README.md

metadata

language:
  - en
license: cc-by-4.0
pretty_name: SemanticAlign-Bench
tags:
  - research-papers
  - machine-learning
  - benchmarking
  - llm-evaluation
  - factuality
  - structured-extraction
  - paper-understanding
  - paper-to-code
task_categories:
  - question-answering
  - text-generation
size_categories:
  - 1K<n<10K
annotations_creators:
  - expert-generated
language_creators:
  - found

SemanticAlign-Bench

A benchmark for evaluating AI agents on structured claim extraction from top-tier ML conference papers. Each paper is decomposed into Semantic Alignment Units (SAU) — atomic, self-contained implementation propositions — across four diagnostic dimensions spanning numerical precision to pipeline-level workflow. Agents are evaluated on whether they can reproduce these claims without hallucination, omission, or misordering.

Dataset Description

• Papers: 30 papers from ICLR 2025, ICML 2025, and NeurIPS 2025, spanning 5 domains (6 papers each):

  Domain	Count
  Probabilistic Inference / Generative Models	6
  Reinforcement Learning	6
  Computer Vision	6
  NLP / LLM	6
  Numerical Methods / Scientific Computing	6

• Total SAU Claims: 1,491

• Size: ~519 MB

The Four SAU Dimensions

Each paper is decomposed into claims across four diagnostic dimensions, ordered from micro to macro:

Dimension	Name	Count	Definition
D1	Numerical Precision	523	Hyperparameters, configuration values, thresholds, scaling factors
D2	Formulas / Algorithms	503	Mathematical formulas, algorithm steps, architectural mechanisms
D3	Experiment Protocols	300	Datasets, baselines, evaluation metrics, experimental scope
D4	Pipelines / Procedures	165	Multi-step execution order: phase ordering, algorithm step sequencing

The D1--D4 hierarchy is universal across all evaluated configurations: D1 > D2 > D4 > D3 in score holds invariant for all 12 generator setups (Claude/DeepSeek/Gemini/GPT-4o × BasicAgent/PaperCoder/OpenHands). D3 (experimental protocol) is the dominant bottleneck, with only 0.7% perfect-score rate — 14× lower than D1. D4 exhibits a distinctive pattern: lowest zero rate (33.7%) but only 5.9% of claims score ≥0.5, meaning agents almost always attempt ordering constraints but rarely get them right.

Paper Venue Distribution

Venue	Count
ICLR 2025	15
ICML 2025	8
NeurIPS 2025	7

Dataset Structure

Per-Paper Directory Layout

<paper_id>/
  config.yaml       # Paper metadata (title, venue, year, domain, arxiv URL)
  paper.md          # Full paper text in markdown
  paper.pdf         # Original PDF
  sau.json          # SAU claims — the core annotation file
  images/           # Paper figures extracted from PDF
  blacklist.txt     # official repo url

SAU Claim Format (sau.json)

{
  "paper_id": "adjoint-matching",
  "paper_title": "Adjoint Matching: Fine-tuning Flow and Diffusion Models with Memoryless SOC",
  "D1": [
    {
      "id": "adjoint-matching-D1-001",
      "claim": "Image resolution for autoencoder pre-training and generation: 512×512",
      "source": "Section 7"
    }
  ],
  "D2": [ ... ],
  "D3": [ ... ],
  "D4": [ ... ]
}

Each claim includes:

• id: Unique identifier ({paper}-{dimension}-{number})
• claim: Self-contained implementation proposition in natural language
• source: Paper section where the claim originates

Annotation Quality

All 1,491 claims have undergone multi-version human review with systematic error checks:

• Verification against source paper for factual accuracy
• Format normalization and consistency validation
• Cross-reference integrity checks between dimensions
• Fairness audit across domains and paper types (theory vs. empirical)

Supported Tasks

1. Claim-Level Factuality: Given a paper, can the agent accurately extract a specific numerical value, formula, experimental detail, or procedural step?
2. Dimension-Level Completeness: Can the agent achieve full recall across all four SAU dimensions for a given paper?
3. Cross-Dimensional Consistency: Are claims in D4 (pipelines) consistent with D2 (formulas) and D3 (experiments)?
4. Hallucination Detection: Can the agent distinguish paper-supported claims from plausible but fabricated ones?

Dataset Creation

Source Data

30 papers selected from ICLR 2025, ICML 2025, and NeurIPS 2025, covering 5 domains with equal representation across task types (classification, generation, RL, theory, scientific computing).

Evaluation Results

In a benchmark study evaluating 360 paper-level runs (12 generators × 30 papers):

• Overall SAS: mean 0.221, median 0.200. 82.4% of SAU claims score ≤0.25.
• Model dominance: Model choice drives 2.35× more score variation than scaffold choice (1.15×). Top 5 configurations all use Claude or DeepSeek; bottom 3 all use GPT-4o.
• Scaffold asymmetry: PaperCoder (+0.116 for GPT-4o) provides more benefit to weaker models. OpenHands adds near-zero value without minimum planning competence.
• Failure pattern: 81% of zero-scored claims contain partial but incorrect code; only 5.7% are completely absent. Improving scores requires better comprehension, not broader coverage.
• Paper difficulty: Numerical methods/PDE papers dominate the easiest tier; multi-modal systems and complex training pipelines the hardest.

Considerations for Using the Data

Limitations

This is a static benchmark: claims test specification fidelity (did the agent encode the right parameters, formulas, and protocols?) rather than runtime correctness. The benchmark does not include execution-based evaluation or dynamic testing.

Intended Use

• Benchmarking LLM factuality on scientific content
• Measuring agent understanding of structured paper content
• Stress-testing retrieval-augmented generation (RAG) over academic papers

Out-of-Scope Uses

• Training data for production LLMs (limited size, single annotator)
• Automated paper review or acceptance prediction

Additional Information

License

SAU annotations are licensed under CC-BY-4.0. Underlying papers are subject to their original copyright terms as posted on arXiv and respective conference proceedings.

Citation

@inproceedings{semanticalign_bench,
  title     = {SemanticAlign-Bench: Evaluating Semantic Alignment in LLM-Based Paper Reproduction},
  author    = {Anonymous Author(s)},
  year      = {2025},
  note      = {Benchmark dataset at \url{https://anonymous-hf.up.railway.app/a/rrgn430zpfui/}}
}

Papers List

Paper ID	Title	Venue
adjoint-matching	Adjoint Matching: Fine-tuning Flow and Diffusion Models with Memoryless SOC	ICLR 2025
avg-reward-pg	Global Convergence of Policy Gradient in Average Reward MDPs	ICLR 2025
ca2-vdm	Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing	ICML 2025
cara	Canonical Rank Adaptation: An Efficient Fine-Tuning Strategy for Vision Transformers	ICML 2025
conformal-bayesian-quadrature	Conformal Prediction as Bayesian Quadrature	ICML 2025
diffusion-convergence-rate	Instance-dependent Convergence Theory for Diffusion Models	ICLR 2025
emergent-planning-rl	Interpreting Emergent Planning in Model-Free RL	ICLR 2025
gated-attention-llm	Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free	NeurIPS 2025
generator-augmented-flows	Improving Consistency Models with Generator-Augmented Flows	ICML 2025
hi-mar	Hierarchical Masked Autoregressive Models with Low-Resolution Token Pivots	ICML 2025
lora-sb	Initialization using Update Approximation is a Silver Bullet for Extremely Efficient Low-Rank Fine-Tuning	ICLR 2025
luno	Linearization Turns Neural Operators into Function-Valued Gaussian Processes	ICML 2025
ma-rlhf	MA-RLHF: Reinforcement Learning from Human Feedback with Macro Actions	ICLR 2025
masked-diffusion-token-ordering	Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions	ICML 2025
moe-pot	Mixture-of-Experts Operator Transformer for Large-Scale PDE Pre-Training	NeurIPS 2025
mrq	Towards General-Purpose Model-Free RL (MR.Q)	ICLR 2025
navil	NaViL: Rethinking Scaling Properties of Native Multimodal LLMs under Data Constraints	NeurIPS 2025
neural-operator-flow-matching-pde	Bridging Neural Operator and Flow Matching for a Generative PDE Foundation Model	NeurIPS 2025
nfig	NFIG: Multi-Scale Autoregressive Image Generation via Frequency Ordering	NeurIPS 2025
ngpt	nGPT: Normalized Transformer with Representation Learning on the Hypersphere	ICLR 2025
olmoe	OLMoE: Open Mixture-of-Experts Language Models	ICLR 2025
prioritized-generative-replay	Prioritized Generative Replay	ICLR 2025
pyramidal-flow-matching	Pyramidal Flow Matching for Efficient Video Generative Modeling	ICLR 2025
robotic-world-model	Robotic World Model: A Neural Network Simulator for Robust Policy Optimization	NeurIPS 2025
sam2	SAM 2: Segment Anything in Images and Videos	ICLR 2025
sc-fno	Sensitivity-Constrained Fourier Neural Operators (SC-FNO)	ICLR 2025
score	Training Language Models to Self-Correct via Reinforcement Learning	ICLR 2025
universal-neural-operators	Towards Universal Neural Operators through Multiphysics Pretraining	NeurIPS 2025
voting-leaderboards	Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards	ICML 2025
wdno	Wavelet Diffusion Neural Operator (WDNO)	ICLR 2025