# Experiment Summary

This file summarizes the experiments run in `SGL_new` during the current iteration.

## 1. Main Model Variants

- `2B vision + 1B text/mlp1` hybrid
- `shared_vision`: `2B guide + 8B decode`
- optional guidance variants on top of `shared_vision`:
  - `guide_text_mode=short_rationale`
  - `guide_reasoning_mode=short_cot`

## 2. 2B Vision + 1B Text Hybrid

Checkpoint build:

- hybrid checkpoint: `/home/yf/snap/data/yf/InternVL2-1B_2Bvision_hybrid`
- build script: [build_hybrid_checkpoint_2bvision_1bllm.sh](/home/yf/snap/SGL_new/build_hybrid_checkpoint_2bvision_1bllm.sh:1)

TextVQA 50-sample results:

| Mode | Accuracy | Result file |
| --- | ---: | --- |
| normal inference | `0.652000` | `/home/yf/snap/SGL_new/outputs/textvqa_largeonly_hybrid_2bvision_1bllm_validation50/textvqa_val_hybrid_2bvision_1bllm_limit50.json` |
| `reasoning-mode=two_pass` | `0.488000` | `/home/yf/snap/SGL_new/outputs/textvqa_largeonly_hybrid_2bvision_1bllm_cot50/textvqa_val_hybrid_2bvision_1bllm_two_pass_limit50.json` |
| `reasoning-mode=prompt` | `0.000000` | `/home/yf/snap/SGL_new/outputs/textvqa_largeonly_hybrid_2bvision_1bllm_prompt50/textvqa_val_hybrid_2bvision_1bllm_prompt_limit50.json` |

Takeaway:

- For the `1B hybrid`, direct CoT prompting hurts TextVQA answer formatting.
- `prompt` mode is the worst because it pushes the model to emit explanatory sentences instead of short answers.
- `two_pass` is better than `prompt`, but still clearly worse than normal inference.

## 3. Shared Vision 50-Sample Ablations

Common setup:

- guide checkpoint: `/home/yf/snap/data/yf/InternVL2-2B`
- decode checkpoint: `/home/yf/snap/data/yf/InternVL2-8B`
- `large-model-prune-layer=0.0`
- `large-model-prune-ratio=0.4`
- `consistency-token-ratio=0.05`

Results:

| Variant | Accuracy | Result file |
| --- | ---: | --- |
| baseline | `0.734000` | `/home/yf/snap/SGL_new/outputs/shared_vision_baseline50/textvqa_shared_vision_baseline_limit50.json` |
| `guide_text_mode=short_rationale` | `0.628000` | `/home/yf/snap/SGL_new/outputs/shared_vision_guide_text50/textvqa_shared_vision_guide_text_limit50.json` |
| `guide_reasoning_mode=short_cot` | `0.734000` | `/home/yf/snap/SGL_new/outputs/shared_vision_guide_attention50/textvqa_shared_vision_guide_attention_limit50.json` |

Takeaway:

- Short text hints hurt on this 50-sample slice.
- Short CoT in the guide branch does not improve accuracy on this slice, but it does not hurt either.

## 4. Did Guide CoT Change Attention?

Answer: yes.

Distribution comparison between:

- baseline: `/home/yf/snap/SGL_new/outputs/shared_vision_baseline50_stats/textvqa_shared_vision_baseline_limit50_stats.json`
- `short_cot`: `/home/yf/snap/SGL_new/outputs/shared_vision_guide_attention50_stats/textvqa_shared_vision_guide_attention_limit50_stats.json`

Measured differences over the same 50 samples:

- `avg_l1 = 0.133287`
- `median_l1 = 0.123993`
- `avg_jsd = 0.004375`
- `avg_top16_overlap = 13.48 / 16`
- `top1_same_count = 50 / 50`
- `avg_entropy_delta = +0.113141`
- `changed_small_answer_count = 38 / 50`
- `changed_large_answer_count = 2 / 50`

Interpretation:

- Guide CoT changes the visual-token importance distribution.
- The main effect is on the secondary mass / overall spread, not on the single top token.
- On this slice, attention changes did not convert into a net accuracy gain.

## 5. Explicit CoT Prompting in Guide Branch

An `explicit_cot` guide mode was prototyped to force a more explicit reasoning format.

Smoke result:

- output: `/home/yf/snap/SGL_new/outputs/vqav2_on_correct_off_wrong_shared_vision_explicitcot_smoke10/vqav2_on_correct_off_wrong_shared_vision_explicitcot_smoke10.json`
- observed behavior: the model only partially followed the intended `Reasoning / Answer` structure

Takeaway:

- Harder prompting is still not enough to guarantee stable step-by-step reasoning format.
- The current guide CoT remains prompt-level control, not a true multi-pass reasoning mechanism.

## 6. `on_correct_off_wrong_nontrunc.json` Cases

Important finding:

- `/home/yf/snap/SGL_new/data/textvqa/on_correct_off_wrong_nontrunc.json` is not aligned with the current `TextVQA val` cache.
- These cases match `/home/yf/snap/data/yf/sgl_vqav2_cache/vqav2_val.jsonl`.
- In practice, this file is a VQAv2-style case list, despite living under `textvqa/`.

Helper used:

- [run_shared_vision_cases.py](/home/yf/snap/SGL_new/tools/run_shared_vision_cases.py:1)

74-case results on this set:

| Variant | Accuracy | Result file |
| --- | ---: | --- |
| baseline | `0.896396` | `/home/yf/snap/SGL_new/outputs/vqav2_on_correct_off_wrong_shared_vision_baseline/vqav2_on_correct_off_wrong_shared_vision_baseline.json` |
| `guide_reasoning_mode=short_cot` | `0.896396` | `/home/yf/snap/SGL_new/outputs/vqav2_on_correct_off_wrong_shared_vision_shortcot/vqav2_on_correct_off_wrong_shared_vision_shortcot.json` |

Takeaway:

- On these 74 VQAv2-style cases, enabling short CoT in the guide branch did not change overall accuracy.

## 7. Full TextVQA Runs

Two full `shared_vision + short_cot guide-attention` runs were launched and completed.

### Keep40

- output dir: `/home/yf/snap/SGL_new/outputs/shared_vision_guide_attention_full_20260429_115658`
- result file: `/home/yf/snap/SGL_new/outputs/shared_vision_guide_attention_full_20260429_115658/textvqa_shared_vision_guide_attention_full.json`
- summary file: `/home/yf/snap/SGL_new/outputs/shared_vision_guide_attention_full_20260429_115658/textvqa_shared_vision_guide_attention_full.summary.json`
- final accuracy: `0.764260`

### Keep09

- output dir: `/home/yf/snap/SGL_new/outputs/shared_vision_guide_attention_keep09_full_20260429_130806`
- result file: `/home/yf/snap/SGL_new/outputs/shared_vision_guide_attention_keep09_full_20260429_130806/textvqa_shared_vision_guide_attention_keep09_full.json`
- summary file: `/home/yf/snap/SGL_new/outputs/shared_vision_guide_attention_keep09_full_20260429_130806/textvqa_shared_vision_guide_attention_keep09_full.summary.json`
- final accuracy: `0.744660`

## 8. Code Changes Relevant to These Experiments

- shared-vision core: [run_shared_vision_guided_textvqa.py](/home/yf/snap/SGL_new/eval/vqa/run_shared_vision_guided_textvqa.py:1)
- shared-vision launcher: [textvqaSharedVision-2Bguide-8Btext.sh](/home/yf/snap/SGL_new/textvqaSharedVision-2Bguide-8Btext.sh:1)
- keep40/keep09 launcher: [run_textvqa_shared_vision_keep40_keep09.sh](/home/yf/snap/SGL_new/run_textvqa_shared_vision_keep40_keep09.sh:1)
- case runner: [run_shared_vision_cases.py](/home/yf/snap/SGL_new/tools/run_shared_vision_cases.py:1)
- hybrid single-model eval: [run_single_model_native.py](/home/yf/snap/SGL_new/eval/vqa/run_single_model_native.py:1)

## 9. Practical Conclusions

- For the `1B hybrid`, normal inference is the safest option so far.
- For `shared_vision`, short text hints are currently harmful on the tested slice.
- Short CoT in the guide branch changes attention distributions, but does not yet give consistent accuracy gains.
- If the goal is real step-by-step guide reasoning, prompt-only control is not enough; a true multi-pass guide mechanism is the next logical step.