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.

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