AIME or HotpotQA or MMLU Benchmark Papers

Researcher Quick Triage

Use this page for benchmark-matched method comparisons and eval protocol selection. Quality band: High .

• 60 papers explicitly name benchmark datasets in the sampled set.
• 44 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.

Start Here (Benchmark-Matched First 6)

Ranked by protocol completeness so you can quickly find papers suitable for comparison studies.

• Evaluating and Benchmarking the System One Model Jev

  Sep 29, 2026 · Citations: 0 · Score: 8.5

  Eval: Automatic Metrics · Metrics: Accuracy

• Authority Bias in Language Models: Source Deference and User Agreement Are Not Interchangeable

  Sep 29, 2026 · Citations: 0 · Score: 8.5

  Eval: Automatic Metrics · Metrics: Accuracy

• Decomposing Wrong-Consensus Agreement in LLM Self-Consistency

  Aug 19, 2026 · Citations: 0 · Score: 8.0

  Eval: Automatic Metrics · Metrics: Accuracy

• Memory Augmentation Unlocks Efficient Chain-of-Thought Reasoning

  Aug 21, 2026 · Citations: 0 · Score: 8.0

  Eval: Automatic Metrics · Metrics: Accuracy

• CAR: Query-Guided Confidence-Aware Reranking for Retrieval-Augmented Generation

  May 6, 2026 · Citations: 0 · Score: 7.5

  Eval: Automatic Metrics · Metrics: F1

• Will Scaling Improve Social Simulation with LLMs?

  Jul 2, 2026 · Citations: 0 · Score: 7.5

  Eval: Automatic Metrics, Simulation Env · Metrics: Accuracy

Protocol Matrix (Top 10)

Compare protocol ingredients quickly before deep-reading full papers.

Top Papers On This Benchmark

• Not All Flips Are Conformity: Decomposing Stance Convergence in Multi-Agent LLM Debate

  Xiqi Hao, Zengqing Wu, Yu-Xuan Qiu, Chuan Xiao, Ruiqi Xu · May 30, 2026 · Citations: 0

  Llm As JudgeAutomatic Metrics

  Multi-agent debate (MAD) among large language models (LLMs) is often judged by answer changes and convergence, yet these outcomes alone cannot establish that peers improved the answer.

• What RL Brings to Reasoning: A Few Decisions the Base Model Would Not Make

  Siyu Zhang, Yuquan Lu, Yoshua Y. Li, Yifan Ding, Kun Zeng · Oct 17, 2026 · Citations: 0

  Automatic Metrics

  Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

• EdgeRock: Strong Reasoning at Constant Memory

  Huishan Ji, Weiming Zhang, Qirui Ye, Jinchao Xu, Yike Guo · Oct 7, 2026 · Citations: 0

  Automatic Metrics

  Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

• When Reasoning Traces Conflict with Evidence: How Answer Configuration Shapes Evidence Following

  Shaoang Li, Yanhang Shi, Jian Li · Oct 14, 2026 · Citations: 0

  Automatic Metrics

  Because trace deletion alone cannot reveal which source an answer follows, evaluations should report both properties together with the answer configuration.

• UNREAL: Unifying Retrieval and Long-Context with a Single Model

  Edan Kinderman, Elad Hoffer, Yochai Blau, Brian Chmiel, Ron Banner · Oct 6, 2026 · Citations: 0

  Automatic Metrics

  Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

• Penalty-Framed No-Valid-Option MCQA: Analyzing LLM Abstention under Invalid Choices

  Jinhyeok Kim, Hye-Young Jung · Oct 6, 2026 · Citations: 0

  Automatic Metrics

  Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

• PiERN: Token-Level Routing for Integrating High-Precision Computation and Reasoning

  Jingyuan Fan, Purui Liu, Hengbo Xiao, Yuxuan Zheng, Jingzhao Zhang · Sep 17, 2025 · Citations: 0

  Automatic Metrics

  Results show that PiERN achieves not only higher accuracy than directly finetuning LLMs but also significant improvements in response latency, token usage, GPU energy consumption, and experts routing accuracy compared with mainstream…

• Stabilizing Off-Policy Training for Long-Horizon LLM Agent via Turn-Level Importance Sampling and Clipping-Triggered Normalization

  Chenliang Li, Adel Elmahdy, Alex Boyd, Zhongruo Wang, Siliang Zeng · Nov 25, 2025 · Citations: 0

  To address these challenges, we propose SORL, Stabilizing Off-Policy Reinforcement Learning for Long-Horizon Agent Train- ing.

• Even Small Reasoners Should Quote Their Sources: Introducing the Pleias-RAG Model Family

  Pierre-Carl Langlais, Pavel Chizhov, Mattia Nee, Carlos Rosas-Hinostroza, Matthieu Delsart · Apr 25, 2025 · Citations: 0

  Pleias-RAG-350m and Pleias-RAG-1B outperform SLMs below 4 billion parameters on standardized RAG benchmarks (HotpotQA, 2wiki) and are competitive with popular larger models, including Qwen-2.5-7B, Llama-3.1-8B, and Gemma-3-4B.

• Test-Time Adaptation of Reasoning Strategies with Bayesian Nonparametric Memory

  Keshav Ramji, Tahira Naseem, Ramón Fernandez Astudillo · Oct 5, 2026 · Citations: 0

  Automatic Metrics

  We demonstrate that Bayesian Cheatsheet achieves clear performance gains relative to existing memory modules across reasoning benchmarks such as AIME'25, Omni-MATH, and PhysReason, even in the cold-start setting.

• Gaokerena: A Small Persian Medical Language Model Family

  Mehrdad Ghassabi, Hamidreza Baradaran Kashani, Pedram Rostami, Sadra Hakim, Zahra Kazemi · Aug 2, 2026 · Citations: 0

  Pairwise PreferenceRlaif Or Synthetic Feedback

  This training improved performance on a translated medical MMLU benchmark from 46.64% to 49.31%.

• Time-Anchored Diffusion Language Models: Latent-Space Caching for Fast Generation

  Joel Anto Paul, Litu Rout, Aditya Akella, Sanjay Shakkottai · Sep 29, 2026 · Citations: 0

  Automatic Metrics

  Applied to DiffusionGemma-26B, TADM:Post-train improves throughput by approximately 49% to 79% on several math, code, and STEM benchmarks (GSM8K, AIME26, GPQA-Diamond, LiveCodeBench-v6, HumanEval, MMLU-Pro).

• Overcoming Scaling Limits in On-Policy Self-Distillation for LLM Reasoning

  Md. Ismail Hossain, Humaira Kousar, Isidora Chara Tourni · Sep 29, 2026 · Citations: 0

  Automatic Metrics

  We separate these roles in a factorial analysis and find that scaffold correctness has a stronger effect on downstream accuracy than context correctness.

• Evaluating and Benchmarking the System One Model Jev

  Tobias Deußer, Lorenz Sparrenberg, Rafet Sifa · Sep 29, 2026 · Citations: 0

  Rubric Rating Automatic Metrics

  We evaluate Jev (jev-1.13.0) zero-shot on 37 datasets spanning classification, routing, natural language inference, reading comprehension, commonsense reasoning, moderation, legal clause analysis and rubric scoring, with one frozen template…

• Authority Bias in Language Models: Source Deference and User Agreement Are Not Interchangeable

  Abhinav Rajeev Kumar, Paras Chopra · Sep 29, 2026 · Citations: 0

  Pairwise Preference Automatic Metrics

  In three open-weight families, removing a fitted source direction lowers source compliance by 65-80 percentage points while removing a user or assistant direction has far smaller effects, and removing the user direction shows the reverse…

• 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 Metrics

  The 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 Preference

  To provide a scalable diagnostic approach, we propose a two-component probabilistic framework for auditing MCQA benchmarks using model output distributions.

• ShardMemo: Scope-Before-Routing for Agentic Memory Retrieval

  Yang Zhao, Chengxiao Dai, Mengying Kou, Yue Xiu, Dusit Niyato · Jan 29, 2026 · Citations: 0

  Automatic Metrics

  We present SHARD-MEMO, an agentic memory system built on scope-before-routing: metadata predicates first identify the admissible shards, and a learned router then selects a small number of them for shard-local approximate nearest neighbor…

• 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 Metrics

  Kascade 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…

• 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 Metrics

  Based 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 Metrics

  Abstract 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 Metrics

  Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

• From Retrieved Context to Runtime Control: Adaptive Compression for Edge-based RAG

  Zlatan Feric, Amir Taherin, Yanzhi Wang, David Kaeli · Aug 20, 2026 · Citations: 0

  Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

• When Should Multi-Round RAG Stop? Structured Stopping Judgments and Retrieval Reduction in Search-R1

  Weimeng Luo · Aug 13, 2026 · Citations: 0

  Automatic Metrics

  We adapt S2G-RAG's structured sufficiency-and-gap judgment to a frozen Search-R1 pipeline and train a Qwen3.5-2B judge on 3,009 states from 900 disjoint HotpotQA questions.

• EviReform: Evidence-Guided Query Reformulation for Multi-Hop Graph Retrieval

  Xinlong Xu, Yoshua Y. Li · Aug 13, 2026 · Citations: 0

  Automatic Metrics

  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.

• CAR: Query-Guided Confidence-Aware Reranking for Retrieval-Augmented Generation

  Zhipeng Song, Yizhi Zhou, Xiangyu Kong, Jiulong Jiao, Xuezhou Ye · May 6, 2026 · Citations: 0

  Pairwise Preference Automatic Metrics

  CAR converts these confidence changes into coarse precedence constraints and returns the feasible ranking with minimum Kendall distance from the baseline, preserving existing pairwise preferences unless generator-side evidence supports…

• SAG: SQL-Retrieval Augmented Generation with Query-Time Dynamic Hyperedges

  Yuchao Wu, Junqin Li, XingCheng Liang, Yongjie Chen, Yinghao Liang · Aug 12, 2026 · Citations: 0

  Automatic Metrics

  Experiments on HotpotQA, 2WikiMultiHopQA, and MuSiQue show that SAG achieves the best retrieval and end-to-end QA performance on every benchmark, with gains that widen as reasoning-chain complexity increases.

• Optimize Cheap, Deploy Strong: Cost-Aware Cross-Tier Transfer for Evolutionary Optimization

  Tal Oved, Roi Pony, Oshri Naparstek, Udi barzelay · Aug 11, 2026 · Citations: 0

  Automatic Metrics

  Evolutionary optimization of LLM prompts and agentic programs (e.g., GEPA) is dominated by fitness evaluation: scoring each candidate runs an answering LLM over a validation set, so the evaluator's price tier dictates total search cost.

• 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 Metrics

  A 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 Metrics

  Across 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 Env

  We 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 Metrics

  Across 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.

• What Survives Into Context: A Diagnostic for Budget-Constrained Multi-Hop RAG and When Submodular Evidence Packing Improves It

  Ananto Nayan Bala · Jul 1, 2026 · Citations: 0

  Automatic Metrics

  Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

• On Compositional Learning Behaviours in Formal Mathematics

  Kevin Yandoka Denamganaï · May 27, 2026 · Citations: 0

  Automatic Metrics

  Self-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 Metrics

  Inference-time safety methods for large language models have proliferated, yet no systematic comparison exists.

• 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.

• The Efficiency Frontier: A Unified Framework for Cost-Performance Optimization in LLM Context Management

  Binqi Shen, Lier Jin, Hanyu Cai, Lan Hu, Yuting Xin · May 21, 2026 · Citations: 0

  Automatic Metrics

  Unlike existing evaluations that compare methods in isolation, the proposed framework enables decision-oriented analysis.

• 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 Metrics

  Across 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.

• Holistic Data Scheduler for LLM Pre-training via Multi-Objective Reinforcement Learning

  Chenhao Dang, Jing Ma, Mingjie Liao · Jun 23, 2026 · Citations: 0

  Automatic Metrics

  On 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 Metrics

  Abstract 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 Metrics

  We 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 Preference

  Across 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 Metrics

  Abstract 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 Metrics

  Large language models (LLMs) frequently achieve impressive scores on standardized benchmarks, yet accuracy alone offers a limited view of their capabilities.

• 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 Metrics

  Extensive 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…

• 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.

• Answer Only as Precisely as Justified: Calibrated Claim-Level Specificity Control for Agentic Systems

  Tianyi Huang, Samuel Xu, Jason Tansong Dang, Samuel Yan, Kimberley Yin · Apr 19, 2026 · Citations: 0

  Agentic systems often fail not by being entirely wrong, but by being too precise: a response may be generally useful while particular claims exceed what the evidence supports.

• 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 Env

  With 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…

• Beyond Factual Accuracy: Evaluating Global Reasoning Integrity in RAG Systems with LogicScore

  Zhichao Yan, Yunxiao Zhao, Jiapu Wang, Jiaoyan Chen, Xiaoli Li · Jan 21, 2026 · Citations: 0

  Automatic Metrics

  Current evaluation methods for Retrieval Augmented Generation (RAG) suffer from factual myopia: they relentlessly emphasize factual accuracy yet neglect global logical integrity in long-form answer generation.

• 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 Metrics

  Paradoxically, 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 Judge

  LLM 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…

• 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.

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