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448d6a5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 | """IdeaBench-style evaluation queries (Sec 5.1 proxy).
The paper's IdeaBench is built on AI Idea Bench 2025 (Qiu et al., 2025; 3,495
papers, 8 domains) which is not openly redistributable here. We build a small,
faithful proxy: one query per domain, each with a topic x_t (problem statement,
solution withheld), related works x_r (inspirational prior papers), and an
expert-written reference idea y* distilled from a real, well-known target paper.
This is a SCALED proxy of the benchmark, not the full 3,495-paper set.
"""
QUERIES = [
{
"domain": "CV",
"topic": "Building a promptable foundation model for image segmentation that generalizes zero-shot to unseen objects and tasks via flexible prompts.",
"related": [
"Fully convolutional and Mask R-CNN style networks segment fixed, pre-defined object categories from labelled masks.",
"Vision transformers (ViT) provide scalable image encoders pre-trained on large image corpora.",
"CLIP aligns images and text with contrastive pre-training, enabling open-vocabulary recognition.",
"Interactive segmentation methods refine masks from user clicks but are trained per-dataset.",
],
"reference": {
"title": "Segment Anything: a promptable segmentation foundation model",
"problem": "Segmentation models are trained per task/category and do not generalize to new objects or prompts; there is no foundation model for segmentation nor a web-scale mask dataset.",
"method": "A promptable model (image encoder + prompt encoder + fast mask decoder) trained to output valid masks for point/box/text prompts; a data engine iteratively co-develops the model and a 1B-mask, 11M-image dataset via model-in-the-loop annotation.",
"evaluation": "Zero-shot transfer to 23 segmentation datasets and downstream tasks (edge detection, proposals, instance segmentation); compare mask quality and human ratings vs interactive and supervised baselines.",
},
},
{
"domain": "NLP",
"topic": "Aligning language models with human preferences without the instability and complexity of reinforcement learning from a separate reward model.",
"related": [
"RLHF fits a reward model to human comparisons and optimizes the policy with PPO, which is unstable and sample-inefficient.",
"The optimal RLHF policy has a closed form: a reweighting of the reference policy by the exponentiated reward.",
"Bradley-Terry models express pairwise preference probabilities via a latent scalar score.",
],
"reference": {
"title": "Direct Preference Optimization: your language model is secretly a reward model",
"problem": "RLHF is complex and unstable, requiring reward-model training plus RL. Can we optimize the same objective directly from preference pairs?",
"method": "Reparameterize the RLHF objective so the reward is expressed via the policy's log-ratio to a reference model, yielding a simple classification-style loss on preference pairs (no reward model, no RL, no sampling).",
"evaluation": "Sentiment control, summarization and single-turn dialogue; compare win-rate vs PPO-RLHF and preferred-FT baselines under GPT-4/human judgment; measure stability and compute.",
},
},
{
"domain": "Multi-modal",
"topic": "Giving a large language model general-purpose visual understanding and instruction-following using limited multimodal supervision.",
"related": [
"CLIP/ViT encoders yield strong transferable image features aligned to text.",
"Instruction tuning on GPT-generated data makes LLMs follow diverse natural-language instructions.",
"Flamingo/BLIP-2 connect frozen vision encoders to LLMs with learned adapters for captioning and VQA.",
],
"reference": {
"title": "Visual Instruction Tuning (LLaVA)",
"problem": "There is little multimodal instruction-following data and no simple recipe to turn an LLM into a general visual assistant.",
"method": "Use a language-only GPT to synthesize multimodal instruction-following data from image captions/boxes, then connect a CLIP visual encoder to an LLM via a projection and instruction-tune end-to-end.",
"evaluation": "A synthetic multimodal instruction benchmark plus ScienceQA; compare against BLIP-2/visual chat baselines with GPT-4 as judge and accuracy.",
},
},
{
"domain": "Audio & Speech",
"topic": "Endowing a large language model with perception and understanding of general audio, speech, and music from raw waveforms.",
"related": [
"Whisper provides robust multilingual speech recognition from weak supervision at scale.",
"Audio encoders (BEATs) learn general acoustic representations for non-speech sounds.",
"Q-Former style adapters bridge frozen encoders and LLMs for cross-modal reasoning.",
],
"reference": {
"title": "SALMONN: towards generic hearing for large language models",
"problem": "Speech LLMs handle only speech; there is no single model that jointly understands speech, audio events and music and generalizes to unseen audio tasks.",
"method": "Fuse a Whisper speech encoder and a BEATs audio encoder via a window-level Q-Former into an LLM; train with cross-modal instruction tuning plus an activation-tuning stage to unlock emergent cross-modal abilities.",
"evaluation": "Speech recognition/translation, audio captioning, music QA and unseen tasks (e.g., audio-based storytelling); compare to task-specific and cascaded baselines.",
},
},
{
"domain": "Robotics & Control",
"topic": "Learning visuomotor manipulation policies that model multimodal, temporally-consistent action distributions from demonstrations.",
"related": [
"Behavior cloning with unimodal Gaussian or MSE heads struggles with multimodal demonstrations.",
"Diffusion models learn expressive multimodal distributions via iterative denoising.",
"Action-chunking and receding-horizon control improve temporal consistency in imitation.",
],
"reference": {
"title": "Diffusion Policy: visuomotor policy learning via action diffusion",
"problem": "Imitation policies collapse multimodal action distributions and produce temporally inconsistent actions, limiting dexterous manipulation.",
"method": "Represent the policy as a conditional denoising diffusion process over action sequences, using receding-horizon control, visual conditioning, and time-series diffusion for stable, multimodal action generation.",
"evaluation": "15 tasks across simulation and real robots; compare success rate vs LSTM-GMM/BET/IBC behavior-cloning baselines and ablate horizon and conditioning.",
},
},
{
"domain": "Science",
"topic": "Generating novel, stable inorganic crystalline materials with targeted properties beyond substitution-based search.",
"related": [
"Graph neural network potentials predict formation energy and stability of crystals.",
"Diffusion and flow models generate molecules and conformers in continuous space.",
"Large DFT-relaxed datasets provide stability labels for inorganic compounds.",
],
"reference": {
"title": "A generative model for inorganic materials design (MatterGen)",
"problem": "Materials discovery relies on screening known/substituted structures, rarely proposing genuinely novel stable crystals with desired properties.",
"method": "A diffusion model over atom types, coordinates and the periodic lattice that generates stable, diverse crystals, with property-conditioned adapters fine-tuned for chemistry, symmetry, and mechanical/magnetic/electronic targets.",
"evaluation": "Fraction of stable-unique-novel structures verified by DFT; property-conditioned generation success and distance-to-hull vs substitution and prior generative baselines.",
},
},
{
"domain": "General ML",
"topic": "Designing a sequence model with Transformer-level quality but linear-time inference and long-context efficiency.",
"related": [
"Transformers have quadratic attention cost and a growing KV cache with sequence length.",
"Structured state-space models (S4) capture long dependencies with linear recurrence but weaker language modeling.",
"Gating and input-dependent dynamics improve selectivity in recurrent models.",
],
"reference": {
"title": "Mamba: linear-time sequence modeling with selective state spaces",
"problem": "Attention is inefficient for long sequences while prior SSMs underperform Transformers on information-dense modalities like language.",
"method": "Make SSM parameters input-dependent (selective) so the model propagates or forgets state per token, and implement a hardware-aware parallel scan for a simplified attention-free architecture.",
"evaluation": "Language modeling perplexity and downstream tasks vs Transformers/Transformer++ at matched size, plus DNA and audio; measure throughput and scaling with context length.",
},
},
{
"domain": "Theory",
"topic": "Determining how to jointly allocate model size and training tokens under a fixed compute budget for large language models.",
"related": [
"Kaplan et al. scaling laws suggested favoring larger models over more data.",
"Compute-optimal training seeks the best loss for a fixed FLOP budget.",
"Power-law fits relate loss to parameters and data separately.",
],
"reference": {
"title": "Training compute-optimal large language models (Chinchilla)",
"problem": "Given a fixed compute budget, how should parameters and training tokens be traded off? Prior laws over-sized models relative to data.",
"method": "Fit a parametric loss surface L(N,D) from >400 runs across model/data scales using three complementary estimation approaches, then solve for the compute-optimal (N,D) frontier.",
"evaluation": "Predict and verify a compute-optimal model (Chinchilla) trained with the derived N,D; compare downstream accuracy vs larger under-trained models (Gopher/GPT-3) at equal compute.",
},
},
]
def get_queries(domains=None):
if domains:
return [q for q in QUERIES if q["domain"] in domains]
return list(QUERIES)
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