Ofir Ben Shoham, Shrutendra Harsola, Vignesh Subrahmaniam, Shravan Mohan, Yakov Gazman, Oded Vainas · Aug 12, 2026 · Citations: 0
Rubric RatingLlm As JudgeGeneral
Our reward is an LLM-as-a-judge rubric that scores each recommendation across multiple binary dimensions of advice quality, augmented with a safety gate for harm prevention.
Since LLM-based evaluation alone cannot confirm whether improvements reflect genuine business value rather than adaptation to the judge, we complement it with a judge-independent audit based on a standard doubly-robust Conditional Average…
Long-form song generation models continue to improve in duration, structural integrity, and acoustic complexity, making reliable aesthetic rewards increasingly important for aligning these models with human preferences.
On the out-of-domain Music Arena benchmark with 733 preference pairs, it achieves the highest accuracy of 71.35%.
As users increasingly turn to Large Language Models (LLMs) for information and advice on political matters, particularly during election periods, the political preferences expressed by these systems have become a matter of public interest.
We demonstrate the framework through an Italian case study, providing a systematic analysis of LLM-generated political evaluations on italian parties and leaders.
Nimet Beyza Bozdag, Emre Can Acikgoz, Gokhan Tur, Dilek Hakkani-Tür · Aug 12, 2026 · Citations: 0
Automatic MetricsGeneral
As LLMs increasingly debate, advise, and think collaboratively with humans and each other, resistance to harmful persuasion becomes a core requirement for reliable behavior.
We formalize this threat as adversarial persuasion and introduce an adversarial reinforcement learning framework that trains persuader agents to change a target model's answer in a single interaction.
Vu Duc Anh, Nhat M. Hoang, Do Xuan Long, Cong-Duy Nguyen, Ponhvoan Srey, Luu Anh Tuan · Aug 12, 2026 · Citations: 0
Pairwise PreferenceGeneral
The first stage strengthens step-level reasoning via step-level preference optimization, while the second stage explicitly trains models to self-verify and self-correct.
Comprehensive in-domain and out-of-domain evaluations across multiple LLMs demonstrate that SFS-DPO and SFS-DPO-R consistently outperform prior step-level training baselines.
For LLM agents, however, the unit of observation is an interactive trajectory, where the model can ask clarifying questions, call tools, update state, and make intermediate decisions whose errors propagate to the final outcome.
Haokai Zhao, Yunze Xiao, Weihao Xuan, Flora Salim, Benjamin Tag, Aditya Joshi · Aug 12, 2026 · Citations: 0
Pairwise PreferenceGeneral
Group alignment adapts a language model to a demographic group to produce responses that reflect the group's opinions, values, and preferences.
However, existing group alignment methods and evaluations focus only on how closely the model matches the group's opinions, overlooking the induced change in sycophantic behaviour.
On the full GPQA Diamond benchmark (198 graduate-level science questions), majority voting reduces per-problem accuracy on a majority of problems for two instruction-tuned models from different families: 56.6% of problems for Qwen2.5-7B and…
Rima Mittal, Ankit Gubrani, Satyanarayana Kakollu · Aug 11, 2026 · Citations: 0
Pairwise PreferenceGeneral
Through controlled experiments on two GPU families spanning the memory-bound to compute-bound regimes (A10G, ridge \approx 117 FLOP/byte; A100, ridge \approx 183 FLOP/byte), we demonstrate: (1) the inference-optimal vocabulary shifts 16x…
Natural language user preferences provide an interpretable interface for LLM personalization.
To this end, we propose AlignXada, a training-free meta-learning framework that induces reusable textual refinement policies for adapting universal preference summaries to task-specific ones.
Alicia Guerra, Yibo Hu · Aug 5, 2026 · Citations: 0
Critique EditGeneral
Open-ended revisions require a different measurement strategy because answer quality is graded, latent, and judged imperfectly.
We introduce an experimental protocol implemented across a pooled main peer-condition corpus and separately constructed decomposition corpora, allowing us to separate ordinary re-answering, candidate-content exposure, a bundled…
Zhilin Wang, Han Song, Runzhe Zhan, Jusen Du, Jiacheng Chen, Tianle Li · Jul 2, 2026 · Citations: 0
Simulation EnvGeneral
Autonomous agents are increasingly expected to improve executable policies through feedback, yet existing evaluations often collapse this process into a final score or confound it with open-ended software-engineering progress.
We introduce Autonomous Policy Evolution, a controlled evaluation setting in which a harness-model agent repeatedly edits an executable policy system under a fixed interaction budget.
Manuel Alonso-Carracedo, Ruben Fernandez-Boullon, Pedro Celard, Francisco J. Rodriguez-Martinez, Lorena Otero-Cerdeira · Jul 2, 2026 · Citations: 0
Rubric RatingAutomatic MetricsGeneral
Gemini~3.0 Pro with rubric-guided prompting achieved the highest human-AI agreement (ICC(3,1) = 0.888, MAE = 0.10, Bland-Altman bias = -0.014).
These results show that question complexity is a reliable predictor of the difficulty LLMs face in grading accurately, and they establish a principled, taxonomy-based framework for determining which questions are suitable for AI-assisted…
Memory for a long-horizon LLM agent is a contract about what each future decision is allowed to see.
A public online benchmark of frontier LLMs on the same game reports zero wins at the lowest difficulty across five configurations, and the developer-reported human win rate at the same difficulty is 16%; the task is hard but not saturated.
Crucially, this robustness does not transfer: it attenuates across domains and, in the safety-critical vaccine domain, can reverse, with myth-rebuttal weakening under skeptical pressure.
We synthesize these into a four-way taxonomy separating active from accidental robustness, and argue that behavioral evaluation alone cannot distinguish a model that resists skepticism because it understands the signal from one that only…
Javier Irigoyen, Roberto Daza, Francisco Jurado, Julian Fierrez, Ruben Tolosana, Alvaro Ortigosa · Jul 2, 2026 · Citations: 0
Rubric RatingAutomatic MetricsGeneral
For each question, the dataset includes an explanation written by a human teacher alongside 11 explanations generated by LLM-simulated teacher profiles associated with distinct pedagogical risks.
We introduce SkillCoach, a self-evolving rubric framework for evaluating and enhancing agentic skill-use.
Experiments show that evolved rubrics substantially improve evaluation quality, expose failures hidden by final accuracy, and provide stronger supervision signals than outcome-only filtering for enhancing agentic skill-use.
Using the PLUME writing personalization benchmark, we extract steering vectors for a range of preferences and evaluate them on summarization and email-writing tasks across two open-source models (Qwen2.5-7B-Instruct and…
Taken together, our results suggest that steering vectors face meaningful limits as a general-purpose tool for preference alignment.
