1 00:00:00,240 --> 00:00:07,140 Imagine a small checkout program. For this illustration, the intended contract is a tax-inclusive sum. 2 00:00:07,860 --> 00:00:10,300 The caller supplies prices of ten and twenty. 3 00:00:10,980 --> 00:00:14,820 A helper named tax multiplies one price by one point one. 4 00:00:15,540 --> 00:00:20,580 A report later displays the total. Between them sits a function whose body has disappeared. 5 00:00:21,400 --> 00:00:25,060 We still see its name, quote total, its argument, prices, 6 00:00:25,500 --> 00:00:30,500 and how the answer will be used. Those relationships give us a contract to reconstruct, 7 00:00:30,920 --> 00:00:33,320 before we write a single line inside the missing function. 8 00:00:34,033 --> 00:00:36,653 The paper connects this pattern to a coding agent. 9 00:00:37,353 --> 00:00:42,813 A function begins with context, makes a call, receives a returned value, and continues. 10 00:00:43,693 --> 00:00:46,713 An agent begins with history, takes an action, 11 00:00:47,253 --> 00:00:51,913 receives an observation from a tool or environment, and chooses its next step. 12 00:00:52,873 --> 00:00:55,373 That correspondence motivates the training task. 13 00:00:56,433 --> 00:01:01,173 During infilling, however, the model predicts a missing implementation from surrounding code; 14 00:01:01,653 --> 00:01:03,853 it does not receive a live tool return. 15 00:01:04,753 --> 00:01:10,313 The proposed connection is a shared need to interpret dependencies and continue coherently. 16 00:01:11,013 --> 00:01:16,353 Return to the checkout example. One consistent body applies tax to every price 17 00:01:16,753 --> 00:01:21,253 and sums the returned values. Eleven plus twenty-two gives thirty-three. 18 00:01:22,193 --> 00:01:28,333 Now the caller, the helper, and the report agree about what quote total returns. 19 00:01:29,553 --> 00:01:33,853 The paper formalizes surrounding relationships as a program dependency graph: 20 00:01:34,673 --> 00:01:41,093 functions become nodes, calls become directed edges, and methods in the same class receive sibling links. 21 00:01:42,173 --> 00:01:47,593 Its targets hide entire function bodies while keeping signatures and the remaining file visible. 22 00:01:48,293 --> 00:01:51,013 A useful hole must contain substantial work. 23 00:01:51,553 --> 00:01:55,153 The complexity score combines code length, branching, and nesting. 24 00:01:56,353 --> 00:02:02,113 The inferability score asks whether visible arguments, helpers, signatures, and sibling methods 25 00:02:02,153 --> 00:02:03,093 provide enough clues. 26 00:02:04,053 --> 00:02:08,353 Target docstrings are hidden with the body, so they cannot simply reveal the answer. 27 00:02:09,553 --> 00:02:14,653 The selector rewards both qualities and penalizes difficulty that outruns contextual support. 28 00:02:15,933 --> 00:02:18,293 It also selects connected function pairs and triples. 29 00:02:19,493 --> 00:02:23,293 When several bodies are hidden together, contextual clues are recomputed, 30 00:02:23,693 --> 00:02:27,293 and the completed functions must agree on their arguments and returned values. 31 00:02:27,973 --> 00:02:31,773 For each selected target, a teacher model sees the masked file 32 00:02:31,833 --> 00:02:35,353 and produces a rationale followed by a candidate implementation. 33 00:02:36,033 --> 00:02:41,793 In the default recipe, that teacher is Gemini three Flash, and it cannot see the original hidden body. 34 00:02:42,533 --> 00:02:46,253 A separate judge compares the candidate with the original code 35 00:02:46,313 --> 00:02:50,533 for qualities including correctness, executability, and API use. 36 00:02:51,393 --> 00:02:56,333 Retained examples become supervised training pairs: the masked file is the input; 37 00:02:56,833 --> 00:02:59,473 the explanation and predicted body form the response. 38 00:03:00,513 --> 00:03:02,673 This trains reconstruction in context, 39 00:03:03,113 --> 00:03:06,413 rather than just copying the teacher's code without its surrounding problem. 40 00:03:06,953 --> 00:03:11,513 This is a mid-training stage. In the Qwen two point five Coder experiments, 41 00:03:12,033 --> 00:03:15,233 the researchers begin with an instruction-tuned coding model, 42 00:03:15,813 --> 00:03:20,013 add function-aware infilling, and then apply agentic post-training. 43 00:03:20,833 --> 00:03:24,873 Their selected corpus comes from nine hundred sixty-eight Python repositories 44 00:03:25,353 --> 00:03:28,193 and contains about two point six billion tokens, 45 00:03:28,553 --> 00:03:31,333 measured with the Qwen two point five Coder tokenizer. 46 00:03:32,133 --> 00:03:35,413 The resulting agent is evaluated after that entire pipeline. 47 00:03:36,393 --> 00:03:41,753 Keep the comparison in view: the added stage prepares the model before it learns the agent task, 48 00:03:42,293 --> 00:03:45,713 while the later agent-training recipe remains the matched component. 49 00:03:46,320 --> 00:03:51,680 Consider one matched result: the fourteen-billion-parameter Qwen two point five Coder model 50 00:03:51,740 --> 00:03:56,180 with R two E Gym post-training, evaluated on SWE Bench Verified. 51 00:03:57,000 --> 00:03:59,060 This benchmark has five hundred instances. 52 00:03:59,820 --> 00:04:03,920 The reproduced post-training-only baseline solves twenty-six point two percent; 53 00:04:04,600 --> 00:04:09,480 adding function-aware mid-training before the same post-training reaches twenty-nine point two percent. 54 00:04:10,000 --> 00:04:15,880 That is a three-percentage-point increase. These are means over three evaluation runs in the authors' setup. 55 00:04:16,500 --> 00:04:20,000 The comparison measures the value of inserting this preparation stage 56 00:04:20,060 --> 00:04:22,260 into a completed coding-agent pipeline. 57 00:04:22,700 --> 00:04:24,920 It does not measure a standalone infilling model. 58 00:04:25,727 --> 00:04:30,027 A separate seven-billion-parameter ablation asks whether the selection rule matters. 59 00:04:30,867 --> 00:04:35,067 With the same two-hundred-thousand-target budget and teacher rationales held fixed, 60 00:04:35,727 --> 00:04:39,227 random function selection scores thirteen point nine one percent 61 00:04:39,287 --> 00:04:41,527 on the unweighted average of Verified and Lite. 62 00:04:42,267 --> 00:04:47,827 The full dependency, complexity, and inferability selector scores fifteen point six one percent. 63 00:04:48,747 --> 00:04:51,447 This is a different model and comparison from the previous chart. 64 00:04:52,047 --> 00:04:56,987 It tests how the training examples are chosen, giving evidence for the contextual selection recipe 65 00:04:57,367 --> 00:05:00,267 rather than treating every missing function as equally useful. 66 00:05:01,013 --> 00:05:06,293 Our small checkout puzzle carries the central idea. A caller tells us what arrives. 67 00:05:06,873 --> 00:05:11,773 A helper tells us what can be done. Downstream code tells us what must come back. 68 00:05:12,633 --> 00:05:16,493 Reconstructing the missing body requires those pieces to fit together. 69 00:05:17,453 --> 00:05:21,253 Function-aware fill in the middle turns that contextual reconstruction 70 00:05:21,333 --> 00:05:23,473 into a preparation task for coding agents. 71 00:05:24,333 --> 00:05:29,473 The question is useful whenever code depends on code: before writing the missing function, 72 00:05:30,133 --> 00:05:32,053 read the relationships that give it meaning.