quick_or_slow

Quick or Slow: Forced-Choice Self-Consistency Inversion in Audio-LLMs

This repository accompanies the ICASSP submission: "Anti-Predictive Self-Consistency in Audio-LLMs: A Forced-Choice Diagnostic and Silent-Audio Probe Recalibrator"

One-sentence claim

On an audio-LLM binary paralinguistic task, forcing a full commit rate does not fix chance-level accuracy — but it does let us prove that the model's confidence is worse than useless (anti-predictive), and a one-extra-forward-pass silent-audio probe can partially repair it.

Repository layout

pivot_c/
├── paper/                  # ICASSP manuscript (IEEEtran)
│   ├── main_merged.tex     # single-file merged manuscript (compile this)
│   ├── main.tex            # split version (uses \input{})
│   ├── sec_*.tex           # per-section sources
│   ├── references.bib      # bibliography
│   └── figures/            # publication-ready PNG + PDF (figures4papers style)
│       ├── forced_choice_intro.{png,pdf}
│       └── selective_risk_curve.{png,pdf}
│
├── code/                   # analysis + inference scripts
│   ├── infer_sc.py         # k-sample SC generation
│   ├── infer_probe_pitchbright.py  # first-token logit probe extraction
│   ├── grid.py             # multi-cell GPU grid runner
│   ├── grid_perturb_q2o.py # perturbation (noise/pink/trunc) grid
│   ├── analyze_rho_sign_predictor.py   # N=54 predictor
│   ├── analyze_forced_choice.py        # forced-choice rescore
│   ├── analyze_fc_paired_bootstrap.py  # paired bootstrap CI
│   ├── analyze_fc_decompose.py         # K-flip redundancy + decomposition
│   ├── analyze_locked_wrong_xfam.py    # cross-family locked-wrong
│   ├── analyze_flipped_calibrator_xfam.py
│   ├── analyze_tau_sweep.py            # τ-sweep
│   ├── analyze_perturb.py              # deployment-degradation
│   ├── analyze_fusion_canonical.py      # canonical fusion (SoT)
│   ├── analyze_fusion_generic.py
│   ├── analyze_fusion_xfam.py           # lp_margin cross-family pooled
│   ├── verify_selective_risk.py        # selective-risk verification
│   ├── make_selective_risk_fig_fp.py   # figure: selective-risk
│   └── make_intro_fig_fp.py            # figure: forced-choice intro
│
├── results/                # raw per-item JSONLs (351 files)
│   ├── sc_{model}__{corpus}__{task}.jsonl          # free-gen SC
│   ├── sc_pert_{cond}_{model}__{corpus}__{task}.jsonl  # perturbed SC
│   └── probe_{task}_{model}__{corpus}__{cond}.jsonl    # logit probes
│
└── analysis/               # canonical analysis outputs + verdicts
    ├── rho_sign_cells.json
    ├── rho_sign_cells_k20.json
    ├── snr_sweep_curve.json
    └── ...

Reproducibility

All numbers in the paper recompute from the raw per-item JSONLs:

# Forced-choice rescore (Table tab:fc + Figure fig:fc)
python3 code/analyze_forced_choice.py

# Paired bootstrap CI (abstract + Finding Layer-2)
python3 code/analyze_fc_paired_bootstrap.py
python3 code/analyze_fc_decompose.py

# Selective-risk curve (Figure fig:selective)
python3 code/verify_selective_risk.py
python3 code/make_selective_risk_fig_fp.py

# Forced-choice intro figure (Figure fig:fc)
python3 code/make_intro_fig_fp.py

# N=54 flip-rate rho-sign predictor (Finding #9)
python3 code/analyze_rho_sign_predictor.py

Models used (weights loaded from HuggingFace / local checkpoints):

• Qwen/Qwen2-Audio-7B-Instruct
• Qwen/Qwen2.5-Omni-7B
• SALMONN-7B (local checkpoint)
• Audio Flamingo-Next (local checkpoint at /ossfs/workspace/af-next, requires afnext_env)

Grid: 4 audio-LLM families × 3 corpora (LibriSpeech test-clean/test-other, VoxPopuli) × 4 binary paralinguistic tasks (pace/loud/pitch/bright) × k=5 at τ=0.7 + perturbations (noise at SNR {10,5,0,-5} dB, pink noise, trunc 1s).

Key numbers (all reproducible)

License

Anonymous for double-blind review. Code + data released for reproducibility.