small language model

Prompt Framing and the Coverage Gap in Automated Security Analysis of Code Generated by Small, Locally Run Language Models

Initial Research Release This is the first public release of Security in LLM-Generated Code. This release contains the research materials, experimental code, datasets, analysis scripts, findings, and supporting documentation associated with the study. Contents Research paper and supporting documentation Experimental da...

三阶意识与 AF-by-discrete 的结构相容性

This bilingual Chinese–English research monograph develops a controlled structural correspondence between a psychological–phenomenological account of third-order consciousness and the mathematics of AF-by-discrete groupoids. On the psychological side, the work asks how experience becomes organized as “my experience,” h...

Stretching a Concept to Breaking Point: An Empirical Audit of a Taxonomy of Conceptual Tensions

A glass-box creativity engine, the Concept Collider, rests on a single primitive: a concept is not merely a point but a structure held under tension, and pressing it along one of seven fracture types breaks it in a characteristic way. That taxonomy is load-bearing—every profile, every homology score, every negative-spa...

A New Architecture of Agent

According to the drawbacks of large language model, I design a new agent which can overcome the drawbacks. The agent contains four deep neural networks, which is abstract network, concrete network, decision network, execution network. Each of the four networks just do one thing, so it focus what it can do just like bra...

ACADEMIC PERMISSIBILITY IN LLM-ASSISTED EFL WRITING: HOW CONTEXTUAL JUSTIFICATIONS SHAPE STUDENTS' MORAL JUDGMENTS IN A WITHIN-SUBJECTS VIGNETTE SURVEY

AbstractThis paper investigates EFL students’ perceptions of ethical acceptability judgments of using Large Language Models (LLMs) into academic writing. In contrast to the simplistic view of acceptable/unacceptable use of LLMs, the present study models how specific contextual justifications shape students’ moral evalu...

ACADEMIC PERMISSIBILITY IN LLM-ASSISTED EFL WRITING: HOW CONTEXTUAL JUSTIFICATIONS SHAPE STUDENTS' MORAL JUDGMENTS IN A WITHIN-SUBJECTS VIGNETTE SURVEY

AbstractThis paper investigates EFL students’ perceptions of ethical acceptability judgments of using Large Language Models (LLMs) into academic writing. In contrast to the simplistic view of acceptable/unacceptable use of LLMs, the present study models how specific contextual justifications shape students’ moral evalu...

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.

LLM-Based Multi-Agent Systems for Code Generation: A Multi-Vocal Literature Review

A Multi-Vocal Literature Review is conducted, combining insights from both academia and industry, including peer-reviewed studies and grey literature to systematically synthesize and analyze existing knowledge on LLM-based multi-agent systems for code generation.

Autonomous Agents in Software Development: A Vision Paper

The vision is to leverage the capabilities of multiple GPT agents to contribute to SE tasks and to propose an initial road map for future work, arguing that multiple G PT agents can perform creative and demanding tasks far beyond coding and debugging.

Reproductive phasiRNAs: At the core of small RNA-mediated male fertility in monocots

HSRM: Hidden-State Reward Models for Test-Time Verification

HSRM is introduced, a lightweight hidden-state reward model that verifies candidate solutions by directly reading the generator's internal representations rather than re-processing its text, providing an efficient alternative to text-only verification by reusing representations already computed during generation.

A.X K2 Technical Report

To support long contexts efficiently, Sparse Gated Attention (SGA), which combines sparse attention with gated attention, and adopt Gated Norm (GN) to stabilize large-scale training is introduced, which keeps 4-bit NVFP4 serving within one point of FP8 accuracy.