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Human Feedback and Eval Paper Explorer

A focused feed for RLHF, preference data, rater protocols, agent evaluation, and LLM-as-judge research. Every paper includes structured metadata for quick triage.

Total papers: 106 Search mode: keyword RSS

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LFQA-HP-1M: A Large-Scale Human Preference Dataset for Long-Form Question Answering

Rafid Ishrak Jahan, Fahmid Shahriar Iqbal, Sagnik Ray Choudhury · Feb 27, 2026

Citations: 0
Pairwise PreferenceRubric Rating General
  • We present LFQA-HP-1M, a large-scale dataset comprising 1.3M human pairwise preference annotations for LFQA.
  • We propose nine rubrics for answer quality evaluation, and show that simple linear models based on these features perform comparably to state-of-the-art LLM evaluators.
Toward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks

Kunihiro Miyazaki, Takanobu Kawahara, Stephen Roberts, Stefan Zohren · Feb 26, 2026

Citations: 0
Pairwise Preference Multi Agent General
  • While mainstream approaches deploy multi-agent systems mimicking analyst and manager roles, they often rely on abstract instructions that overlook the intricacies of real-world workflows, which can lead to degraded inference performance and…
  • Therefore, we propose a multi-agent LLM trading framework that explicitly decomposes investment analysis into fine-grained tasks, rather than providing coarse-grained instructions.
AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning

Yutong Wang, Siyuan Xiong, Xuebo Liu, Wenkang Zhou, Liang Ding, Miao Zhang · Feb 26, 2026

Citations: 0
Automatic Metrics Multi Agent MathCoding
  • We propose AgentDropoutV2, a test-time rectify-or-reject pruning framework designed to dynamically optimize MAS information flow without retraining.
  • Empirical results on extensive math benchmarks show that AgentDropoutV2 significantly boosts the MAS's task performance, achieving an average accuracy gain of 6.3 percentage points on math benchmarks.
Citations: 0
Pairwise Preference General
  • Inspired by Humphrey's ipsundrum hypothesis, we implement ReCoN-Ipsundrum, an inspectable agent that extends a ReCoN state machine with a recurrent persistence loop over sensory salience Ns and an optional affect proxy reporting…
  • Across fixed-parameter ablations (ReCoN, Ipsundrum, Ipsundrum+affect), we operationalize Humphrey's qualiaphilia (preference for sensory experience for its own sake) as a familiarity-controlled scenic-over-dull route choice.
Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching

Roy Miles, Aysim Toker, Andreea-Maria Oncescu, Songcen Xu, Jiankang Deng, Ismail Elezi · Feb 26, 2026

Citations: 0
Automatic Metrics Long Horizon MathCoding
  • This modular pipeline separates exploration (diffusion) from evaluation and solution synthesis, avoiding monolithic unified hybrids while preserving broad search.
  • Across math reasoning benchmarks, we find that step-level recombination is most beneficial on harder problems, and ablations highlight the importance of the final AR solver in converting stitched but imperfect rationales into accurate…
Decentralized Ranking Aggregation: Gossip Algorithms for Borda and Copeland Consensus

Anna Van Elst, Kerrian Le Caillec, Igor Colin, Stephan Clémençon · Feb 26, 2026

Citations: 0
Pairwise Preference Multi Agent General
  • The concept of ranking aggregation plays a central role in preference analysis, and numerous algorithms for calculating median rankings, often originating in social choice theory, have been documented in the literature, offering theoretical…
  • peer-to-peer networks, IoT, multi-agent systems), extending the ability to calculate consensus rankings with guarantees in a decentralized setting, i.e., when preference data is initially distributed across a communicating network, remains…
Moral Preferences of LLMs Under Directed Contextual Influence

Phil Blandfort, Tushar Karayil, Urja Pawar, Robert Graham, Alex McKenzie, Dmitrii Krasheninnikov · Feb 26, 2026

Citations: 0
Pairwise Preference General
  • Moral benchmarks for LLMs typically use context-free prompts, implicitly assuming stable preferences.
  • We introduce a pilot evaluation harness for directed contextual influence in trolley-problem-style moral triage: for each demographic factor, we apply matched, direction-flipped contextual influences that differ only in which group they…
RLHFless: Serverless Computing for Efficient RLHF

Rui Wei, Hanfei Yu, Shubham Jain, Yogarajan Sivakumar, Devesh Tiwari, Jian Li · Feb 26, 2026

Citations: 0
Pairwise Preference Automatic Metrics General
  • Reinforcement Learning from Human Feedback (RLHF) has been widely applied to Large Language Model (LLM) post-training to align model outputs with human preferences.
Same Words, Different Judgments: Modality Effects on Preference Alignment

Aaron Broukhim, Nadir Weibel, Eshin Jolly · Feb 26, 2026

Citations: 0
Pairwise PreferenceRlaif Or Synthetic Feedback Automatic Metrics General
  • Preference-based reinforcement learning (PbRL) is the dominant framework for aligning AI systems to human preferences, but its application to speech remains underexplored.
  • We present a controlled cross-modal study of human and synthetic preference annotations, comparing text and audio evaluations of identical semantic content across 100 prompts.
Citations: 0
Pairwise Preference Automatic Metrics General
  • Although initially formulated for human truth-telling under asymmetric stakes, the same phase-dynamic architecture extends to AI systems operating under policy constraints and alignment filters.
  • The framework therefore provides a unified structural account of both human silence under pressure and AI preference-driven distortion.
CCCaption: Dual-Reward Reinforcement Learning for Complete and Correct Image Captioning

Zhijiang Tang, Linhua Wang, Jiaxin Qi, Weihao Jiang, Peng Hou, Anxiang Zeng · Feb 25, 2026

Citations: 0
Pairwise Preference Automatic Metrics General
  • Image captioning remains a fundamental task for vision language understanding, yet ground-truth supervision still relies predominantly on human-annotated references.
  • Because human annotations reflect subjective preferences and expertise, ground-truth captions are often incomplete or even incorrect, which in turn limits caption models.
Duel-Evolve: Reward-Free Test-Time Scaling via LLM Self-Preferences

Sweta Karlekar, Carolina Zheng, Magnus Saebo, Nicolas Beltran-Velez, Shuyang Yu, John Bowlan · Feb 25, 2026

Citations: 0
Pairwise Preference Automatic Metrics Math
  • Building on this observation, we introduce Duel-Evolve, an evolutionary optimization algorithm that replaces external scalar rewards with pairwise preferences elicited from the same LLM used to generate candidates.
  • Results show that pairwise self-preferences provide strong optimization signal for test-time improvement over large, discrete output spaces.
Alignment-Weighted DPO: A principled reasoning approach to improve safety alignment

Mengxuan Hu, Vivek V. Datla, Anoop Kumar, Zihan Guan, Sheng Li, Alfy Samuel · Feb 24, 2026

Citations: 0
Pairwise PreferenceRed Team General
  • Recent advances in alignment techniques such as Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Direct Preference Optimization (DPO) have improved the safety of large language models (LLMs).
  • Furthermore, inspired by failure patterns in CoT fine-tuning, we introduce Alignment-Weighted DPO, which targets the most problematic parts of an output by assigning different preference weights to the reasoning and final-answer segments.
Probing Graph Neural Network Activation Patterns Through Graph Topology

Floriano Tori, Lorenzo Bini, Marco Sorbi, Stéphane Marchand-Maillet, Vincent Ginis · Feb 24, 2026

Citations: 0
Pairwise Preference Automatic Metrics General
  • However, it remains unclear how the topology of a graph interacts with the learned preferences of GNNs.
  • Our findings on synthetic graphs and molecular benchmarks reveal that MAs do not preferentially concentrate on curvature extremes, despite their theoretical link to information flow.
HiSAC: Hierarchical Sparse Activation Compression for Ultra-long Sequence Modeling in Recommenders

Kun Yuan, Junyu Bi, Daixuan Cheng, Changfa Wu, Shuwen Xiao, Binbin Cao · Feb 24, 2026

Citations: 0
Pairwise Preference General
  • Modern recommender systems leverage ultra-long user behavior sequences to capture dynamic preferences, but end-to-end modeling is infeasible in production due to latency and memory constraints.
  • While summarizing history via interest centers offers a practical alternative, existing methods struggle to (1) identify user-specific centers at appropriate granularity and (2) accurately assign behaviors, leading to quantization errors…
Citations: 0
Pairwise PreferenceRlaif Or Synthetic Feedback Human Eval General
  • Preference-based RL offers an appealing alternative by learning from human preferences over pairs of behavioural outcomes.
  • More recently, RL from AI feedback (RLAIF) has demonstrated that large language models (LLMs) can generate preference labels at scale, mitigating the reliance on human annotators.

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