeric results and statistical significance
Verificationpartial
partial
Evidencepartial
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%
Implicationpartial
Directly stated in abstract with specific error percentages
Verificationpartial
partial
Evidencepartial
long CoT reverses the gain, yielding 28.0% wrong selections and 18.0% hallucinated functions at d = 256
Implicationpartial
Directly stated in abstract with specific error percentages
Verificationpartial
partial
Evidencepartial
Oracle analysis shows that 88.6% of solvable tasks require at most 32 reasoning tokens, with an average of 27.6 tokens
Implicationpartial
Explicitly stated in abstract with specific percentages and averages
Verificationpartial
partial
Evidencepartial
a finer-grained sweep indicates that the true optimum lies at 8-16 tokens
Implicationpartial
Directly stated in abstract based on finer-grained sweep analysis
Verificationpartial
partial
Evidencepartial
FR-CoT achieves accuracy statistically equivalent to free-form d = 32 CoT while reducing function hallucination to 0.0%
Implicationpartial
Directly stated in abstract with specific performance claims
Verificationpartial
partial
Evidencepartial
Our central finding is a striking non-monotonic pattern on Qwen2.5-1.5B-Instruct
Implicationpartial
Central finding explicitly stated in abstract with supporting evidence
Verificationpartial
partial

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Brief Is Better: Non-Monotonic Chain-of-Thought Budget Effects in Function-Calling Language Agents

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