2025-05-30
Generative AI for Assisting Software Developers
SWE-rebench: An Automated Pipeline for Task Collection and Decontaminated Evaluation of Software Engineering Agents
Relevance: This paper directly addresses the use of LLM-based agents in software engineering tasks. It introduces a pipeline to extract real-world interactive SWE tasks from GitHub, creating a large dataset for reinforcement learning of SWE agents. The paper focuses on evaluating and advancing these agents, contributing to code generation, bug fixing, and documentation generation aspects. It is relevant because it aims to improve AI’s assistance in real-world software development scenarios.
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Text2Grad: Reinforcement Learning from Natural Language Feedback
Relevance: This paper presents a novel reinforcement learning paradigm (Text2Grad) which is designed to take human feedback in the form of natural language and turn it into span-level gradient updates, more precisely adjusting the model’s policy. It directly applies to the area of generative AI assisting software developers because the code generation task is used to demonstrate the effectiveness of the proposed RL paradigm. Such specific feedback can be used to, for example, help an AI assistant generate code that is correct and satisfies the developer’s needs.
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AI Agents
Reinforcing Multi-Turn Reasoning in LLM Agents via Turn-Level Credit Assignment
Relevance: This paper investigates enhancing the reasoning capabilities of LLM agents using reinforcement learning in multi-turn tool-use scenarios, directly relevant to AI agent research. The focus on turn-level credit assignment addresses the challenge of training agents for complex decision-making, where the agent needs to break down complex tasks, use reasoning, and interact with digital tools. This work contributes directly to improving AI agents’ ability to accomplish user-defined goals through better planning and execution.
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Personalized Safety in LLMs: A Benchmark and A Planning-Based Agent Approach
Relevance: This paper introduces personalized safety in LLMs and presents PENGUIN, a benchmark for evaluating safety risks based on individual user vulnerabilities. It also develops RAISE, a training-free agent framework that strategically acquires user-specific background information to improve safety scores. This relates to AI agents as it focuses on creating agents that maintain safety and alignment with human values, a crucial aspect of responsible AI agent development and deployment.
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AITEE – Agentic Tutor for Electrical Engineering
Relevance: This paper presents AITEE, an agent-based tutoring system for electrical engineering, which demonstrates an AI agent that can provide individualized support and promote self-directed learning. The system uses Socratic dialogue to foster learner autonomy, making it an example of an AI agent that interacts with digital tools and environments to accomplish a specific user-defined goal.
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Prompt Engineering Techniques
Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM’s Instruction-Following Capabilities
Relevance: This paper systematically examines how fine-tuning reconfigures LLM computations for instruction-following. It analyzes instruction-specific sparse components (neurons and experts), which directly relates to prompt engineering. This work offers a novel framework and insightful analysis to understand how instructions are processed within LLMs, contributing to better techniques for prompt engineering.
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Human-in-the-loop Machine Learning
Text2Grad: Reinforcement Learning from Natural Language Feedback
Relevance: This paper aligns with human-in-the-loop ML because it presents a new reinforcement-learning paradigm that turns free-form textual feedback into span-level gradients. It exemplifies using human preferences to directly refine the portions of the model’s policy that generate code, summaries, or answers. Thus, natural-language feedback becomes a signal for fine-grained policy optimization.
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Techniques for Explaining AI Behavior
Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM’s Instruction-Following Capabilities
Relevance: This paper introduces SPARCOM, an analytical framework, to explore the relationship between fine-tuning-induced adaptations and sparse computational substrates in LLMs. It falls under Explainable AI (XAI) as it examines how fine-tuning reconfigures LLM computations by isolating and analyzing instruction-specific sparse components (neurons and experts). The work provides deeper insights into how LLMs internalize instruction-following behavior, helping make the models’ decision-making processes more transparent.
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Meta-Learning an In-Context Transformer Model of Human Higher Visual Cortex
Relevance: This paper relates to XAI because it demonstrates that BraInCoRL facilitates better interpretability of neural signals in higher visual cortex by attending to semantically relevant stimuli. By jointly conditioning on image features and voxel activations, the models learn to directly generate better performing voxelwise models of higher visual cortex. Further, BraInCoRL facilitates better interpretable mappings from natural language queries to voxel selectivity. These methods and analysis techniques can be used to help visualize which part of the input is important and understand how a model attends to them.
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