LLM API
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Token-Efficient Change Detection in LLM APIs
Announce Type: replace Abstract: Remote change detection in LLMs is a difficult problem. Existing methods are either too expensive for deployment at scale, or require initial white-box access to model weights or grey-box access to log probabilities. We aim to achieve both low cost and strict black-box operation, observing only output tokens.
Lightfall: An API-first, LLM-addressable control platform for synchrotron beamlines
arXiv:2606.06711v1 Announce Type: new Abstract: Synchrotron beamlines differ in hardware, technique, and workflow, making customized control interfaces necessary; bespoke per-beamline graphical user interfaces (GUIs) do not scale well, one-size-fits-all facility software forces compromises that leave most of the interface unused, and even recent component-library approaches keep per-scientist tweaks on a developer's queue. We present Lightfall, a control platform designed for facility-wide...
Show HN: Mnemo – local-first AI memory layer for any LLM (Rust, SQLite,petgraph)
Local-first AI memory layer for any LLM. Persistent knowledge graph, entity extraction, semantic retrieval — no cloud required. Most LLMs forget everything the moment a conversation ends.
Diagnosing Knowledge Gaps in LLM Tool Use: An Agentic Benchmark for Novel API Acquisition
Announce Type: new Abstract: Large language models for code generation often need to use APIs that are absent from their pretraining data. This requires more than recalling a function name: models must coordinate signatures, module paths, input-output contracts, semantics, and executable usage patterns. Existing novel-API benchmarks are typically static, rely on coarse pass/fail metrics, or use synthetic APIs that may not reflect real library evolution.
Alibaba/Open-Code-Review
The open source AI code review agent. English | 简体中文 Open Code Review is an AI-powered code review CLI tool. It originated as Alibaba Group's internal official AI code review assistant — over the past two years, it has served tens of thousands of developers and identified millions of code defects.
Online Pandora's Box for Contextual LLM Cascading
Announce Type: new Abstract: Motivated by Large Language Model (LLM) cascading, we propose an online contextual Pandora's Box model for adaptively querying and selecting LLM APIs. In each period, a decision-maker observes a request context and faces a two-phase decision problem. In the query phase, the decision-maker sequentially queries APIs, where each query reveals a generated output and the decision-maker incurs an (output-dependent) cost.
AppAgent-Claw: CLI Is All You Need for GUI Automation
arXiv:2606.05171v1 Announce Type: new Abstract: The OpenClaw platform provides a practical foundation for automation through its skill-oriented architecture, organizing external capabilities into lightweight, reusable components that can be invoked efficiently through a command-line interface (CLI). However, a significant bottleneck remains: many real-world tasks are confined to graphical user interfaces (GUIs) with no stable API available. While LLM-based GUI agents offer generality, their...
Framing Migration News with LLMs: Structured CoT as a Support for Human Interpretation
Announce Type: new Abstract: Frame analysis of migration news is a socially consequential task: media scholars and researchers who study how migration is narrated need tools that are not only accurate, but transparent, auditable, and accessible within the resource constraints typical of academic research groups. Existing LLM-based approaches rely on proprietary APIs and large models that raise concerns about data privacy, reproducibility and equitable access among media researchers. This...
ADK Arena: Evaluating Agent Development Kits via LLM-as-a-Developer
Announce Type: new Abstract: The rapid proliferation of Agent Development Kits (ADKs), SDK-level frameworks for building LLM-powered autonomous agents, has outpaced any empirical understanding of how framework choice affects agent performance. We propose \textbf{LLM-as-a-Developer}, a methodology that replaces human developers with an LLM coding agent that learns each framework's API from documentation, writes agent code, and iteratively repairs it through a validate-and-feedback loop until...
Statistical Priors for Implicit Preferences: Decoupling Skill Selection as a Local Harness in Personal Agents
new Abstract: As Large Language Model (LLM) capabilities advance, locally deployed personal agents relying on API-based remote models and external skills have emerged as a novel paradigm. With the rapid expansion of available skills, enabling personal agents to learn and adapt to implicit user preferences becomes a critical challenge. However, local deployment constraints preclude complex centralized selection algorithms, creating an urgent need for a lightweight local preference harness.