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HFEPX Hub

Math Or Medicine Papers

Updated from current HFEPX corpus (Feb 27, 2026). 176 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Ranking. Frequent quality control: Calibration. Frequently cited benchmark: MATH. Common metric signal: accuracy. Use this page to compare protocol setup, judge behavior, and labeling design decisions before running new eval experiments. Newest paper in this set is from Feb 26, 2026.

Papers: 176 Last published: Feb 26, 2026 Global RSS Tag RSS
MathMedicine

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 176 papers for Math Or Medicine Papers. Dominant protocol signals include automatic metrics, simulation environments, human evaluation, with frequent benchmark focus on MATH, Retrieval and metric focus on accuracy, cost. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.

Why This Matters For Eval Research

Protocol Takeaways

Benchmark Interpretation

  • MATH appears in 11.4% of hub papers (20/176); use this cohort for benchmark-matched comparisons.
  • Retrieval appears in 8.5% of hub papers (15/176); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 34.7% of hub papers (61/176); compare with a secondary metric before ranking methods.
  • cost is reported in 11.9% of hub papers (21/176); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Close gap on Papers with explicit human feedback. Coverage is a replication risk (11.9% vs 45% target).
  • Close gap on Papers reporting quality controls. Coverage is a replication risk (6.3% vs 30% target).
  • Tighten coverage on Papers naming benchmarks/datasets. Coverage is usable but incomplete (34.7% vs 35% target).
  • Maintain strength on Papers naming evaluation metrics. Coverage is strong (56.3% vs 35% target).
  • Close gap on Papers with known rater population. Coverage is a replication risk (18.2% vs 35% target).
  • Close gap on Papers with known annotation unit. Coverage is a replication risk (9.7% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (11.9% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (6.3% vs 30% target).

Papers naming benchmarks/datasets

Coverage is usable but incomplete (34.7% vs 35% target).

Papers naming evaluation metrics

Coverage is strong (56.3% vs 35% target).

Papers with known rater population

Coverage is a replication risk (18.2% vs 35% target).

Papers with known annotation unit

Coverage is a replication risk (9.7% vs 35% target).

Suggested Reading Order

  1. 1. An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  2. 2. AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning

    High citation traction makes this a useful baseline for method and protocol context.

  3. 3. Why Diffusion Language Models Struggle with Truly Parallel (Non-Autoregressive) Decoding?

    High citation traction makes this a useful baseline for method and protocol context.

  4. 4. InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models

    High citation traction makes this a useful baseline for method and protocol context.

  5. 5. A Decision-Theoretic Formalisation of Steganography With Applications to LLM Monitoring

    Adds automatic metrics for broader coverage within this hub.

  6. 6. Toward Automatic Filling of Case Report Forms: A Case Study on Data from an Italian Emergency Department

    Adds automatic metrics for broader coverage within this hub.

  7. 7. NoRA: Breaking the Linear Ceiling of Low-Rank Adaptation via Manifold Expansion

    Adds automatic metrics for broader coverage within this hub.

  8. 8. Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching

    Adds automatic metrics for broader coverage within this hub.

Known Limitations

  • Only 6.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (18.2% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Links

human_eval vs llm_as_judge

both=0, left_only=3, right_only=1

0 papers use both Human Eval and Llm As Judge.

human_eval vs automatic_metrics

both=0, left_only=3, right_only=161

0 papers use both Human Eval and Automatic Metrics.

llm_as_judge vs automatic_metrics

both=0, left_only=1, right_only=161

0 papers use both Llm As Judge and Automatic Metrics.

Benchmark Brief

MATH

Coverage: 20 papers (11.4%)

20 papers (11.4%) mention MATH.

Examples: Overconfident Errors Need Stronger Correction: Asymmetric Confidence Penalties for Reinforcement Learning , Gradient Regularization Prevents Reward Hacking in Reinforcement Learning from Human Feedback and Verifiable Rewards , RFEval: Benchmarking Reasoning Faithfulness under Counterfactual Reasoning Intervention in Large Reasoning Models

Benchmark Brief

Retrieval

Coverage: 15 papers (8.5%)

15 papers (8.5%) mention Retrieval.

Examples: AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning , TCM-DiffRAG: Personalized Syndrome Differentiation Reasoning Method for Traditional Chinese Medicine based on Knowledge Graph and Chain of Thought , Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance

Benchmark Brief

GSM8K

Coverage: 13 papers (7.4%)

13 papers (7.4%) mention GSM8K.

Examples: InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models , Black-Box Reliability Certification for AI Agents via Self-Consistency Sampling and Conformal Calibration , Pyramid MoA: A Probabilistic Framework for Cost-Optimized Anytime Inference

Metric Brief

accuracy

Coverage: 61 papers (34.7%)

61 papers (34.7%) mention accuracy.

Examples: AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning , InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models , Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching

Metric Brief

cost

Coverage: 21 papers (11.9%)

21 papers (11.9%) mention cost.

Examples: Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching , Sparsity Induction for Accurate Post-Training Pruning of Large Language Models , Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages

Metric Brief

latency

Coverage: 11 papers (6.3%)

11 papers (6.3%) mention latency.

Examples: Why Diffusion Language Models Struggle with Truly Parallel (Non-Autoregressive) Decoding? , InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models , Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching

Most Cited In This Hub

Fast path to methods with the strongest citation traction in this scope.

Papers: AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning , Why Diffusion Language Models Struggle with Truly Parallel (Non-Autoregressive) Decoding? , InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models

Most Recent

Fast path to latest protocol changes and newly published evaluation setups.

Papers: AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning , Why Diffusion Language Models Struggle with Truly Parallel (Non-Autoregressive) Decoding? , InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models

Best Protocol Detail

Papers with explicit rater/unit metadata and quality-control signals for reproducibility.

Papers: An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models

Top Papers

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