Papers with ASD
CodeDistiller: Automatically Generating Code Libraries for Scientific Coding Agents (2026.acl-demo)
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| Challenge: | Automated Scientific Discovery (ASD) systems rely on parametric knowledge to generate and run code-based experiments. |
| Approach: | They propose a system that distills large collections of scientific Github repositories into a vetted library of working domain-specific code examples. |
| Outcome: | The proposed system produces more accurate, complete, and scientifically sound experiments than an agent with only general materials-science code examples. |
Predicting pragmatic discourse features in the language of adults with autism spectrum disorder (2021.acl-srw)
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| Challenge: | Existing tools to quantify atypicality in discourse and pragmatics are difficult to precisely identify and quantify. |
| Approach: | They present a corpus of transcribed natural conversations produced in an experimental setting and annotate them for three pragmatic features on a three-point scale. |
| Outcome: | The proposed model yields higher accuracy than previous approaches for deriving these features, with F1 exceeding 0.82 for all three pragmatic features. |
Evaluating the Performance of Transformer-based Language Models for Neuroatypical Language (2022.coling-1)
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| Challenge: | Difficulties with social aspects of language are among the hallmarks of autism spectrum disorder (ASD). |
| Approach: | They propose a transformer-based framework for identifying linguistic features associated with social aspects of communication using a corpus of conversations between adults with and without ASD and neurotypical conversational partners. |
| Outcome: | The proposed framework yields strong accuracy overall, but performance is significantly worse for the language of participants with ASD, suggesting they use a more diverse set of strategies for some social linguistic functions. |
Activation Steering Decoding: Mitigating Hallucination in Large Vision-Language Models through Bidirectional Hidden State Intervention (2025.acl-long)
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| Challenge: | Large Vision Language Models (LVLMs) suffer from hallucination where generated textual descriptions fail to align accurately with visual semantics. |
| Approach: | They propose a training-free approach that mitigates hallucination through targeted intervention in the model’s intermediate activations by identifying directional patterns of hallucinism in the activation space using a small calibration set. |
| Outcome: | The proposed approach reduces hallucination across multiple benchmarks while maintaining performance on general visual understanding tasks. |
CodeScientist: End-to-End Semi-Automated Scientific Discovery with Code-based Experimentation (2025.findings-acl)
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Peter Jansen, Oyvind Tafjord, Marissa Radensky, Pao Siangliulue, Tom Hope, Bhavana Dalvi Mishra, Bodhisattwa Prasad Majumder, Daniel S Weld, Peter Clark
| Challenge: | Automated scientific discovery (ASD) systems are limited in their evaluation of software artifacts and large volumes of research artifs are typically evaluated using conference-style paper review with limited evaluation of code. |
| Approach: | They propose a novel ASD system that frames ideation and experiment construction as a form of genetic search jointly over combinations of research articles and codeblocks defining common actions in a domain. |
| Outcome: | The proposed system returns 19 discoveries on machine-generated ideas in the domain of agents and virtual environments. |
From Synthesis to Clinical Assistance: A Strategy-Aware Agent Framework for Autism Intervention based on Real Clinical Dataset (2026.acl-long)
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| Challenge: | Applied Behavior Analysis (ABA) is the gold standard for clinical intervention, but large language models struggle to adhere to its standardized procedures. |
| Approach: | They propose a strategy-aware framework to unify high-fidelity intervention dialogue synthesis and clinical decision support. |
| Outcome: | Experiments show that ASDAgent achieves nearly 80% strategic consistency with human experts. |
Speech Corpus for Korean Children with Autism Spectrum Disorder: Towards Automatic Assessment Systems (2024.lrec-main)
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| Challenge: | Despite the growing demand for digital therapeutics for children with autism spectrum disorder, there is currently no speech corpus for Korean children with ASD. |
| Approach: | They propose to use Korean children with ASD to improve pronunciation and severity evaluation by transcribed speech and language evaluation sessions to assess their articulatory and linguistic characteristics. |
| Outcome: | The proposed corpus will be 300 children with ASD and 50 typically developing (TD) children. |
The Slovak Autistic and Non-Autistic Child Speech Corpus:Task-Oriented Child-Adult Interactions (2024.lrec-main)
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| Challenge: | Presented is the Slovak Autistic and Non-Autistic Child Speech Corpus . corpus contains over 15 hours of speech . |
| Approach: | They present a Slovak autistic and non-autistic child speech corpus . the corpus was primarily recorded to investigate lexical alignment . |
| Outcome: | The Slovak Autistic and Non-Autistic Child Speech Corpus contains over 15 hours of speech . the corpus can be shared with researchers and replicated in future research . |
A Syntactic and Semantic Probe into Language Evolution based on Large Language Models (2026.findings-acl)
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| Challenge: | Existing studies on language evolution have relied on manual annotated resources and rely on dependency parsing. |
| Approach: | They propose to use attention-based structural distance and semantic space distance to measure language development. |
| Outcome: | The proposed measures show that human and LLMs share common characteristics in language processing. |