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Total papers: 9 Search mode: keyword Shortlist (0) RSS

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Match reason: Matches selected tags (Math, Critique Edit).

Score: 58% Moderate protocol signal Freshness: Warm Status: Ready
Critique Edit Long Horizon Math
  • 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.
  • To address this limitation, we introduce Retrieval-Augmented Self-Supervised Prompt Refinement (RASPRef), a framework that improves prompts without requiring human annotations or task-specific supervision.
Open paper
PAVE: Premise-Aware Validation and Editing for Retrieval-Augmented LLMs

Tianyi Huang, Caden Yang, Emily Yin, Eric Wang, Michael Zhang · Mar 21, 2026

Citations: 0

Match reason: Matches selected tags (Math, Critique Edit).

Score: 58% High protocol signal Freshness: Warm Status: Ready
Critique Edit Automatic Metrics Math
  • In controlled ablations with a fixed retriever and backbone, PAVE outperforms simpler post-retrieval baselines in two evidence-grounded QA settings, with the largest gain reaching 32.7 accuracy points on a span-grounded benchmark.
Open paper
Adaptive Robust Estimator for Multi-Agent Reinforcement Learning

Zhongyi Li, Wan Tian, Jingyu Chen, Kangyao Huang, Huiming Zhang, Hui Yang · Mar 23, 2026

Citations: 0

Match reason: Matches selected tags (Math, Critique Edit).

Score: 55% Moderate protocol signal Freshness: Warm Status: Ready
Critique Edit Multi Agent Math
  • Multi-agent collaboration has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language models, yet it suffers from interaction-level ambiguity that blurs generation, critique, and revision, making credit…
  • To address both issues, we propose a robust multi-agent reinforcement learning framework for collaborative reasoning, consisting of two components: Dual-Agent Answer-Critique-Rewrite (DACR) and an Adaptive Robust Estimator (ARE).
Open paper
Unlocking Reasoning Capability on Machine Translation in Large Language Models

Sara Rajaee, Sebastian Vincent, Alexandre Berard, Marzieh Fadaee, Kelly Marchisio, Tom Kocmi · Feb 16, 2026

Citations: 0

Match reason: Matches selected tags (Math, Critique Edit).

Score: 55% Moderate protocol signal Freshness: Warm Status: Ready
Critique Edit Long Horizon MathCoding
  • We systematically evaluate several open- and closed-weights RLMs on the WMT24++ benchmark and find that enabling explicit reasoning consistently degrades translation quality across languages and models.
Open paper
Citations: 0

Match reason: Matches selected tags (Math, Critique Edit).

Score: 53% High protocol signal Freshness: Cold Status: Ready
Pairwise PreferenceCritique Edit Automatic Metrics Math
  • 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…
  • The protocol is model-agnostic and operates purely through role-structured prompting, requiring no training, access to model internals, or symmetrically strong agents.
Open paper
MAS-ZERO: Designing Multi-Agent Systems with Zero Supervision

Zixuan Ke, Austin Xu, Yifei Ming, Xuan-Phi Nguyen, Ryan Chin, Caiming Xiong · May 21, 2025

Citations: 0

Match reason: Matches selected tags (Math, Critique Edit).

Score: 53% High protocol signal Freshness: Cold Status: Ready
Critique Edit Automatic Metrics Multi Agent MathCoding
  • Multi-agent systems (MAS) leveraging the impressive capabilities of Large Language Models (LLMs) hold significant potential for tackling complex tasks.
  • It achieves substantial average accuracy improvements of up to 16.69% on reasoning, 16.66% on coding, and 5.45% on agentic tasks, while maintaining cost efficiency.
Open paper
SAHOO: Safeguarded Alignment for High-Order Optimization Objectives in Recursive Self-Improvement

Subramanyam Sahoo, Aman Chadha, Vinija Jain, Divya Chaudhary · Mar 6, 2026

Citations: 0

Match reason: Matches selected tags (Math, Critique Edit).

Score: 52% Sparse protocol signal Freshness: Warm Status: Fallback
Critique Edit MathCoding
  • We introduce SAHOO, a practical framework to monitor and control drift through three safeguards: (i) the Goal Drift Index (GDI), a learned multi-signal detector combining semantic, lexical, structural, and distributional measures; (ii)…
Open paper

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