The Tool Call Investigation: Reconstructing Kimi-K2.5 Token IDs Across API Boundaries

In the high-stakes world of training speculative decoding models like EAGLE-3, data fidelity is everything. When the assistant in this opencode session pivoted from local GPU inference to the OpenRouter API for generating training data, it introduced a fundamental tension: the local pipeline produced exact token ID sequences, while the API returned text strings. Bridging that gap required meticulous investigation, and nowhere was this more critical than in handling tool calls. Message [msg 4044] captures a pivotal debugging moment—the assistant's third attempt to understand how OpenRouter handles tool-calling behavior, and the most carefully constructed probe yet.

The Motivation: Why Tool Call Correctness Mattered

The context leading to this message is essential. The session had been building an EAGLE-3 speculative decoding drafter for the Kimi-K2.5 model, a months-long effort spanning environment setup, SGLang deployment, performance tuning, hidden state extraction, and multiple rounds of drafter training. By segment 29 (the current segment), the pipeline had reached a critical juncture: generating training data at scale. The assistant had written a sophisticated run_inference_openrouter.py script that could fire 2,000 concurrent requests to OpenRouter, processing all eight B-datasets (B3 through B8) in approximately 33 minutes at a cost of roughly $86.

But there was a catch. The local SGLang inference pipeline produced responses as token ID arrays—exact integer sequences that could be directly fed into the training loop. OpenRouter, being a standard OpenAI-compatible API, returned text strings. The training pipeline needed to reconstruct exact Kimi-K2.5 token IDs from these text responses, a process fraught with peril. Special tokens like <|im_end|> (token 163586), <|tool_calls_section_begin|> (token 163595), and the response separator all needed to be perfectly reconstructed. A single token mismatch could corrupt the training data.

The user's instruction in [msg 4041]—"Look at tool call correctness too"—had triggered this investigation. The user recognized that tool calls represented a particularly fragile edge case in the reconstruction pipeline. When Kimi-K2.5 generates a tool call, it produces a structured sequence of special tokens:

<|tool_calls_section_begin|><|tool_call_begin|>functions.calculate_discounted_price:0<|tool_call_argument_begin|>{"price": 200, "discount": 0.2}<|tool_call_end|><|tool_calls_section_end|>

The question was: how does OpenRouter return this? Does it pass the raw special tokens through in the content field, or does it parse them into the structured tool_calls field (as OpenAI's API does)? The answer would determine whether the reconstruction code needed to handle structured tool call objects or could simply concatenate the text content.

The First Two Attempts: Learning from Failure

Message [msg 4044] is the third probe in a rapid debugging sequence. The first attempt ([msg 4042]) was ambitious: a script that tested three scenarios—with the tools parameter, without it but with a tool-calling system prompt, and with an actual B1_glaive prompt. This script timed out after 120 seconds, likely because one of the three parallel requests hit a slow provider. The assistant learned from this: shorter timeouts and single requests.

The second attempt ([msg 4043]) was more focused: a single test using the first prompt from B1_glaive. This succeeded but revealed a problem—the model correctly identified that it "can't book flights" and returned a text response without tool calls. The prompt, which asked about booking a flight from New York to London, didn't actually trigger tool-calling behavior because the model recognized it lacked the capability.

This brings us to message [msg 4044]. The assistant now understands a crucial insight: not all prompts trigger tool calls. To test tool call handling, you need a prompt that actually makes the model generate tool calls. The assistant's reasoning is explicit: "This particular sample didn't generate tool calls (model correctly identified it can't book flights). Let me find a B1 prompt that's more likely to trigger tool calls."

The Third Probe: A More Sophisticated Approach

Message [msg 4044] demonstrates a significant leap in sophistication. Instead of guessing which prompts might trigger tool calls, the assistant writes a script that:

1. Loads the Kimi-K2.5 tokenizer to decode token IDs into readable text, enabling comparison between local and API outputs.
2. Searches the local inference results for samples that actually contained tool calls. It does this by scanning raw_responses.jsonl for the token ID 163595 (<|tool_calls_section_begin|>), the definitive marker of tool-calling behavior.
3. Finds sample 241 from B1_glaive, which did generate tool calls locally. The prompt is about calculating a discounted price—a perfect test case because it's a simple calculation that the model can perform with a tool.
4. Displays the local SGLang output for comparison, showing the reasoning and content as they appeared in the local inference run.
5. Sends the exact same prompt to OpenRouter with the same parameters (temperature 0.6, provider filtering to exclude Fireworks and BaseTen, INT4 quantization preferred). The script is carefully structured. It first establishes the ground truth from local inference, then sends the probe to OpenRouter, and finally compares the two. The comparison checks for: - Whether OpenRouter returns tool calls in the structured tool_calls field - Whether the raw special tokens appear in the content field - The provider used and the finish reason

The Technical Depth: Token IDs and Special Tokens

One of the most technically interesting aspects of this message is the use of raw token IDs for detection. The assistant knows that <|tool_calls_section_begin|> has token ID 163595 in the Kimi-K2.5 vocabulary. This knowledge came from earlier analysis in the chunk (as described in the chunk summary): "discovering <|im_end|> is token 163586, not 163533, verifying BPE boundary behavior across response separators."

The script uses this token ID directly:

TC_BEGIN = 163595  # <|tool_calls_section_begin|>

This is a clever optimization. Instead of decoding every response to check for tool calls (which would be expensive with 10,000+ samples), the script can scan the raw token ID arrays for a single integer. This works because the local inference pipeline stored output_ids as lists of integers in the JSONL files.

The assistant also demonstrates understanding of the Kimi-K2.5 output format. The local output is split on response to separate reasoning from content:

if " response" in decoded:
    think_part, rest = decoded.split(" response", 1)

This reflects the model's architecture: Kimi-K2.5 generates reasoning text, then a response separator token, then the actual response content (which may include tool calls). The assistant needs to understand this structure to correctly reconstruct the full output from OpenRouter's split format (where reasoning and content are returned in separate fields).

Assumptions and Their Validity

The assistant makes several assumptions in this message:

1. That the local SGLang output is representative: The assistant assumes that sample 241's local output is a valid baseline for comparison. This is reasonable—the local inference used the same model (Kimi-K2.5 INT4) and similar parameters.
2. That OpenRouter will produce similar outputs: The assistant assumes that sending the same prompt through OpenRouter will produce a similar response (including tool calls). This is not guaranteed—different providers may use different quantization levels, sampling parameters, or even model versions. The assistant mitigates this by specifying quantizations: ["int4"] in the provider configuration.
3. That tool calls are deterministic enough: The assistant assumes that the model will generate tool calls for the same prompt consistently. With temperature 0.6, there's some randomness, but tool-calling behavior is usually fairly deterministic for well-defined tasks like price calculation.
4. That the tokenizer is available: The script loads the tokenizer from /shared/kimi-k2.5-int4, which assumes the model files are still present on the machine. This is a reasonable assumption given the earlier setup work. These assumptions are generally sound, but they introduce some uncertainty. The most significant risk is that OpenRouter might return a different output format than expected—for instance, if the provider uses a different model version or if OpenRouter's post-processing strips tool call tokens differently.

The Output: What the Message Reveals

The message's output is truncated in the conversation data, but it reveals several important findings:

1. Sample 241 was found: The script successfully identified a tool-calling sample (sample_id 241) from B1_glaive. The prompt involves calculating a discounted price for a dress originally priced at $200 with a 20% discount.
2. The local output has 101 tokens: The SGLang inference produced a relatively short response, with the reasoning portion visible: "The user wants to know the discounted price of a dress. The original price is $200 and the discount i..."
3. The script was deployed and executed: The scp and ssh commands successfully transferred and ran the script on the remote machine (10.1.230.174), and the output was captured. The full output of the OpenRouter comparison is not shown in the message (it continues in subsequent messages), but the groundwork is laid. The assistant now has a reliable test case and a clear comparison methodology.

The Thinking Process: Debugging Methodology

The assistant's thinking process in this message reveals a mature debugging methodology:

Iterative refinement: Each failed attempt provides information for the next. The timeout in [msg 4042] teaches the assistant to use shorter timeouts. The non-tool-calling response in [msg 4043] teaches the assistant to find a prompt that actually triggers tool calls.

Ground truth establishment: Before probing the unknown (OpenRouter), the assistant establishes ground truth from the known (local SGLang). This is a fundamental debugging principle: understand the expected behavior before testing the actual behavior.

Minimal reproduction: Instead of running the full pipeline and checking results post-hoc, the assistant creates a minimal test script that isolates the specific question: "What does OpenRouter return for a tool-calling prompt?"

Parallel thinking and execution: The assistant writes the script, deploys it, and runs it in a single message. The reasoning about what the script should do is embedded in the code comments and structure, not in separate analysis text.

The Broader Context: Why This Matters

This message sits at a critical transition point in the EAGLE-3 training pipeline. The assistant had pivoted from local GPU inference to OpenRouter API because the local inference was too slow for the scale needed (83K prompts across multiple datasets). But this pivot introduced a data format mismatch that threatened the entire training pipeline.

The tool call investigation is one facet of a larger reconstruction challenge. The chunk summary notes that the assistant had already solved several related problems:

• Discovering that <|im_end|> is token 163586, not 163533
• Verifying BPE boundary behavior across response separators
• Confirming that tool call tokens survive as raw text in content when the tools parameter isn't sent The last point is the most relevant: the assistant had already confirmed that tool call tokens survive as raw text when tools isn't sent. So why the additional investigation? The user's request in [msg 4041] suggests they wanted more rigorous validation. Perhaps the earlier confirmation was based on a different test, or perhaps the user wanted to verify with an actual training sample rather than a synthetic test.

Conclusion

Message [msg 4044] exemplifies the meticulous, iterative debugging that characterizes high-quality ML engineering work. The assistant doesn't assume that a single test is sufficient—it probes, fails, learns, and probes again with increasing sophistication. The tool call investigation, while seemingly a narrow technical question, has broad implications for the entire training pipeline. If OpenRouter strips tool call tokens from the content field, the reconstruction code would need to handle structured tool call objects, adding complexity and potential failure modes. If the tokens survive as raw text, the reconstruction is simpler but must handle the exact byte-level representation.

The message also demonstrates the importance of understanding the underlying model's tokenization and output format. The assistant's knowledge of Kimi-K2.5's special token IDs, the response separator, and the tool call syntax reflects deep engagement with the model architecture. This is not surface-level API usage—it's the kind of detailed understanding required to build production-grade training pipelines for state-of-the-art language models.

In the end, the tool call investigation succeeded. The chunk summary confirms that "structural validation of 1637 OpenRouter responses showed 0 issues, with token counts matching billing within 0.04%." The meticulous probing in message [msg 4044] and its predecessors paid off, ensuring that the EAGLE-3 training data would be faithful to the model's native output format, tool calls included.