- ReasonScaffold: A Scaffolded Reasoning-based Annotation Protocol for Human-AI Co-Annotation
Smitha Muthya Sudheendra, Jaideep Srivastava · Mar 22, 2026 · Citations: 0
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We evaluate the approach on sentiment classification and opinion detection tasks, analyzing changes in inter-annotator agreement and revision behavior.
- PaperBanana: Automating Academic Illustration for AI Scientists
Dawei Zhu, Rui Meng, Yale Song, Xiyu Wei, Sujian Li · Jan 30, 2026 · Citations: 0
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To lift this burden, we introduce PaperBanana, an agentic framework for automated generation of publication-ready academic illustrations.
- 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
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We show that plateaued RL models can successfully refine failed solutions when given natural language critiques.
- How Much LLM Does a Self-Revising Agent Actually Need?
Sungwoo Jung, Seonil Son · Apr 8, 2026 · Citations: 0
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Recent LLM-based agents often place world modeling, planning, and reflection inside a single language model loop.
- CAMEL: Confidence-Gated Reflection for Reward Modeling
Zirui Zhu, Hailun Xu, Yang Luo, Yong Liu, Kanchan Sarkar · Feb 24, 2026 · Citations: 0
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Building on this insight, we propose CAMEL, a confidence-gated reflection framework that performs a lightweight single-token preference decision first and selectively invokes reflection only for low-confidence instances.
- Distilling Feedback into Memory-as-a-Tool
Víctor Gallego · Jan 9, 2026 · Citations: 0
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We propose a framework that amortizes the cost of inference-time reasoning by converting transient critiques into retrievable guidelines, through a file-based memory system and agent-controlled tool calls.
- BeliefShift: Benchmarking Temporal Belief Consistency and Opinion Drift in LLM Agents
Praveen Kumar Myakala, Manan Agrawal, Rahul Manche · Mar 25, 2026 · Citations: 0
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LLMs are increasingly used as long-running conversational agents, yet every major benchmark evaluating their memory treats user information as static facts to be stored and retrieved.
- Error-Aware Knowledge Distillation via Targeted Revision for Customer-Service Summarization
Hee-Jin Lee, Zhen Guo, Luchao Jin, Morteza Moazami Goudarzi · Nov 4, 2025 · Citations: 0
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We introduce an Analyze-Revise-Finetune (ARF) pipeline that enables smaller open-source language models (LLMs) to surpass substantially larger proprietary models in customer service summarization tasks.
- Beyond Refusal: Probing the Limits of Agentic Self-Correction for Semantic Sensitive Information
Umid Suleymanov, Zaur Rajabov, Emil Mirzazada, Murat Kantarcioglu · Feb 25, 2026 · Citations: 0
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To address this, we introduce SemSIEdit, an inference-time framework where an agentic "Editor" iteratively critiques and rewrites sensitive spans to preserve narrative flow rather than simply refusing to answer.
- Large Language Models and Impossible Language Acquisition: "False Promise" or an Overturn of our Current Perspective towards AI
Ziyan Wang, Longlong Ma · Feb 9, 2026 · Citations: 0
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In Chomsky's provocative critique "The False Promise of CHATGPT," Large Language Models (LLMs) are characterized as mere pattern predictors that do not acquire languages via intrinsic causal and self-correction structures like humans, there
- ClearFairy: Capturing Creative Workflows through Decision Structuring, In-Situ Questioning, and Rationale Inference
Kihoon Son, DaEun Choi, Tae Soo Kim, Young-Ho Kim, Sangdoo Yun · Sep 18, 2025 · Citations: 0
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Furthermore, exploratory applications demonstrate that captured steps can enhance generative AI agents in Figma, yielding predictions better aligned with professionals and producing coherent outcomes.