Papers

Long-horizon agents need to remember what happened over weeks and months, yet today's external memory systems either store flat recent history or perform destructive one-shot compaction that discards the detail needed for evidence and reasoning. We present Chronomem ( 忆 时 塔 , "Tower of Remembering Time"), a time-layered external memory service exposed through the Model Context Protocol (MCP). Inspired by two everyday mechanisms that roll detail up layer by layer—financial consolidation and the daily-report chain (task → daily → weekly → monthly)—Chronomem organizes episodic records into a hierarchy of time windows—from ten minutes up to a week—where each level is a materialized snapshot that references finer levels and can be drilled down to original records. Its central design principle is the separation of two kinds of recall with different costs: exact recall, which navigates reference chains to raw records and costs no LLM tokens, and semantic recall, which reads LLMgenerated summaries and is the form that truly compresses coarse windows. Summary records are themselves append-only temporal entries, so summaries can be recursively summarized, realizing progressive, non-destructive compression. Memory types are user-defined with per-type granularity chains, so high-frequency channels (chat, tool calls) keep minute-level granularity while sparse channels (work logs) converge to day/week. We position the design, discuss open problems (semantic drift, token-budgeted trigger strategies, privacy), and outline an evaluation plan; empirical results are forthcoming.

This document presents a rebuttal to the paper “ChatGPT is bullshit” (CiB), which claims that Large Language Models (LLMs) like ChatGPT are inherently unconcerned with truth, merely imitating human-like text without the capacity for genuine intention. Our counterargument emphasizes four main points: the critical role of Reinforcement Learning from Human Feedback (RLHF), the conflation of specific domain failures with a lack of overall truth-orientation, the false dichotomy of “truth-valuing humans vs. bullshitting LLMs”, and the risk that CiB itself meets its own criteria for “bullshit”. We conclude with reflections on how responsible, transparent, and up-to-date research is essential for understanding the evolving capacities of AI systems. This text was coauthored by a human and ChatGPT (o1 version), following a transparent collaborative process documented in the appendices. A noteworthy point is that o1, unprompted, identifies the critical error in CiB’s argument after only a few seconds of thought.

"Vibe coding" describes a style of software development in which a human works interactively with an AI coding assistant, specifying goals and evaluating results while delegating much of the detailed implementation, testing, debugging, and explanation to the AI. Similar methods can now be applied in the academic world. We suggest the term "vibe research" for sustained research collaboration of this kind. The analogy raises an immediate puzzle. If vibe coding and vibe research involve closely related forms of human-AI collaboration, why has vibe coding already become a generally accepted software development practice, while vibe research remains difficult to accommodate within mainstream academia? We consider three possible explanations. First, even if vibe research is technically possible, AI contributions may be too limited or trivial for the question to matter in practice. Second, research collaboration may require forms of responsibility or accountability which AI systems cannot assume. Third, current academic institutions may simply not provide a place for this form of collaboration within their established governance structures. We argue that each explanation is less straightforward than it initially appears. With regard to the substantive importance of AI-generated results, we cite multiple recent examples from mathematics in which frontier AI systems have made contributions regarded by the mathematical research community as highly non-trivial. With regard to responsibility, it is indeed true that an AI cannot assume institutional accountability in the same way as a human. We argue, however, that for many of the questions at issue the operationally useful requirement is reliable intellectual provenance: the ability to establish where ideas and decisions came from, inspect the evidence on which they were based, and obtain an explanation of them. The infrastructure used to preserve project history, trace decisions, inspect artefacts, and explain technical choices in vibe-coding projects carries over naturally to vibe-research projects and can deliver this kind of provenance. We present four examples from our own work showing that this is not merely a theoretical possibility but can readily be realised in practice. In the fourth, the present paper itself, we go beyond preserving final research artefacts and supporting resources: the complete human-AI session trace through which the paper was developed is retained as part of the provenance record, together with lightly processed representations and metadata supporting navigation and search. With regard to institutional acceptance, we argue that an important sociological difference between the software and academic worlds is that academic publishing is governed by formal authorship and ethics policies which change through relatively slow institutional processes, while software-development methodology can often change directly through team and organisational practice. We examine the ACL Policy on Publication Ethics as a particularly relevant example. Our general aims in the paper are not to argue that vibe research is risk-free, nor to propose a general rule for AI authorship. They are rather to identify an apparent asymmetry between software engineering and academic research, examine some of its conceptual, technical, and institutional foundations , and ask whether current academic conventions accurately describe emerging forms of human-AI research collaboration.

本文提出了一种面向中国独立研究者需求量身定制的新型预印本平台模型。与现有需要机构隶属关系或背书的平台不同，拟建系统结合了两个核心功能：（1）零门槛预印本发布；（2）社区驱动的讨论与协作。该平台集成了一个AI辅助质量验证系统，对提交的稿件进行多轮基于对话的验证。同时设计了一套分级用户成长机制，逐步将有潜力的独立研究者与知名学者和机构连接起来。本方案旨在弥合中国日益增长的研究产出与其对独立研究者有限的开放获取基础设施之间的差距

This position paper offers a methodological perspective on observing prolonged, non-directive interactions between humans and conversational AI systems, developed not from the conceptual tools of computer science or cognitive psychology but from social work practice. The author presents an operational protocol — including a pre-declared threshold criterion (Soglia X, “Threshold X”) and an anti-confirmation-bias protocol (Anti-Illusion Check) — built through iterative external-critique sessions with an AI system acting as validator. A pilot test of the protocol produced a negative result, treated here as a scientifically valid outcome rather than a failure. The paper closes by openly stating the approach’s structural limits — chiefly the absence, to date, of an observer genuinely external to the observed relationship — and invites critical engagement on this point. Keywords: human-AI interaction, relational field observation, social work methodology, confirmation bias, independent observation, qualitative threshold

本文通过形式化认知状态空间，定义无限追问算子与还原论投影算子，严格证明无限追问与还原论在极限行为上同构，均导致锚定空洞、复杂度发散、认知价值衰减至零，最终陷入认知热寂。结论揭示了无限追问是还原论的最精致伪装，其数学本质与还原论无异，为整体论优于还原论提供了严密的形式化支撑。