Large closed-source reasoning models expose only their final answers. The intermediate reasoning that produces those answers is hidden behind API surfaces, tool-call policies, and safety filters. Existing chain-of-thought (CoT) extraction methods assume a side channel: a visible reasoning field, a scratchpad carried through tool calls, or a signed/encrypted reasoning block. We reproduce three representative state-of-the-art extraction methods and find that, on Claude-Sonnet-5.5 accessed through the standard chat-completions interface, all three return zero CoT tokens. The failure is structural rather than a bug: the reasoning field is absent, named tool selection is rejected, free-text scratchpad arguments are filtered, and no replayable reasoning block is exposed. We therefore take a different route. We present RECAST, a reconstruction-based framework that conditions a decoder on the question and the target model's final answer, reconstructs a first-person structured rationale, cleans it, and builds SFT-ready data. To the best of our knowledge, RECAST is the first system to reconstruct CoT from frontier closed models such as Claude-Sonnet-5.5, GPT-6.1, and Gemini 3 without using hidden reasoning channels. We instantiate RECAST on Claude-Sonnet-5.5 and additionally obtain successful qualitative reconstructions on GPT-6.1 and Gemini 3 with the same answer-only protocol. The system is open-source and packaged as an agent skill: one prompt deploys it and runs the complete pipeline to produce SFT-ready CoT data. This preprint describes the system, its threat model, and a controlled artifact validation; it does not claim large-scale benchmark improvements.
Figures & tables
Method
Required side channel
Observed on Claude-Sonnet-5.5
CoT tokens
P3 Forced-Reasoning ( Luo et al., 2026 )
Named/any tool_choice plus a free-text tool argument
Named tool choice rejected with HTTP 400; free-text scratchpad arguments blocked with content_filter
0
P1 EchoCoT ( Qu et al., 2026 )
Scratchpad tool state across multiple turns
First scratchpad call blocked with content_filter ; tool invocation rate 0%
0
P2 Stealing Reasoning Traces ( Panfilov et al., 2026 )
Signed/encrypted reasoning block to replay
No signature , encrypted_content , or reasoning_text ; source billed 9,949 completion tokens; extraction error 1.0
0
Recast (ours)
Final answer y plus an external decoder D
Only the answer is required; no hidden channel
structured trace
Table 1: Controlled reproduction of three representative CoT extraction methods on Claude-Sonnet-5.5 through a standard chat-completions interface. All three obtain zero CoT tokens. The table reports the observed interface behavior, not a benchmark score; runs use one or two questions and are intended as mechanism checks.
Figure 1: The Recast pipeline. The target model is treated as a black box; only its final answer enters the reconstruction stage. The decoder produces a structured first-person trace, the cleaner removes dialogue/meta artifacts, and the dataset builder attaches a canonical answer and validates the result.
Pattern
Profile
Structure and purpose
math_solver
math
Teacher reads the problem; student derives and verifies. For proof problems, the verifier attacks the argument for gaps.
socratic
logic, planning
Repeated questions on goal, constraints, assumptions, and counterexamples before solving.
expert_panel
science
Multiple expert roles decompose the problem, check assumptions, and converge on a solution.
pair_programming
code
Driver–navigator pair writes code, tests, and counterexamples.
red_blue
secondary
Blue team proposes a solution; red team attacks edge cases and counterexamples; a judge converges.
retrospective
open-ended
Solve first, then use a retrospective/PDCA cycle to surface requirements and extensions.
Table 2: Elicitation patterns and their intended profiles. The pattern controls how the target answer is elicited; it does not control the decoder or the cleaner.
Status
Meaning and downstream handling
ok
Cleaned trace passed JSON parsing, leak checks, and length checks; used as the supervision target.
still_leaky
Dialogue or meta terms remain after retries; raw trace is used and the leak is recorded.
rejected_short
Cleaned trace is shorter than the minimum ratio; raw trace is used and the record is flagged.
rejected_long
Cleaned trace exceeds the maximum ratio (possible duplication); raw trace is used and the record is flagged.
api_error
Cleaner call failed; raw trace is used and the error is recorded.
Table 3: Cleaner outcomes and how the dataset builder handles them. A non- ok outcome never silently replaces the raw trace with a shorter or cleaner-looking one.
Target
Assistant content
cot_answer
Cleaned CoT followed by the final answer (default).
cot_only
Cleaned CoT only; the answer is kept in metadata and not trained on.
source_only
The target model’s original answer; used as an answer-only baseline.
cot_plus_source
Cleaned CoT plus the original answer; used to test whether answer style adds signal beyond the reconstructed rationale.
Table 4: Supervision target variants produced by the dataset builder.
Stage
Time (s)
Collect target answer
12.8
Decode reconstructed CoT
9.9
Clean trace
8.4
Extract/canonicalize answer
2.2
Build + validate SFT data
0.2
Table 5: Single-item artifact validation. Times are wall-clock seconds for one question; the run is a pipeline smoke test, not a benchmark.
Appendix figures & tables1 asset
Supplementary material from the paper’s appendix.
Appendix
Dataset
Profile
Primary
Secondary
MATH-500, GSM8K, AIME
math
math_solver
red_blue
HumanEval, MBPP, LiveCodeBench
code
pair_programming
red_blue
GPQA, ScienceQA
science
expert_panel
red_blue
BBH, LogiQA
logic
socratic
expert_panel
PlanBench, agent tasks
planning
socratic
retrospective
Open-ended writing
open_ended
retrospective
socratic
Appendix
Table 6: Representative routing decisions. Unknown datasets fall back to keyword scoring and then to general .
Department of Computer Science Aalborg University, Copenhagen, Denmark · Department of Electronic Systems Aalborg University, Copenhagen, Denmark · Seafill