MiniCPM-o 4.5: greedy decoding degenerates (empty / repetition-loop outputs) on long multimodal prefixes (>12k tokens)

Summary

MiniCPM-o 4.5's greedy decoding degenerates on long multimodal prefixes: once the audio+video prompt grows past roughly 12k tokens, an increasing fraction of generations come back as an empty string, a single-character repetition loop (CCCCCC… for 130 tokens), or stray junk (5, 44) instead of an answer. The same inputs fed to another omni model (Qwen3-Omni-30B-A3B) at even longer prefix lengths produce 100% well-formed outputs, so this looks like a model/training-distribution issue rather than a harness issue.

Environment

• Model: MiniCPM-o 4.5 (HF snapshot, config.json transformers_version: 4.51.0), trust_remote_code=True
• torch.bfloat16, attn_implementation="sdpa", init_tts=False, single 48G GPU (L40S)
• Decoding: greedy — do_sample=False, num_beams=1, repetition_penalty=1.02 (we call model.generate mirroring chat() preprocessing; also reproduced through the stock model.chat(..., do_sample=False, max_new_tokens=128, max_inp_length=32768) path, see below)
• Inputs: long-video QA (public benchmarks: TraceAV-Bench, MMOU, LVOmniBench, Video-Odyssey). Prompt = several 75s video segments (10 frames each) interleaved with their audio, plus an MCQ question. Prefix length varies with how many segments are packed.

Dose–response: failure rate climbs with prefix length

Rate of well-formed answers (non-empty, parseable option letter) vs. prompt length, greedy decoding, same prompt template throughout — only the number of packed segments varies (n in parentheses):

Failure signatures at ≥13k on TraceAV (286 failures): 192 off-answer prose / option echo, 71 short junk (5, 44), 23 completely empty generations, plus repetition loops like:

CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC…  (130 tokens of "C")

Also reproduces through the stock chat() whole-video path

Feeding whole long clips through model.chat() itself (official-style ingest, do_sample=False, max_inp_length=32768) on TraceAV-Bench: 24.9% of items return a fully empty string and 57.7% are unparseable overall. Bucketing by prefix length shows the same cliff: parse rate 99.1% at 8–12k tokens → 73.6% at 12–16k → worse beyond.

Evidence it's the LM decoding, not the media ingest

• Same-input A/B: with the identical packed prompts, attaching a LoRA fine-tuned on long-prefix QA data brings every bucket in the table above to 100% well-formed. The base weights' output distribution at long prefixes is the only thing that changes.
• Cross-model control: the identical segment-union prompts (same videos, same windows, longer prefixes up to ~18k tokens) on Qwen3-Omni-30B-A3B greedy decode: 100% well-formed (0 empty / 0 loops).

So the failure is recoverable by light SFT, which suggests the pretraining/SFT mix has little coverage of >12k-token interleaved audio-video prefixes, and greedy decoding falls off the manifold there (empty EOS-first or repetition loops).

Questions

1. Is a >12k-token multimodal prefix outside the intended operating range for MiniCPM-o 4.5? The docs advertise long-video understanding, and max_inp_length=32768 is accepted silently.
2. Is there a recommended decoding configuration (sampling, repetition penalty, min_new_tokens) that mitigates this?
3. If this is a known training-coverage gap, it would be worth documenting on the model card — the empty-string failure mode silently scores as wrong answers in any benchmark harness.

Happy to provide item-level IDs / exact prompts for any of the public benchmarks above if useful.