HFEPX Benchmark Hub

GSM8K Or MMLU Or AIME Benchmark Papers

Updated from current HFEPX corpus (Jun 30, 2026). 119 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Jun 30, 2026). 119 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics, Llm As Judge. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequent quality control: Calibration. Frequently cited benchmark: GSM8K. 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 Mar 28, 2026.

Papers: 119 Last published: Mar 28, 2026 Global RSS

Researcher Quick Triage

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

Analysis blocks are computed from the loaded sample (60 of 119 papers).

High-Signal Coverage

100.0%

60 / 60 sampled papers are not low-signal flagged.

Replication-Ready Set

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

1.7%

1 papers report calibration/adjudication/IAA controls.

33 papers explicitly name benchmark datasets in the sampled set.
28 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

13.4% of papers report explicit human-feedback signals, led by pairwise preferences.
automatic metrics appears in 22.7% of papers in this hub.
GSM8K is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Notes (Expanded)

Protocol Takeaways

Most common quality-control signal is rater calibration (0.8% of papers).
Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.

Benchmark Interpretation

GSM8K appears in 48.7% of hub papers (58/119); use this cohort for benchmark-matched comparisons.
MMLU appears in 37.8% of hub papers (45/119); use this cohort for benchmark-matched comparisons.

Metric Interpretation

accuracy is reported in 43.7% of hub papers (52/119); compare with a secondary metric before ranking methods.
cost is reported in 20.2% of hub papers (24/119); compare with a secondary metric before ranking methods.

Start Here (Benchmark-Matched First 6)

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

Cliff Tokens: Identifying Single-Token Failure Triggers in LLM Mathematical Reasoning
Jun 24, 2026 · Citations: 0 · Score: 8.0

Eval: Automatic Metrics · Metrics: Accuracy
PubMed Reasoner: Dynamic Reasoning-based Retrieval for Evidence-Grounded Biomedical Question Answering
Mar 28, 2026 · Citations: 0 · Score: 7.5

Eval: Llm As Judge, Automatic Metrics · Metrics: Accuracy
Hidden Measurement Error in LLM Pipelines Distorts Annotation, Evaluation, and Benchmarking
Apr 13, 2026 · Citations: 0 · Score: 7.5

Eval: Llm As Judge · Metrics: Precision
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
$V_1$: Unifying Generation and Self-Verification for Parallel Reasoners
Mar 4, 2026 · Citations: 0 · Score: 7.5

Eval: Automatic Metrics · Metrics: Pass@1
Entropy trajectory shape predicts LLM reasoning reliability: A diagnostic study of uncertainty dynamics in chain-of-thought
Mar 19, 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
Cliff Tokens: Identifying Single-Token Failure Triggers in LLM Mathematical Reasoning Jun 24, 2026	Automatic Metrics	Pairwise Preference	Accuracy, Pass@64	Not reported
PubMed Reasoner: Dynamic Reasoning-based Retrieval for Evidence-Grounded Biomedical Question Answering Mar 28, 2026	Llm As Judge, Automatic Metrics	Expert Verification	Accuracy, Relevance	Not reported
Hidden Measurement Error in LLM Pipelines Distorts Annotation, Evaluation, and Benchmarking Apr 13, 2026	Llm As Judge	Demonstrations	Precision, Agreement	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
$V_1$: Unifying Generation and Self-Verification for Parallel Reasoners Mar 4, 2026	Automatic Metrics	Pairwise Preference	Pass@1	Not reported
Entropy trajectory shape predicts LLM reasoning reliability: A diagnostic study of uncertainty dynamics in chain-of-thought Mar 19, 2026	Automatic Metrics	Not reported	Accuracy, Calibration error	Calibration
Stabilizing Iterative Self-Training with Verified Reasoning via Symbolic Recursive Self-Alignment Mar 23, 2026	Automatic Metrics	Pairwise Preference	Accuracy	Not reported
Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation Oct 5, 2025	Automatic Metrics, Simulation Env	Rubric Rating	Accuracy, Pass@k	Not reported
How Reliable is Language Model Micro-Benchmarking? Oct 9, 2025	Automatic Metrics	Pairwise Preference	Accuracy, Cost	Not reported
GIFT: Group-Relative Implicit Fine-Tuning Integrates GRPO with DPO and UNA Oct 27, 2025	Automatic Metrics	Pairwise Preference	Mse	Not reported

Researcher Workflow (Detailed)

Checklist

Gap: Papers with explicit human feedback

Coverage is a replication risk (13.4% vs 45% target).
Gap: Papers reporting quality controls

Coverage is a replication risk (0.8% vs 30% target).
Strong: Papers naming benchmarks/datasets

Coverage is strong (100% vs 35% target).
Strong: Papers naming evaluation metrics

Coverage is strong (68.9% vs 35% target).
Gap: Papers with known rater population

Coverage is a replication risk (0.8% vs 35% target).
Gap: Papers with known annotation unit

Coverage is a replication risk (13.4% vs 35% target).

Strengths

Most papers provide measurable evaluation context (100% benchmarks, 68.9% metrics).
Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

Only 0.8% of papers report quality controls; prioritize calibration/adjudication evidence.
Rater population is under-specified (0.8% coverage).
Annotation unit is under-specified (13.4% coverage).

Suggested Next Analyses

Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
Stratify by benchmark (GSM8K vs MMLU) before comparing methods.
Track metric sensitivity by reporting both accuracy and cost.
Add inter-annotator agreement checks when reproducing these protocols.

Recommended Queries

Judge vs Human Agreement Benchmark Slice: GSM8K Metric Slice: accuracy Recent High-Signal Papers

Known Limitations

Only 0.8% of papers report quality controls; prioritize calibration/adjudication evidence.
Rater population is under-specified (0.8% coverage).
Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Snapshot (Detailed)

Evaluation Modes

Automatic Metrics (27)
Llm As Judge (3)
Human Eval (1)
Simulation Env (1)

Human Feedback Mix

Pairwise Preference (11)
Critique Edit (3)
Demonstrations (1)
Expert Verification (1)

Top Benchmarks

GSM8K (58)
MMLU (45)
AIME (34)
GPQA (18)

Top Metrics

Accuracy (52)
Cost (24)
Pass@1 (7)
Inference cost (6)

Top Papers On This Benchmark

PubMed Reasoner: Dynamic Reasoning-based Retrieval for Evidence-Grounded Biomedical Question Answering
Yiqing Zhang, Xiaozhong Liu, Fabricio Murai · Mar 28, 2026 · Citations: 0

Expert Verification Llm As JudgeAutomatic Metrics

In this context, we introduce PubMed Reasoner, a biomedical QA agent composed of three stages: self-critic query refinement evaluates MeSH terms for coverage, alignment, and redundancy to enhance PubMed queries based on partial (metadata)…
Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation
Mohsen Hariri, Amirhossein Samandar, Michael Hinczewski, Vipin Chaudhary · Oct 5, 2025 · Citations: 0

Rubric Rating Automatic MetricsSimulation Env

We present a principled Bayesian evaluation framework that replaces Pass@k and average accuracy over N trials (avg@N) with posterior estimates of a model's underlying success probability and credible intervals, yielding stable rankings and…
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.
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 Metrics

Mathematical reasoning is a key benchmark for large language models.
$V_1$: Unifying Generation and Self-Verification for Parallel Reasoners
Harman Singh, Xiuyu Li, Kusha Sareen, Monishwaran Maheswaran, Sijun Tan · Mar 4, 2026 · Citations: 0

Pairwise Preference Automatic Metrics

On code generation (LiveCodeBench, CodeContests, SWE-Bench) and math reasoning (AIME, HMMT) benchmarks, V_1-Infer improves Pass@1 by up to 10% over pointwise verification and outperforms recent test-time scaling methods while being…
How Reliable is Language Model Micro-Benchmarking?
Gregory Yauney, Shahzaib Saqib Warraich, Swabha Swayamdipta · Oct 9, 2025 · Citations: 0

Pairwise Preference Automatic Metrics

We introduce a meta-evaluation measure for micro-benchmarking which investigates how well a micro-benchmark can rank two models as a function of their performance difference on the full benchmark.
Entropy trajectory shape predicts LLM reasoning reliability: A diagnostic study of uncertainty dynamics in chain-of-thought
Xinghao Zhao · Mar 19, 2026 · Citations: 0

Automatic Metrics

Chain-of-thought (CoT) reasoning improves LLM accuracy, yet detecting failures cheaply remains elusive.
RASPRef: Retrieval-Augmented Self-Supervised Prompt Refinement for Large Reasoning Models
Rahul Soni · Mar 27, 2026 · Citations: 0

Critique Edit

Recent reasoning-focused language models such as DeepSeek R1 and OpenAI o1 have demonstrated strong performance on structured reasoning benchmarks including GSM8K, MATH, and multi-hop question answering tasks.
Think$^{2}$: Grounded Metacognitive Reasoning in Large Language Models
Abraham Paul Elenjical, Vivek Hruday Kavuri, Vasudeva Varma · Feb 21, 2026 · Citations: 0

Pairwise Preference Human Eval

We introduce a psychologically grounded metacognitive framework that operationalizes Ann Brown's regulatory cycle (Planning, Monitoring, and Evaluation) as a structured prompting architecture, and study its integration within a lightweight…
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…
Stabilizing Iterative Self-Training with Verified Reasoning via Symbolic Recursive Self-Alignment
Xinyu Zhang · Mar 23, 2026 · Citations: 0

Pairwise Preference Automatic Metrics

We further demonstrate that constructing DPO preference pairs from NSRSA verification teaches the model to distinguish sound from flawed reasoning (reward accuracy 46% to 63%).
GIFT: Group-Relative Implicit Fine-Tuning Integrates GRPO with DPO and UNA
Zhichao Wang · Oct 27, 2025 · Citations: 0

Pairwise Preference Automatic Metrics

This paper proposes Group-relative Implicit Fine-Tuning (GIFT), a reinforcement learning framework for aligning large language models (LLMs) that unifies on-policy optimization with implicit preference learning.
FOR-Prompting: From Objection to Revision via an Asymmetric Prompting Protocol
He Zhang, Anzhou Zhang, Jian Dai · Oct 2, 2025 · Citations: 0

Pairwise PreferenceCritique Edit Automatic Metrics

Beyond structured math tasks, FOR-Prompting supports refinement in open-ended and multi-stage tasks: qualitative analysis shows improved exploration, coverage, and specificity, and a blind study of human preferences found that participants…
Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback
Xiaoying Zhang, Yipeng Zhang, Hao Sun, Kaituo Feng, Chaochao Lu · Jun 3, 2025 · Citations: 0

Critique Edit Automatic Metrics

We show that plateaued RL models can successfully refine failed solutions when given natural language critiques.
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 Metrics

We 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.
S0 Tuning: Zero-Overhead Adaptation of Hybrid Recurrent-Attention Models
Jack Young · Apr 1, 2026 · Citations: 0

Automatic Metrics

Using roughly 48 execution-verified HumanEval training solutions, tuning a single initial state matrix per recurrent layer, with zero inference overhead, outperforms LoRA by +10.8 pp (p < 0.001) on HumanEval.
Top-b: Entropic Regulation of Relative Probability Bands in Autoregressive Language Processes
Deepon Halder, Raj Dabre · Mar 15, 2026 · Citations: 0

Automatic Metrics

Empirical validation on GPQA and GSM8K benchmarks indicates that Top-b significantly reduces generation entropy and inter-decoding variance while maintaining competitive reasoning accuracy, effectively approximating a self-regulating…
Learning When to Sample: Confidence-Aware Self-Consistency for Efficient LLM Chain-of-Thought Reasoning
Juming Xiong, Kevin Guo, Congning Ni, Chao Yan, Katherine Brown · Mar 9, 2026 · Citations: 0

Automatic Metrics

Recent self-consistency-based approaches further improve accuracy but require sampling and aggregating multiple reasoning trajectories, leading to substantial additional computational overhead.
D-COT: Disciplined Chain-of-Thought Learning for Efficient Reasoning in Small Language Models
Shunsuke Ubukata · Feb 25, 2026 · Citations: 0

Automatic Metrics

In this study, we propose Disciplined Chain-of-Thought (D-CoT), a novel framework that enforces a structured reasoning process using control tags -- such as <TEMP_LOW> for fact-checking and <TEMP_HIGH> for multi-perspective exploration --…
The Flexibility Trap: Why Arbitrary Order Limits Reasoning Potential in Diffusion Language Models
Zanlin Ni, Shenzhi Wang, Yang Yue, Tianyu Yu, Weilin Zhao · Jan 21, 2026 · Citations: 0

Automatic Metrics

We demonstrate that effective reasoning can be better elicited by intentionally forgoing arbitrary order and applying standard Group Relative Policy Optimization (GRPO) instead.
Cache What Lasts: Token Retention for Memory-Bounded KV Cache in LLMs
Ngoc Bui, Shubham Sharma, Simran Lamba, Saumitra Mishra, Rex Ying · Dec 3, 2025 · Citations: 0

Automatic Metrics

Across mathematical reasoning (GSM8K, MATH-500, AIME24), procedural generation (LongProc), conversational long-memory benchmarks (LongMemEval), and long-context understanding (LongBenchV2 and SCBench), TRIM-KV consistently outperforms…
SPARE: Single-Pass Annotation with Reference-Guided Evaluation for Automatic Process Supervision and Reward Modelling
Md Imbesat Hassan Rizvi, Xiaodan Zhu, Iryna Gurevych · Jun 18, 2025 · Citations: 0

Automatic Metrics

To address this, we introduce Single-Pass Annotation with Reference-Guided Evaluation (SPARE), a novel structured framework that enables efficient per-step annotation by jointly aligning solution steps to reference solutions and determine…
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…
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…
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 Metrics

Our 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…
Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference
Bo-Wei Chen, Chung-Chi Chen, An-Zi Yen · Feb 25, 2026 · Citations: 0

Automatic Metrics

Experiments on the Massive Multitask Language Understanding (MMLU) benchmark show that our approach achieves accuracy comparable to the largest model while reducing computational costs by 20\% to 40\%.
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 Metrics

Across 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…
Fragile Knowledge, Robust Instruction-Following: The Width Pruning Dichotomy in Llama-3.2
Pere Martra · Dec 27, 2025 · Citations: 0

Automatic Metrics

We evaluated seven expansion ratio configurations using comprehensive benchmarks assessing factual knowledge, mathematical reasoning, language comprehension, instruction-following, and truthfulness.
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 Metrics

To address this, we introduce the Dynamic Auction-based Language Agent (DALA), a novel framework that treats communication bandwidth as a scarce and tradable resource.
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…
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 Metrics

Empirical results on the PRM800K dataset show that HRM, together with HNC, provides more stable and reliable evaluations than PRM.
Inducing Epistemological Humility in Large Language Models: A Targeted SFT Approach to Reducing Hallucination
Cem Uluoglakci, Tugba Taskaya Temizel · Mar 18, 2026 · Citations: 0

Pairwise Preference

We also release HypoTermQA-Enhanced, a benchmark for hallucination tendency strengthened through multiple validations.
Long Grounded Thoughts: Synthesizing Visual Problems and Reasoning Chains at Scale
David Acuna, Chao-Han Huck Yang, Yuntian Deng, Jaehun Jung, Ximing Lu · Nov 7, 2025 · Citations: 0

Pairwise Preference

We introduce a framework able to synthesize vision-centric problems spanning diverse levels of complexity, and the resulting dataset with over 1M high-quality problems including: reasoning traces, preference data, and instruction prompts…
Masked Language Flow Models
Iskander Azangulov, Kianoosh Ashouritaklimi, Leo Zhang, Simon Vary, Patrick Rebeschini · Jun 26, 2026 · Citations: 0
EntMTP: Accelerating LLM Inference with Entropy Guided Multi Token Prediction
Carrie Chen · Jun 25, 2026 · Citations: 0
BrahmicTokenizer-131K: An Indic-Capable Drop-In Replacement for o200k_base
Rohan Shravan · May 28, 2026 · Citations: 0
ReasonOps: Operator Segmentation for LLM Reasoning Traces
Daniel Lee, Owen Queen, James Zou · May 28, 2026 · Citations: 0
ACC: Compiling Agent Trajectories for Long-Context Training
Qisheng Su, Zhen Fang, Shiting Huang, Yu Zeng, Yiming Zhao · May 21, 2026 · Citations: 0
X-Token: Projection-Guided Cross-Tokenizer Knowledge Distillation
Sharath Turuvekere Sreenivas, Adithyakrishna Venkatesh Hanasoge, Mingyu Yang, Ali Taghibakhshi, Saurav Muralidharan · May 20, 2026 · Citations: 0
Prompt Compression in Diffusion Large Language Models: Evaluating LLMLingua-2 on LLaDA
Sterling Huang, Abigayle Brown, Jiyoo Noh, Jiakang Xu, Wantong Huo · May 18, 2026 · Citations: 0
Weak-to-Strong Elicitation via Mismatched Wrong Drafts
Wei Deng · May 17, 2026 · Citations: 0
Roll Out and Roll Back: Diffusion LLMs are Their Own Efficiency Teachers
Fanqin Zeng, Feng Hong, Geng Yu, Huangjie Zheng, Xiaofeng Cao · May 16, 2026 · Citations: 0
Confidence Geometry Reveals Trace-Level Correctness in Large Language Model Reasoning
Shuo Liu, Ding Liu, Shi-Ju Ran · May 16, 2026 · Citations: 0
The Metacognitive Probe: Five Behavioural Calibration Diagnostics for LLMs
Rafael C. T. Oliveira · May 11, 2026 · Citations: 0
Learning Multi-Indicator Weights for Data Selection: A Joint Task-Model Adaptation Framework with Efficient Proxies
Jingze Song, Zihao Chen, Wenqing Chen, Zibin Zheng · May 10, 2026 · Citations: 0
Statistical Scouting Finds Debate-Safe but Not Debate-Useful Cases: A Matched-Ceiling Study of Open-Weight LLM Reasoning Protocols
Julia Hu, Alfred Shen, Kumar Lakshmipathi · May 10, 2026 · Citations: 0
Edit-Based Refinement for Parallel Masked Diffusion Language Models
Houxing Ren, Mingjie Zhan, Zimu Lu, Ke Wang, Yunqiao Yang · May 10, 2026 · Citations: 0
Not All Thoughts Need HBM: Semantics-Aware Memory Hierarchy for LLM Reasoning
Aojie Yuan, Tianqi Shen, Dajun Zhang · May 10, 2026 · Citations: 0
Rubric-Grounded RL: Structured Judge Rewards for Generalizable Reasoning
Manish Bhattarai, Ismael Boureima, Nishath Rajiv Ranasinghe, Scott Pakin, Dan O'Malley · May 8, 2026 · Citations: 0
Prune-OPD: Efficient and Reliable On-Policy Distillation for Long-Horizon Reasoning
Zhicheng Yang, Zhijiang Guo, Yifan Song, Minrui Xu, Yongxin Wang · May 8, 2026 · Citations: 0
Unsolvability Ceiling in Multi-LLM Routing: An Empirical Study of Evaluation Artifacts
Saloni Garg, Amit Sagtani · May 8, 2026 · Citations: 0
Gradient-Based LoRA Rank Allocation Under GRPO: An Empirical Study
Yash Ganpat Sawant · May 8, 2026 · Citations: 0
LaTER: Efficient Test-Time Reasoning via Latent Exploration and Explicit Verification
Xuan Li, Yining Wang, Yuchen Liu, Guanjun Liu, Delai Qiu · May 8, 2026 · Citations: 0
From 0-Order Selection to 2-Order Judgment: Combinatorial Hardening Exposes Compositional Failures in Frontier LLMs
Hanmeng Liu, Shichao Weng, Xiulai Liu, Zhicai Zhang, Anli Yan · May 8, 2026 · Citations: 0
When Are Experts Misrouted? Counterfactual Routing Analysis in Mixture-of-Experts Language Models
Youngsik Yoon, Siwei Wang, Wei Chen, Jungseul Ok · May 8, 2026 · Citations: 0
Beyond Negative Rollouts: Positive-Only Policy Optimization with Implicit Negative Gradients
Mingwei Xu, Hao Fang · May 7, 2026 · Citations: 0
Is Escalation Worth It? A Decision-Theoretic Characterization of LLM Cascades
Dylan Bouchard · May 7, 2026 · Citations: 0
More Is Not Always Better: Cross-Component Interference in LLM Agent Scaffolding
Ming Liu · May 7, 2026 · Citations: 0
Decodable but Not Corrected by Fixed Residual-Stream Linear Steering: Evidence from Medical LLM Failure Regimes
Ming Liu · May 7, 2026 · Citations: 0
ZAYA1-8B Technical Report
Robert Washbourne, Rishi Iyer, Tomas Figliolia, Henry Zheng, Ryan Lorig-Roach · May 6, 2026 · Citations: 0

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