HFEPX Benchmark Hub
AIME or GSM8K or MMLU Benchmark Papers
Updated from current HFEPX corpus (2026-09-29). This page tracks 60 papers reporting AIME or GSM8K or MMLU benchmark evidence, with protocol and metric context for comparison.
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Researcher Quick Triage
Use this page for benchmark-matched method comparisons and eval protocol selection. Quality band: High .
High-Signal Coverage
100.0%
60 / 60 sampled papers are not low-signal flagged.
Replication-Ready Set
37
Papers with explicit benchmark + metric + eval mode fields.
Quality Controls
3.3%
2 papers report calibration/adjudication/IAA controls.
- 60 papers explicitly name benchmark datasets in the sampled set.
- 40 papers report at least one metric term in metadata extraction.
- Start with the ranked shortlist below before reading all papers.
Primary action: Start with the top 2 benchmark-matched papers, then compare evaluation modes in the protocol matrix.
Why This Matters (Expanded)
Why This Matters For Eval Research
- Use this page to compare AIME or GSM8K or MMLU papers by evaluation mode, metric, and evidence quality before reusing reported results.
Protocol Notes (Expanded)
Protocol Takeaways
- AIME or GSM8K or MMLU papers are often paired with automatic_metrics, llm_as_judge.
Benchmark Interpretation
- GSM8K: 29 papers
- MMLU: 26 papers
- AIME: 15 papers
- MATH-500: 9 papers
Metric Interpretation
- accuracy: 22 papers
- cost: 11 papers
- latency: 6 papers
- perplexity: 4 papers
Start Here (Benchmark-Matched First 6)
Ranked by protocol completeness so you can quickly find papers suitable for comparison studies.
- Decomposing Wrong-Consensus Agreement in LLM Self-Consistency
Aug 19, 2026 · Citations: 0 · Score: 8.0
Eval: Automatic Metrics · Metrics: Accuracy
- TwinKV: A Composable Repair Pass for KV Cache Eviction via Pairwise Key Redundancy
Aug 27, 2026 · Citations: 0 · Score: 8.0
Eval: Automatic Metrics · Metrics: Spearman
- Memory Augmentation Unlocks Efficient Chain-of-Thought Reasoning
Aug 21, 2026 · Citations: 0 · Score: 8.0
Eval: Automatic Metrics · Metrics: Accuracy
- Will Scaling Improve Social Simulation with LLMs?
Jul 2, 2026 · Citations: 0 · Score: 8.0
Eval: Automatic Metrics, Simulation Env · Metrics: Accuracy
- Cliff Tokens: Identifying Single-Token Failure Triggers in LLM Mathematical Reasoning
Jun 24, 2026 · Citations: 0 · Score: 7.5
Eval: Automatic Metrics · Metrics: Accuracy
- Enhanced LLM Reasoning by Optimizing Reward Functions with Search-Driven Reinforcement Learning
May 3, 2026 · Citations: 0 · Score: 7.5
Eval: Automatic Metrics · Metrics: Accuracy
Protocol Matrix (Top 10)
Compare protocol ingredients quickly before deep-reading full papers.
| Paper | Eval Modes | Human Feedback | Metrics | Quality Controls |
|---|---|---|---|---|
| Decomposing Wrong-Consensus Agreement in LLM Self-Consistency Aug 19, 2026 | Automatic Metrics | Pairwise Preference | Accuracy | Not reported |
| TwinKV: A Composable Repair Pass for KV Cache Eviction via Pairwise Key Redundancy Aug 27, 2026 | Automatic Metrics | Pairwise Preference | Spearman | Not reported |
| Memory Augmentation Unlocks Efficient Chain-of-Thought Reasoning Aug 21, 2026 | Automatic Metrics | Demonstrations | Accuracy, Latency | Not reported |
| Will Scaling Improve Social Simulation with LLMs? Jul 2, 2026 | Automatic Metrics, Simulation Env | Not reported | Accuracy | Calibration |
| Cliff Tokens: Identifying Single-Token Failure Triggers in LLM Mathematical Reasoning Jun 24, 2026 | Automatic Metrics | Pairwise Preference | Accuracy, Pass@64 | Not reported |
| Enhanced LLM Reasoning by Optimizing Reward Functions with Search-Driven Reinforcement Learning May 3, 2026 | Automatic Metrics | Pairwise Preference | Accuracy, F1 | Not reported |
| Hidden Measurement Error in LLM Pipelines Distorts Annotation, Evaluation, and Benchmarking Apr 13, 2026 | Llm As Judge | Demonstrations | Precision, Agreement | Not reported |
| Auditing MCQA Benchmarks through Probability Landscapes Aug 31, 2026 | Not reported | Pairwise Preference | Not reported | Not reported |
| RARE: Decoupling Representation Steering from Expert Routing in Mixture-of-Experts Language Models Aug 21, 2026 | Automatic Metrics | Not reported | Accuracy, Success rate | Not reported |
| Inject, Align, Recover: Staged Post-Training for Retrieval-Free Document Knowledge Internalization Aug 20, 2026 | Automatic Metrics | Not reported | Accuracy | Not reported |
Researcher Workflow (Detailed)
Checklist
-
Gap: Human feedback
Human feedback is present in 8 of 60 papers.
-
Gap: Quality controls
Quality controls is present in 2 of 60 papers.
-
Strong: Benchmarks
Benchmarks is present in 60 of 60 papers.
-
Strong: Metrics
Metrics is present in 40 of 60 papers.
-
Gap: Known rater population
Known rater population is present in 6 of 60 papers.
-
Gap: Known annotation unit
Known annotation unit is present in 10 of 60 papers.
Strengths
- Benchmarks is present in 60 of 60 papers.
- Metrics is present in 40 of 60 papers.
- Agentic evaluation is present in 13 of 60 papers.
Known Gaps
- Human feedback is present in 8 of 60 papers.
- Quality controls is present in 2 of 60 papers.
- Known rater population is present in 6 of 60 papers.
Suggested Next Analyses
- Review the most recent AIME or GSM8K or MMLU papers first, then compare reported metrics and quality-control context before treating results as comparable.
Recommended Queries
Known Limitations
- This synthetic persisted page is generated from extraction data because the cached benchmark payload was missing for either-aime-or-gsm8k-or-mmlu.
Research Utility Snapshot (Detailed)
Evaluation Modes
- Automatic Metrics (36)
- Llm As Judge (2)
- Simulation Env (2)
Human Feedback Mix
- None (52)
- Pairwise Preference (6)
- Demonstrations (2)
Top Benchmarks
- GSM8K (29)
- MMLU (26)
- AIME (15)
- MATH-500 (9)
Top Metrics
- Accuracy (22)
- Cost (11)
- Latency (6)
- Perplexity (4)
Top Papers On This Benchmark
- Decomposing Wrong-Consensus Agreement in LLM Self-Consistency
Lizhuo Zhang, Mengmeng Tang, Chenfeng Long, Xiaoyong Tang, Xiang Luo · Aug 19, 2026 · Citations: 0
Pairwise Preference Automatic MetricsThe mechanical reference is leak-free: each case's preference and accuracy are estimated from its other runs only.
- Auditing MCQA Benchmarks through Probability Landscapes
Minsoo Song, Chanjun Park · Aug 31, 2026 · Citations: 0
Pairwise PreferenceTo provide a scalable diagnostic approach, we propose a two-component probabilistic framework for auditing MCQA benchmarks using model output distributions.
- Kascade: A Practical Sparse Attention Method for Long-Context LLM Inference
Dhruv Deshmukh, Saurabh Goyal, Nipun Kwatra, Ramachandran Ramjee · Dec 18, 2025 · Citations: 0
Automatic MetricsKascade achieves up to 4.1x speedup in decode attention and 2.2x speedup in prefill attention over FlashAttention-3 baseline on H100 GPUs while closely matching dense attention accuracy on long-context benchmarks such as LongBench and…
- TwinKV: A Composable Repair Pass for KV Cache Eviction via Pairwise Key Redundancy
Hong Chen, Yudong Zeng, Yongwei Huang, Zuhao Ouyang, Dongnan Zheng · Aug 27, 2026 · Citations: 0
Pairwise Preference Automatic MetricsWe introduce TwinKV, a training-free, attention-free redundancy signal that detects whether a token's key has a near-duplicate elsewhere in context.
- Tool Verification for Test-Time Reinforcement Learning
Ruotong Liao, Nikolai Röhrich, Xiaohan Wang, Yuhui Zhang, Yasaman Samadzadeh · Mar 2, 2026 · Citations: 0
Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.
- Reasoning or Rambling? Exploring the Effect of Thinking on Agent Persuasion
Haodong Zhao, Jidong Li, Zhaomin Wu, Tianjie Ju, Zhuosheng Zhang · Sep 25, 2025 · Citations: 0
Understanding persuasion is critical for the safety and reliability of multi-agent systems built on large language models (LLMs).
- Memory Augmentation Unlocks Efficient Chain-of-Thought Reasoning
Simeng Zhang, Yilong Chen, Wenyuan Zhang, Zhenyu Zhang, Yao Chen · Aug 21, 2026 · Citations: 0
Demonstrations Automatic MetricsBased on this principle, we propose Memory-Augmented Compression, a training-free framework that constructs reusable reasoning memories from historical traces and retrieves them as prefill-side scaffolds.
- RARE: Decoupling Representation Steering from Expert Routing in Mixture-of-Experts Language Models
Zhibo Zhang, Zhen Ouyang, Ling Shi, Kailong Wang · Aug 21, 2026 · Citations: 0
Automatic MetricsAbstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.
- Inject, Align, Recover: Staged Post-Training for Retrieval-Free Document Knowledge Internalization
Qian Kou, Xiaofeng Shi, Xiaosong Qiu, Hua Zhou · Aug 20, 2026 · Citations: 0
Automatic MetricsAbstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.
- LoRA-GA$^2$: Low Rank Adaptation with Multi-step Gradient Adaptive Alignment
Haonan He, Xinyue Fan · Aug 20, 2026 · Citations: 0
Automatic MetricsFor instance, LoRA-GA^2 surpasses the leading baseline by an average of 0.66 points on the GLUE benchmark, and outperforms the strongest baseline by 1.03 points on GSM8K and 0.87 points on HumanEval, respectively.
- Masked diffusion LLMs can use EoS tokens for hidden reasoning
Sarah Breckner, Sebastian Schuster · Mar 5, 2026 · Citations: 0
Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.
- Decoupled Contrastive Decoding via Expert-Aligned Drafting
Zhixuan Liu, Zhichen Dong, Yuanfu Wang, Chao Yang · Aug 13, 2026 · Citations: 0
Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.
- TEMPER: Testing Emotional Perturbation in Quantitative Reasoning
Atahan Dokme, Benjamin Reichman, Larry Heck · Apr 9, 2026 · Citations: 0
Automatic MetricsBeyond emotion specifically, the benchmark construction procedure offers a controllable instrument for stylistic translation and robustness evaluation.
- The Wording Effect: Quantifying Two-Way Drift in LLM Benchmark Performance
Shailja Thakur, Sungeun An, Chad DeLuca, Hima Patel · Aug 12, 2026 · Citations: 0
Automatic MetricsA benchmark score comes from a single phrasing of each problem.
- Shorthand for Thought: Compressing LLM Reasoning via Entropy-Guided Supertokens
Zhenyu Zhao, Sander Land, Daniel M. Bikel, Waseem Alshikh · Apr 29, 2026 · Citations: 0
Automatic MetricsAcross three model families and five mathematical reasoning benchmarks, our approach shortens reasoning traces by 8.1% on average; under a TOST equivalence analysis at a +/- 2pp margin, accuracy is equivalent or inconclusive on 13/15 model…
- Will Scaling Improve Social Simulation with LLMs?
Caleb Ziems, William Held, Su Doga Karaca, David Grusky, Tatsunori Hashimoto · Jul 2, 2026 · Citations: 0
Automatic MetricsSimulation EnvWe use scaling laws to study the relationship between LLMs' compute scale, general capability benchmarks, and the fidelity of social simulation in three representative sub-domains: opinion modeling, behavioral simulation, and longitudinal…
- NITP: Next Implicit Token Prediction for LLM Pre-training
Xiangdong Zhang, Debing Zhang, Shaofeng Zhang, Xiaohan Qin, Yu Cheng · May 24, 2026 · Citations: 0
Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.
- PARTREP: Learning What to Repeat for Decoder-only LLMs
Andikawati P Widjaja, Yongjun Kim, Hyounghun Kim, Jaeho Lee · Jul 2, 2026 · Citations: 0
Automatic MetricsAcross eight benchmarks (including MMLU, GSM8K, and RULER) and three model families (Qwen2.5, Llama3.2, Gemma4), PartRep retains most of the gains of full repetition while using only 59.4\% of its KV cache and 79.0\% of its prefill FLOPs.
- On Compositional Learning Behaviours in Formal Mathematics
Kevin Yandoka Denamganaï · May 27, 2026 · Citations: 0
Automatic MetricsSelf-evolving scientific agents capable of conquering the hard tail of formal mathematics require Compositional Learning Behaviours (CLBs) -- the capacity to ground and recombine novel symbolic structures in context, beyond mere…
- Closing the Activation-Cone Blind Spot: Response-Time Probing and Unified Defense
Subhadip Mitra · Jun 28, 2026 · Citations: 0
Automatic MetricsInference-time safety methods for large language models have proliferated, yet no systematic comparison exists.
- Epiphany-Aware KV Cache Eviction Without the Attention Matrix
Steven Kolawole, Virginia Smith · Jun 25, 2026 · Citations: 0
Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.
- The Hidden Cost of Structured Generation in LLMs: Draft-Conditioned Constrained Decoding
Avinash Reddy, Thayne T. Walker, James S. Ide, Amrit Singh Bedi · Feb 8, 2026 · Citations: 0
Automatic MetricsAcross structured reasoning benchmarks, DCCD improves strict structured accuracy by up to +24 percentage points over standard constrained decoding (e.g., 15.2\% to 39.0\% on GSM8K with a 1B model), and enables smaller model pairs to match…
- Symbiotic-MoE: Unlocking the Synergy between Generation and Understanding
Xiangyue Liu, Zijian Zhang, Miles Yang, Zhao Zhong, Liefeng Bo · Apr 9, 2026 · Citations: 0
Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.
- CeRA: Breaking the Linear Ceiling of Low-Rank Adaptation with Non-linearity Retained at Inference
Hung-Hsuan Chen · Feb 26, 2026 · Citations: 0
Automatic MetricsOn the SlimOrca benchmark, CeRA breaks this linear barrier: at rank 64 (PPL 3.89), it outperforms LoRA at rank 512 (PPL 3.90), demonstrating superior spectral efficiency.
- Cliff Tokens: Identifying Single-Token Failure Triggers in LLM Mathematical Reasoning
Jaeyong Ko, Pilsung Kang, Yukyung Lee · Jun 24, 2026 · Citations: 0
Pairwise Preference Automatic MetricsAcross seven models and three mathematical reasoning benchmarks (GSM1K, MATH500, AIME 2025), cliff tokens act as failure triggers; deleting the first cliff token and resampling recovers pass@64 to 1.0, while keeping it limits recovery to…
- SARA: Unlocking Multilingual Knowledge in Mixture-of-Experts via Semantically Anchored Routing Alignment
Tianyu Dong, Yangyang Liu, Jiang Zhou, Xinwei Wu, Xiaohu Zhao · Jun 24, 2026 · Citations: 0
We conduct experiments on 2 LLMs across 5 low-resource languages and 3 benchmarks.
- Riazi-8B: An Urdu Large Language Model for Mathematical Reasoning
Azher Ali, Ibtsam Haider, Raja Khurram Shahzad, Seemab Latif, Mehwish Fatima · Jun 24, 2026 · Citations: 0
Recent LLMs demonstrate strong mathematical reasoning capabilities, but existing gains rely heavily on English-centric training resources and benchmarks.
- Holistic Data Scheduler for LLM Pre-training via Multi-Objective Reinforcement Learning
Chenhao Dang, Jing Ma, Mingjie Liao · Jun 23, 2026 · Citations: 0
Automatic MetricsOn The Pile benchmark, HDS reaches the final validation perplexity of the next best method with 44% fewer training iterations.
- RoPE-Aware Bit Allocation for KV-Cache Quantization
Fengfeng Liang, Yuechen Zhang, Jiaya Jia · Jun 23, 2026 · Citations: 0
Automatic MetricsAbstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.
- GEMS: Geometric Constraints Enable Multi-Semantic Superposition in LLMs
Yu Deng · Jun 18, 2026 · Citations: 0
Automatic MetricsAbstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.
- SIGMA: Search-Augmented On-Demand Knowledge Integration for Agentic Mathematical Reasoning
Ali Asgarov, Umid Suleymanov, Aadyant Khatri · Oct 31, 2025 · Citations: 0
Automatic MetricsWe introduce SIGMA (Search-Augmented On-Demand Knowledge Integration for AGentic Mathematical reAsoning), a unified framework that orchestrates specialized agents to independently reason, perform targeted searches, and synthesize findings…
- Where Does Social Reasoning Come From? Capability Provenance in Language Models
Glenn Matlin, Chandreyi Chakraborty, Saehee Eom, Mika Okamoto, Rayan Castilla · Jun 17, 2026 · Citations: 0
Training-data attribution measures how strongly each training document influences a model's predictions on a benchmark, but document-level scores are too noisy to identify which corpus regions support which capabilities, and prior work has…
- Resolution Diagnostics for Paired LLM Evaluation
Anany Kotawala · May 28, 2026 · Citations: 0
Pairwise PreferenceAcross two public LLM leaderboards, many displayed pairwise rankings do not meet a conventional paired-test resolution target under the actual paired evaluation design: 11 of 40 Open LLM Leaderboard v1 pairwise comparisons and 4 of 9…
- Reasoning Theater: Disentangling Model Beliefs from Chain-of-Thought
Siddharth Boppana, Annabel Ma, Max Loeffler, Raphael Sarfati, Eric Bigelow · Mar 5, 2026 · Citations: 0
Automatic MetricsAbstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.
- Latent Performance Profiling of Large Language Models
Tanmoy Chakraborty, Ayan Sengupta, Suparna Bhattacharya, Partha Pratim Chakrabarti, Amlan Chakrabarti · May 28, 2026 · Citations: 0
Automatic MetricsLarge language models (LLMs) frequently achieve impressive scores on standardized benchmarks, yet accuracy alone offers a limited view of their capabilities.
- The Importance of Being Statistically Earnest: A Critical Re-evaluation of GSM-Symbolic
Dominika Agnieszka Długosz, Arlindo Oliveira, Natalia Díaz-Rodríguez · May 27, 2026 · Citations: 0
The GSM-Symbolic benchmark (Mirzadeh et al., 2025) reported consistent performance drops across 25 Large Language Models (LLMs) when tested on template-generated variants of GSM8K problems, concluding that the models lack genuine reasoning…
- Modeling Hierarchical Thinking in Large Reasoning Models
G M Shahariar, Erfan Shayegani, Ali Nazari, Nael Abu-Ghazaleh · Oct 25, 2025 · Citations: 0
Experiments across four benchmarks (AIME25, MATH-500, GSM8K, and GPQA Diamond) using three state-of-the-art open reasoning models demonstrate that Q-Value steering policy achieves significant performance gains with "surgical" efficiency,…
- Accelerated Test-Time Scaling with Model-Free Speculative Sampling
Woomin Song, Saket Dingliwal, Sai Muralidhar Jayanthi, Bhavana Ganesh, Jinwoo Shin · Jun 5, 2025 · Citations: 0
Automatic MetricsExtensive evaluations across multiple models and reasoning tasks (AIME-2024, GPQA-Diamond, and LiveCodeBench) demonstrate that STAND reduces inference latency by 60-65% compared to standard autoregressive decoding while maintaining…
- InnerQ: Hardware-Aware Tuning-Free Quantization of KV Cache for Large Language Models
Sayed Mohammadreza Tayaranian Hosseini, Amir Ardakani, Warren J. Gross · Feb 26, 2026 · Citations: 0
Automatic MetricsOur evaluation experiments on Llama models shows that InnerQ maintains a few-shot GSM8K performance comparable to non-quantized KV caches and surpasses prior KV cache quantization methods.
- Benchmarking EngGPT2-16B-A3B against Comparable Italian and International Open-source LLMs
Andrea Sassella, Andrea Chizzola, Tommaso Bianchi, Luca Alessandrelli, Mark James Carman · May 8, 2026 · Citations: 0
This report benchmarks the performance of ENGINEERING Ingegneria Informatica S.p.A.'s EngGPT2MoE-16B-A3B LLM, a 16B parameter Mixture of Experts (MoE) model with 3B active parameters.
- Enhanced LLM Reasoning by Optimizing Reward Functions with Search-Driven Reinforcement Learning
Arash Ahmadi, Sarah Sharif, Yaser, Banad · May 3, 2026 · Citations: 0
Pairwise Preference Automatic MetricsMathematical reasoning is a key benchmark for large language models.
- Tracing Uncertainty in Language Model "Reasoning"
Nils Grünefeld, Bertram Højer, Philipp Mondorf, Barbara Plank, Anna Rogers · May 8, 2026 · Citations: 0
Automatic MetricsLanguage model (LM) "reasoning", commonly described as Chain-of-Thought or test-time scaling, often improves benchmark performance, but the dynamics underlying this process remain poorly understood.
- SOD: Step-wise On-policy Distillation for Small Language Model Agents
Qiyong Zhong, Mao Zheng, Mingyang Song, Xin Lin, Jie Sun · May 8, 2026 · Citations: 0
To address this, we propose SOD, a step-wise on-policy distillation framework for small language model agents, which adaptively reweights distillation strength at each step based on step-level divergence.
- Mathematical Reasoning via Intervention-Based Time-Series Causal Discovery Using LLMs as Concept Mastery Simulators
Tsuyoshi Okita · May 8, 2026 · Citations: 0
Simulation EnvWith a 7B-parameter LLM whose weights are entirely frozen, CIKA achieves 69.7\% on the contamination-free Omni-MATH-Rule benchmark and 64.0\% overall, compared to 60.5\% for o1-mini, and 97.2\% on GSM8K, 46--50\% on AIME 2024--2026, and…
- Teaching Thinking Models to Reason with Tools: A Full-Pipeline Recipe for Tool-Integrated Reasoning
Qianjia Cheng, Yuchen Zhang, Zhilin Wang, Yuxin Zuo, Shunkai Zhang · May 7, 2026 · Citations: 0
Automatic MetricsParadoxically, we observe that tool-enabled evaluation can degrade reasoning performance even when the strong thinking models make almost no actual tool calls.
- Hidden Measurement Error in LLM Pipelines Distorts Annotation, Evaluation, and Benchmarking
Solomon Messing · Apr 13, 2026 · Citations: 0
Demonstrations Llm As JudgeLLM evaluations drive which models get deployed, what safety standards get adopted, which research conclusions get published, and how projections of AI's labor-market impact get made.
- FIT to Forget: Robust Continual Unlearning for Large Language Models
Xiaoyu Xu, Minxin Du, Kun Fang, Yaxin Xiao, Zhicong Huang · Jan 29, 2026 · Citations: 0
Furthermore, to facilitate rigorous evaluation, we introduce PCH, a unified benchmark encompassing Personal, Copyrighted, and Harmful content, alongside two symmetric metrics, Forget Degree (F.D.) and Retain Utility (R.U.), to…
- Cross-Tokenizer Likelihood Scoring Algorithms for Language Model Distillation
Buu Phan, Ashish Khisti, Karen Ullrich · Dec 16, 2025 · Citations: 0
Automatic MetricsOur method enables sequence likelihood evaluation for vocabularies different from the teacher model native tokenizer, addressing two specific scenarios: when the student vocabulary is a subset of the teacher vocabulary, and the general case…
- Beyond Majority Voting: Towards Fine-grained and More Reliable Reward Signal for Test-Time Reinforcement Learning
Weiqin Wang, Yile Wang, Kehao Chen, Hui Huang · Dec 17, 2025 · Citations: 0
We conduct experiments across various models and benchmarks, experimental results show that SCOPE consistently outperforms recent baselines.
- Fragile Knowledge, Robust Instruction-Following: The Width Pruning Dichotomy in Llama-3.2
Pere Martra · Dec 27, 2025 · Citations: 0
Automatic MetricsWe evaluated seven expansion ratio configurations using comprehensive benchmarks assessing factual knowledge, mathematical reasoning, language comprehension, instruction-following, and truthfulness.
- UR$^2$: Unify RAG and Reasoning through Reinforcement Learning
Weitao Li, Boran Xiang, Xiaolong Wang, Zhinan Gou, Weizhi Ma · Aug 8, 2025 · Citations: 0
Experiments on open-domain QA, MMLU-Pro, medical, and mathematical reasoning tasks show that UR^2, built on Qwen-2.5-3/7B and LLaMA-3.1-8B, consistently outperforms existing RAG and RL baselines, and achieves performance comparable to…
- Toward Automated Robustness Evaluation of Mathematical Reasoning
Yutao Hou, Zeguan Xiao, Fei Yu, Yihan Jiang, Ma Shuguang · Jun 5, 2025 · Citations: 0
Existing robustness evaluations predominantly rely on hand-crafted templates or a limited set of perturbation rules.
- When Does LLM Self-Correction Help? A Control-Theoretic Markov Diagnostic and Verify-First Intervention
Aofan Liu, Jingxiang Meng · Apr 24, 2026 · Citations: 0
Iterative self-correction is widely used in agentic LLM systems, but when repeated refinement helps versus hurts remains unclear.
- Cost-Effective Communication: An Auction-based Method for Language Agent Interaction
Yijia Fan, Jusheng Zhang, Kaitong Cai, Jing Yang, Chengpei Tang · Nov 17, 2025 · Citations: 0
Automatic MetricsTo address this, we introduce the Dynamic Auction-based Language Agent (DALA), a novel framework that treats communication bandwidth as a scarce and tradable resource.
- Where Should LoRA Go? Component-Type Placement in Hybrid Language Models
Hector Borobia, Elies Seguí-Mas, Guillermina Tormo-Carbó · Apr 24, 2026 · Citations: 0
We systematically study component-type LoRA placement across two hybrid architectures -- Qwen3.5-0.8B (sequential, GatedDeltaNet + softmax attention) and Falcon-H1-0.5B (parallel, Mamba-2 SSM + attention) -- fine-tuned on three domains and…
- Multi-Token Prediction via Self-Distillation
John Kirchenbauer, Abhimanyu Hans, Brian Bartoldson, Micah Goldblum, Ashwinee Panda · Feb 5, 2026 · Citations: 0
Automatic MetricsAbstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.
- DeepPrune: Parallel Scaling without Inter-trace Redundancy
Shangqing Tu, Yaxuan Li, Yushi Bai, Lei Hou, Juanzi Li · Oct 9, 2025 · Citations: 0
Llm As JudgeAutomatic MetricsOur method features a specialized judge model trained with out-of-distribution data (AIME 2022, AIME 2023, and MATH 500) using oversampling techniques to accurately predict answer equivalence from partial reasoning traces, achieving 0.7072…
- Towards Hierarchical Multi-Step Reward Models for Enhanced Reasoning in Large Language Models
Teng Wang, Zhangyi Jiang, Zhenqi He, Shenyang Tong, Wenhan Yang · Mar 16, 2025 · Citations: 0
Automatic MetricsEmpirical results on the PRM800K dataset show that HRM, together with HNC, provides more stable and reliable evaluations than PRM.
- Don't Overthink It: Inter-Rollout Action Agreement as a Free Adaptive-Compute Signal for LLM Agents
Khushal Sethi · Apr 9, 2026 · Citations: 0
Automatic MetricsWe introduce TrACE (Trajectorical Adaptive Compute via agrEement), a training-free controller that allocates LLM calls adaptively across agent timesteps by measuring inter-rollout action agreement.
- Diffusion Language Models Know the Answer Before Decoding
Pengxiang Li, Yefan Zhou, Dilxat Muhtar, Lu Yin, Shilin Yan · Aug 27, 2025 · Citations: 0
Automatic MetricsEmpirical evaluations of LLaDA-8B and Dream-7B across multiple tasks show that Prophet reduces the number of decoding steps by up to 3.4x while preserving high generation quality.
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