yzhao062

UNIFY AGENT CONFIGURATIONS IN ONE FILE! 📄 Yzhao062 Anywhere AgentsUnifies agent configurations for Claude Code and Codex via a single AGENTS.md file. yzhao062/anywhere-agents t.co

ap loads 14 public data sources into one format, keeping messages, tool calls, results and labels together. Use it to inspect traces, compare datasets or export data for training. Code: yzhao062/agentpile #AIAgents #OpenSource

🔭 Today's find yzhao062.github.io — My personal website #indiedev #opensource #html5 🔗 check it out

agent-risk: Risk Assessment for Changes to AI Agents Before changing a deployed AI agent (a stricter safety gate, a new model), organizations must approve the change, starting from existing logs. Extending classical risk analysis to partial records and partial changes to running systems, agent-risk replays logged episodes, assuming nothing else changes and the agent does not anticipate the change. The log settles episodes acting as before, a declared stop value settles stopped ones, and the rest need logged support, a saved-state rerun, or a bound. We prove what logs must record for exact replay and which saved states serve a gate family. For independent episodes, we bound the reruns a decision needs and tolerable drift from live behavior. In a bash sandbox, a 7,200-episode log alone decided neither of two retry gates, which let the agent act after refusals the log never shows. Rerunning only each gate's blocked episodes from saved states reached both live decisions at half the agent calls of a live test sized post hoc for them, given the log. Without per-step harm, a stopping gate's certified harm ceiling rose up to 2.5-fold, since the log hides whether harm preceded the stop. Approval thus needs evidence designed in advance: log fields, saved states, targeted reruns. Comments: 43 pages, 5 figures, 7 tables. Code, registrations, and logs: yzhao062/agent-risk

AI4Fire: Evaluating Large Language Models on Wildfire Tasks Yue Zhao, Xiyang Hu, Zuobin Xiong, Zhangyu Wang, Ruolin Li arxiv.org [𝚌𝚜.𝙲𝙻 𝚌𝚜.𝙻𝙶] 💬Code: yzhao062/AI4Fire t.co

AI4Fire: Evaluating Large Language Models on Wildfire Tasks Large language models (LLMs) are entering wildfire management, where overstated evaluations can cost property and lives. How do they perform on wildfire tasks, with and without grounding? Bare means a model receives the task input alone. Grounded means it also receives one task-specific addition: for smoke detection, a smoke-free reference frame from the same camera. AI4Fire runs six core models bare and grounded on five wildfire tasks, zero-shot; a sweep adds 29 more. Our literature search on fire tasks found 138 works; none combines this roster, task coverage, and paired bare and grounded runs. We report three findings. (1) Grounding helped most where the addition carried the answer: a read-only SQL tool lifted every core model's database accuracy from at most 16 to at least 88 percent. (2) Simple rules were hard to beat: no core model outperformed repeating today's staffing count, and two open-weight models mostly copied the median of similar earlier fire-days, a worse forecast. (3) Public releases carry hazards: a fire-danger column separates the holdout perfectly, and 67 aerial fire frames carry smoldering or fire-free labels read from a clipped thermal maximum. We release prompts, responses, scores, code, and the survey record. Comments: 51 pages. Code: yzhao062/AI4Fire

GRADE: Graph Representation of LLM Agent Dependency and Execution Traces record what LLM agents did, leaving implicit what each step relied on. GRADE holds both in one graph; six published graph views are its projections. Execution edges come free; each costly dependency edge is graded as observed in the trace, declared by added logging, or inferred under an assumption, a schema we test one grade at a time. We recover the dependency layer from six tool-use, coding, and web corpora and measure its failure-prediction gain over run size. First, over a linear run-size baseline, it improves prediction on three corpora, but the gain depends on the learner and task sampling. On held-out corpora the layer stays above chance where run size does not, under one learner, with the same task-sampling caveat. Second, structure can pass for signal it does not carry: a layer inferred under full history restates run size. Within a corpus, a preregistered test does not separate the recovered layer from a degree-matched counterfeit that rewires each step's dependencies but keeps their number. The recovered layer's largest transfer lead over it reverses when the held-out corpus's task family is withheld. Before crediting structure, grade each edge, keep a run-size baseline, and score degree-matched attachment controls. Comments: 35 pages. Expanded analyses and revised figures. Code: yzhao062/grade