--- 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 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 ``` / 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`) ```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 ```bibtex @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 |