2026-02-27
AI for Software Development
ISO-Bench: Can Coding Agents Optimize Real-World Inference Workloads?
Relevance: This paper presents a benchmark specifically for coding agents tasked with optimizing real-world software performance. It evaluates the ability of AI to generate optimization patches for popular frameworks like vLLM. From an HCI perspective, it highlights a critical gap: agents often correctly identify software bottlenecks but struggle to execute functional code solutions. This insight is vital for designing better human-AI collaborative tools where the human might need to intervene in the execution phase even when the AI’s diagnostic reasoning is correct.
RankEvolve: Automating the Discovery of Retrieval Algorithms via LLM-Driven Evolution
Relevance: RankEvolve demonstrates how LLMs can be used to automatically discover and evolve complex retrieval algorithms by representing them as executable code. This is a prime example of AI-driven code generation and refactoring, where the model iteratively improves upon existing codebases (like BM25). For software developers, this suggests a future where AI doesn’t just complete boilerplate code but actively optimizes algorithmic logic through evolutionary search, significantly reducing the manual effort required for performance tuning in specialized software domains.
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Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptions
Relevance: This research examines the quality of natural-language tool descriptions that AI agents rely on to interact with external software systems. By identifying ‘smells’ or defects in these descriptions, the paper provides a framework for improving how developers document APIs and tools for AI consumption. This is highly relevant to AI for software development as it establishes best practices for creating ‘machine-readable’ documentation, which is essential for the seamless integration of LLM-based agents into modern software engineering workflows and toolchains.
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AI Agents
GUI-Libra: Training Native GUI Agents to Reason and Act with Action-aware Supervision and Partially Verifiable RL
Relevance: GUI-Libra focuses on training agents to navigate and interact with graphical user interfaces (GUIs), a core challenge in creating autonomous digital assistants. It introduces a training recipe that balances reasoning with grounding, ensuring agents can translate plans into valid screen actions. This work is critical for HCI because it improves the reliability of agents that act on behalf of users in complex, multi-step tasks across web and mobile environments, directly impacting how humans delegate digital chores to autonomous software systems.
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TAPE: Tool-Guided Adaptive Planning and Constrained Execution in Language Model Agents
Relevance: TAPE addresses the fragility of AI agents in environments where single errors lead to failure. By introducing adaptive planning and constrained decoding, it allows agents to re-evaluate their strategies based on environmental feedback. This is a significant advancement for agent autonomy and reliability. In HCI contexts, such robustness is essential for building user trust, as it ensures that agents can recover from unexpected states or tool failures without requiring constant human oversight or manual restarts.
DREAM: Deep Research Evaluation with Agentic Metrics
Relevance: DREAM proposes an agentic framework for evaluating the complex outputs of research agents. It uses tool-calling agents to verify factual correctness and temporal validity, addressing the ‘mirage of synthesis’ where fluent text hides errors. This is relevant to the AI Agents topic as it demonstrates ‘meta-agent’ collaboration—using agents to monitor and evaluate other agents. This approach is vital for ensuring that autonomous research systems remain aligned with human requirements for accuracy and grounded reasoning.
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LLM Evaluation Methods
Intent Laundering: AI Safety Datasets Are Not What They Seem
Relevance: This paper provides a critical evaluation of existing AI safety benchmarks, revealing that they often rely on superficial ‘triggering cues’ rather than actual malicious intent. It introduces ‘intent laundering’ to test model robustness more accurately. This is a vital contribution to LLM evaluation, as it exposes the disconnect between current standardized tests and real-world adversarial behavior. For HCI researchers, this emphasizes the need for evaluation methods that consider the nuanced ways humans might interact with or subvert AI safety mechanisms.
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NanoKnow: How to Know What Your Language Model Knows
Relevance: NanoKnow introduces a benchmark to distinguish between a model’s internal parametric knowledge and its ability to use external evidence. By partitioning data based on the pre-training corpus, it allows researchers to systematically evaluate how models handle known versus unknown information. This is crucial for HCI because understanding the source of a model’s ‘knowledge’ helps in designing interfaces that better communicate uncertainty, manage user trust, and mitigate the risks of hallucinations when models encounter topics not seen during training.
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Benchmark Test-Time Scaling of General LLM Agents
Relevance: This study evaluates the performance of general-purpose agents across multiple domains (coding, search, reasoning) and analyzes how they scale with additional compute at test-time. It identifies fundamental limitations like ‘context ceiling’ and ‘verification gaps’ that prevent simple scaling from solving complex tasks. This is highly relevant for evaluation methods as it shifts the focus from static benchmarks to understanding the dynamic behavior of agents during long-horizon interactions, which directly impacts the design of responsive and efficient AI-human interfaces.
Reinforcement Learning
ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning
Relevance: ARLArena addresses the inherent instability and ‘training collapse’ often seen when applying reinforcement learning to LLM-based agents. It provides a stable training recipe and a systematic analysis of policy gradient dimensions. This is essential for the RL field as it moves toward training agents for complex, multi-step tasks. From an HCI perspective, stable RL is the foundation for creating agents that can learn from human feedback or environmental rewards consistently, leading to more predictable and reliable collaborative behaviors.
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TextPecker: Rewarding Structural Anomaly Quantification for Enhancing Visual Text Rendering
Relevance: This paper utilizes RL to solve a specific generative challenge: rendering accurate text within images. It proposes a structural anomaly perceptive RL strategy that uses a specialized reward signal to penalize distortions and misalignments. This demonstrates how RL can be used to align generative models with human visual expectations. In HCI, this approach can be extended to optimize any generative output where human perception of ‘correctness’ or ‘quality’ is difficult to capture with simple loss functions.
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Learning to Detect Language Model Training Data via Active Reconstruction
Relevance: This work introduces a novel application of on-policy reinforcement learning to detect training data membership. By using RL to ‘sharpen’ a model’s ability to reconstruct specific texts, the authors can determine if a model was previously exposed to that data. This highlights a unique intersection of RL and model interpretability/privacy. It shows how RL techniques can be used to ‘interrogate’ a model’s memory, which has significant implications for auditing AI systems and ensuring they respect data privacy and copyright.
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Explainable AI
The Truthfulness Spectrum Hypothesis
Relevance: This paper investigates how LLMs represent truthfulness internally, proposing that they contain directions in their representational space ranging from domain-general to domain-specific. By using linear probes and causal interventions, it explains patterns like sycophancy (telling the user what they want to hear). This is a classic XAI task that makes the ‘black box’ of LLM decision-making more transparent. Understanding these internal geometries helps HCI researchers design interventions to steer models toward more honest and less biased interactions.
See and Fix the Flaws: Enabling VLMs and Diffusion Models to Comprehend Visual Artifacts via Agentic Data Synthesis
Relevance: This paper introduces ArtiAgent, which not only identifies artifacts in AI-generated images but also generates local and global explanations for why a specific part of an image is considered a flaw. This is highly relevant to XAI as it moves beyond simple error detection to providing human-readable justifications for model assessments. In HCI, this capability is essential for creative tools, allowing users to understand model limitations and providing a clear path for iterative refinement of generative content.
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